text:
Home > Proceedings > Volume 2493
PROCEEDINGS VOLUME 2493
SPIE'S 1995 SYMPOSIUM ON OE/AEROSPACE SENSING AND DUAL USE PHOTONICS | 17-21 APRIL 1995
Applications of Fuzzy Logic Technology II
Editor(s): Bruno Bosacchi, James C. Bezdek
1 Sessions, 36 Papers, 0 Presentations
Section (36)
SPIE'S 1995 SYMPOSIUM ON OE/AEROSPACE SENSING AND DUAL USE PHOTONICS
Present at an SPIE Conference
VIEW ALL ABSTRACTS +
Fuzzy sets and neurocomputation: knowledge-based networks
Witold Pedrycz
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, pg 2 (13 June 1995); doi: 10.1117/12.211789
Read Abstract +
Fuzzy neural networks combining advantages of numerical computations of neural networks and symbolic processing originating from artificial intelligence are typical constructs of computational intelligence. The paper analyzes basic processing components (fuzzy neurons), proposes several general architectures, elaborates on learning algorithms, and provides a series of application cases.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print format on SPIE.org.
Fuzzy neural network machine prognosis
Patrick K. Simpson, Thomas M. Brotherton
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, pg 21 (13 June 1995); doi: 10.1117/12.211798
The ability to predict failures in machinery before they occur would save time, money, and lives. The Army has several areas that would benefit from this ability. Mechanical components could be replaced before they caused catastrophic damage. Electronic components could be replaced in communication and weapon systems before they endangered a mission or lives. One area that would benefit immediately from this ability is predicting the fatique life of the Army's CH-47 helicopter. The CH-47 is a twin-rotor platform that depends on the reliability of its engine, transmissions, rotors, flight controls, and a myriad of other equipment. Predicting the fatigue life of a CH-47 would save the Army operation and support costs through spares elimination and more timely maintenance cycles. We have developed a methodology for a machine fatigue life predictor that utilizes a combination of parameter estimation, model generation, and condition identification. Using data collected from various fault conditions on the tail rotor assembly of a helicopter, we have simulated fatigue conditions and demonstrated the developed methodology.
Neurofuzzy clustering approach for quadtree segmentation of images
Suryalakshmi Pemmaraju, Sunanda Mitra
Segmentation of images is used for several purposes such as estimation of the boundary of an object, shape analysis, contour detection, texture segmentation, and classification of objects within an image. Despite the existence of several methods and techniques for segmenting images, this task still remains a crucial problem. In our research we have developed a neural network-based fuzzy clustering technique to segment images into regions of specific interest using a quadtree segmentation approach. Since different regions of an image contain varying amount of detail, it is advantageous to segment the regions into blocks of different sizes depending on the content of information present within each block. As the global features of an image are distributed over a wider span of the image and the finer details are concentrated in limited regions, a quadtree segmentation algorithm can efficiently tackle the problem of segmenting images of all kinds. However, block-based techniques tend to introduce blocking artifacts and this problem can be avoided by using a neuro-fuzzy clustering scheme to merge the neighboring blocks of similar regions in a smooth fashion. The proposed algorithm has been applied to images of different kinds and has yielded promising results.
Connectionist approach to adaptive reasoning
Mohan S. Reddy, Abhijit S. Pandya, D. V. Reddy
This paper illustrates the neural net approach to constructing a fuzzy logic decision system. This technique employs an artificial neural network (ANN) to recognize the relationships that exist between the various inputs and outputs. An ANN is constructed based on the variable present in the application. The network is trained and tested. After successful testing, the ANN is exposed to new data and the results are grouped into fuzzy membership sets. This data grouping forms the basis of a new ANN. The network is now trained and tested with the fuzzy membership data. New data is presented to the trained network and the results from the fuzzy implications. This approach is used to compute skid resistance values from G-analyst accelerometer readings on open grid bridge decks.
Generalized predictive neurofuzzy control model for nonlinear dynamical systems
Emre Kaymaz, Sunanda Mitra
A generalized controller based on fuzzy clustering and fuzzy generalized predictive control has been developed for nonlinear systems. The identification of the system to be controlled is realized by a three-layer feed-forward neural network model. The speed of convergence of the neural network is improved by the introduction of a fuzzy logic controlled backpropagation learning algorithm. The neural network model is then used as the system simulator for developing the predictive fuzzy logic controller. The use of fuzzy clustering facilitates automatic generation of membership relations of the input-output data. Unlike the linguistic fuzzy logic controller which requires approximate knowledge of the shape and the numbers of the membership functions in the unput and output universes of the discourse, this integrated neuro-fuzzy approach allows one to find the fuzzy relations and the membership functions more accurately. Furthermore, there is no need for tuning the controller. A nonlinear heating/cooling system is chosen as an application. The performance of this predictive fuzzy controller is shown to be superior to that of on/off, PI, and linguistic fuzzy logic controllers in terms of both the accuracy and the consumption of energy.
Fuzzy neuron modeling based on algebraic approach to fuzzy sets
Wladyslaw Homenda, Witold Pedrycz
Emerging approaches to modeling fuzzy neurocomputing are solely based on logic operators and/or represented by min and max operators and their extensions--triangular norms. The max-min based models of fuzzy neurons suffer from a kind of insensitivity: only the weakest (strongest) argument(s) affects the result of min (max) operator. While this feature could be taken as an advantage in some cases, in genral it may produce highly undesireable results. Aggregating inputs of a neuron with max-min model results in ignoring most of incoming pieces of information. On the other hand, replacing max-min operations by triangular norms, though removing insensitivity drawback, creates a problem related to a relevant handling of negative nature of information to be processed in neural network. In this paper, new fuzzy-set oriented model of neuron is introduced and analyzed. The architecture of this neuron is based on the selection of positive and negative types of information. The idea of such selection was applied in fuzzy neurons introduced by Hirota and Pedrycz and Pedrycz and Rocha. While the models of those neurons involve max and min operators and triangular norms, the neuron presented here utilizes a certain extension of fuzzy sets. This extension is based on algebraic operators rather than on triangular norms. Subsequently, the formalism is capable of representing negative as well as repetitive information--an evident advantage over conventional fuzzy set connectives. Moreover, processing fuzzy information with aglebraic operators is compatible with the nature of common models of nonfuzzy neurons. Main features of introduced models of neuron are presented and some characteristics of the neuron are discussed. The comparison with other models of fuzzy neurons is also summarized.
Fuzzy logic approach to enhance the performance of remote manipulator systems
Robert N. Lea, Joe A. Mica, Yashvant Jani, Edgar Dohmann
In this paper we extend previous work done at the NASA Goddard Space Flight Center on the development of a fuzzy logic controller to generate individual joint rate commands to the robotic arm in response to human input, via remote manipulation of translational and rotational hand controllers. In this phase of the study we have utilized the Goddard Space FLight Center Remote Manipulator hardware/software system in which the fuzzy logic controller has currently been implemented, and have used this system for tuning the controller for near optimal response. Performance results from the system studies are presented.
Least-biased fuzzy clustering method for inhomogeneous data
Gerardo Beni, Xiaomin Liu
We have extended the least biased fuzzy clustering algorithm to inhomogeneous data sets. The resolution parameter is generalized from a scalar to a vector with the dimension of the feature space. We fix the orientation of the resolution vector to measure the relative inhomogeneities of each cluster of data points in the different dimensions; and we study the effect of the magnitude of the resolution parameter on the phase transitions yielding the clusters. Based on the detection of the onset of a phase transition, a new technique for truncating the iteration scheme of solution reduces the computational complexity to the order of the number of data points. The actual computational load of the algorithm is discussed and examples are given to illustrate the performance of the algorithm in clustering inhomogeneous data sets.
Applications of fuzzy integrals to handwriting recognition
Paul D. Gader, Magdi A. Mohamed, James M. Keller
Proc. SPIE 2493, Applications of Fuzzy Logic Technology II, pg 102 (13 June 1995); doi: 10.1117/12.211792
Two applications of fuzzy integrals to handwritten word recognition are discribed. Fuzzy integrals are used in a dynamic programming based, offline handwritten word recognition algorithm. This algorithm finds optimal matches between word images and strings in lexicons. Fuzzy integrals are used to compute the match scores. Fuzzy integrals are also used in a hidden Markov model based, offline handwritten word recognition algorithm. In this case, fuzzy measures and integrals are used as alternatives to probabilistic measures and ordinary integrals. Results are presented on standard data sets consisting of real images collected form United States Postal Service mail stream.
Fuzzy controllers and fuzzy expert systems: industrial applications of fuzzy technology
Piero P. Bonissone
We will provide a brief description of the field of approximate reasoning systems, with a particular emphasis on the development of fuzzy logic control (FLC). FLC technology has drastically reduced the development time and deployment cost for the synthesis of nonlinear controllers for dynamic systems. As a result we have experienced an increased number of FLC applications. In a recently published paper we have illustrated some of our efforts in FLC technology transfer, covering projects in turboshaft aircraft engine control, stream turbine startup, steam turbine cycling optimization, resonant converter power supply control, and data-induced modeling of the nonlinear relationship between process variable in a rolling mill stand. These applications will be illustrated in the oral presentation. In this paper, we will compare these applications in a cost/complexity framework, and examine the driving factors that led to the use of FLCs in each application. We will emphasize the role of fuzzy logic in developing supervisory controllers and in maintaining explicit the tradeoff criteria used to manage multiple control strategies. Finally, we will describe some of our FLC technology research efforts in automatic rule base tuning and generation, leading to a suite of programs for reinforcement learning, supervised learning, genetic algorithms, steepest descent algorithms, and rule clustering.
Fuzzy control and multimedia with examples from law enforcement
Susan Hackwood
We present an extension of fuzzy controllers to include multimedia rules, i.e., rules which do not include verbal or numerical descriptors. We describe the structure and construction of such a multimedia fuzzy controller. In particular, we describe an empirical but unbiased methodology to measure, from human subjects, distances in feature space and hence determine fuzzy memberships. We also propose a practical multimedia fuzzy controller and describe its application examples are given from the law enforcement field where man-machine interactions are important and applications of the methodology described in this paper appear promising.
Intelligent robotic systems: control and cooperation
Mohan M. Trivedi
In this paper, we discuss development of useful intelligent systems, emphasizing an integrated systems perspective. In particular we will highlight two important ideas. The first is associated with the sensor-based control, for realizing effective and robust approaches for processing and analysis of sensory information associated with the state of the environment and the state of the intelligent system for reflexive and deliberative tasks. The second is associated with cooperation, for developing teams consisting of heterogenerous and complementary intelligent modules. The aim is to develop useful and effective intelligent systems involving teams of players which are either all robotic devices or some robotic and some human.
Adaptive SAR raw data reduction using fuzzy logic
Ursula C. Benz
The Earth's surface and subsurface can be mapped with a very high resolution using synthetic aperture radar (SAR). It is an effective tool for remote sensing, since it works nearly independent from daylight and weather conditions. However, the huge amount of signal data difficults the on-board storage and the data downlink to the ground station. This paper presents a raw data compression algorithm using fuzzy logic, the fuzzy block adaptive quantizer (FBAQ), which improves the efficiency of the system. The data rate is reduced by a factor of 3 with a slight deterioration of the image quality. The performance of the FBAQ is compared with the traditional algorithm, a block adaptive quantizer and its hardware design for implementation in an airborne SAR system is shown.
Study and optimization of a neural network with fuzzy logic preprocessing for particle identification on a cosmic ray detector
Marco Casolino, M. Candusso, M. P. DePascale, Aldo Morselli, Piergiorgio Picozza, M. Ricci
A fuzzy logic preprocessing is used in connection with a back propagation neural network in particle recognition. As application on 4 GeV CERN experimental and Monte Carlo data of e- and (pi) (superscript -, taken with the prototype of the silicon Tungsten calorimeter of the Wizard collaboration, is shown. This preprocessing consists in giving as input to the net the membership value, for a given discriminating parameter value, to belong to a given particle class. In this way the input layer receives a normalized input. The net can then exploit the correlations between different parameters, resulting in an increased convergence speed and recognition capability of the net. Other advantages of this approach are its noise robustness and the simple generalization of other particle classes or energies.
Soft learning vector quantization
James C. Bezdek, Nikhil R. Pal
Learning vector quantization (LVQ) often requires extensive experimentation with the learning rate distribution and update neighborhood used during iteration towards good prototypes. A single winner prototype controls the updates. This paper discusses two soft relatives of LVQ: the soft competition scheme (SCS) of Yair et al. and fuzzy LVQ equals FLVQ. These algorithms both extend the update rates that are partially based on posterior probabilities. FLVQ is a batch algorithm whose learning rates are derived from fuzzy memberships. We show several relationships between SCS and FLVQ; and we show that SCS learning rates can be interpreted in terms of statistical decision theory. Finally, we show the relationship between FLVQ, fuzzy c-means, hard c-means, a batch version of LVQ, and SCS.
Principle of least commitment in the analysis of chromosome images
James M. Keller, Paul D. Gader, Charles W. Caldwell
The automation of chromosome identification and visualization for a complete cell (karyotyping) has been the subject of considerable research. While rather high classification rates are possible on individual chromosomes, the cell level classification rates are still quite low. We describe a system which uses partial confidence values generated by neural and fuzzy classifiers with optimization to increase the cell level recognition rates. This is consistent with Marr's Principle of Least Commitment for the design of intelligent computer vision algorithms.
Motion control of a mobile robot traveling in a convoy using fuzzy logic
Suresh B. Marapane, Martin B. Holder, Mohan M. Trivedi
In many applications, the use of multiple robots of different capabilites will be desirable. In a teaming approach, multiple robots will cooperate to accomplish a task which a single robot alone is incapable, inefficient, or impractical at performing. When multiple robots are deployed, the motion of each robot may have to be coordinated with the rest of the robots. This paper deals with development of a fuzzy logic based motion controller for convoying the coordinated motion between a leader and a follower. The fuzzy logic based real-time motion controller uses relative distance and speed information to adust the speed of the trailing robot to smoothly follow the leader without either collisions or loosing sight of it. The performance and robustness of the system has been verified in a series of extensive laboratory trials.
Mobile robots: human analysis of color and surface for fast object identification
Dario Mancini, Annalina Auricchio, Massimo Brescia
