Research on the Application of Machine Learning Big Data Mining Algorithms in Digital Signal Processing

The design and analysis of software which implements digital signal processing (DSP) algorithms on multiprocessor systems is examined. The DSP algorithms that are considered exhibit certain characteristics which are common of DSP algorithms used in communication and control systems. The analysis methods developed use linear complexity algorithms to analyze the software that implements the DSP algorithms. The first step involves finding the repeat factors associated with a basic cycle of the algorithm. Using these repeat factors it is straightforward to (1) find the precedence relationships between tasks and finally a feasible schedule for the multiprocessor, and (2) coordinate and organize the efforts to optimize the software. >

Validation of Peripheral Pulse Wave Analysis Technique to Detect Aortic Valve State in Continuous-Flow LVADs1

Multiplication is an important fundamental function in arithmetic operations. Multiplication-based operations such as Multiply and Accumulate(MAC) and inner product are among some of the frequently used computation Intensive Arithmetic Functions(CIAF) currently implemented in many Digital Signal Processing (DSP) applications such as convolution, Fast Fourier Transform(FFT), filtering and in microprocessors m its arithmetic and logic unit. Since multiplication dominates the execution tune of most DSP algorithms, so there is a need of high speed multiplier. Currently, multiplication time is still the dominant factor in determining the instruction cycle time of a DSP chip. The demand for high speed processing has been increasing as a result of expanding computer and signal processing applications. Higher throughput arithmetic operations are important to achieve the desired performance in many real-time signal and image processing applications . One of the key arithmetic operations in such applications is multiplication and the development of fast multiplier circuit has been a subject of interest over decades. Multiplier based on Vedic Mathematics is one of the fast and low power multiplier. Employing this technique in the computation algorithms will reduce the complexity, execution time, power etc. This vedic based multiplier is compared with binary multiplier(partial products method).

Numerical linear algebra, digital signal processing, and parallel algorithms are three disciplines with a great deal of activity in the last few years. The interaction between them has been growing to a level that merits an Advanced Study Institute dedicated to the three areas together. This volume gives an account of the main results in this interdisciplinary field. The following topics emerged as major themes of the meeting: - Singular value and eigenvalue decompositions, including applications, - Toeplitz matrices, including special algorithms and architectures, - Recursive least squares in linear algebra, digital signal processing and control, - Updating and downdating techniques in linear algebra and signal processing, - Stability and sensitivity analysis of special recursive least squares problems, - Special architectures for linear algebra and signal processing. This book contains tutorials on these topics given by leading scientists in each of the three areas. A consider- able number of new research results are presented in contributed papers. The tutorials and papers will be of value to anyone interested in the three disciplines.

Wireless communications is evolving in different directions from several starting points. In virtually all cases, evolution has proceeded from analogue systems to digital systems having more capability to cope with the radio environment and at a lower cost. The synergy between digital radio communications and VLSI is revolutionising the design of wireless terminals. More and more these terminals look like dedicated computers capable of executing digital signal processing (DSP) algorithms of extremely high complexity. On the one side, these algorithms are interfaced with the radio channel via the RF analogue front end and antenna of the terminal. On the other side with the user through a microphone, speaker and keyboard. In nearly all cases, the RF subsystem also contains a frequency synthesiser which is again a dedicated digital hardware. The evolution of these terminals is characterised by the disappearance of analogue signal processing components and subsystems and their replacement in the software domain by DSP code which runs on highly optimised hardware for maths. At the same time the DSP number crunching heart of the terminals is going faster, stronger, smaller and cheaper. One should expect that this type of evolution may continue until a point where the analogue hardware will be minimum dealing with signal amplification at RF frequencies, conversion to, and from, near baseband and interfacing via a discrete time analogue circuitry to the DSP domain.

Context. The digital signal processing is applied in many fields of science, technology and human activity. One of the ways of implementing algorithms of digital signal processing is the development of coprocessors as an integral part of well-known architectures.
 In the case of developing a pipelined device, the presented approach will allow to use software and hardware tools of the appropriate architecture, provide the faster execution of signal processing algorithms, reduce the number of cycles and memory accesses.
 Objective. Objectives are design and characterization study of a pipelined RISC-V processor and coprocessor of digital signal processing which performs fast Fourier transform.
 Method. Analyzing technical literature and existing decisions allow to assess advantages and disadvantages of modern developments and on the basis of which to form the relevance of the selected topic. Model designing and simulation results allow to examine a model efficiency, to determine weak components’ parts and to improve model parameters.
 Results. The pipelined RISC-V processor has been designed which executes a basic set of instructions. Execution time of assembly program on the single-cycled and the pipelined processors have been analyzed. According to the results, the test program on the pipelined processor is executed in 29 cycles, while on the single-cycle processor it takes 60 cycles. The structure of the coprocessor for the fast Fourier transform algorithm and a set of processor instructions that allow working with the coprocessor have been developed. The number of cycles of the coprocessor based on Radix-2 fast Fourier transform algorithm for 512 points is 2358 cycles, and for 1024 points is 5180 cycles.
 Conclusions. Conducted researches and calculations have showed that the application of the developed hardware coprocessor reduces the fast Fourier transform algorithm execution time and the load of the pipelined processor during calculations.

