text:
\section{Introduction}
Waste generation has increased dramatically in the 21st century due to the growth in the global population. According to the World Bank Group's report \cite{WorldBankGroup2018What2.0}, 2.01 billion tonnes of municipal solid waste are generated worldwide every year, with only about 19\% adequately recycled  \cite{WorldBankGroup2018What2.0}. Recycling not only helps to preserve raw material but, more importantly, reduces the landfills required, which is an undesirable way of waste disposal due to its high demand for space and the danger of introducing contaminants into the ground or even groundwater system. A major step of recycling is to separate the waste into specific categories according to its material. Failure to do so will significantly harm the effectiveness of recycling. Traditionally, workers at the recycling company sort the waste into corresponding categories by hand, which is an inefficient method and requires unnecessary labor. Therefore, people are now in dire need of a more advanced and automated waste separation system.\par
Image classification machine learning (ML) algorithms have been used to build automatic waste classification systems and assist waste management. This research aimed to improve the existing classification system and pursue the commercialization of an artificial intelligence (AI) street bin. Specifically, we focused on reducing the power consumption of the controller board to extend AI bin's battery life, increasing its waste classification accuracy and reducing its price. \par
The only intelligent recycling bin on the market, Bin-e  \cite{Bin-e}, uses image classification algorithms to separate the trash into four categories: plastic, paper, metal and glass. It achieves a waste segmentation accuracy of 92\% \cite{Bin-e}. However, the price USD 5200 \cite{Bin-e} is expensive for a rubbish bin. Also, Bin-e's operation requires a 230V power supply, so it cannot replace the traditional trash bins at the locations without plug sockets. Therefore, it can hardly increase the household and street recycling rate. As a result, this paper proposes low-cost battery-powered real-time recycling waste segmentation bin systems to fill this gap.\par
In this research, we trained a light MobileNet image classification model for Jetson Nano with TrashNet  \cite{GaryThungGarythung/trashnet:Classification}, an open-source waste dataset, and 1800 new training samples collected by us. The model demonstrated great performance with a high test accuracy of 95.98\% and a small parameter size of 3.0 M. Jetson Nano only consumed 4.7 W when it ran the model. We also found a cheaper and less energy-hungry device, K210 \cite{CanaanKendryteK210} to further reduce power consumption. The K210 board only consumed 0.89 W at inference time and the model on it achieved a high test accuracy of 96.64\%, which made it a more practical solution for trash bin applications. The code used in this study is available on Github \cite{XueyingLiMolvcanAN-SMART-RECYCLING-BIN-USING-WASTE-IMAGE-CLASSIFICATION-AT-THE-EDGE}.\par
This paper is organized as follows. In Section \ref{sec:2}, the related works performed in waste classification and AI trash bins are introduced. Section \ref{sec:3} introduces the system design and explains the background theories that support this study. The methodology of data collection and model training is introduced in section \ref{sec:4}. Section \ref{sec:5} analyses the test results and evaluates the system performance on Jetson Nano and K210. Finally, Section \ref{sec:6} concludes the achievements and provides suggestions for future research.

\section{Related works}\label{sec:2}

The United Kingdom (UK) government has planned to increase the household recycling rate in England to 50\% by 2020, but only 44\% of municipal waste was reused and recycled in 2020 \cite{2022Progress2020}. The inconvenience and lack of knowledge are two crucial factors that prevent people from recycling \cite{Knickmeyer2020SocialAreas}. Automatic waste segmentation bins were designed to overcome these problems by helping people classify and send the waste into the corresponding containers, making waste disposal more convenient. Table \ref{tab:1} outlines the approaches and the primary hardware components involved in related studies to construct the waste segmentation systems.

\begin{table*}[!ht]
 \caption{Related works in AI bins}
  \centering
  \begin{adjustbox}{width=1\textwidth}
      \small
\begin{tabular}{p{0.7cm}p{2cm}p{4cm}p{4cm}p{4cm}}
\toprule
\cmidrule(r){1-5}
Year &	Author &	Waste classification method &	Classification category &	The device \\
\midrule
2014    & Rajkamal et   al. \cite{Rajkamal2014AGREENBIN}  &Inductive   metal sensor,\newline capacitive based moisture sensor, methane sensor, odor sensor & metal/glass, food,   bio,\newline paper/plastic, inert                         & PIC18F4580   \newline controller             \\
2017                                               & kumar et al. \cite{B.R.S.Kumar2017Eco-FriendlyManagement}                       & Inductive   and capacitive metal sensor, gas sensor, bacteria sensor, moisture sensor   & Biodegradable,   plastic, metal   & STM32   controller  \\
2019                                               & Pereira et al. \cite{Pereira2019SmartOptimisation}                          & Infrared radiation   sensor, capacitive sensor                                          & Dry, wet, plastic                                                      & Atmega 328P \newline microcontroller         \\
2019                                               & Ziouzios et al. \cite{Ziouzios2019AClassification}                          & CNN image   classfication                                                               & Glass, paper,   metal, plastic, cardboard, trash                       & Xilinx Pynq-Zl FPGA                 \\
2020                                               & White et al. \cite{White2020WasteNet:Bins}                             & CNN image   classification                                                              & Paper, plastic,   metal, glass,\newline cardboard, other                       & Jetson Nano                         \\
2021                                               & Jimeno et al. \cite{Jimeno2021DevelopmentProcessing}                            & CNN   image classification                                                              & Aluminum   cans, plastic bottles, plastic cups, paper, spoons/forks    & Computer   with \newline Nvidia RTX 2060 GPU \\
2021\newline                                               & Sallang et al. \cite{Sallang2021AEnvironment}                           & CNN   image classification \newline                                                             & Glass, paper,   metal, plastic, cardboard                              & Raspberry   Pi 4 \newline         \\
\bottomrule
\end{tabular}
  \end{adjustbox}
  \label{tab:1}
\end{table*}

In early research, a microcontroller is connected to a variety of sensors to determine the composition of the waste. For example, inductive and capacitive sensors can detect the metal element \cite{Rajkamal2014AGREENBIN}\cite{B.R.S.Kumar2017Eco-FriendlyManagement}\cite{Pereira2019SmartOptimisation} and moisture sensors separate wet waste from dry waste  \cite{B.R.S.Kumar2017Eco-FriendlyManagement}\cite{Pereira2019SmartOptimisation}. The microcontroller makes decisions based on its readings \cite{Rajkamal2014AGREENBIN}\cite{B.R.S.Kumar2017Eco-FriendlyManagement}. However, although the sensor-based classification method can detect the composition precisely, it lacks the ability to classify waste into more specific groups. The sensors cannot discriminate between plastic, paper, glass, and other unrecyclable dry waste, which are important categories in recycling. \par

The development of machine learning and image classification enables the bin to sort the waste based on visual input like a human. The convolutional neural network (CNN) is a branch of image classification algorithms that performs mainly convolution operations on the pixels \cite{Yamashita2018ConvolutionalRadiology}. It is the most popular choice due to its high accuracy and power efficiency compared to other methods and is used in all the four papers \cite{Ziouzios2019AClassification}\cite{White2020WasteNet:Bins}\cite{Jimeno2021DevelopmentProcessing}\cite{Sallang2021AEnvironment}. It gives the bin ability to differentiate between spoons and cups, which is tremendous progress compared to the sensor-based approaches. Traditionally, the CNN models run on a cloud raising the data transmission latency and user privacy security problems. To solve these problems, recent research moved the computation to an edge embedded system. However, edge computing has the drawback of limiting computation resources, so the model size is important in selecting the CNN model structure.\par

To develop, evaluate and select the proper CNN structures, most research in this field used the TrashNet dataset developed by Mindy Yang and Gary Thung \cite{GaryThungGarythung/trashnet:Classification} in 2017, the first open-access recycling waste dataset. This high-quality dataset contains 2527 waste photos from 6 groups: metal, glass, plastic, paper, cardboard, and trash. It provides a foundation for later research and our study also used this dataset. \par

Efforts have been made to increase the segmentation accuracy of CNN models based on TrashNet. The state-of-art accuracy of 97.86\% on TrashNet was reached in \cite{OzkayaFine-TuningRecyclability}. However, the CNN architecture, GoogLeNet, used in this research will cause out-of-memory (OOM) on Jetson Nano \cite{Abdulmahmood2021ImprovingAnalysis}. It demonstrated the potential of CNN classification, but the model size and computation cost must be cut down to implement the machine learning algorithm on embedded systems. A lighter CNN model, WasteNet, was constructed by White et al. and achieved an accuracy of 97\% on the TrashNet dataset \cite{White2020WasteNet:Bins}. The paper claimed that the model could be loaded to Jetson Nano, but it did not provide details regarding the edge implementation, such as the classification speed. Nevertheless, from the model structure, we could estimate that the classification speed for one image would be too slow for real-time classification. It can only be used in a bin application that classifies the waste objects based on one photo. \par

The classification speed is quantified by inference time, which refers to the time taken for one classification to be completed. Our application looks for a smaller model that can interact with the user in real-time, with an inference time that must be smaller than 0.1 s, which is the human visual reaction time. The CNN model, EfficientNet B0, used in \cite{Abdulmahmood2021ImprovingAnalysis} is the starting point of this research and it achieved an accuracy of 95.38\% and an inference time of 0.07 s on Jetson Nano. While the model is fast enough to be used in real-time applications, the 96\% high memory usage needs optimization for bin applications. \par