Object recognition is usually obtained by using some kind of different approach based on profile reconstruction, whose results are not always reliable or sufficient for a right object identification. The present paper describes an alternative method to profile calculation, based on color and surface information associated to the images. The method is also used to define the system typology on the basis of a detailed analysis of human instinctive behaviors associated to the task to be performed by the system.
Generalized fuzzy k-means algorithms and their application in image compression
Nicolaos B. Karayiannis
This paper presents the development of generalized fuzzy k-means algorithms and their application in image compression based on vector quantization. The development of generalized fuzzy k-means algorithms is based on the search for partitions of the feature vector space other than those generated by existing fuzzy k-means algorithms. These alternative partitions can be obtained by relaxing one of the conditions imposed on the membership functions. The clustering problem is formulated as a constrained minimization problem, whose solution depends on the selection of a constrain function that satisfies certain conditions. The solution of this minimization problem results in a broad family of generalized fuzzy k-means algorithms, which include the existing fuzzy k-means algorithms as a special case. Moreover, the proposed formulation results in the minimum fuzzy k-means algorithms, which are computationally less demanding than the existing fuzzy k-means algorithms. A broad family of admissible constrain functions result in an extended family of fuzzy k-means algorithms, which at the limit provide the fuzzy k-means and minimum fuzzy k-means algorithms. The resulting algorithms are used in image compression based on vector quantization.
Fuzzy logic information fusion for object recognition
Byron H. Chen, Ching-Fang Lin, Stelios C.A. Thomopoulos
Object recognition is often performed in an environment full of uncertainties. Typical factors are the imprecision introduced by the limitations of image processing algorithms, the misinterpretation of the feature vector due to noise or occlusion, and the infinite variability of the object features due to continuous environment change as well as countermeasures. An integrated approach (statistical methods, multisensor fusion, and fuzzy logic) for automatic object recognition if presented in this paper. A fuzzy scene representation is proposed to cope with uncertainties. The features of the object and the background are obtained from both a priori knowledge and the data collected by a multisensor suite and then reconstructed for object recognition. A recognition scheme, based on fuzzy logic, has been developed to merge the information from multiple sources of differing resolution and confidence into a combined assessment of the object identity. It has been found that the fuzzy logic based information fusion architecture provides a platform to accommodate the output of different types of image/signal processing algorithms. In addition, it allows the input of the temporal scene development through an expanded feature vector. Details of the fuzzy scene representation and the recognition process are discussed. Experimental results are presented to show the potential of the approach.
Fuzzy logical semantics of Bayesian decision making
Peter V. Golubtsov
An approach to uncertainty and decision making problems alternative to probabilistic one is studied. It is based on fuzzy sets technique and has deep relations with the underlying first order fuzzy logic. In this logic not only logical connectives but also quantifiers have fuzzy interpretation. It is shown that all fundamental concepts of probability and statistics such as joint distribution, conditional distribution, etc., have meaningful analogs in new context. Thus the classical concepts obtain fuzzy-logical semantics. As a result one may treat them as formulas of first order logic. It is shown, that this appraoch makes it possible to utilize rich conceptual experience of statistics. In particular it leads to fuzzy Bayesian approach in decision making, which plays the same role in fuzzy problems of optimal decision making as its probabilistic prototype in the theory of statistical games, and provides methods for construction of optimal strategies. Connection with underlying fuzzy logic provides the logical semantics for fuzzy decision making. In this approach a priori information is represented by a fuzzy predicate and an experiment--by fuzzy universal quantifier, etc. As a result the notion 'good decision strategy' is expressed by a first order formula in this logic.
Adaptive fuzzy logic tracking controller
Jatinder S. Bedi, Yeongseob Kueon
The membership functions are the critical factors of the system performance for fuzzy logic control. The degree of membership is determined by a membership function, which is defined based on experience or intuition of engineers. In general, it is accepted that membership functions changes several times as the system is tuned to achieve desired responses to given inputs. Once the system is in operation, the membership functions do not change. So the membership functions must be tuned properly to obtain the desired system performance. In this paper, we propose the technique to overcome the tuning difficulties of membership functions by trial and error method. This technique has been applied to the system with parameter uncertainties to investigate the performance.
Use of sliding mode controller functions in a TSK-type FLC
Min Xu, Samuel M. Smith
In this paper, a TSK type fuzzy logic controller (FLC) with sliding mode controllers (SMC) as its rule consequents is developed, namely a fuzzy sliding controller (FSC). As FSC is a nonlinear controller and its parameters can be easily decided through an analysis of the desired slope and the boundary layer's thickness of the SMC in different regions in the state space. Moreover, the FSC can provide a nonlinear static mapping with fewer rules, and its stability can be analyzed through the sliding condition of the SMC. The derivation of gradient descent based update equations for learning the parameters in each rule of the FSC is included in this paper. Simulation results with an inverted pendulum model are presented. Comparisons between the FSC and a TSK type FLC with linear output functions are also included in this paper.
Fuzzy logic controller and real-time implementation of a ball balancing beam
Kim C. Ng, Mohan M. Trivedi
A ball balancing beam is a nonlinear dynamic system which is quite difficult to control using convention methods since some special mathematical techniques and control theory knowledge are required to derive the equations. There are difficult issues in this system: it has delayed feedback associated with control actions; and the 'jumping ball' phenomenon brings sensor uncertainty. Balancing is essential in carrying out robotic tasks such as transporting dynamic system. Relative to conventional controllers, a fuzzy logic controller requires less mathematical derivation in design; it has high noise tolerance, probably gravity independence, and it is portable to other scales of same system setup such as with a shorter beam or a heavier ball. In this paper, membership function construction, and fuzzy rule generation are discussed in detail. The hardware setup is shown. In addition, ways to overcome feedback delays and noisy signals are presented. Finally, the performance of the fuzzy controller will be evaluated and compared to conventional and neural network controllers.
Fuzzy inference system for semiconductor manufacturing processes
Raymond L. Chen, Costas J. Spanos
A systematic approach is presented for modeling qualitative properties in semiconductor manufacturing processes. This approach is based on the fuzzy logic theory, and on the statistical analysis of categorical data. A fuzzy inference system can be designed and created by training data obtained either from human expert knowledge, or automatically extracted from statistically designed experiments. Before being used to design the fuzzy system, the data extracted from the designed experiments can be processed and filtered with the help of linear and logistic regression analysis. After the establishment of the initial inference system, the fuzzy membership functions can be tuned adaptively to accommodate process changes.
Fuzzy logic and manufacturing science
Bruno Bosacchi
This paper discusses the potential of fuzzy logic for manufacturing science. After a general introduction of manufacturing science as a complex systems to which Zadeh's Incompatibitlity Principle applies, the paper presents a bird's eye view of fuzzy logic applications to operational and engineering aspects of manufacturing science, and, in particular, to reliability studies.
Modeling product quality from a flexible machining center using fuzzy Petri nets with neural networks
Moheb Maurice Hanna
In this paper, an intelligent control architecture has been proposed to model, monitor, and control the product quality in a CNC milling machining center. The adopted architecture uses a fuzzy timed Petri net (fuzzy logic with timed Petri nets) and synergistic neural networks. The paper demonstrates how fuzzy input variables, fuzzy marking, fuzzy time-firing sequences, fuzzy outputs reasoning, and a global output variable should be defined for use with fuzzy timed Petri nets. The technique employed with the fuzzy timed Petri net uses two fuzzy input variables, spindle speed, and feed rate, through the milling process in order to maintain product quality characteristics such as surface finish. It also illustrates how the technique reacts when the product quality is high, medium, or low.
Fuzzy logic approach to vulnerability assessment
Kirk A. Dunkelberger
Within the context of our sensor fusion systems, we define an entity's vulnerability as the certainty with which other entities have the capability to detect and/or strike the entity; vulnerability assessment (VA) is the inference of vulnerability certainties. This investigation considers two issues: the feasibility of a fuzzy VA algorithm and the interface of a fuzzy VA algorithm into an existing sensor fusion system, including human-machine interface aspects. Relative kinematics, sensor/weapon technical capabilities, sensor/weapon system state, contextual electronic signatures, physics, terrain, atmospherics, and doctrinal bias are certainly all viable inputs to a VA algorithm. These data are traditionally characterized by a mix of continuous, discrete, and/or symbolic values with associated error bounds in various mathematical forms. Hence, the algorithmic infusion of a fuzzy VA into this systemic environment implies resolving the uncertainty information content of these representations and integrating them into a coherent fuzzy reasoning context. The information overload facing the tactical operator has necessitated the reduction of many data to prioritized simple alerts. While there is a reasonable understanding of the visual representations and implications of thresholding probabilistic data, the presentation and thresholding of fuzzy data is not well understood; some of the more critical implications on the human-machine interface are presented herein.
Interactive query specification in fuzzy document retrieval systems using genetic algorithms
Tetsuya Murai, Hideo Kanemitsu, Masaoki Miyakoshi, Masaru Shimbo
An interactive query specification method in fuzzy document retrieval using a genetic algorithm is proposed based on a possible-worlds perspective where every world is represented by a bit code of 1 and 0 which corresponds to truth and false, respectively. Given a query, several seed worlds are generated in a set of worlds and seed worlds. Schemata generated through selection, crossover, and mutation can be regarded as candidates for possible needs implicit in the user, and then he can select some of these schemata to specify his need and revise his query interactively.
Input-output fuzzy systems and the inverse problem
Yeongseob Kueon, Jatinder S. Bedi
The paper presents the inverse problem concerned with fuzzy relations. A method for the inverse solution of a max-min compositional relation equation, based on the intermediate value theorem, is called the bisection algorithm, or binary-search method. The benefits of the inverse problem is used not only for its theoretical implications but also for its applicability on real problems of optimization and control of fuzzy logic systems.
Determining data clusters' fine center, extents, and membership functions using a modified mountain method
James A. Kelly, Ronald R. Yager
This paper describes a stand alone Mountain Method (MM) to determine clusters' fine center position, extents, and membership functions. The motivation for this fuzzy clustering algorithm is to improve the approximate cluster centers resulting from the MM without experiencing the problem of local minima, and provide the additional cluster information necessary to fuzzify a system. After briefly reviewing the MM, this paper discusses the methodological considerations for effective cluster determination, modifications to the MM, and classical data set results using the MM, fuzzy c-mean (FCM) and the modified Mountain Method (MMM). The MMM compared to FCM algorithm achieved cluster centers resulting in a standard error of 0.166 and 0.052 with respect to Anderson's Iris, and Yager and Filev's data sets. The methodology discussed herein is the subject of a US patent, entitled 'Multiple Target Discrimination Algorithm', serial number 5,396,252, assigned to United Technologies Corporation. The patent addresses discriminating multiple targets or clusters of digitized sensor data for determining fine position and extents thereof.
Fuzzy understanding of neighborhoods with nearest unlike neighbor sets
Belur V. Dasarathy
A new fuzzy learning and classification scheme based on developing a fuzzy understanding of neighborhoods with nearest unlike neighbor sets (NUNS) is reported in this study. NUNS, by definition, the set of samples identified as the nearest from the other class(es) for each given sample, represent in essence the boundaries between pattern classes known in the problem environment. Accordingly, samples close to the NUNS are likely to have more ambiguity or uncertainty in their labels than those farther away from these NUNS. This information about the uncertainty or imprecision in the labels of the given training set can be extracted and represented in terms of fuzzy memberships. These fuzzy membership values, which may be determined in the learning phase using appropriate fuzzy membership models, can then be utilized in the classification phase to derive the identity of an unknown sample. This classification can be accomplished using any one of the established fuzzy classification techniques.
Fuzzy logic anti-skid control for commercial trucks
Mark L. Akey
A fuzzy logic (FL) anti-skid brake controller (ABS) is proposed as the next generation ABS replacing current generation finite state (FS) control. The FL controller is part of a commercial truck braking system, encompassing reverse front-back braking proportions on an articulated vehicle as compared to that found on fixed, passenger car systems. In this early research, the FL controller must satisfy three goals. The first goal is to produce superior braking distances over that of the finite state controller, specifically under low (mu) conditions. The second goal is to provide superior braking under varying system conditions (road surface conditions, physical brake parameters, wheel velocity sensor parameters). The third goal is to provide a convenient, flexible, and tractable ABS solution which is amenable to redevelopemnt to different vehicular platforms. Monte Carlo simulation results illustrate stopping distance improvements of 5 to 10 % averaged over all (mu) surfaces for varying wheel loads. On low (mu) surfaces, the improvement increases to 15% (up to a full tractor-trailer length). These results are obtained while varying other system parameters demonstrating robustness. Finally, the fuzzy logic rule sets and the overall configuration illustrate a straight-forward design and maturation process for the rule sets.
Adaptive method for the creation of mapping models
Nikita V. Dolgushev, Stanislav A. Suvorov
In this work the principles of making a method for creating an object with required attributes are discussed. The method was illustrated by the example of the creation of analytical function, which has a geometrical image closely spaced to a required one. The computer program was made to implement the discussed method. The convergence of the algorithm was analyzed.
Image understanding in terms of semiotics
E. Zakharko, Roman M. Kaminsky, V. Shpytko
Human perception of pictorial visual information is investigated from iconical sign view-point and appropriate semiotical model is discussed. Image construction (syntactics) is analyzed as a complex hierarchical system and various types of pictorial objects, their relations, regular configurations are represented, studied, and modeled. Relations between image syntactics, its semantics, and pragmatics is investigated. Research results application to the problems of thematic interpretation of Earth surface remote imgages is illustrated.
Structure-changeable fuzzy controller
Chenghua Fu, Lenian Ye, Jian-Guo Bai
Referring to the different feedback information, the structure--changeable fuzzy controller presented in this paper select different control modes, such as PID, FLC, separately. It is known by studying the simulation results that the structure--changeable fuzzy controller, can eliminate the steady state error and result better robustness than classic PID controller.