Data acquisition and Signal Processing is an important part of frequency modulated continuous wave (FMCW) radar system. In recent year P2ET-LIPI was developing FMCW navigation radar system in portable size. The main component of data acquisition for navigation radar is highspeed Analog to Digital Conversion (ADC) USB-2020 board and signal processing algorithm has been developed using Visual C# 2015 Community Edition. USB-2020 board has two channel ADC on board with 12 bits resolution and operated in a burst-io mode to get maximum sampling rate at 20 MS/s. A timing generator for synchronization between ADC and linear frequency modulation (LFM) chirp generator is developed using Complex Programmable Logic Device (CPLD) Max II series based on VHSIC hardware description language (VHDL). Signal processing unit consists of windowing function to reduce side- lobe suppression, Fast Fourier Transform (FFT) function to get range information and adaptive threshold using three algorithms of Constant False Alarm Rate (CFAR): Cell Averaging (CA), Order Statistic (OS) and Variable Index (VI) CFAR. The experiment shows that the data acquisition module can capture In-phase and Quadrature (I/Q) beat signal and signal processing algorithm can detect object/target from the noise.

Through the analysis of a data-flow-graph representation of an algorithm, the recent work by P.W. Lee (Ph.D. dissertation, Rutgers Univ., NJ, 1988) produces static, nonpreemptive schedules capable of implementing the algorithm on a multiple-processor system. These schedules are based on the assumptions that algorithms of the type that are typical of a class of digital signal processing (DSP) algorithms are computation bound (as opposed to I/O bound) and that data transfer (I/O operations) occurs instantaneously. This raises the question of the effects on the efficiency of the multiprocessor's utilization as the execution of the algorithm changes from computation bound to I/O bound. In the present work the author examines these effects for two cases of an algorithm (typical of this class of DSP algorithms). These cases are with and without a dominant run-time task, where a dominant run-time task is characterized by a far larger execution time than any other task in the algorithm. It is concluded that the ideal I/O assumption put forth by Lee is valid for software structures that exhibit dominant tasks. >

As a low-energy method to increase the data rate of optical links in data centers, we propose self-homodyne Nyquist optical time division multiplexing (OTDM). In Nyquist OTDM, spectrally efficient high-baud rate signals can be generated exceeding the limit of electronic signal processing. However, full integration of OTDM systems has not been reported, mainly because of the complicated signal detection scheme, which involves demultiplexing and clock recovery. In our proposal, the Nyquist pulse train is transmitted to the receiver as a local oscillator (LO) to leverage self-homodyne detection, which allows using large linewidth lasers and a simplified digital signal processing (DSP) algorithm. As the transmitted pulse train serves as an optical clock, demultiplexing and detection of the OTDM signal can be performed without using power-intensive high-bandwidth electronics and DSP. In this method, the LO pulse train needs to enter the coherent detector in exact synchronization with the OTDM signal for detecting the individual tributary correctly. For this purpose, we present a pulse delay control method suitable for photonic integration. A Nyquist pulse train with m carriers enables m-time multiplexing of optical signals. We explain and demonstrate the proposed concept in the case of m = 2, as it is the most feasible implementation. In the O-band where the chromatic dispersion (CD) is negligible, DSP-free operation can be achieved using the QPSK format. At the band edges where CD is non-negligible, it can be compensated by the DSP as in the conventional coherent detection. We verify this numerically and in an experiment involving the transmission of a 64-Gbaud QPSK signal at 1550 nm over a single-mode fiber. In terms of low energy, self-homodyne Nyquist OTDM is advantageous in wavelength division multiplexing (WDM). Taking it into consideration, we perform 4-channel WDM transmission of the 64-Gbaud QPSK signal over a 1-km dispersion shifted fiber without CD compensation. The results demonstrate a data rate of 512 Gb/s with a BER of <1 × 10-10.

Digital signal processing (DSP) has been widely applied in optical communication systems to mitigate various signal distortions and has become one of the key technologies that have sustained data traffic growth over the past decade. However, the strict energy budget of application-specific integrated circuit-based DSP chips has prevented the deployment of some powerful but computationally costly DSP algorithms in real applications. As a result, fiber nonlinearity-induced signal distortions impede fiber communication systems, especially in wavelength-division multiplexed (WDM) transmission systems. To solve these challenges in DSP, there has been a surge of interest in implementing neural networks-based signal processing using photonics hardware (i.e., photonic neural networks). Photonic neural networks promise to break performance limitations in electronics and gain advantages in bandwidth, latency, and power consumption in solving intellectual tasks that are unreachable by conventional digital electronic platforms. This work proposes a photonic recurrent neural network (RNN) capable of simultaneously resolving dispersion and both intra and inter-channel fiber nonlinearities in multiple WDM channels in the photonic domain, for the first time to our best knowledge. Furthermore, our photonic RNN can directly process optical WDM signals in the photonic domain, avoiding prohibitive energy consumption and speed overhead in analog to digital converters (ADCs). Our proposed photonic RNN is fully compatible with mature silicon photonic fabrications. We demonstrate in simulation that our photonic RNN can process multiple WDM channels simultaneously and achieve a reduced bit error rate compared to typical DSP algorithms for all WDM channels in a pulse-amplitude modulation 4-level (PAM4) transmission system, thanks to its unique capability to address inter-channel fiber nonlinearities. In addition to signal quality performance, the proposed system also promises to significantly reduce the power consumption and the latency compared to the state-of-the-art DSP chips, according to our power and latency analysis.