Power consumption is another factor that needs to be considered in the final product. Unfortunately, rare research has paid attention to it. For instance, none of the trash bin cases listed in Table \ref{tab:1} has measured the power consumption of the proposed system. Still, it is evident that the CNN-based approaches have higher power consumption than the sensor-based one and the Pynq-Zl and Raspberry Pi will typically have lower power consumption than Jetson Nano used in \cite{White2020WasteNet:Bins} and \cite{Abdulmahmood2021ImprovingAnalysis}. The Raspberry Pi 4 has a typical power consumption of between 2.7 W and 6.4 W. It reduces the power consumption but results in undesirable low performance \cite{Sallang2021AEnvironment}. As a result, the CNN architecture, MobileNet V2, has a low average per-class precision of 91.76\% and a long inference time of 0.358 s on it. \par

The limitation of the current research is that the energy-saving systems will have unacceptable low performance in the classification task for commercialization. As a result, this study designed two high-accuracy waste classification systems with lower power consumption than previous studies. The first system reduced the power consumption of the application on Jetson Nano by using a lighter model, MobileNet, to reduce power consumption while maintaining the accuracy. \par

The second system is developed based on K210, a less expensive and more energy-saving embedded device.  K210 is an unpopular choice and is used in fewer than 100 research. Most research implemented YOLO object detection models on it \cite{Henderi2020AnArea}\cite{Li2021ADRC-basedFeedback} and demonstrated its outstanding power efficiency. K210 has not been used for recycling waste classification when this paper was written, so this paper proposed and evaluated an innovative approach.

\section{System design}\label{sec:3}
\subsection{The AI recycling bin design }

The AI bin consists of five recycling waste containers and a detection box. The waste will be sorted after it is placed in the detection box. The whole system can be controlled by a Jetson Nano or a K210 board. \par

The bin design using Jetson Nano is summarized in Figure \ref{fig:1}. Jetson Nano will interact with the users and collects feedback through the touch screen in front of it, displaying the instructions for using the bin and the camera inputs. The Raspberry Pi camera at the top takes photos of the waste in the detection box. The images will be fed to the classification algorithm in Jetson Nano, which classifies the photos into seven groups.
 \begin{figure}
        \includegraphics[width=0.5\textwidth]{1.png}
        \caption{System block design of WasteNet.}
        \label{fig:1}
\end{figure}
The waste classification models in the previous study \cite{Abdulmahmood2021ImprovingAnalysis} have five output classes: "paper", "metal", "plastic", "cardboard" and "glass". They will be reproduced in the next section as the benchmark models of our study. Our model will be trained with two new classes, "empty" and "hand". "Empty" means there is nothing in the photo, while the "hand" group detects a human's hand to avoid trapping the user's hand by the door. When these two groups are detected, the bin waits and continues detecting.
\subsection{The Benchmark models }\label{bench}
The waste classification models in \cite{Abdulmahmood2021ImprovingAnalysis} consist of an input layer, a pre-processing augmentation layer, an EfficientNet B0 base model layer, a global average pooling 2D layer and a dense layer. The first model has an input layer size of 512x384 pixels, while the second model input has 384x288 pixels. Table \ref{tab:2} shows the configuration of the augmentation layer and the model training hyperparameters for reproduction. \par
\begin{table}
  \centering
    \small
    \caption{Training parameters used in \cite{Abdulmahmood2021ImprovingAnalysis} }
  \begin{tabular}{ll}
    \toprule
    \cmidrule(r){1-2}
    Pre-Processing Method     & Value \\
    \midrule
    Random flip & Horizontal and vertical \\
    Random rotation & Up to 180\(^{\circ}\) \\
    Random translation & Up to 10\% \\
    Random zoom & Up to 75\% \\
    \bottomrule
    \cmidrule(r){1-2}
    Training Parameter     & Value \\
    \midrule
    Learning rate scheduler & Constant learning rate scheduler \\
    Train/Validation/Test split ratio & 72/18/10\\
    Optimizer & Adam optimiser \\
    Training epochs & 50\\
    Learning rate&	4.3e-05\\
    Fine-tuning training epochs	&8\\
Fine-tuning learning rate&	4e-06\\
Loss functions&	Sparse categorical cross entropy\\
Classifier activation function	&Softmax\\
Include top layers in base model&	False\\
    Batch size & 16 \\
    \bottomrule
  \end{tabular}
  \label{tab:2}
\end{table}
The models were trained with the TrashNet dataset, which consists of six categories of images: glass, paper, cardboard, plastic, metal, and trash. Only the first five groups of data were used to build the model that classified recycling into five categories. In total, 2152 images were used for training and validation, and 238 images were used for testing. The train/validation/test split is 72/18/10.\par

To begin with, the base model is initialized with pre-trained weights on ImageNet. The two models are trained separately by setting the corresponding input sizes. The learning rate was set to 4.3e-05 at the first 50 epochs and was reduced to 4e-06 at the eight epochs afterwards. Eventually, both models achieved the same test accuracy of 95.38\%. Figure \ref{fig:2} shows the confusion matrix of the test results and indicates that most mistakes are made in classifying between paper and cardboard.\par
 \begin{figure}[H]
 \centering
        \includegraphics[width=0.4\textwidth]{2.png}
        \caption{Confusion matrix of the benchmark models}
        \label{fig:2}
\end{figure}
After the model was reproduced, it was saved into TensorFlow SavedModel format and transferred to Jetson Nano. The 384x288 model was optimized successfully through TensorRT on Jetson Nano, but the 512x384 model caused an OOM error. The throughput of the accelerated model was 19 inferences per second (IPS) at the maximum power mode.\par
Nevertheless, the 384x288 model occupied 94.9\% of memory. The system would freeze up if we ran other applications simultaneously. To implement the classification model in an AI bin application, it is essential to replace the original base model with the lighter model, such as MobileNet V3, which has lower memory usage and power consumption.
\subsection{MobileNet V3 model}
The EfficientNet architecture used in the benchmark model has demonstrated outstanding performance on the ImageNet dataset. In particular, EfficientNet B7 has the same top-1 accuracy, 84.3\%, as the GPipe while being 8.4x \cite{Howard2017MobileNets:Applications}. However, as EfficientNet B0 is the smallest model in the family, with a parameter size of 4.0 M without top fully connected layers, so MobileNet V3 Large, which only has 3.0M parameters and top-1 accuracy of 1.1\% less than EfficientNet on ImageNet, is chosen as the substitute.
MobileNet is a series of CNN architectures developed by Google. It contains three architectures: MobileNet V1 \cite{Howard2017MobileNets:Applications}, V2 \cite{Sandler2018MobileNetV2:Bottlenecks} and V3 \cite{Howard2019SearchingMobileNetV3}. MobileNet V2 achieved higher accuracy on ImageNet with a smaller parameter size than MobileNet V1. The MobileNet V3 optimized the latency of MobileNet V2. \par

Other CNN models also achieved similar accuracies as benchmark models, such as ShuffleNet V2, ResNet50 and Dense-169 as listed in Table \ref{tab:3}, but their parameter size is too large and results in high latency. The parameter size of the MobileNet V3 Large is smaller than EfficientNet B0 when the fully connected layers are removed in both models.

\begin{table}[H]
  \centering
    \small
    \caption{Model classification accuracy on ImageNet  }
  \begin{tabular}{p{3cm}p{2cm}p{2.5cm}}
    \toprule
    \cmidrule(r){1-3}
    CNN   architecture          & Number of   \newline parameters (M) & Top-1   accuracy on ImageNet (\%) \\
    \midrule
EfficientNet   B0 \cite{Tan2019EfficientNet:Networks}  & 5.3                        & 76.3                              \\
MobileNet V3 Large\newline  \cite{Howard2019SearchingMobileNetV3}& 5.4                        & 75.2                              \\
MobileNet V1 \cite{Howard2017MobileNets:Applications}       & 4.2                        & 70.6                              \\
ShuffleNet V2 \cite{MaShuffleNetDesign}     & 7.4                        & 74.9                              \\
Dense-169 \cite{Huang2016DenselyNetworks}         & 14.3                       & 76.2                             \\
ResNet50 \cite{He2015DeepRecognition}          & 25. 6                        & 74.9                       \\
    \bottomrule