Incandescent bulbs, CFL bulbs, LED bulbs. What light bulb is best?
What Light Bulb is Best?
Lighting in our homes has changed as our technology changes. With several choices in light bulbs available on the market now, it's important to know what light bulb is best for you.
Incandescent bulbs are the traditional bulbs found in homes for years. These bulbs use electrical current to create heat in the metal filament, which produces light when brought up to an incandescent temperature.
Since light fixtures were made with these bulbs in mind, they fit everywhere. There are many different wattages, colors, and shapes. Because of their design, though, they can be hot enough to burn when touched, or cause fires if placed too closely to flammable material. Their thin glass breaks extremely easily. They are also much more expensive to use than LED or CFL bulbs.
Using only about 20% of that used by the incandescent alternative, CFL bulbs became a thrifty alternative in the early 2000s. Although they had a twisty-tubular design, they caught on fast as a "green" bulb. Until it became general knowledge that they were made with mercury and had to be disposed of carefully. GE announced they would stop producing them in 2016, but they can still be bought in stores.
The light from these bulbs can be unpleasant, and even gives some people headaches. Because of the chemical danger a broken CFL bulb can cause, All Seasons does not allow these bulbs to be used in our homes. For more information about this, please call your Property Manager.
LED light was first discovered in 1907 and put to use in 1972 as the indicator light in electronics. The main thing keeping them from use was the cost of production, which has mainly been solved. But even today, they are more expensive to buy, although stores like Amazon sell them for a little as $14.00 for 6 bulbs.
LED light is not produced from heat, so the energy savings is 90% over incandescent bulbs. Smaller wattage means less electricity is needed to produce the same amount of light. Because no dangerous chemicals are used, the LED lights are much safer than CFL bulbs.
One of the main advantages of LED bulbs is their long lifetime. They can last up to 40,000 hours of use, quickly paying for themselves in saved utility costs.
A new feature of LED bulbs is their ability to be used as smart bulbs. Their electronic design lends control to the bulb itself through internal WiFi, instead of having to control the power outlet to turn the bulb on and off.
More than just on and off control, though, comes with these bulbs. A smartphone or hub can control their color, timing, and some have Bluetooth speakers built-in. You can schedule lights to come on while you're on vacation.
Here are some of the pros and cons of LED bulbs.
Incandescent bulbs have always been allowed in All Seasons homes. CFL light bulbs are not allowed, due to the mercury content in them and risk of mercury exposure to the Resident if they were to break.
LED bulbs are now allowed and encouraged! If you would like to talk to All Seasons about swapping out your incandescent bulbs for the LED alternative, call our office at: (719) 632-3368, and speak with your Property Manager.