Assigning service requests in Voice-over-Internet gateway multiprocessors

Background. Far-Field R-Wave (FFRW) oversensing and P wave blanking are the major causes of inaccurate atrial signal classification in current pacemaker (PM) devices leading to inaccurate stored diagnostic information and to inadequate PM reactions like mode-switching or preventive pacing. The prospective and multi-center MARS (Morphology of Atrial Signals) study therefore investigated during daily life the capabilities of Digital Signal Processing (DSP) in an implanted dual chamber PM to improve P-wave classification accuracy. Methods . 55 patients with an implanted dual chamber PM (Vitatron T- or C-series) were included: During 24 hours with daily life activity and a stress test, a Holter ECG collected surface ECG, PM markers and all atrial EGM’s. The DSP algorithm analysed atrial events sensed nearby a ventricular event using the minimum and the maximum amplitude of the digitalized and filtered signal and the maximum and minimum amplitude of the slope signal. After comparing to the characteristics of a normal P wave, each of these atrial signals were then classified as a true atrial signal or FFRW. DSP classification results were then compared to the conventional PM signal classification by three independent reviewers. Results . Classification accuracy for the traditional PM functionality after optimizing blanking and sensitivity settings was 97.1% (19.5–100%) as compared to 99.4% (76.9–100%) using the DSP algorithm (p&lt;0.001). Conclusion . The DSP algorithm significantly improved the classification accuracy of sensed atrial events as an important prerequisite for a correct diagnostic and therapeutic PM function. The herewith presented approach of analysing atrial signals by form and timing may allow to reduce or even eliminate atrial blanking periods thus leading to larger PM sensing windows to prevent from atrial tachyarrhythmia undersensing without an increase in FFRW sensing.

Problem statement. The main trend in the development of vibroacoustic technology for monitoring geotechnical objects is aimed at automating the receipt, transmission, collection and processing of information. However, the mathematical apparatus and algorithms for digital processing of vibroacoustic signals require further development. Purpose of the study. Definition of features and development of algorithms for the implementation of the spectral analysis method for vibroacoustic monitoring of the geotechnical system functioning safety. Methods. Analysis and generalization of data, analytical and laboratory studies of acoustic signals oscillograms, methods for constructing digital environmental control systems. Research results. The methods and algorithms used in systems for monitoring the state of rocks, mine workings, underground and surface structures are investigated. Digital signal processing algorithms usually include filtering and spectral analysis of input data. The features of the spectral analysis method implementation for processing vibroacoustic monitoring data of a geotechnical system are determined. It is established that a decaying aperiodic signal can be considered as a superposition of multiplications of imaginary periodic signals and shifted single rectangular pulses. In this case, the frequency of the first oscillation mode is inversely proportional to the double thickness of the studied elements of the geotechnical system. To control the characteristics of pulsed acoustic signals, in contrast to the previously known method, which uses one measurement during the partition interval and manual construction of point fields, it is proposed to use a series of points at each of the time intervals of signal change and analyze the relationships between the spectrum of the real aperiodic signal and the spectra of its imaginary periodic components. Algorithms for automatic analysis of vibroacoustic response spectrograms and calculating control parameters using the discrete ordinate method are developed. The method is implemented by sequential cyclic processing of the signal from high frequencies to low frequencies, followed by division into time intervals, within each of which the change in the deviation value is taken as the period of the unquenchable oscillatory process. Scientific novelty. The methodology for identifying hidden damage in structural elements of buildings and structures has been further developed, which is distinguished by taking into account the parameters of vibroacoustic control of the destruction consequences and assessing the risks of the structures stability loss. Practical significance. The research results make it possible to improve the method for assessing changes in the structure of the medium as a result of the occurrence of observed and hidden systems of cracks using vibroacoustic monitoring.

This paper presents a newly developed laboratory paradigm for teaching applied or real-time digital signal processing courses. It involves the utilization of smartphones to implement digital signal processing algorithms in real-time using ARM processors of smartphones. Representative laboratory experiments together with an application project running on smartphones as apps are discussed in the paper. It is shown that such a paradigm provides a cost-free and a truly mobile laboratory environment for students to learn implementation aspects of signal processing algorithms.

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.