  \end{tabular}
  \label{tab:3}
\end{table}

\subsection{Jetson Nano and Kendryte K210 chip}
The model is deployed to Jetson Nano in the previous study \cite{Abdulmahmood2021ImprovingAnalysis}. Jetson Nano \cite{NvidiaJetsonKit} is a powerful single-board computer designed by Nvidia for AI applications. It runs the Linux embedded system, which can execute multiple applications and graphics processing unit (GPU) accelerated neural networks in parallel. Its developer kit has a high-definition multimedia interface (HDMI) port, two camera serial interface (CSI) camera slots and four universal serial bus (UCB) ports that can connect to accessories. The 40-pin header allows it to drive motors and receive signals from sensors. It has a 5 W mode and a maximum power mode with typical power consumption between 5 W and 10W. \par
In this study, we also explored the use of a K210 developer kit to run the waste classification model. K210, in contrast, only runs one model stored in its flash memory during operation without an operating system. Canaan developed it to perform tasks specialized in image and audio processing. It has a neural network processor called knowledge processor unit (KPU) used to accelerate CNN computations, such as convolution, batch normalization, activation, and pooling operations. Images can be read from a digital video port (DVP) camera and displayed on a liquid-crystal display (LCD) screen connected to board. The Sipeed M1w dock K210 developer kit has a maximum power consumption of 3 W. \par
Table \ref{tab:4} shows the specifications of the two embedded devices. Jetson Nano has greater flexibility and computation power for applications, while K210 has lower price and power consumption. K210 only supports 1x1 and 3x3 kernels in CNN, so it cannot implement most CNN structures such as MobileNet V3. The low random-access memory (RAM) limits the model size to 6 MB and the input resolution to 320x240 pixels. In contrast, the 4GB RAM allows Jetson Nano to run more complex models such as Efficient Net B0 with an input of 512x384 pixels. Therefore, K210 is a cheaper substitute for Jetson Nano which runs models lighter than MobileNet V1.\par

\begin{table}[H]
  \centering
    \small
    \caption{Comparison of Jetson Nano and K210 }
  \begin{tabular}{p{3cm}p{2cm}p{2.5cm}}
    \toprule
    \cmidrule(r){1-3}
                       & Nvidia Jetson   Nano 4GB\cite{NvidiaJetsonKit} & Sipeed M1w   dock K210 kit\cite{2019SipeedFeatures} \\
    \midrule

Clock                  & 1.43GHz                  & 0.4GHz                     \\
RAM                    & 4GB                      & 8 MB                       \\
Power consumption      & 5-10W                    & 0.3W-3W   (typically 1W)   \\
Programming   language & Python                   & Micropython                \\
Cost                   & GBP 133                  & GBP 23 \\
    \bottomrule

  \end{tabular}
  \label{tab:4}
\end{table}

After the models are trained in TensorFlow, they are optimized for on-device machine learning before deployment to reduce the size and latency. TensorFlow Lite library \cite{TensorFlowTensorflow.org/lite/performance/model_optimization} can compress the TensorFlow model through quantization, which reduces the precision of the weights from 32-bit floats to 16-bit floats or 8-bit integers. K210 uses nncase \cite{KendryteKendryteNncase}, an open-source library developed by Kendryte, to accelerate the TensorFlow Lite model into its unique kmodel format that optimizes KPU usage through constant-folding, operator replacement and operator fusion. Firstly, constant-folding computes the expressions that only contain known constants and substitutes the answers to save computation cost at runtime. Then, operator replacement changes some operators in the TensorFlow Lite model, which KPU cannot accelerate, with several operators that perform a similar task. Finally, operator fusion combines several operators into a single KPU accelerated operator. These operations optimizes the latency of model running on K210.\par
For Jetson Nano, Nvidia's TensorRT \cite{NVDIANVIDIATensorRT}  library is used to optimize inference on its GPU and provides post-training quantization for TensorFlow model.

\section{Methodology}\label{sec:4}

This section introduces the development of the classification model on Jetson Nano and K210. We collected 1872 new recycling waste data to train new models. Experiments were carried out to evaluate the models' accuracy and power consumption.\par
\subsection{Collect more training data}
Firstly, more recycling waste images were collected to increase the training dataset. As the TrashNet dataset only contains waste images with white background, making the classification model vulnerable to variation in lighting conditions and dirt or contamination in the background. Waste photos with a black background were taken to tell the model the background color is irrelevant in classification and increase the robustness of real-world cases. Additionally, we want to add two classes, "empty" and "hand" to the model, but we do not have images belonging to these categories to train the model. Therefore, we built a detection model with plywood material and collected a domain-specific waste dataset.\par
The waste samples were collected from a nearby recycling bin and from people directly. Figure \ref{fig:3} shows a waste photo taken by the camera at the top.\par
 \begin{figure}[H]
 \centering
        \includegraphics[width=0.3\textwidth]{3.png}
        \caption{A sample waste image taken by the cameras}
        \label{fig:3}
\end{figure}
The "hand" group contains photos of the waste held by hand. Figure \ref{fig:4} shows an example of the "hand" class. If there is a hand in the images, it belongs to the "hand" group, regardless of the type of waste present.\par
  \begin{figure}[H]
 \centering
        \includegraphics[width=0.3\textwidth]{4.png}
        \caption{Sample "hand" images taken by the top camera}
        \label{fig:4}
\end{figure}
The "empty" class only contains a black background. This class tells the bin application that there is nothing in the detection box. An example is shown in Figure \ref{fig:5}.
   \begin{figure}[H]
 \centering
        \includegraphics[width=0.3\textwidth]{5.png}
        \caption{"Empty" images taken by the top camera}
        \label{fig:5}
\end{figure}
We collected 1871 images and combined them with the TrashNet dataset. Table \ref{tab:5} lists the number of images in each class and each dataset.

\begin{table}[H]
  \centering
    \small
    \caption{Number of waste items in each category}
  \begin{tabular}{p{1.5cm}p{3cm}p{3cm}}
    \toprule
    \cmidrule(r){1-3}
 Category  & Number of images in TrashNet & Number of images in our dataset \\
    \midrule

Cardboard & 403                          & 268                             \\
Glass     & 501                          & 130                             \\
Paper     & 594                          & 89                              \\
Plastic   & 482                          & 298                             \\
Metal     & 410                          & 140                             \\
Hand      & 0                            & 920                             \\
Empty     & 0                            & 26               \\
    \bottomrule

  \end{tabular}
  \label{tab:5}
\end{table}

Although recycling waste was divided into five categories, some of the waste was a mixed waste that contained more than one group of materials. For example, a coca-cola tin had a plastic label wrapped around the metal body and a wine glass bottle had a metal cap. In this case, we labelled the items according to the largest component of them. Additionally, different countries and recycling companies have different recycling rules. In the UK, the paperboard materials, such as cartons, are recycled with paper, while in the United States of America (USA), they belong to cardboard. In this paper, we follow the recycling guide of a waste management company in the USA \cite{WMIntellectualPropertyHoldings2020Recycling-Myths}. When it comes to commercialization in the future, it is crucial to consider the recycling rules of the local community.\par

\subsection{Train the models with transfer learning}
The two datasets were used to train new classification models with TensorFlow version 2.9. Firstly, we replaced the EfficientNet B0 model layer in the benchmark model with MobileNet V3 architecture initialized with the pre-trained weights on ImageNet. The MobileNet model has a smaller parameter size and memory usage of the model. Then, the output size of the dense layer was changed to seven to add two new classes to the model. Finally, a resizing layer is inserted between the data augmentation layer and the base model layer to resize the input from 512x384 pixels to 224x224 pixels. By compressing the input after data augmentation, we obtained a clearer image for base model input than compressing the data before the data augmentation layer. \par
Figure \ref{fig:6} illustrates the structure of our model. The model was trained with the hyperparameters shown in Table \ref{tab:6}. The final model was converted to the TensorRT model with TensorFlow version 2.5, and its performance was measured on Jetson Nano.

   \begin{figure}[H]
 \centering
        \includegraphics[width=0.5\textwidth]{image6.png}
        \caption{Waste classification model structure}
        \label{fig:6}
\end{figure}
\begin{table}[H]
  \centering
    \small
    \caption{Training parameters used to build the classification model on Jetson Nano}
  \begin{tabular}{ll}
    \toprule
    \cmidrule(r){1-2}
    Training Parameter     & Value \\
    \midrule
    Learning rate scheduler & Constant learning rate scheduler \\
    Train/Validation/Test split ratio & 72/18/10\\
    Optimizer & Adam optimiser \\
    Training epochs & 50\\
    Learning rate&	4.3e-05\\
    Fine-tuning training epochs	&10\\
Fine-tuning learning rate&	4e-06\\
Loss functions&	Sparse categorical cross entropy\\
Classifier activation function	&Softmax\\
Include top layers in base model&	False\\
    Batch size & 16 \\
    Base model dropout rate& 0.2 \\
    \bottomrule
  \end{tabular}
  \label{tab:6}
\end{table}
\subsection{Build and implement the K210 model}
After building the AI application on Jetson Nano, we used a cheaper and less power-hungry processor, K210, to run the waste classification model. K210 used its own model format called kmodel. Sipeed provides sample code to train classification model based on MobileNet V1 and convert it to kmodel model format with nncase \cite{SipeedSipeedMaix_train}. They applied data augmentation to the image data, as shown in Table \ref{tab:7}, and built a model with structure with TensorFlow, shown in Figure \ref{fig:7}.
\begin{table}[H]
  \centering
    \small
    \caption{Data augmentation used in \cite{SipeedSipeedMaix_train}}
  \begin{tabular}{ll}
    \toprule
    \cmidrule(r){1-2}
    Pre-Processing Method     & Value \\
    \midrule
    Random width and height shift& Up to 20\% \\
    Random rotation & Up to 180\(^{\circ}\) \\
    Random translation & Up to 10\% \\
    Random shear & Up to 50\% \\
    \bottomrule
  \end{tabular}
  \label{tab:7}
\end{table}