text:
WASHINGTON (AP) - Federal prosecutors have drafted an indictment against six Blackwater Worldwide security guards in last year's deadly Baghdad shootings of 17 Iraqi civilians, The Associated Press has learned.
The draft is being reviewed by senior Department of Justice officials, but no charging decisions have been made. A decision is not expected until at least later this month, people close to the case said.
Also still undecided is whether the justice department would charge the guards with manslaughter or assault, said the people, all of whom spoke on condition of anonymity because they were not authorized to speak publicly about the case.
It's also possible that prosecutors ultimately will seek charges against as few as three of the guards, whose identities are still secret. Depending on the charges, an indictment would carry maximum sentences of five to 20 years.
An indictment would send the message that the justice department believes U.S. contractors do not operate with legal impunity in war zones.
It's an untested legal theory because the law is murky on whether contractors could be charged in U.S. courts, or anywhere, for crimes committed overseas.
Blackwater guards opened fire in a busy intersection Sept. 16, 2007, in what witnesses said was an unprovoked attack. Young children were among the 17 civilians killed. The shootings outraged Iraqis and embarrassed the United States, further straining relations between the two nations.
Blackwater is adamant that its guards, who protect U.S. diplomats, were ambushed by insurgents in Baghdad's Nisoor Square.

redpajama_set_name: RedPajamaCommonCrawl


I admit, I wouldn't have predicted its popularity. But of course, the show isn't really about clutter. Like other shows in the "rehabilitation" genre, "Hoarding" is about transformation.
From despair to redemption. From dirty to pure. From helpless to empowered.
It's a modern spin on the hero's journey, and it may be a perfect model for your AdWords campaign. If your prospects are searching because they have a problem that they've so far been unable to solve, you can structure your ads and landing pages based on the Hoarding template.
Each of these short teasers is a premise of a story. Our human brains are hardwired to want to know what happens next, and how it all turns out. Your AdWords ads can function in much the same way as teasers, and will stand out from all the boring ads that focus just on features and benefits.
(I just copied and pasted those quotes straight from a Google search for [landscaping durham nc]).
The first ad represents a straight list of features.
The second ad is slightly better, highlighting an outcome and sharing an offer.
The lawn company's chemicals made her children sick.
Harry gazed as his neighbor's yard in envy and shame.
In order to write a teaser ad, you have to get clear on the deepest pain that triggered the search. Why does someone really want landscaping? Each of the above teaser ads aims at a different motivator, and triggers a different emotional response.
In either case, the real subject of the story is your prospect, either directly or through identification. That's why it's so important to make your hero likeable, even though they may be deeply flawed.
Meet Cheryl and Joe, who just bought their first house and want to create a vegetable garden in their side yard, to inspire and feed their kids.
Everything they plant withers and dies.
Cheryl feels like crying every times she goes outside or looks out the window.
They neighbors, with their tidy green lawns, give her dirty looks and mutter about "falling property values." She complains to Joe that he isn't spending enough time helping her. Tension builds in the home.
Finally, Cheryl orders a soil test and discovered diazinon, malathion, and lead in the soil.
Shocked into action, she call in Bountiful Backyards landscaping company.
The landscaper comes over and shows Cheryl and Joe how to bio-remediate the soil using Japanese Knotweed and Salt Cedar plants.
Skip "Challenges and Setbacks" for now.
Cheryl and Joe now grow dozens of fruits, nuts and vegetables in healthy soil. The neighbors all want to know how they did it. The couple and their children are happy and healthy.
Testimonials from Cheryl and Joe and dozens of other clients.
Call to Action – you can be Bountiful Backyard's next success story.
Return to Challenges and Setbacks in the form of Objections Raised and Answered.
Repeat the Call to Action.
The foundation of this style of search advertising is deep empathy with your prospect. If you tell their story and highlight what matters to them, you'll command their attention and stoke their hunger for transformation.


