Consider a discrete source producing a sequence of message letters from a finite alphabet. A single-letter distortion measure is given by a non-negative matrix (d ij ). The entryd ij measures the ?cost? or ?distortion? if letteriis reproduced at the receiver as letterj. The average distortion of a communications system (source-coder-noisy channel-decoder) is taken to bed= ? i.j P ij d ij whereP ij is the probability ofibeing reproduced asj. It is shown that there is a functionR(d) that measures the ?equivalent rate? of the source for a given level of distortion. For coding purposes where a leveldof distortion can be tolerated, the source acts like one with information rateR(d). Methods are given for calculatingR(d), and various properties discussed. Finally, generalizations to ergodic sources, to continuous sources, and to distortion measures involving blocks of letters are developed. In this paper a study is made of the problem of coding a discrete source of information, given afidelity criterionor ameasure of the distortionof the final recovered message at the receiving point relative to the actual transmitted message. In a particular case there might be a certain tolerable level of distortion as determined by this measure. It is desired to so encode the information that the maximum possible signaling rate is obtained without exceeding the tolerable distortion level. This work is an expansion and detailed elaboration of ideas presented earlier [1], with particular reference to the discrete case. We shall show that for a wide class of distortion measures and discrete sources of information there exists a functionR(d) (depending on the particular distortion measure and source) which measures, in a sense, the equivalent rateRof the source (in bits per letter produced) whendis the allowed distortion level. Methods will be given for evaluatingR(d) explicitly in certain simple cases and for evaluatingR(d) by a limiting process in more complex cases. The basic results are roughly that it is impossible to signal at a rate faster thanC/R(d) (source letters per second) over a memoryless channel of capacityC(bits per second) with a distortion measure less than or equal tod. On the other hand, by sufficiently long block codes it is possible to approach as closely as desired the rateC/R(d) with distortion leveld. Finally, some particular examples, using error probability per letter of message and other simple distortion measures, are worked out in detail.

Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959.

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Two Dimensional Phase Unwrapping Theory Algorithms And Software

Sliding-mode control (SMC) has been studied extensively for over 50 years and widely used in practical applications due to its simplicity and robustness against parameter variations and disturbances. Despite the extensive research activities carried out, the key technical problems associated with SMC remain as challenging research questions due to demands for new industrial applications and technological advances. In this respect, soft computing (SC) is a rather recent development in intelligent systems which has provided alternative means for adaptive learning and control to overcome the key SMC technical problems. Substantial efforts in integration of SMC with SC have been placed in recent years with various successes. In this paper, we provide the state of the art of recent developments in SMC systems with SC, examining key technical research issues and future perspectives.

Sliding-Mode Control With Soft Computing: A Survey

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

https://journals.sagepub.com/doi/pdf/10.1155/2015/431047

Data mining for the Internet of Things: literature review and challenges

Optimal selection of wavelet basis function applied to ECG signal denoising

A supervisory fuzzy neural network (FNN) control system is designed to track periodic reference inputs in this study. The control system is composed of a permanent magnet (PM) synchronous servo motor drive with a supervisory FNN position controller. The supervisory FNN controller comprises a supervisory controller, which is designed to stabilize the system states around a defined bound region and an FNN sliding-mode controller, which combines the advantages of the sliding-mode control with robust characteristics and the FNN with online learning ability. The theoretical and stability analyses of the supervisory FNN controller are discussed in detail. Simulation and experimental results show that the proposed control system is robust with regard to plant parameter variations and external load disturbance. Moreover, the advantages of the proposed control system are indicated in comparison with the sliding-mode control system.

A supervisory fuzzy neural network control system for tracking periodic inputs

A new fast kNN classification algorithm is presented for texture and pattern recognition. The algorithm identifies the fat k closest vectors in the design set of a kNN classifier for each input vector by performing the partial distance search in the wavelet domain. Simulation results show that, without increasing the classification error rate, the algorithm requires only 12.94% of the computational time of the original kNN technique.

Fast kNN classification algorithm based on partial distance search

This paper proposes a novel flexible VLSI architecture for the implementation of variable block size motion estimation (VBSME). The architecture is able to perform a full motion search on integral multiples of 4/spl times/4 blocks sizes. To use the architecture, each 16/spl times/16 macroblock of the source frames should be partitioned into sixteen 4/spl times/4 non-overlapping subblocks, called primitive subblocks. The architecture contains sixteen modules and one VBSME processor. Each module, realized by cascading ID systolic arrays, is responsible for the block-matching operations of a different primitive subblock. The realization has the advantages of high throughput, high flexibility and 100 % processing element (PE) utilization. The motion estimation of all the primitive subblocks is performed in parallel. Because these primitive subblocks can be used to form the 41 subblocks of different sizes specified by the H.264, the VBSME processor is employed to concurrently compute the sums of absolute differences (SADs) of all the 41 subblocks from the SADs of the primitive subblocks. This new architecture has lower latency and higher throughput over other exiting VBSME architectures for the hardware implementation of H.264 encoders.

An efficient VLSI architecture for H.264 variable block size motion estimation

In this paper, a novel medical data compression algorithm, termed layered set partitioning in hierarchical trees (LSPIHT) algorithm, is presented for telemedicine applications. In the LSPIHT, the encoded bit streams are divided into a number of layers for transmission and reconstruction. Starting from the base layer, by accumulating bit streams up to different enhancement layers, we can reconstruct medical data with various signal-to-noise ratios (SNRs) and/or resolutions. Receivers with distinct specifications can then share the same source encoder to reduce the complexity of telecommunication networks for telemedicine applications. Numerical results show that, besides having low network complexity, the LSPIHT attains better rate-distortion performance as compared with other algorithms for encoding medical data.

Scalable medical data compression and transmission using wavelet transform for telemedicine applications

An ultrasonic motor (USM) servo-drive with an online trained neural-network model-following controller is proposed. First, the driving circuit for the USM, which is a two-phase chopper-inverter combination, is introduced. Since the dynamic characteristics of the USM are difficult to obtain and the motor parameters are time varying, an online trained neural-network model-following controller is proposed to control the rotor position of the USM. An accurate tracking response can be obtained by random initialisation of the weights and biases of the network owing to the powerful online learning capability. Moreover, the influences of parameter variations and external disturbances of the USM servo-drive can be effectively reduced by the neural-network controller.

Wen-Jyi Hwang

Papers

A supervisory fuzzy neural network control system for tracking periodic inputs

Fast kNN classification algorithm based on partial distance search

An efficient VLSI architecture for H.264 variable block size motion estimation

Scalable medical data compression and transmission using wavelet transform for telemedicine applications

Ultrasonic motor servo-drive with online trained neural-network model-following controller