   \begin{figure}
 \centering
        \includegraphics[width=0.5\textwidth]{image7.png}
        \caption{MobileNet V1 structure used by K210}
        \label{fig:7}
\end{figure}
We kept the same data augmentation setup and used the same training hyperparameters applied in the previous training, as shown in Table \ref{tab:8}. All training data is resized into 224x224 pixels before training. The model used the same test/validation split 70/15 as our previous models on Jetson Nano. We first trained 110 epochs to find the stopping epochs and then trained the model with the stopping epochs.
\begin{table}[H]
  \centering
    \small
    \caption{Training parameters used to build K210 model }
  \begin{tabular}{p{4cm}p{4cm}}
    \toprule
    \cmidrule(r){1-2}
    Training Parameter     & Value \\
    \midrule
    Learning rate scheduler & Constant learning rate scheduler \\
    Optimizer & Adam optimiser \\
    Learning rate&	4.3e-05\\
Loss functions&	Sparse categorical cross entropy\\
Classifier activation function	&Softmax\\
Include top layers in base model&	False\\
    Batch size & 16 \\
Dropout rate in base model&	0.001\\
Dropout rate at output&	0.001\\
    \bottomrule
  \end{tabular}
  \label{tab:8}
\end{table}
We tested both the implementation of MobileNet V1 and V2 on K210. K210 only supported MobileNet V1 with alpha equal to 0.75 and MobileNet V2 with alpha equal to 0.5. OOM error occurs when a larger model is deployed. The ImageNet  accuracies of the V1 (alpha = 0.75) and V2 (alpha = 0 .5) were 68.4\% \cite{Tensorflow/models/research/slim/nets/mobilenet_v1.md} and 65.4\% \cite{Tensorflow/models/research/slim/nets/mobilenet/} correspondingly, so only MobileNet V1 architecture was trained.\par
The trained model is accelerated into kmodel format and loaded onto K210 together with the firmware using Kflash provided by Kendryte \cite{KendryteKendryteKflash.py}. We used the firmware that only supports basic Application Programming Interface (API) and integrated development environment (IDE) on K210 to reduce memory usage.

\subsection{Measure the performance of the model on the devices}
   \begin{figure}
 \centering
        \includegraphics[width=0.4\textwidth]{14.png}
        \caption{Photo of the final waste classification device}
        \label{fig:75}
\end{figure}
After the classification model was trained, the throughput of each model on Jetson Nano was measured by recording the latency. Figure \ref{fig:75} demonstrated the final waste detection device. The memory usage and power consumption of the device were measured when only the model was running and when the AI application was running at the same time. The memory usage was recorded from the system tool of Jetson Nano. We used Keweisi voltage current meter to measure the power. It was connected between the power supply and the device to test the power consumption of Jetson Nano and K210.

\section{Results and discussion}\label{sec:5}
The accuracies of models obtained from transfer learning will be analyzed in this section. Our best model, the 224x224 pixels MobileNet V3 Large model, achieved an accuracy of 95.98\% in classifying the images into seven groups. The model had 40 IPS on Jetson Nano at 5 W power mode and consumed 30\% less power than the EfficientNet B0 model. The K210 model achieved a further 96.64\% on waste classification while the board's power only drew 0.89 W.
\subsection{Accuracies of models trained with TrashNet dataset on Jetson Nano }

As mentioned in section \ref{bench}, The EfficientNet B0 model has achieved high top-1 accuracy on original test data. However, we could not use this compute-intensive model directly in the application. So, we aimed to construct a less resource-intensive model while maintaining higher accuracy than the benchmark model.\par

Firstly, we applied a 70/15/15 training/validation/test split to the TrashNet dataset by randomly separating 15\% of TrashNet data to form the TrashNet test set. The rest of the TrashNet dataset became a new TrashNet training set. This ratio was used because the author in \cite{YuTowardsClassification} demonstrated that both 15\% and 10\% could produce the same level of classification accuracy on the TrashNet dataset, which is higher than 20\% and 25\% with statistical analysis. Other research \cite{Aral2019ClassificationModels}\cite{Bircanoglu2018RecycleNet:Networks} also utilized 70\% of the TrashNet data as training data. So, we consider that 70/15/15 would also be suitable for model training.\par

We trained and tested EfficientNetB0, MobileNet V3 Large and MobileNet V3 Small with new TrashNet training set using the hyperparameters in Table \ref{tab:6}. All the models converged quickly and reached 90\% of the highest validation accuracy after 8 epochs. Table \ref{tab:9} compares the results we obtained.

\begin{table*}[!ht]
 \caption{Accuracy of three models on 70/15/15 split}
  \centering
  \begin{adjustbox}{width=1\textwidth}
      \small
\begin{tabular}{p{3cm}p{2cm}p{2.5cm}p{2.5cm}p{2.5cm}p{2.5cm}}
\toprule
\cmidrule(r){1-6}
Model \newline Architecture & Total \newline parameters (M) & Total keras(.h5)\newline model size (MB) & Training \newline accuracy (\%) & Top-1 Validation \newline Accuracy (\%) & Top-1 Accuracy \newline on test set (\%) \\
\midrule
EfficientNetB0     & 4.2                  & 15.8                               & 99.44                  & 96.32                          & 94.40                           \\
MobileNet V3 Large & 3.0                  & 11.7                               & 99.50                  & 94.94                          & 95.52                           \\
MobileNet V3 Small & 0.94                 & 3.87                               & 98.94                  & 93.33                          & 93.56                          \\
\bottomrule
\end{tabular}
  \end{adjustbox}
  \label{tab:9}
\end{table*}
As it can be observed, the MobileNet V3 Small model had lower training, validation, and test accuracy than the other two models. In contrast, MobileNet V3 large reached similar training accuracy as EfficientNet B0. The test accuracy was 1.12\% higher, while the validation accuracy was 1.38\% lower. The two models had similar performance on this task while MobileNet V3 Large was lighter and faster, so MobileNet V3 Large was used to build our new model. \par

As the original TrashNet test set only has five classes, 15\% of the "hand" and "empty" group data were separated from our new dataset and combined with the TrashNet test set to form the seven-class test set, which evaluated the model's performance on the two new class. The rest of our dataset was added to TrashNet training data and formed the final training data.\par

We trained EfficientNetB0 and MobileNet V3 Large with the new training data. The same hyperparameters in Table \ref{tab:6} were used to train both models.
The result is shown in Table \ref{tab:10}. The accuracy on 5-class test set of the EfficientNet B0 model increased from 94.40\% in Table \ref{tab:9} to 95.23\%. This indicates that the new dataset had a positive effect on the models and helped them to learn more general features that assist classification tasks. Our photos have different waste samples, lighting and background, so the accuracy on TrashNet will not increase significantly. \par

\begin{table*}[!ht]
 \caption{Accuracies of models trained by the final training data}
  \centering
  \begin{adjustbox}{width=1\textwidth}
      \small
\begin{tabular}{p{3cm}p{2cm}p{1.2cm}p{2cm}p{2.4cm}p{2cm}p{2.4cm}}
\toprule
\cmidrule(r){1-7}
Model \newline Architecture & Base model \newline input resolution (pixels) & Training accuracy (\%) & Top-1 Accuracy on 5-class test set (\%) & Per-class precision of 5-class test set (\%) & Top-1 Accuracy \newline on new 7-class test set (\%) & Per-class precision of 7-class test set (\%) \\
\midrule
EfficientNet B0    & 512x384                                                                           & 99.59                  & 95.23                                   & 94.79                                        & 95.98                                       & 95.97                                        \\
MobileNet V3 Large & 512x384                                                                           & 99.46                  & 94.68                                   & 94.53                                        & 95.38                                       & 95.68                                        \\
MobileNet V3 Large & 224x224                                                                           & 99.46                  & 95.79                                   & 95.70                                        & 95.98                                       & 96.41                                         \\
\bottomrule
\end{tabular}
  \end{adjustbox}
  \label{tab:10}
\end{table*}

The data was also fed to MobileNet V3 Large models with base model resolutions of 512x384 pixels and 224x224 pixels, as we wanted to reduce the computation resources used. Eventually, the 22x224 pixels model achieved the highest accuracy on the TrashNet test set among the three models. The downscaling increased the accuracy. The 224x224 pixels model maintained the original test accuracy as the Efficient Net B0 benchmark model. It achieved a higher per-class precision on both the original test set and the new test set. This suggested that it was less biased towards different categories, which was expected for the application. \par

The larger input size does not always result in better accuracy. In \cite{Hesamian2015EffectNetwork}, the author demonstrated that the misclassification rate of the model reduced gradually when the resolution reduced from 2048x2048 pixels to 128x128 pixels. In our case, 224x224 might be a more suitable input size than 512x384, so the accuracy of MobileNet V3 Large increased when the input size was reduced. However, more experiments must be carried out to determine the best resolution for this application.\par

The confusion matrix in Figure \ref{fig:8} and \ref{fig:9} shows that the model has the highest error rate in classifying between cardboard and paper. The MobileNet V3 model had the lowest sensitivity of 90\% on cardboard against paper, followed by the sensitivity of 94.37\% on plastic against glass. The EfficientNet and MobileNet model showed the same characteristics.