text:
Top Las Vegas Tours by Duration
357 results | All Las Vegas Tours
2-Day Private Tour: Grand Canyon, Zion Canyon and Bryce Canyon from Las Vegas
Minimum of 2 people required Private tour. COVID-19 Privet Tour for Safety. Departing from Las Vegas, the tour course includes visits to the Grand Canyon & Zion Canyon & Bryce Canyon. We offer you the most unforgettable memories on the west desert. The Tour provide the best tour service with limited capacity of no more than 10 people per group. We assure that you will have plenty of time to enjoy the scenery, sightseeing, as well as trekking. Furthermore, our tour guide will help you to take amazing pictures at the hidden spots that people generally don't know about. We will do our best to provide comfort and safety to everyone who travels with tour for 1 night and 2 days Departure time around 7am Arrival time in Las Vegas is around 7pm.
5-day Tour: Utah Mighty 5 from Las Vegas
Ideal for first-time visitors to Utah, this introduction to Utah's craggy scenery and interesting rock formations covers five national parks in five days. Round-trip transportation from Las Vegas aboard an air-conditioned coach, four nights' hotel accommodations, daily breakfasts, and entrance to all the parks is included. Your guide points out all the key sights and shares the geological and human history in each park.
Grand Canyon and Antelope Canyon Small Group Overnight Tour
See the iconic landscapes of the Southwest on this 2-day tour from Las Vegas to Arizona's top natural attractions. While it's difficult to fit in all these sights while traveling on your own, this efficiently organized guided tour squeezes in three Grand Canyon overlooks, Antelope Canyon, Horseshoe Bend, and Hoover Dam. Plus, you even have time to stop for photos and learn about the sights along the way.
Grand Canyon, Monument Valley, and Zion Winter 3-Day Tour from Las Vegas
Explore the Southwest's iconic National Parks on a 3-day tour from Las Vegas to the Grand Canyon, Monument Valley, Lake Powell, and Zion. Discover sweeping views as you walk the South Rim of the Grand Canyon, and watch sunset illuminate the desert landscape. Take a jeep tour of Monument Valley's back roads with a Navajo guide, and then capture classic images of Lake Powell's rocky shoreline. Trace America's geologic history in the cliffs at Zion National Park, and hear stories about the Southwest's earliest inhabitants before returning to Las Vegas.
3-Day Tour: Sedona, Monument Valley and Antelope Canyon from Las Vegas
Experience the best of the desert Southwest on a 3-day tour from Las Vegas to Sedona, Monument Valley, and Antelope Canyon. Cross the scenic Mojave, and take in views from the Colorado Plateau as you travel historic Route 66 through the rocky landscape. Explore Sedona's soaring formations and downtown boutiques, then go deep into Navajo nation on a jeep tour of Monument Valley. Watch the Colorado River rush through Horseshoe Bend, see the evening light on Lake Powell, and visit towering dunes and sandstone cliffs in parks near Las Vegas.
Small-Group 3 Hour Hoover Dam Mini Tour With Strip Pickup
Visit Hoover Dam on a Las Vegas "mini" tour that doesn't take up a whole day so you don't waste your precious vacation time. Or, perhaps you have a late flight and want to do something fun after hotel check-out. After departing the Strip, head to the dam, where you visit lookout points for views and photo ops.
Valley of Fire & Hoover Dam Photo Tour from Las Vegas
Tour the Valley of Fire with a guide and get the best possible photos of the otherworldly landscape on this photography tour. With round-trip transportation from Las Vegas included, there's no need to rent a car and you can enjoy the scenery on the journey. Spend the day exploring the unique landscape and capturing photos of the area's flora, fauna, and natural formations.
7-Days Zion, Bryce, Monument Valley, Arches, Canyonlands, Grand Canyon South Rim
Visit some of the most popular national parks in the United States with an expert guide on a 7-day tour from Las Vegas. Admire the natural beauty of Zion, Bryce Canyon, Arches, Canyonlands, and the Grand Canyon national parks, plus a trip through Monument Valley. Select optional activities such as a Grand Canyon helicopter flight to customize your tour. Includes transport by climate-controlled minivan, some meals, six nights of accommodation, and all national park entrances fees.
3-Day National Parks Tour from Las Vegas: Grand Canyon, Zion and Bryce Canyon
Explore the Western United States' stunning desert landscapes on this 3-day tour from Las Vegas. With an expert guide, visit Zion National Park, Bryce Canyon National Park, and the Grand Canyon. See Lake Powell and other land and water formations as you travel through the beautiful deserts of Arizona and Utah. All ground transportation, two nights of accommodation, entrance fees, and daily breakfast are included.
Big Bus Las Vegas Open Top Night Tour
As dusk falls, the city comes to life illuminated by the dazzling neon lights Las Vegas is famous for. Take in dancing fountains and erupting volcanoes as you travel the Infamous Las Vegas Strip. The tour leads you past casinos, wedding chapels and historic landmarks as you enter the heart of downtown. Choose to go on an adventure with your tour guide as you walk down Fremont Street. During the walking tour you will see sights such as a 61 pound golden nugget! After a brief guided walking tour, you will be able to immerse yourself in the best that downtown has to offer: live entertainment, street artists, costumed entertainers and of course the word famous Fremont Street Light and Sound show.
Small-Group Zion National Park Day Tour from Las Vegas
Escape from the hustle of the Las Vegas Strip to a place of peace, stunning beauty and complete relaxation on this full-day adventure to Zion National Park. This park is located just 163 miles from Las Vegas near Springdale, Utah. See Zion's many breathtaking viewpoints, its vast array of plant and animal life, plus Checkerboard Mesa, the Virgin River and much more. Also, travel through the Zion-Mount Carmel Tunnel and along the Zion Canyon Scenic Drive. Lunch, snacks, bottled water and a hotel pickup and drop-off are included.
Red Rock Canyon & Hoover Dam Half-Day Photo Tour from Las Vegas
Whether you're shooting for print or social media, capture brilliant images of scarlet cliffs, blue sky, and muted desert flora on this private photo tour of Red Rock Canyon, just minutes from Las Vegas. Your personal guide knows all the best viewpoints to capture landscapes and selfies, and can let you into the area's secrets as well. Needless to say, your day includes door-to-door round-trip private transfers.
Ultimate Hoover Dam Tour from Las Vegas With Lunch
On this enjoyable tour, you will get a comprehensive VIP tour of the Hoover Dam. This tour gets you to the dam before the crowds arrive, giving you more time to enjoy your destination! Included is a bonus stop at the Las Vegas Welcome sign, a stop at the Hoover Dam Museum in Boulder City, the Hoover Dam memorial bridge (where you can get beautiful photos of the dam from afar), and this tour also includes the exclusive, signature tour, "Walk on the Top". This is a 45 min fully guided tour over the Hoover Dam, with your guide touching on a wide variety of subjects relating to the dam including stories of its construction, unbelievable facts and figures related to the dam, and information regarding the fascinating operations of the dam. A seated hot freshly cooked lunch at a restaurant is also included as well as complimentary Mac King tickets ($45 value)
Grand Canyon West Rim Bus Tour
Marvel at the incredible scenery of the Grand Canyon's West Rim thanks to this 12-hour flexible sightseeing trip from Las Vegas. Take a hop-on, hop-off bus tour along the edge of the canyon, stopping at highlights like Eagle Point, Guano Point and Hualapai Cowboy Ranch. Optional trip upgrades including a helicopter flight or access to the Skywalk viewing platform are also available.
Award Winning Red Rock Canyon Tour
Leave behind the hectic crowds of Las Vegas and spend the day exploring desert landscapes on this doors-open private van tour. Cruising through picture-perfect scenery with the doors open ensures you don't miss any of the sights along the way. As you stop at highlights such as Calico Hills and American Indian heritage sites, your guide explains the geological and cultural significance that you might overlook on your own.
Grand Canyon National Park VIP Tour from Las Vegas
Explore the most popular areas of the Grand Canyon on this guided, full-day sightseeing adventure around the South Rim. Beginning in Las Vegas, travel past Hoover Dam, Eldorado Canyon State Park and famous Route 66 in Kingman, Arizona en route to the Grand Canyon's South Rim for an entertaining and educational 3-hour tour. See a number of different panoramic views from various lookout locations while learning about the history and geology of the canyon from the expert tour guide. Continental breakfast, lunch, bottled water and Las Vegas hotel pickup/drop-off included.
Grand Canyon South Rim Bus Tour with Optional Upgrades
Explore the Grand Canyon National Park on this guided, full-day sightseeing adventure tour around the South Rim. Travel to the South Rim and the National Geographic Visitors Center. See panoramic views from various lookout locations while learning about the history and geology of the canyon from your guide. Upgrade to include an IMAX movie and/or tour of the canyon rim by Jeep or a scenic helicopter flight. Las Vegas hotel pickup and drop-off are included.
Private Tour: Death Valley Day Tour with Evening Milky Way Viewing
❥-❥-❥ This place where I have experienced the most stars and Milky way in my life ❥-❥-❥ Minimum of 2 people required Private tour. COVID-19 Privet Tour for Safety. It's the only tour to day and night in the Death Valley. You can enjoy a leisurely tour in the day and see the stars and the Milky Way at the same time in the evening. We leave Las Vegas around 9am ~ 1pm. and have a tour of about 11 hours. Death Valley has the darkest night sky in the United States, and is a great place to gaze awe in the expanse of numerous stars, milky Way, see the details of the moon, and trace the meteor shower. And Death Valley National Park, one of the hottest, driest and lowest in North America, is believed to feature miles of deep sand dunes, colorful rocks and canyons, a wide variety of wildlife and one type of evaporated salt. We invite you to an unbelievable beautiful place. This product is not a trekking tour but a photo tour. Schedule may vary depending on local weather and situation.
Small-Group Hollywood and Los Angeles Day Tour from Las Vegas
When visiting fabulous Las Vegas, escape the casino scene for a day to visit the home of the US film industry — Hollywood, California. On this guided, small-group adventure, travel through Death Valley and Joshua Tree National Park en route to Los Angeles. Explore iconic local spots like the Hollywood Walk of Fame, Santa Monica and much more. A continental breakfast, plus lunch, snacks, bottled water, any necessary entry fees and a Las Vegas hotel pickup and drop-off are included.
Hoover Dam Hummer Tour from Las Vegas
Travel to the Hoover Dam in a luxury Hummer H2 during this half-day excursion from Las Vegas. Upon arrival, take the Discovery Tour, an experience that includes riding an elevator into the depths of the Black Canyon's walls to check out the power plant generators.
Small-Group Hoover Dam VIP Tour from Las Vegas
Enjoy an educational and entertaining trip to Hoover Dam on this guided, 5-hour, small-group sightseeing adventure from Las Vegas. Visit the Hoover Dam Visitor Center and Generator Room, enjoy an informational movie and check out the view from the top of the dam and Lake Mead. Learn about the history of Hoover Dam, with an abundance of staggering facts. On the return trip to Las Vegas, make a short visit to a local park where herds of bighorn sheep typically graze. All entrance fees, snacks and bottled water are included.
Valley of Fire Day Trip from Las Vegas
Valley of Fire State Park is a public recreation and nature preservation area covering nearly 46,000 acres (19,000 ha) located 16 miles (26 km) south of Overton, Nevada.The state park derives its name from red sandstone formations, the Aztec Sandstone, which formed from shifting sand dunes 150 million years ago.These features, which are the centerpiece of the park's attractions, often appear to be on fire when reflecting the sun's rays. It is Nevada's oldest state park, as commemorated with Nevada Historical Marker #150. It was designated as a National Natural Landmark in 1968.
Small-Group Death Valley National Park Day Trip from Las Vegas
Discover the lowest point in the Western Hemisphere at Death Valley National Park, the largest National Park in the U.S., on this guided, full-day excursion from Las Vegas. Travel on board an air-conditioned minibus to one of the most desolate places on earth, and learn about Death Valley's history, landscape, and wildlife along the way. See other desert highlights including Furnace Creek, Artist's Palette, Badwater Basin, Zabriskie Point, and more. A boxed lunch, plenty of bottled water, and hotel pickup and drop-off are included.
Area 51 Day Tour from Las Vegas
Discover Area 51 on this guided, full-day adventure from Las Vegas. Travel through some of the most desolate, uninhabited and scenic desert landscape in the world to witness this infamous location. See top-secret Janet Airlines at Las Vegas International Airport, the mutant Joshua Tree Forest, mysterious Dry Lake, plus alien petroglyphs, the Black Mailbox and more. Stop for lunch at the Little A'Le'Inn. Lunch, snacks, bottled water and a hotel pickup/drop-off included.
What are the best Tours by Duration in Las Vegas?
The best Tours by Duration in Las Vegas according to Viator travelers are:
See all Tours by Duration in Las Vegas on Viator.
What Tours by Duration in Las Vegas are taking additional safety precautions?
What Tours by Duration in Las Vegas are good for avoiding crowds?