  \begin{figure}[H]
 \centering
        \includegraphics[width=0.36\textwidth]{image8.png}
        \caption{Confusion matrix of EffecientNet }
        \label{fig:8}
\end{figure}
  \begin{figure}[H]
 \centering
        \includegraphics[width=0.36\textwidth]{image9.png}
        \caption{ Confusion matrix of MobileNet 224x224}
        \label{fig:9}
\end{figure}
Finally, The model was accelerated into the TensorRT model and ran on the Jetson Nano. Table \ref{tab:11} shows the inference speed of the three models. Compared to the 384x288 EfficientNet model, the MobileNet V3 computation speed increased because of the reduction of parameter size. The speed doubled for MobileNet V3 with a resolution of 224x224 pixels
\begin{table}[H]
  \centering
    \small
    \caption{Inference speed on Jetson Nano at maximum power mode}
  \begin{tabular}{p{3cm}p{2.5cm}p{2cm}}
    \toprule
    \cmidrule(r){1-3}
Model Architecture   & Input Resolution \newline (pixels) & Inference Per \newline second \\
    \midrule
EfficientNetB0       & 384x288                   & 19                   \\
MobileNet V3   Large & 512x384                   & 25                   \\
MobileNet V3   Large & 224x224                   & 40          \\
    \bottomrule

  \end{tabular}
  \label{tab:11}
\end{table}

To save power, we will run the models at 5 W power mode. This reduces the computation resources available and limits the inference speed. Table \ref{tab:12} shows that although the inference speed is reduced to 13 IPS for EfficentNet, the MobileNet model remains the same inference rate. The memory usage is reduced to 90.9\%. \par
\begin{table}[H]
  \centering
    \small
    \caption{Inference speed on Jetson Nano at 5 W power mode}
  \begin{tabular}{p{1.6cm}p{2cm}p{1cm}p{0.8cm}p{1cm}}
    \toprule
    \cmidrule(r){1-5}
Model \newline Architecture   & Base model Input Resolution (pixels) & Inference Per \newline second & Memory (\%) & Power \newline consumption (W)\\
    \midrule
EfficientNetB0       & 384x288                              & 13                   & 94.93       & 6.682                                                                  \\
MobileNet V3   Large & 224x224                              & 40                   & 90.90       & 4.698                                                                        \\
    \bottomrule

  \end{tabular}
  \label{tab:12}
\end{table}
An AI bin program is programmed using Python Tkinter library. The program interacts with the user through the graphics user interface (GUI) and calls the model to perform classifications. A demonstration video of the program can be found at \cite{XueyingLiDemonstrationPrototype}. The program occupied 6\% of Jetson Nano's memory, so although it cannot run EfficientNet B0 model, MobileNet V3 large model worked fine for it. \par

Finally, as shown in Table \ref{tab:12}, the power consumption was reduced by 29.7\% when MobileNet V3 Large model was used. Running the GUI program did not increase the power consumption. The AI bin program consumed 3.97 W when the model was not inferencing, and Jetson Nano itself consumed 3 W when no program was running on it. Therefore no matter how light model of the model is, it will still consume 3 W power. Also, it took more than 20 minutes to load the model before inferencing, so Jetson Nano had to keep the program running at 3.97 W. \par

If a 92.5Wh battery pack is used, it will only last 23.3 hours in standby mode. The battery life will be shorter if the power consumption of the 10W bulb and the motors are taken into account. The battery pack must be changed manually every day if an independent battery source is used. However, the waste collection frequency in London is once a week.\par

So, the bin needs to connect to a cable or use a larger and more expensive power bank instead. This motivates us to test the performance of K210.\par

\subsection{Accuracy of the model trained with two datasets on K210}

The accuracy of the K210 model was investigated in this section. The variation in training accuracy during the training process is shown in Figure \ref{fig:10}.
  \begin{figure}[H]
 \centering
        \includegraphics[width=0.5\textwidth]{10.png}
        \caption{Change of Model accuracy during training}
        \label{fig:10}
\end{figure}
At 50 epochs, the training accuracy was still increasing. After 100 epochs, it began to oscillate around 97\% and stopped rising further. So, the training was stopped at 100 epochs. \par

The model eventually achieved an accuracy of 96.64\% on the 5-class test set and 95.58\% on 7-class test set. The confusion matrix of the result is shown in Figure \ref{fig:11} . The accuracy of waste classification was higher than the previous models, while the 7-class accuracy was 0.5\% lower than the MobileNet V3 Large 224x224 model. This suggests that K210 could be a substitute for Jetson Nano on the classification task.
  \begin{figure}[H]
 \centering
        \includegraphics[width=0.4\textwidth]{image11.png}
        \caption{Confusion matrix of MobileNet V1 on K210}
        \label{fig:11}
\end{figure}
Then, we loaded the model onto the K210 developer kit and ran the model. Figure \ref{fig:12} shows the photo of K210 when the model was running on it. The camera connected to it took photos of the object and the classification result is displayed on the top of the screen. The inference time was 66 ms, and the device only consumed 0.89W at interference. The power consumption was reduced by 80\% compared to the MobileNet model on Jetson Nano.

  \begin{figure}
 \centering
        \includegraphics[width=0.4\textwidth]{image12.jpeg}
        \caption{K210 is running the classification model}
        \label{fig:12}
\end{figure}
We only demonstrated K210's ability to run the classification model. In future research, the hyperparameters of the model can be fine-tuned to improve the model. Additionally, K210 also supports user interface programming with MircoPython, which could be used in the commercialized product.\par

As the power consumption was less than 1 W, it can be powered by solar panels in the UK. Photovoltaic Geographical Information System \cite{EuropeanCommissionPHOTOVOLTAICSYSTEM} provided daily and monthly average solar radiation data in Europe and other areas through satellites. The monthly radiation on a plane normal to the sunrays in London in 2019 was downloaded from the database. The solar panel could be placed at the top of the bin, which will have at least 1600 $cm^{2}$ area. By using a solar panel with an efficiency of 22\% \cite{LixadaActivity}, the average energy produced a day in an ideal case can be calculated as:\cite{SaurNewsBureau2016HereSystem}
\begin{equation}
    \label{eq}
    E=A*r*H
\end{equation}
where E is the maximum power output, A equals the solar panel area normal to the sunshine, r is the conversion efficiency, and H is the monthly irradiation on the plane normal to the sun rays. Figure \ref{fig:13} shows the electric energy produced by the solar panel per day.\par
  \begin{figure}[H]
 \centering
        \includegraphics[width=0.5\textwidth]{image13.png}
        \caption{Estimated electric energy produced by solar panel in 2019}
        \label{fig:13}
\end{figure}
As it can be observed, the solar panel will have the lowest output in November. Assuming that an ideal 48Wh battery is used to store the power energy produced by the solar panel and powers the K210 board, the battery can supply 1.9 W power in November ideally, while our application only takes 0.89 W. Additionally, it only takes 5 seconds to be switched on and starts detection immediately. This means it will not consume any power in standby mode, significantly reducing the requirements of energy consumption per day. So, using renewable energy to power it may be a feasible choice that can be explored in future studies. The combination of solar panels of 1600 $cm^{2}$ and 48 Wh board would typically cost between GBP 100 and GBP 200. It would be a cheaper option than Jetson Nano.

\section{Conclusion and future work}\label{sec:6}

This research aimed to build a real-time waste classification application on Jetson Nano with an accuracy above 95.38\% and a power consumption below 5 W. To eliminate the power supply cable connection and further encourage an eco-friendly charging method, K210 was proposed to replace Jetson Nano for the applications without self-improvement. \par

The application utilized MobileNet V3 Large as the primary classification model to reduce the model size and latency. We collected 1800 waste images in the target domain of this application with the detection box. The data was added to the TrashNet recycling waste dataset to improve the model's accuracy. Additionally, two new classes were inserted into the classification model that avoids trapping the user's hand and detects the cases when waste is not present. The model eventually achieved a 95.98\% accuracy on the test data and gave a throughput of 40 IPS at 4.698 W. Finally, we trained the MobileNet V1 model with the same training data, and it reached an accuracy of 96.64\%. The model was accelerated and ran on K210. It only consumed 0.89 W power. \par

The K210 developer board costs GBP 23, while the existing intelligent bin product costs USD 5200 \cite{Bin-e}. Therefore, considering the cost of solar batteries, conveyor belts, and physical shells, the production cost may still be attractive for commercialization. \par

Future works can continue to develop the trash bin with the two embedded systems. While Jetson Nano targets the highly accurate bin used in households and offices, K210 can be used in cheaper along-road self-powered bins. For Jetson Nano, the research can investigate the hyperparameters for continual training and implement the algorithm on Jetson Nano with limited computation resources that restricts the training time and samples. For K210, future works can build the circuit board for powering K210 with a solar panel. As the energy generated by the solar panel fluctuates depending on the sunshine, a power management system is required to ensure constant voltage output to K210. Moreover, the performance of the two applications could be tested with a larger unseen real-world test set with a size greater than 10\% of the training data. This will give a better estimation of their performance.\par

In conclusion, this paper has provided a state-of-the-art solution that reduced the power consumption of Jetson Nano by 30\% and increased the inference speed by 200\%. K210 only used 20\% of the power of Jetson Nano while maintaining accuracy. The use of this bin can increase the recycling rate up to 95\%, significantly improving the recycling rate in the UK.
\section{Acknowledgements}
The authors would like to acknowledge their gratitude to Mindy Yang and Gary Thung who open-sourced the TrashNet dataset. The authors would also like to show gratitude to the Institute of Making staff for technical assistance with constructing the detection box.