Grafton – Bruce L. Quilliam Sr., 72, passed away Saturday, Nov. 17, 2018. His beloved wife and best friend Jean passed away in February of this year. They were married and in love for 50 years.
He leaves a son, Bruce L. Quilliam Jr. and his wife Jing of NewYork; a daughter, Robyn J. Quilliam and her partner Scott Foster of Worcester; three grandchildren, Andrew Quilliam, Ian and Seth Foster; and a niece, Kim Deely of Grafton and her three children, Sean, Matthew, and Thomas Deely. He leaves behind two sisters, Janet (Quilliam) Tompkins of Peoria, Ill. and Ilva Quilliam of Grafton, and his two brothers, Jerome Quilliam and William Quilliam, both of Grafton. Bruce was predeceased by his sister, Marcia (Quilliam) O'Neil. He also leaves numerous sisters-in-law and brothers-in-law, nieces, nephews, and friends.
Bruce was born in Worcester, son of William Quilliam and Beatrice (Nicholson) Quilliam. He lived in Grafton much of his life; in childhood and then with his own family across the way from his parents' home on family land. Bruce and his family moved to Wappingers Falls, N.Y. in 1982, and Jean and Bruce lived there for 33 years.
He worked as a Vice President of Sales and Marketing at Perretta Graphics, where he traveled the world from the tip of South Africa to countries in Asia to countries in Europe. He returned to Grafton with his dear wife in 2015 after retirement.
He renovated his old home to his liking and enjoyed it for several years, especially the yard and the kitchen as he was an excellent gardener and cook. He enjoyed spending time with his family, especially his grandchildren as he loved them dearly.
Jean and Bruce cultivated an amazing flower garden for over 20 years. Every morning their ritual was to sit on the patio, watch the birds, and look out at their garden.
Bruce was an extraordinary man who was very interested and learned in politics, history, and economics. An often stoic man, he was devoted to his family and would do anything for their well-being. He was witty, intelligent, caring, and walked with integrity. He was a great husband, father, and friend. He will be greatly missed.
Memorial calling hours will be held Saturday, Dec. 1, from 2-4 p.m., followed by personal remembrances at 4 p.m. at Roney Funeral Home, 152 Worcester St., North Grafton. There is no burial. A family and friends gathering will be at Bruce and Jean's house directly following the memorial service.
A Book of Memories to share a story or message of sympathy with Bruce's family is available online at www.RoneyFuneralHome.com.


LONDON, ENGLAND--(Marketwire - April 20, 2012) - Tethys Petroleum (TSX:TPL)(LSE:TPL) today provided an Annual Information Update for the period 25 July 2011 to 19 April 2012.
In accordance with Prospectus Rule 5.2, the Company sets out below a summary of the information, which has been published or made available to the public during the period. The period is less than a full year as the Company's secondary listing onto the London Stock exchange commenced 25 July 2011. The information referred to in this update was current at the time of publication, but some information may now be out of date and some of it will have been prepared in accordance with requirements of jurisdictions other than England & Wales. It is not provided for any other purpose and neither the Company, nor any other person, takes any responsibility for, or makes any representation, express or implied, as to the accuracy or completeness of, the information, which it contains. Further, such information prepared in accordance with the laws or regulations of a particular jurisdiction other than England & Wales may not comply with or meet the relevant standards of disclosure in any other jurisdiction.
information, whether as a result of new information, future events or otherwise.


text:
#ifndef CGMSERVICE_H
#define CGMSERVICE_H
#ifdef __cplusplus
extern "C"
{
#endif

/*
 * INCLUDES
 */
#include "OSAL_clock.h"

/*
 * CONSTANTS
 */

//ATT level constants. They can be allocated in "TEXAS INSTRUMENTS\BLE-CC254X-1.4.0\COMPONENTS\ble\include\att.h"
#ifndef ATT_ERR_MISSING_CRC
#define ATT_ERR_MISSING_CRC              0x80 //!< The CRC is missing (defined in the CGMS v1.0)
#endif
#ifndef ATT_ERR_INVALID_CRC
#define ATT_ERR_INVALID_CRC              0x81 //!< The CRC is invalid (defined in the CGMS v1.0)
#endif
#ifndef ATT_ERR_INVALID_VALUE
#define ATT_ERR_INVALID_VALUE            0x82 //!< The attribute value is invalid for the operation
#endif
#ifndef ATT_ERR_INVALID_OPCP_OPCODE
#define ATT_ERR_INVALID_OPCP_OPCODE      0x83 //!< The op code for CGM control point is not supported
#endif

// Glucose Service bit fields
#define CGM_SERVICE				0x00000001	///< Flag identifier for CGM service

// Characteristic Value sizes
#define CGM_CTL_PNT_MIN_SIZE			1               ///< Minimum size of the CGM specific operation control point buffer
#define CGM_CTL_PNT_MAX_SIZE			20              ///< Maximum size of the CGM specific operation control point buffer
#define CGM_RACP_MIN_SIZE			2               ///< Minimum size of the RACP buffer
#define CGM_RACP_MAX_SIZE			20              ///< Minimum size of the RACP buffer

//CGM  Callback events
#define CGM_MEAS_NTF_ENABLED			1               ///< Measurement Characteristic notification enabled
#define CGM_MEAS_NTF_DISABLED			2               ///< Measurement Characteristic notification disabled
#define CGM_CONTEXT_NTF_ENABLED			3               ///< Context notification enabled
#define CGM_CONTEXT_NTF_DISABLED		4               ///< Context notification disabled
#define CGM_CTL_PNT_IND_ENABLED			5               ///< OPCP indication is enabled
#define CGM_CTL_PNT_IND_DISABLED		6               ///< OPCP indication is disabled
#define CGM_CTL_PNT_CMD				7               ///< OPCP control command received
#define CGM_RACP_CTL_PNT_CMD			8               ///< RACP control command received
#define CGM_SESSION_START_TIME_CHANGED		9               ///< Session start time has changed
#define CGM_FEATURE_READ_REQUEST		10              ///< Feature read request from the lower layer
#define CGM_STATUS_READ_REQUEST			11              ///< Status read request from the lower layer
#define CGM_START_TIME_READ_REQUEST		12              ///< Start time read request from the lower layer
#define CGM_RUN_TIME_READ_REQUEST		13              ///< Run time read request from the lower layer
#define CGM_START_TIME_WRITE_REQUEST		14              ///< Start time write request from the lower layer
#define	CGM_RACP_IND_DISABLED			15              ///< RACP indication is disabled
#define	CGM_RACP_IND_ENABLED			16              ///< RACP indication is enabled

// ATT status values
#define	CGM_ERR_IN_PROGRESS		     	0xFE            ///< GATT error, transmission in progress
#define CGM_ERR_CCC_CONFIG		     	0xFD            ///< GATT error, incompatible CCC configuration

// CGM Service UUID
#define CGM_SERV_UUID				0x181F	///< GM service

// CGM Service Characteristic UUID
#define CGM_MEAS_UUID				0x2AA7	///< CGM Measurement
#define CGM_FEATURE_UUID			0x2AA8	///< CGM Feature
#define	CGM_STATUS_UUID				0x2AA9	///< CGM Status
#define	CGM_SES_START_TIME_UUID			0x2AAA	///< CGM Session Start Time
#define CGM_SES_RUN_TIME_UUID			0x2AAB	///< CGM Session Run Time
#define REC_ACCESS_CTRL_PT_UUID			0x2A52	///< Record Access Control Point
#define CGM_SPEC_OPS_CTRL_PT_UUID		0x2AAC	///< CGM Specific Ops Control Point

// values for CGM meas flags
#define	CGM_TREND_INFO_PRES			0x01	///< CGM Trend Information Present
#define	CGM_QUALITY_PRES			0x02	///< CGM Quality Present
#define	CGM_STATUS_ANNUNC_WARNING_OCT		0x20	///< Sensor Status Annuciation Field, Warning Octet present
#define	CGM_STATUS_ANNUNC_CAL_TEMP_OCT		0x40	///< Sensor Status Annuciation Field, Cal/Temp-Octet present
#define	CGM_STATUS_ANNUNC_STATUS_OCT		0x80	///< Sensor Status Annuciation Field, Status Octet present

// value for CGM status annuciation field
#define	CGM_STATUS_ANNUNC_SES_STOP		0x000001///< Session Stop
#define	CGM_STATUS_ANNUNC_BAT_LOW		0x000002///< Device Battery Low
#define	CGM_STATUS_ANNUNC_WRONG_SENSOR_TYPE	0x000004///< Sensor type incorrect for device
#define	CGM_STATUS_ANNUNC_SENSOR_MALFUNC	0x000008///< Sensor malfunction
#define	CGM_STATUS_ANNUNC_DEVICE_SPEC_ALERT	0x000010///< Device specific alert
#define	CGM_STATUS_ANNUNC_GNERAL_DEVICE_FAULT	0x000020///< General device fault has occurred in the sensor
#define	CGM_STATUS_ANNUNC_REQ_TIME_SYNC		0x000100///< Time synchronization between sensor and collector required
#define	CGM_STATUS_ANNUNC_NOT_ALLOWED_CAL	0x000200///< Calibration not allowed
#define	CGM_STATUS_ANNUNC_RECOM_CAL		0x000400///< Calibration recommended
#define	CGM_STATUS_ANNUNC_REQ_CAL		0x000800///< Calibration required
#define	CGM_STATUS_ANNUNC_HIGH_TEMP		0x001000///< Sensor temperature too high for valid test/result at the time of measurement
#define	CGM_STATUS_ANNUNC_LOW_TEMP		0x002000///< Sensor temperature too low for valid test/result at the time of measurement
#define	CGM_STATUS_ANNUNC_LOW_PATIENT		0x010000///< Sensor result lower than the patient low level
#define	CGM_STATUS_ANNUNC_HIGH_PATIRNT		0x020000///< Sensor result higher than the patient high level
#define	CGM_STATUS_ANNUNC_LOW_HYPO		0x040000///< Sensor lower than the hypo level
#define	CGM_STATUS_ANNUNC_HIGH_HYPER		0x080000///< Sensor result higher than the hyper level
#define	CGM_STATUS_ANNUNC_EXCEEDED_DESC_RATE	0x100000///< Sensor rate of decrease exceeded
#define	CGM_STATUS_ANNUNC_EXCEEDED_INCR_RATE	0x200000///< Sensor rate of increase exceeded
#define	CGM_STATUS_ANNUNC_LOW_NIR		0x400000///< Sensor result lower than the device can process
#define	CGM_STATUS_ANNUNC_HIGH_NIR		0x800000///< Sensor result higher than the device can process

// value for CGM feature flag org.bluetooth.characteristic.cgm_feature.xml
#define CGM_FEATURE_CAL				0x000001///< Calibration Support
#define CGM_FEATURE_ALERTS_HIGH_LOW		0x000002///< Patient High/Low Alerts supported
#define CGM_FEATURE_ALERTS_HYPO			0x000004///< Hypo Alerts supported
#define CGM_FEATURE_ALERTS_HYPER		0x000008///< Hyper Alerts supported
#define CGM_FEATURE_ALERTS_INC_DEC		0x000010///< Rate of Increase/Decrease Alerts support
#define CGM_FEATURE_ALERTS_DEVICE_SPEC		0x000020///< Device Specific Alert supported
#define CGM_FEATURE_DETECTION_SENSOR_MALFUNC	0x000040///< Sensor Malfunction Detection support
#define CGM_FEATURE_DETECTION_TEMP_HIGH_LOW	0x000080///< Sensor temperature high-low detection support
#define CGM_FEATURE_DETECTION_RESULT_HIGH_LOW	0x000100///< Sensor result high-low detection support
#define CGM_FEATURE_DETECTION_BAT_LOW		0x000200///< Low battery detection support
#define CGM_FEATURE_DETECTION_SENSOR_TYPE_ERR	0x000400///< Sensor type error detection supported
#define CGM_FEATURE_FAULT_GENERAL_DEVICE	0x000800///< General device fault support
#define CGM_FEATURE_E2E_CRC			0x001000///< E2E-CRC supported
#define CGM_FEATURE_MULTI_BOND			0x002000///< Multiole Bond support
#define CGM_FEATURE_MULTI_SES			0x004000///< Multiple Sessions support
#define CGM_FEATURE_TREND_INFO			0x008000///< CGM Trend information support
#define CGM_FEATURE_QUALITY			0x010000///< CGM quality support

// CGM Type-Sample byte
// values for CGM type information flag 4 bit lower nimble
#define CGM_TYPE_CAPILLARY_WHOLE_BLOOD		0x01	///< Capillary whole blood
#define CGM_TYPE_CAPILLARY_PLASMA		0x02	///< Capillary plasma
#define CGM_TYPE_CAPILLARY_WHOLE_BLOOD2		0x03	///< Capillary whole blood again, possibly a typo
#define CGM_TYPE_VENOUS_PLASMA			0x04	///< Venous Plasma
#define CGM_TYPE_ARTERIAL_WHOLE_BLOOD		0x05	///< Arterial whole blood
#define CGM_TYPE_ARTERIAL_PLASMA		0x06	///< Arterial Plasma
#define CGM_TYPE_UNDET_WHOLE_BLOOD		0x07	///< Undetermined whole blood
#define CGM_TYPE_UNDET_PLASMA			0x08	///< Undetermined plasma
#define CGM_TYPE_ISF				0x09	///< Interstitial fluid (ISF)
#define CGM_TYPE_CTRL_SOLUTION			0x0A	///< Control solution

// values for CGM sample location 4 bit higher nimble
#define CGM_SAMPLE_LOC_FINGER			0x01	///< Sample location Finger
#define CGM_SAMPLE_LOC_AST			0x02	///< Sample location Alternate Site Test (AST
#define CGM_SAMPLE_LOC_EARLOB			0X03	///< Sample location Earlobe
#define CGM_SAMPLE_LOC_CTRL_SOLUTION		0x04	///< Sample location Control solution
#define CGM_SAMPLE_LOC_SUBCUT_TISSUE		0x05	///< Sample location Subcutaneous tissue
#define CGM_SAMPLE_LOC_UNAVAIL			0x0F	///< Sample location not available

// The time value for start time/ run time etc. is obviously implemented
// https://developer.bluetooth.org/gatt/characteristics/Pages/CharacteristicViewer.aspx?u=org.bluetooth.characteristic.date_time.xml
// Time Zone Infomation sint8
#define TIME_ZONE_UTC_M12			-48	///< TC-12:00
#define TIME_ZONE_UTC_M11			-44	///< TC-11:00
#define TIME_ZONE_UTC_M10			-40	///< TC-10:00
#define TIME_ZONE_UTC_M9p5			-38	///< TC-9:30
#define TIME_ZONE_UTC_M9			-36	///< TC-9:00
#define TIME_ZONE_UTC_M8			-32	///< TC-8:00
#define TIME_ZONE_UTC_M7			-28	///< TC-7:00
#define TIME_ZONE_UTC_M6			-24	///< TC-6:00
#define TIME_ZONE_UTC_M5			-20	///< TC-5:00
#define TIME_ZONE_UTC_M4p5			-18	///< TC-4:30
#define TIME_ZONE_UTC_M4			-16	///< TC-4:00
#define TIME_ZONE_UTC_M3p5			-14	///< TC-3:30
#define TIME_ZONE_UTC_M3			-12	///< TC-3:00
#define TIME_ZONE_UTC_M2			-8	///< TC-2:00
#define TIME_ZONE_UTC_M1			-4	///< TC-1:00
#define TIME_ZONE_UTC_P0			0	///< TC+0:00
#define TIME_ZONE_UTC_P1			4	///< TC+1:00
#define TIME_ZONE_UTC_P2			8	///< TC+2:00
#define TIME_ZONE_UTC_P3			12	///< TC+3:00
#define TIME_ZONE_UTC_P3p5			14	///< TC+3:30
#define TIME_ZONE_UTC_P4			16	///< TC+4:00
#define TIME_ZONE_UTC_P4p5			18	///< TC+4:30
#define TIME_ZONE_UTC_P5			20	///< TC+5:00
#define TIME_ZONE_UTC_P5p5			22	///< TC+5:30
#define TIME_ZONE_UTC_P5p75			23	///< TC+5:45
#define TIME_ZONE_UTC_P6			24	///< TC+6:00
#define TIME_ZONE_UTC_P6p5			26	///< TC+6:30
#define TIME_ZONE_UTC_P7			28	///< TC+7:00
#define TIME_ZONE_UTC_P8			32	///< TC+8:00
#define TIME_ZONE_UTC_P8p75			35	///< TC+8:45
#define TIME_ZONE_UTC_P9			36	///< TC+9:00
#define TIME_ZONE_UTC_P9p5			38	///< TC+9:30
#define TIME_ZONE_UTC_P10			40	///< TC+10:00
#define TIME_ZONE_UTC_P10p5			42	///< TC+10:30
#define TIME_ZONE_UTC_P11			44	///< TC+11:00
#define TIME_ZONE_UTC_P11p5			46	///< TC+11:30
#define TIME_ZONE_UTC_P12			48	///< TC+12:00
#define TIME_ZONE_UTC_P12p75			51	///< TC+12:45
#define TIME_ZONE_UTC_P13			52	///< TC+13:00
#define TIME_ZONE_UTC_P14			56	///< TC+14:00
#define TIME_ZONE_UNKNOWN			-128	///< nknown

// DST Offset information uint8
// https://developer.bluetooth.org/gatt/characteristics/Pages/CharacteristicViewer.aspx?u=org.bluetooth.characteristic.dst_offset.xml
#define DST_STANDARD_TIME			0x00	///< Standard Time
#define	DST_HALF_HOUR_DAYLIGHT			0x02	///< Half An Hour Daylight Time (+0.5h)
#define	DST_ONE_HOUR_DAYLIGHT			0x04	///< Daylight Time (+1h)
#define	DST_DOUBLE_HOUR_DAYLIGHT		0x08	///< Double Daylight Time (+2h)
#define	DST_UNKNOWN				0xFF	///< Daylight Time unknown

// Record Control Access Point
//Record Control Point values OP Codes
//https://developer.bluetooth.org/gatt/characteristics/Pages/CharacteristicViewer.aspx?u=org.bluetooth.characteristic.record_access_control_point.xml
#define CTL_PNT_OP_REQ                  	0x01	///< Report stored record (Operator: Value from Operator Table)
#define CTL_PNT_OP_CLR                       	0x02	///< Delete stored records (Operator: Value from Operator Table)
#define CTL_PNT_OP_ABORT                     	0x03	///< Abort operation (Operator: Null 'value of 0x00 from Operator Table')
#define CTL_PNT_OP_GET_NUM                   	0x04	///< Report number of stored records (Operator: Value from Operator Table)
#define CTL_PNT_OP_NUM_RSP                   	0x05	///< Number of stored records response (Operator: Null 'value of 0x00 from Operator Table')
#define CTL_PNT_OP_REQ_RSP                   	0x06	///< Response Code (Operator: Null 'value of 0x00 from Operator Table')

//Record Control Point operator
#define CTL_PNT_OPER_NULL                   	0x00	///< Null
#define CTL_PNT_OPER_ALL                     	0x01  	///< All records
#define CTL_PNT_OPER_LESS_EQUAL              	0x02	///< Less than or equal to
#define CTL_PNT_OPER_GREATER_EQUAL           	0x03	///< Greater than or equal to
#define CTL_PNT_OPER_RANGE                   	0x04	///< Within range of (inclusive)
#define CTL_PNT_OPER_FIRST                   	0x05	///< First record(i.e. oldest record)
#define CTL_PNT_OPER_LAST                    	0x06  	///< Last record (i.e. most recent record)