\normalsize
\bibliographystyle{IEEEtran}

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Is Pamela Adlon Lesbian ?
Pamela Adlon, the American actress is known for her voice acting in a number of popular TV series. She is a voice actress, screenwriter and a producer who made her Hollywood debut in 1982. She has won many awards including Primetime Emmy Award and Peabody Award.
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text:
Q: What do you exactly mean by HashMap's iterator is fail-fast and HashTable's enumerator isn't? I was looking up the difference between the two classes and this point came up in a lot of the answers with this blog being the source:
http://javarevisited.blogspot.com/2010/10/difference-between-hashmap-and.html
However I don't completely get it.
Can someone elaborate this? Perhaps with an example?
Thanks for looking in!

A: The best way is to probably look at the source for each class as implemented by the Open JDK implementation for each class; that way, you can get your answer straight from the horse's mouth, as it were :-)
That aside, essentially, "fail-fast" in this sense means that an Iterator over a HashMap will throw an exception if it detects that another thread has modified the targeted HashMap - if you look in the source for HashMap, you will see this is done by simply checking a counter for the number of expected modifications. If the modification count is different than the Iterator expected, that means that someone else has come in since the last check and messed around with the HashMap, and so the Iterator throws a ConcurrentModificationException.
A "non fail-fast" Iterator wouldn't bother to check, and happily go along it's business in the underlying data structure. Therefore, you gain some flexibility (probably dubious flexibility in this case) in exchange for possibly running into errors later; i.e. attempting to access a value that is no longer present.
As with all fail-fast strategies, the idea is that the earlier an error is detected, the easier it is to recover from or debug.

A: When calling iterator.next(), if any modification has been made between the moment the iterator was created and the moment next() is called, a ConcurrentModificationException is immediately thrown. This is what fail-fast means.
The Enumerations returned by Hashtable don't have this behavior. They assume you know what you're doing, and their behavior, AFAIK, is undefined if you modify the map while iterating over it using one of its enumerations.

A: Fail-fast means when you try to modify the content when you are iterating thru it, it will fail and throw ConcurrentModificationException.
Set keys = hashMap.keySet();
for (Object key : keys) {
    hashMap.put(someObject, someValue); //it will throw the ConcurrentModificationException here
}

For HashTable enumeration:
 Enumeration keys = hashTable.keys();
 while (keys.hasMoreElements()) {
          hashTable.put(someKey, someValue);  //this is ok
    }

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Essay Instructions: Please address the topics and questions below in a write-up not exceeding 10 pages with reasonable font and spacing choices. The assignment grade will contribute 10% towards the total grade. It will depend on the quality of the writing and the originality of the answers, that is, the use of sources that you identify. You are expected to properly cite sources; verbatim quotes should be used sparingly, if at all.
2. What is working well in the EU? What isn?t, and why?
3. Describe the transition to the Euro. Has the common currency been a success?
In your answers to 2. and 3., also address the ongoing Eurozone debt crisis.
1. Describe the political landscape in Germany. What are the most pressing political issues?
2. Describe the educational system in Germany and contrast it with that in the U.S.
3. Has the reunification of East and West Germany been a success?
4. What role does immigration play in Germany? Are foreigners well integrated?
1. How is Germany doing economically right now? In particular, how has the sovereign debt crisis in the Eurozone affected Germany?s economy?
2. What are the key drivers of Germany?s economic performance?
3. How does the German way of doing business differ from the American way?
The purpose of the European Union Environmental Trading System or EU ETS is to combat climate change by reducing greenhouse gas emissions cost-effectively. Governments from many countries have agreed to participate in the program. The ideal proposed by the measure is one that has been accepted across different cultures and backgrounds. The program was created to allow for specific measurments to guage its success.
For this exam you will need to write an argument paper that contains the following:?Research the European Union's Emissions Trading System (EU ETS) and answer the following questions (5pts each):1.What is the stated purpose behind the EU ETS?
2.Why do you agree or disagree with the concept of the EU ETS?
3.What are the current and potential environmental results of the EU ETS?
4.What other effects have occurred because of the EU ETS?
5.Why or why not should the U.S. participate in the EU ETS?
6.What are two other options for achieving the stated purpose behind the EU ETS?
?Create an ethical matrix to support your alternative options. Your matrix should identify key stakeholders and ethical concerns.
?Discuss actions taken by different parties in the discussion that promote or discourage open communication.
?Make sure that your paper follows correct APA formatting and includes a title page as well as all appropriate citations. Your paper should be at least 5 pages long and include at least 4 references.
Essay Instructions: The paper I need written must abide by the following guidelines set by the course and is an argument paper that will not give a personal opinion. The paper must include an abstract as the first page. The references page must be at the end and in-text citations. Thanks so much.
What is the stated purpose behind the EU ETS?
Why do you agree or disagree with the concept of the EU ETS?
What are the current and potential environmental results of the EU ETS?
What other effects have occurred because of the EU ETS?
Why or why not should the U.S. participate in the EU ETS?
What are two other options for achieving the stated purpose behind the EU ETS?
Essay Instructions: Critique the European Union's 'holistic security policy' in its neighbouring region (Eastern Europe/Ex-USSR - Ukraine/Russia/Belarus). Analyse this region from different perspectives, 'holistic security policy' includes economic, political and traditional military power. Policies appropriate: EU Neighbourhood Policy, Common Security and Defence policy, Issues: Russia and oil, Belarus and democracy.
Bukkvoll, Tor (1997) Ukraine and European Security, London: Pinter.
Dannreuther, Roland. (2006) ?Developing the Alternative to Enlargement: The European Neighbourhood Policy?. European Foreign Affairs Review, Vol. 11 No. 2, p183-201.
Emerson, Michael and G. Noutcheva (2004) Europeanisation as a Gravity Model of Democratisation, Centre for European Policy Studies: Working Document No. 214.
European Commission (2004) European Neighbourhood Policy Strategy Paper, COM 2004/ 373 final, Brussels 12.05.2004.
European Commission ?Europe in the World? Website. Country reports, Action Plans and progress evaluations related to the European Neighbourhood Policy are available.
European Commission (2001), The EU and Kaliningrad, COM (2001) 26/3-4.
European Commission (1999), Common Strategy of the European Union on Russia, 4 June, (1999/414/CFSP), Official Journal (L/157).
, accessed 3 December 2007.
Forsberg, Tuomas. (2004) ?The EU --Russia Security Partnership: Why the Opportunity was Lost? European Foreign Affairs Review, 2004, Vol. 9 No. 2, p247-267.
Robinson, Neil (2004) Reforging the weakest link: global political economy and post-Soviet change in Russia, Ukraine, and Belarus Aldershot: Ashgate.
Sachs,Jeffrey ?Consolidating Capitalism?, Foreign Policy, no. 98, spring 1995.
Smith, Michael E, and Mark Weber (2008) ?Political Dialogue and Security in the European Neighbourhood: The Virtues and Limits of ?New Partnership Perspectives?
Tassinari, Fabrizion (2006) A Synergy for Black Sea Regional Cooperation: Guidelines for an EU Initiative, CEPS Policy Brief No. 103.
Weber, Mark (2000) Russia and Europe: Conflict and Cooperation Basingstoke, Macmillan.
White, S., Korosteleva, J. and I. McCallister, (2008). ?A Wider Europe? The View from Russia, Belarus and Ukraine?, Journal of Common Market Studies, Vol. 46. No. 2. pp. 219?241.


text:
If Yes to Kosovo Why not Balochistan?
By Dr. Jumma Khan Marri
Until 1989, the Kosovo enjoyed a high degree of autonomy within the former Yugoslavia, when Serbian leader Slobodan Milosevic altered the status of the region, removing its autonomy and bringing it under the direct control of Belgrade. The Kosovar Albanians strenuously opposed the move.
The independence declaration of Kosovo has long divided the Major world powers most notably the United States and Russia, each one respectively championed for their client state the USA Kosovo's and Russians Serbia's cause. The Kosovo took their case to World Court for a yes ruling, According to the World Court's President, Judge Hisashi Owada, and supported by a majority ruling, "The court considers that general international law contains no applicable prohibition of declaration of independence. Accordingly it concludes that the declaration of independence of the 17th of February 2008 did not violate general international law." http://www.ibtimes.com/articles/37583/20100722/icj-kosovo-serbia-world-court-independence-court-sovereignty.htm
Baloch case is similar to that of Kosovo only the Baloch are an orphan nation without their own powerful uncle to look after their national interests, it is up to Baloch national leaders and the intellectual to find an uncle who could lobby for them at international court of justice, the united nations and other world forums to press their interests, Baloch must stand united and clearly state their goals to international community to understand.
The author in his previous article "Energy security in Exchange for Independence" published in many sites highlighted the goals for just and peaceful independent Balochistan. http://countercurrents.org/marri260610.htm
http://thebalochhal.com/2010/06/%e2%80%9cenergy-security-in-exchange-for-independence%e2%80%9d/
Clear explanation of goals is one of the conditions of primary importance in achieving the goals outlined. Proceeding from this principle, the Baloch side has to formulate the legitimate goals of its movement of national and state independence plainly and clearly ever since the very beginning: the movement for restoration of national and state independence of the Baloch Nation, which was lost partially during the British colonial rule and completely after artificial creation of Pakistan.
It is important to mention that in its actions from the very beginning the Baloch national movement for restoration of national and state independence has been sticking to the principles of the International Law, while most scrupulously complying with the juristic and legal regulations proclaimed by the UN, other international organizations.
Regardless of Baloch people's fundamental principles and choice that the Baloch side had been following the laws of the international community, the international community has ignored all of those steps, which were legitimate from the standpoint of the International Law. The principle of "political rationality" was assigned paramount importance. Proceeding from this fact, the attitude of the so-called international community (represented by G8) towards the Baloch State was predetermined by the relationship with Pakistan. Thus the Western politicians violated the basic legal principles that they themselves had ratified and that were supposed to settle the postwar (1945) relations in the world.
Thus, starting from the very first days of its existence the Baloch State encountered hypocrisy, unscrupulousness, lies and cynical practice of "double standards", which are dominating all around the world most notably the united states. We cannot even hear mere verbal condemnations of Pakistani state sponsored terrorism officially proven by recent Wiki leaks and methods of warfare imposed on Baloch nation by Pakistani and Iranian occupying forces in Balochistan and its "concern about violations of human rights in Balochistan" were in sharp contrast with the Pakistani blockade of international journalists and humanitarian assistance that the Baloch people faced.
If the leaders of Western states had not been adhering to the cynical position that "Balochistan is Pakistan's internal affair", which they have mutely expressed, then the situation would not have been that tragic, because Pakistan would not have been able to be waging a long-term war in the Balochistan with the obvious scarcity of its economic and political resources and with total absence of moral resources.
Possessing incomparable moral advantage, the Baloch side would have been able to put the "evil Pakistani army" back where it belongs long time ago. But today they are not even making a secret of the fact that Pakistani military clique is continuing its genocidal war in the Balochistan exclusively with financial, economic, political and informational support from the Western states most notably the United states of America.
The reality has delivered Baloch from the last illusions. Along with the illusions concerning the "international community" and the "international law" virtually all disagreements of ideological and moral nature among the forces of the Baloch Resistance have disappeared as well. The Baloch nation got consolidated and turned to their historical experience, where freedom was the main value as its practical embodiment in the relations within the Baloch society.
Baloch objectives in relation to the conflict with Pakistan are:
1. A verifiable stop to all military action and the immediate ending of violence and repression;
2. Withdrawal of the Pakistani military, police and paramilitary forces from Balochistan;
3. The unconditional release of all political and other prisoners and safe return of all refugees and displaced persons and unhindered access to them by humanitarian aid organisations;
4. The establishment of a political framework agreement for Balochistan on the same basis of the Kosovo Accords, in conformity with international law and the Charter of the United Nations.
5. Two year time frame must be given to Baloch people without occupation to heal their wounds.
6. A free and fair referendum must be held under supervision of international community so that people of Balochistan give their verdict, how they see their future?
The achievement of these objectives, accompanied by measures to ensure their full implementation, has been regarded by the Baloch side as the prerequisite for bringing to an end the violence and human suffering in Balochistan.
The Baloch national goal declared above had been clearly and unambiguously formulated and ratified by the Baloch people represented by their legitimate political leadership: protection and restoration of their statehood and state independence of the Baloch nation based on the international law.
Any attempts to ignore this fundamental and basic goal of the Baloch nation, or any attempts to block the achievement of this goal (by using military force, political machinations, all sorts of tricky concepts, blackmail, creating artificial new leaders etc.), whoever they may be coming from, are the main source and the main reason for instability in the region and threat of permanent war.
On the contrary, respecting and understanding the goals of the Baloch nation, and helping the Baloch State in achieving the proclaimed goal is the main condition of bringing stability, security, peace and predictability to this strategically very important region and to the entire world's stability and economical development.


My name is Seth Blais, and I am a person in recovery from a Substance Use Disorder. I have personally struggled with the misuse of drugs and alcohol for more than a decade. I think it's important to openly and honestly share my experience, and I'm fortunate to be in a position where I feel comfortable doing so.
Over a period of ten years, I experienced many personal failures and major consequences because of my substance use. During this time, I was [mostly] successful at hiding my use from everyone around me. As an entrepreneur and salesperson, I worked hard to maintain the appearance of success. I never seemed able to reach out for help or be fully honest with myself and those who cared about me.
Today, much of my time is spent reaching out to peers who also struggle with Substance Use Disorders. I volunteer as a Recovery Coach with the Westbrook Recovery Liaison Program and give time advocating for organizations like Young People in Recovery. Giving back is what allows me to remain healthy and happy.
The idea behind this blog is to share my personal experience, honestly and with purpose. I don't believe there should be stigma attached to being a person in recovery, and there should be no shame in asking for help. If I'm going to make that statement, I feel that I also need to be willing to raise my hand and identify as someone with a Substance Use Disorder.
I'm not an expert or a medical professional, and I'm not qualified to provide treatment or advice. What I can provide, are honest self-reflection, hard-earned life lessons, and relevant commentary on issues surrounding substance use.
To get my latest articles in your inbox, sign up for my newsletter.
Please follow and like my page on Facebook.


Like and fluffy, but also rich and decadent, this classic Cheese Souffle with roasted asparagus will have your family and friends begging for more. Ready in under an hour, this is the perfect Spring recipe!
Butter 6 ramekins and place in the fridge to chill while preparing the filling.
Roast the asparagus on a baking sheet in a 400 degree oven for 15-20 minutes, until browned and tender. Set aside.
Beat the egg whites until stiff peaks form. Set aside.
Make the bechamel sauce (see below) and add the asparagus, bacon, and brandy. Stir until combined.
Add the cheese, stir and remove from heat and place in a large bowl. Add any salt to taste at this point.
Gently fold the egg whites into the bechamel sauce, making sure the egg whites don't deflate. The filling should still be light and fluffy when you're done. It's ok if it doesn't look fully mixed.
Fill the ramekins to the top edge with the souffle mixture. Place ramekins in an ovenproof baking dish filled with 1/2 to 1" of water. This helps keep the souffles from cracking.
Bake for 20-25 minutes, until golden brown and souffles have risen up over the edge of the ramekins.
Serve immediately, alone or drizzled with hollandaise sauce (totally try the hollandaise sauce).
Heat the butter in a skillet over medium heat. Add the flour and stir until combined.
Slowly add the warmed milk, just a little at a time to keep lumps from forming. Make sure to whisk constantly as you're adding the milk.
Once milk is incorporated, add the black pepper, nutmeg and cream.
Make sure the egg whites reach stiff peaks. Otherwise the souffle may not rise correctly.
Gently fold the bechamel mixture into the egg whites. Over mixing can cause your souffle to deflate quickly or not rise at all.
Always generously grease the ramekins before adding your batter. If the ramekins are not greased, the batter may stick to the edges and not rise as intended when baking.
For a clean look, run a knife edge across the top of the ramekin to smooth the batter.
Always bake souffles on the bottom rack of the oven.
Use a toothpick to check for doneness. If it comes out clean, the souffle is done. If it has batter on it, cook for another 2-3 minutes until toothpick comes out clean.


text:
Knud Erik Hansen (født 12. marts 1951) er en dansk socialdemokratisk politiker, som fra 2010 til 2017 var borgmester i Faxe Kommune, hvor han efterfulgte René Tuekær fra Borgerlisten. Ved årsskiftet 2017-18 blev han efterfulgt af Ole Vive fra Venstre.

Eksternt link
 Danske Kommuners borgmesterfakta

Tidligere borgmestre fra Danmark
Borgmestre fra Socialdemokraterne

redpajama_set_name: RedPajamaWikipedia


Product prices and availability are accurate as of 2019-04-12 20:17:56 EDT and are subject to change. Any price and availability information displayed on http://www.amazon.com/ at the time of purchase will apply to the purchase of this product.
The ideal choice to hold your printer, fax machine or personal computer, the Vertiflex Mobile Underdesk Machine Stand is a must for any company. Made of durable steel and wire construction, this cart is mobile with four twin-wheel casters, two of which are locking. It features two convenient drawers to hold printer supplies, specialty paper, etc. This easy to assemble stand comes in a black finish that coordinates with any decor. Advantus Corp. is dedicated to providing innovative quality products at fair prices.


LightAware's comments are in response to EU plans to further restrict the availability of different types of lighting in the European Union.
The regulations will be effective from September 2020 and will be published in October 2018 assuming completion of the impact assessment, the approval of committees, and the approval of the European Parliament.
The current draft proposes to extend the ban to mains tungsten halogen lamps, most compact fluorescent lamps and T8 linear fluorescent. This draft also removes many of the categories of special purpose lamps including those or people with photo sensitivities. and for theatrical lighting.