//Record Control Point Response Codes
#define CTL_PNT_RSP_SUCCESS                	0x01	///< Normal response for successful operation
#define CTL_PNT_RSP_OPCODE_NOT_SUPPORTED     	0x02	///< Normal response if unsupported Op Code is received
#define CTL_PNT_RSP_OPER_INVALID             	0x03	///< Normal response if Operator received does not meet the requirements of the service (e.g. Null was expected)
#define CTL_PNT_RSP_OPER_NOT_SUPPORTED       	0x04	///< Normal response if unsupported Operator is received
#define CTL_PNT_RSP_OPERAND_INVALID          	0x05	///< Normal response if Operand received does not meet the requirements of the service
#define CTL_PNT_RSP_NO_RECORDS              	0x06	///< Normal response if request to report stored records or request to delete stored records resulted in no records meeting criteria.
#define CTL_PNT_RSP_ABORT_FAILED             	0x07	///< Normal response if request for Abort cannot be completed
#define CTL_PNT_RSP_PROC_NOT_CMPL            	0x08	///< Normal response if unable to complete a procedure for any reason
#define CTL_PNT_RSP_FILTER_NOT_SUPPORTED     	0x09	///< Normal response if unsupported Operand is received

//Record Control Point Resposne Filter
#define CTL_PNT_FILTER_TIME_OFFSET             	0x01	///< Time Offset
// CGM Specific operation codes
#define CGM_SPEC_OP_SET_INTERVAL		1	///< Set CGM Communication Interval
#define CGM_SPEC_OP_GET_INTERVAL		2	///< Get CGM Communication Interval
#define CGM_SPEC_OP_RESP_INTERVAL		3	///< CGM Communication Interval response
#define CGM_SPEC_OP_SET_CAL			4	///< Set Glucose Calibration Value
#define CGM_SPEC_OP_GET_CAL			5	///< Get Glucose Calibration Value
#define CGM_SPEC_OP_RESP_CAL			6	///< Glucose Calibration Value response
#define CGM_SPEC_OP_SET_ALERT_HIGH		7	///< Set Patient High Alert Level
#define CGM_SPEC_OP_GET_ALERT_HIGH		8	///< Get Patient High Alert Level
#define CGM_SPEC_OP_RESP_ALERT_HIGH		9	///< Patient High Alert Level Response
#define CGM_SPEC_OP_SET_ALERT_LOW		10	///< Set Patient Low Alert Level
#define CGM_SPEC_OP_GET_ALERT_LOW		11	///< Get Patient Low Alert Level
#define CGM_SPEC_OP_RESP_ALERT_LOW		12	///< Patient Low Alert Level Response
#define CGM_SPEC_OP_SET_ALERT_HYPO		13	///< Set Hypo Alert Level
#define CGM_SPEC_OP_GET_ALERT_HYPO		14	///< Get Hypo Alert Level
#define CGM_SPEC_OP_RESP_ALERT_HYPO		15	///< Hypo Alert Level Response
#define CGM_SPEC_OP_SET_ALERT_HYPER		16	///< Set Hyper Alert Level
#define CGM_SPEC_OP_GET_ALERT_HYPER		17	///< Get Hyper Alert Level
#define CGM_SPEC_OP_RESP_ALERT_HYPER		18	///< Hyper Alert Level Response
#define CGM_SPEC_OP_SET_ALERT_RATE_DECREASE	19	///< Set Rate of Decrease Alert Level
#define CGM_SPEC_OP_GET_ALERT_RATE_DECREASE	20	///< Get Rate of Decrease Alert Level
#define CGM_SPEC_OP_RESP_ALERT_RATE_DECREASE	21	///< Rate of Decrease Alert Level Response
#define CGM_SPEC_OP_SET_ALERT_RATE_INCREASE	22	///< Set Rate of Increase Alert Level
#define CGM_SPEC_OP_GET_ALERT_RATE_INCREASE	23	///< Get Rate of Increase Alert Level
#define CGM_SPEC_OP_RESP_ALERT_RATE_INCREASE	24	///< Rate of Increase Alert Level Response
#define CGM_SPEC_OP_RESET_ALERT_DEVICE_SPEC	25	///< Reset Device Specific Alert
#define CGM_SPEC_OP_START_SES			26	///< Start the Session
#define CGM_SPEC_OP_STOP_SES			27	///< Stop the Session
#define CGM_SPEC_OP_RESP_CODE			28	///< Response Code

// CGM specific op code - resposne codes
#define	CGM_SPEC_OP_RESP_SUCCESS		1	///< Success
#define	CGM_SPEC_OP_RESP_OP_NOT_SUPPORT		2	///< Op Code not supported
#define	CGM_SPEC_OP_RESP_OPERAND_INVALID	3	///< Invalid Operand
#define	CGM_SPEC_OP_RESP_PROCEDURE_NOT_COMPLETE	4	///< Procedure not completed
#define	CGM_SPEC_OP_RESP_PARAM_NIR		5	///< Parameter out of range

// CGM Calibration Status
#define	CGM_CALIBRATION_REJECT			0x01	///< Calibration Data rejected (Calibration failed)
#define	CGM_CALIBRATION_NIR			0x02	///< Calibration Data out of range
#define	CGM_CALIBRATION_PENDING			0x04	///< Calibration Process Pending
#define CGM_CALIBRATION_CLEAR			0x00	///< Reset all bits back to 0

// Unit UUID
#define UNIT_MASSDENSITY_MG_PER_DL		0x27B1	///< ass density (milligram per decilitre)
#define UNIT_TIME_MIN				0x2760	///< ime (minute)

// The following definition is inferred from the CGM profile specification
#define CGM_CHAR_VAL_SIZE_FEATURE                   6              ///< he size of the feature characteristic without the CRC
#define CGM_CHAR_VAL_SIZE_STATUS                    5              ///< he size of the status characteristic without the CRC
#define CGM_CHAR_VAL_SIZE_START_TIME                9              ///< he size of the start time characteristic without the CRC
#define CGM_CHAR_VAL_SIZE_RUN_TIME                  2              ///< he size of the run time characteristic without the CRC

/*
 * TYPEDEFS
 */
// Glucose Service callback function
typedef void (*CGMServiceCB_t)(uint8 event, uint8* data, uint8 *dataLen,uint8 *result);

/*
 * MACROS
 */

/*
 * Profile Callbacks
 */

/*
 * API FUNCTIONS
 */

extern bStatus_t CGM_AddService( uint32 services );

extern void CGM_Register( CGMServiceCB_t pfnServiceCB);

/**
 * @fn          CGM_MeasSend
 * @brief       Send a CGM measurement.
 * @param       connHandle - connection handle
 * @param       pNoti - pointer to notification structure
 * @return      Success or Failure
 */
extern bStatus_t CGM_MeasSend( uint16 connHandle, attHandleValueNoti_t *pNoti, uint8 taskId );

/**
 * @brief       Send a CGM measurement context.
 * @param       connHandle - connection handle
 * @param       pNoti - pointer to notification structure
 * @param	taskId - the task id of the CGM application
 * @return      Success or Failure
 */
extern bStatus_t CGM_ContextSend( uint16 connHandle, attHandleValueNoti_t *pNoti, uint8 taskId );

/**
 * @brief       Send an indication containing a CGM
 *              measurement.
 * @param       connHandle - connection handle
 * @param       pInd - pointer to indication structure
 * @param	taskId - the taskId of the CGM application
 * @return      Success or Failure
 */
extern bStatus_t CGM_CtlPntIndicate( uint16 connHandle, attHandleValueInd_t *pInd, uint8 taskId );

/**
 * @fn          CGM_RACPIndicate
 * @brief       Send an indication containing RACP operation result to RACP characteristic.
 * @param       connHandle - connection handle
 * @param       pInd - pointer to indication structure
 * @param	taskId - the taskId of teh CGM application
 * @return      Success or Failure
 */
extern bStatus_t CGM_RACPIndicate( uint16 connHandle, attHandleValueInd_t *pInd, uint8 taskId );

/**
 * @brief       Set the state of the CGM database transmission indicator
 * @param       input - the state of the transmission
 *

0	data transmission is not in progress

1	data transmission is in progress

 * @return      none*/
extern bool CGM_SetSendState(bool input);

#ifdef __cplusplus
}
#endif

#endif /* CGMSERVICE_H */

redpajama_set_name: RedPajamaGithub


The South Hutchinson Fire Department's Command Staff consists of the Fire Chief, Assistant Chief, a Division Chief, and a Fire Captain.
The Fire Chief is a part-time position and is appointed annually by the Mayor and approved by the City Council. The Fire Chief assigns the other staff positions.


ignite new passion and zeal for our Savior, Jesus Christ. The powerful truths presented by John Bunyan, John Wesley, William Carey, and Sir Walter Scott, among others, have brought many to accept His amazing grace and fervently follow His ways.
are reminded of the great things He has done for us!


text:
Q: How to send packets from one ethernet card to another in the same system? I have a system in which I have two NIC cards enp2s0 and enp3s0. Each of the NIC card is connected to packet generator. The packet generator will generate the packets and sent it to enp2s0 and I want to  forward the packets from enp2s0  to enps30, whcih later on passes it to the packet generator. I am able to transmit the packet till enp2s0 from the packet generator, but I am not able to forward it.
I have tried running the below commands:
    Open /etc/sysctl.conf and uncomment  net.ipv4.ip_forward=1
sudo iptables -t nat -A POSTROUTING --out-interface enp2s0 -j MASQUERADE
 sudo iptables -A FORWARD --in-interface enp3s0 -j ACCEPT

But still I see not changes.

A: Connect a cable between the two NICs. ;)

redpajama_set_name: RedPajamaStackExchange