DaVinci Long Flexi StrawLong Flexi-Straw provides cooler vapor for your DaVinci Vaporizer.
DaVinci Oil Can SetOil Can Set is for use with liquids and solid concentrates in DaVinci vaporizer.
Ascent Glass SetSet includes glass mouthpiece and internal glass piece.
Ascent Oil Jar SetOil Jars allow for vaporization of liquids with the DaVinci Ascent. Simply fill with liquid and insert into Ascent's chamber.
Ascent Wall ChargerReplacement or extra 110v-240v Wall Charger for Ascent.
DaVinci IQ Soft CaseAlways on the go? Protect your vaporizer and accessories with the DaVinci soft case. This soft case features a stylish exterior with a smell proof zipper, exterior pocket, and pockets for your vaporizer, accessories and anything else you want to pair with your vaporizer.Works perfect with the Davinci IQ Vaporizer!
DaVinci IQ Glass SpacersPerfectly pack the bowl of your IQ Vaporizer every time with these glass spacers. Set includes  10mm glass spacer and  6mm glass spacer.
DaVinci IQ 18650 Battery ChargerWe don't want you left with a low battery so we added an external battery charger to our lineup. With the 18650 battery charger, you can charge two 18650 batteries on the charger at once and FAST. Works with any 18650 batteries but we recommend using the OEM DaVinci 18650 Batteries with this battery charger.
DaVinci IQ Flavor ChamberCool your vapor, enhance your flavor or elevate your experience with the Flavor Chamber for the iQ. It is made of 100% zirconia ceramic making it highly durable and the perfect channel to add natural herbs, flowers and more with the purest flavor in mind. Elevate your experience today!
DaVinci IQ PearlThe IQ Pearl helps you to pack your herb in the bowl firmly. It pushes your material to maintain surrounding radiant temperature for your herb to vaporize evenly.
DaVinci IQ 6000MaH Powerbank Powerbank for the DaVinci IQ Vaporizer. Works with any USB device. Gives about 2 full charges to the Davinci IQ. You can even use the IQ while it's charging!
DaVinci IQ Accessory KitThis kit includes a chimney brush, alcohol wipes, a pair of orings and a spare mouthpiece gasket. Make sure to use this for cleaning and properly maintaining your vaporizer unit. A must have kit if you are a heavy IQ user!
Davinci IQ USB AC Adapter (US)Get this adapter to make sure that you chargers will function to any outlet here in the US. You'll never know when you need 'em. Does not come with a USB cord, recommended to use the OEM Davinci IQ USB cord with this item.
DaVinci IQ USB CableLow on battery? Make sure you keep this fast-braided USB cable for your USB charger in your bag or pocket. Standard 110-220v which compliments the IQ both in style and functionality.
DaVinci IQ USB Car ChargerLeft your charger at home and need to charge your IQ when driving to work? This is the time when having a USB car charger in your vehicle makes perfect sense.Does not come with a USB cord. We recommend using the OEM Davinci IQ USB cord with this car charger.
Davinci IQ Zirconia Mouthpiece w/ GasketDesigned for ultimate discretion in mind, the IQ mouthpiece is also made of 100% zirconia ceramic which has the same pure flavor and smooth taste when you use glass but only 20x times more durable. Pick up an extra mouthpiece today and always have a clean mouthpiece waiting.


text:
News | 16th Apr, 2014
Call for more money for Vic mine clean-up
COMMUNITY groups want a $90 million fund for new brown coal technology in Victoria's Latrobe Valley to be instead used to pay for the Hazelwood coalmine fire clean-up.
The Voices of the Valley spokesman Simon Ellis said $2 million in support announced by the Victorian government had already been spent, but the clean-up was not close to being finished.
The federal and Victorian governments each pledged $45 million to the Advanced Lignite Demonstration Program back in 2012, to entice companies to develop ways to use brown coal from the valley more profitably with reduced emissions.
"It would be an insult to Latrobe Valley residents if the state and federal government's first significant response to the Hazelwood fire was to find new coal mines and technologies," Mr Ellis said.
He said the priority should be to clean up Morwell homes and prevent any further fires at the Hazelwood mine.
Read the full article >
Make a submission to the Hazelwood mine fire inquiry >


BELLARMINE-JEFFERSON HIGH — For the third-straight season, the Bellarmine-Jefferson High girls' volleyball team played its way to the CIF Southern Section quarterfinals.
The Guards found themselves in a tough fight early on in their elite-eight contest against a talented Pacifica Christian team. Although the Seawolves showed their ability to fight back, Bell-Jeff displayed an aptitude for closing out games, as it secured a 26-24, 22-25, 25-18, 25-16 Division IVAA victory Saturday at home.
"We were having problems with our serve, and that really threw us off," said Bell-Jeff Coach Miguel Segura, whose squad advanced to the semifinals for the second straight season. "We lost a lot of points with our service mistakes."
In the match, the Guards committed 15 service errors, including seven in the third game, which Pacifica Christian (22-8 and winners of the Heritage League) won.
In the first three games, Bell-Jeff — champion of the Santa Cruz League — had leads of six or seven points late in the stanzas, only to see Pacific Christian battle back.
The Guards were able to stave off the charges in every game but the second. Bell-Jeff enjoyed a seven-point advantage, 19-12, until Pacific Christian embarked on a 10-2 run to take the lead, 22-21. That run helped give the team momentum to win the game.
Jocelyn Cervantes paced the Guards with 22 kills.
"When we lost the second game I told them that we're just going to have to fight back," Cervantes said. "We knew we had to play better."


text:
One week left to nominate for the Godolphin Stud and Stable Staff Awards
09 Nov 2016 Education/Training
Nominations close on Wednesday 16 November
£120,000 in prize money on offer to winners and runners up
Nominations can be made by anyone within the industry, including trainers, owners, managers and colleagues
Awards benefit the whole team, with separate prizes awarded to the yard of each winner
Today marks the final week to nominate stud and stable staff for the 2017 Godolphin Stud and Stable Staff Awards.
The nomination period closes Wednesday 16 November for the 13th running of the annual awards that recognise and reward the outstanding skills, commitment and contribution of racing's 6,000 stud and stable staff.
Nominations can be made by anyone within the industry, so trainers, owners, managers and colleagues are all encouraged to nominate during the final days.
Sponsored by Godolphin and run by the British Horseracing Authority in association with the Racing Post, the awards offer more than £120,000 in prize money, with up to £40,000 available to a single yard or stud.
The 2017 award winners will be selected from a shortlist who will be interviewed by a panel of judges, overseen by Brough Scott.
The winners will then be announced during a ceremony to be hosted by the newly-appointed face of ITV Racing, Ed Chamberlin on 20 February 2017 at the Jumeirah Carlton Tower Hotel in Knightsbridge, London.
The awards are an important event in the racing calendar and are a good example of the racing community coming together to acknowledge the work that goes on behind the scenes.
Nick Rust, Chief Executive of the BHA, said:
"With just one week left to nominate, I'd like to remind and encourage owners, trainers, colleagues and managers to show your appreciation to the workforce racing couldn't be without. These awards are so important in giving employers and the industry the opportunity to recognise and reward the outstanding work stud and stable staff do all day, every day."
Nomination forms can be downloaded from the recently launched website www.studandstablestaffawards.co.uk, via www.racingpost.com and submitted by post. Alternatively, simply text AWARDS to 07469 145 150 and a member of the team will arrange for a form to be sent to you.
1. The categories are: Leadership Award, Dedication Award, Rider/Groom Award, Stud Staff Award, Rory MacDonald Community Award and, David Nicholson Newcomer Award.
2. The winner of each of the first four categories will receive a prize of £5,000 with an additional £5,000 also being awarded to the winner's yard or stud. Two runners-up in each of these categories will win £2,000 each, with the same amount going to their yard or stud.
The winner of the Rory MacDonald Community Award will receive £5,000, with the same amount going to a charity of their choice. The two runners-up will receive £2,000, with the same amount going to a charity of their choice.
The winner of the David Nicholson Newcomer Award will receive £2,000, with the same amount going to their yard or stud, while two runners-up will receive £1,000 each, with the same amount for their yard. The winner of this award will also receive an additional prize of a five-day educational trip to Dubai. Return flights and accommodation in a five-star hotel included.
The Awards will also recognise an Employee of the Year, who will be selected from the winners of the first four categories. That winner will receive an additional prize of £15,000, with the same amount going to their yard or stud.
3. Last year saw the launch of the 'Rory MacDonald Community Award', which will acknowledge those who support stud and stable staff or have made an outstanding contribution to benefit the wider racing community. The Awards provide an opportunity for the racing industry to highlight and thank the hard work put in by stud and stable staff across the country, and this award in particular acknowledges people who have had a positive impact on their community.


Drinker suitable for all larger animals. Heavy gauge steel, galvanised after manufacture. Finished with no sharp edges or corners to protect your animals. Fitted with BS1212/PT2 Mains Pressure Ball Valve as standard. Supplied with drain hole and plug.


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text:
package samples.junit4.rules;

import org.junit.Before;
import org.junit.Rule;
import org.junit.Test;
import org.junit.rules.TemporaryFolder;
import org.junit.runner.RunWith;
import org.powermock.modules.junit4.PowerMockRunner;

import static org.hamcrest.CoreMatchers.*;
import static org.junit.Assert.assertThat;

@RunWith(PowerMockRunner.class)
public class RuleOrderTest {

    private static final String EMPTY_STRING = "";

    @Rule
    public TemporaryFolder folder = new TemporaryFolder();

    private String temporaryFileName = EMPTY_STRING;

    @Before
    public void setup() throws Exception {
        temporaryFileName = folder.newFile("tempFile").getPath();
    }

    @Test
    public void rulesAreExecutedBeforeSetupMethods() throws Exception {
        assertThat(temporaryFileName, not(nullValue()));
        assertThat(temporaryFileName, not(equalTo(EMPTY_STRING)));
    }
}

redpajama_set_name: RedPajamaGithub