Extracting Periodic Features and Immunity of Electronic Communication Signals Using Adaptive Filtering Algorithms

The principal issue in acoustic echo cancellation (AEC) is to estimate the impulse response between the loudspeaker and the microphone of a hands-free communication device. Basically, we deal with a system identification problem, which can be solved by using an adaptive filter. The most common adaptive filters for AEC are the normalized least-mean-square (NLMS) algorithm and the affine projection algorithm (APA). These two algorithms have to compromise between fast convergence rate and tracking on the one hand and low misadjustment and robustness (against the near-end signal) on the other hand. In order to meet these conflicting requirements, the step-size parameter of the algorithms needs to be controlled. This is the motivation behind the development of variable step-size (VSS) algorithms. Unfortunately, most of these algorithms depend on some parameters that are not always easy to tune in practice. The goal of this chapter is to present a family of non-parametric VSS algorithms (including both VSS-NLMS and VSS-APA), which are very suitable for realistic AEC scenarios. They are developed based on another objective of AEC application, i.e., to recover the near-end signal from the error signal of the adaptive filter. As a consequence, these VSS algorithms are equipped with good robustness features against near-end signal variations, like double-talk. This idea can be extended even in the case of the recursive least-squares (RLS) algorithm, where the overall performance is controlled by a forgetting factor. Therefore, a variable forgetting factor RLS (VFF-RLS) algorithm is also presented. The simulation results indicate that these algorithms are reliable candidates for real-world applications.

Signal processing had a significant impact on the development of many elements of modern life, including telecommunications, education, healthcare, industry, and security. The semiconductor industry is the primary driver of signal processing innovation, producing ever-more sophisticated electronic devices and circuits in response to global demand. In addition, the central processing unit (CPU) is described as the “brain” of a computer or all electronic devices and signal processing. CPU is a critical electronic device that includes vital components such as memory, multiplier, adder, etc. Also, one of the essential components of the CPU is the arithmetic and logic unit (ALU), which executes the arithmetic and logical operations within all types of CPU operations, such as addition, multiplication, and subtraction. However, delay, occupied areas, and energy consumption are essential parameters in ALU circuits. Since the recent ALU designs experienced problems like high delay, high occupied area, and high energy consumption, implementing electronic circuits based on new technology can significantly boost the performance of entire signal processing devices, including microcontrollers, microprocessors, and printed devices, with high-speed and low occupied space. Quantum dot cellular automata (QCA) is an effective technology for implementing all electronic circuits and signal processing applications to solve these shortcomings. It is a transistor-less nanotechnology being explored as a successor to established technologies like CMOS and VLSI due to its ultra-low power dissipation, high device density, fast operating speed in THz, and reduced circuit complexity. This research proposes a ground-breaking ALU that upgrades electrical devices such as microcontrollers by applying cutting-edge QCA nanotechnology. The primary goal is to offer a novel ALU architecture that fully utilizes the potential of QCA nanotechnology. Using a new and efficient approach, the fundamental gates are skillfully utilized with a coplanar layout based on a single cell not rotated. Furthermore, this work presents an enhanced 1-bit and 2-bit arithmetic logic unit in quantum dot cellular automata. The recommended design includes logic, arithmetic operations, full adder (FA) design, and multiplexers. Using the powerful simulation tools QCADesigner, all proposed designs are evaluated and verified. The simulation outcomes indicates that the suggested ALU has 42.48 and 64.28% improvements concerning cell count and total occupied area in comparison to the best earlier single-layer and multi-layer designs.

In view of slow convergence for fixed-step Equivariant Adaptive Separation for Independent (EASI )algorithm and the problem for variable step-size algorithm which bases on kurtosis is sensitive to outlier, a new variable step-size EASI algorithm is proposed, which applys the negative entropy maximization method of non-polynomial functions to the approximate calculation of the mutual information. Experiments results show that the proposed algorithm not only achieves faster convergence and smaller stead-state error than fixed-step EASI and other variable step-size algorithms, but also demonstrate better stability for the problem of outlier.

To attain good performance in an acoustic echo cancellation system, it is important to have a variable step size (VSS) algorithm as part of an adaptive filter. In this paper, we are concerned with the development of a VSS algorithm for a recently proposed subband affine projection (SAP) adaptive filter. Two popular VSS algorithms in the literature are the methods of delayed coefficients (DC) and variable regularization (VR). However, the merits and demerits of them are mutually exclusive. We propose a VSS algorithm* that is a hybrid of both methods and combines their advantages. An extensive study of the new algorithm in different scenarios like the presence double-talk (DT) during the transient phase of the adaptive filter, DT during steady state, and varying DT power is conducted and reasoning is given to support the observed behavior. The importance of the method of VR as part of a VSS algorithm is emphasized.

Electronic digital signal processing is currently introduced in various subsystems of modern optical high-speed transceivers. Traditionally, electronic processing was basically limited to tasks like MUX/DEMUX, clock and data recovery and various synchronization loops as well as analog circuitry like modulator drivers or limiting amplifiers. In order to deal with advanced systems and with the need for increased performance at long reach and higher speed, additional signal processing requirements have appeared. Examples are FEC and electronic equalizers (EDC), electronic pre-compensation, differential encoders, clock and carrier recovery, and polarization control. Recent challenges employ pure digital processing, where ADCs and DACs are required, like OFDM (orthogonal frequency division multiplexing) modulation or MLSE (maximum likelihood sequence estimation) equalizers. In this paper some of these signal processing algorithms are considered in detail and their impact on performance and cost in terms of implementation effort is reviewed.

Aiming at the challenge that traditional methods can not meet the requirements of high precision and high efficiency in complex scenes, a method of introducing deep learning model to improve the accuracy and efficiency of signal processing is proposed. Specifically, this paper designs an improved convolutional neural network model named "signal recognition convolutional network" (SRCN), which is optimized on the basis of traditional CNN to better adapt to the characteristics of electronic signals. In SRCN model, the local features of the signal are automatically extracted in the convolution layer through one-dimensional convolution kernel, and the dimension of the feature map is reduced by maximum pooling, and finally the classification results are output through softmax function in the fully connected layer. The SRCN model is trained and verified by using 30,000 data sets including radar signals, communication signals and other types of electronic signals. The results show that the SRCN model has a high classification performance on all kinds of signals, with an accuracy rate of 95.0%, which is significantly better than the traditional support vector machine (SVM) and decision tree methods. The research in this paper provides new ideas and methods for the field of electronic signal processing and pattern recognition, and shows the great potential and application value of deep learning technology in this field.

This paper presents an analysis of the impact of intentional electromagnetic (EM) interferences on the performance of Wi-Fi communications. The goal is to analyze the susceptibility of orthogonal frequency-division multiplexing (OFDM) signals and to understand how certain parameters of the interference signal act on the degradation of the communication, taking into account the signal processing performed at the reception stage of the communication system. We analyze the relationship between the parameters of the interference and the signal processing at the reception. This study is a contribution for agile communication solutions that would have the ability to reconfigure the signal processing in order to reduce the impact of interference signals. The scenario corresponds to an IEEE 802.11n wireless network affected by a frequency-sweeping interference signal. The interference signal sweeps over a given frequency band, and different sweep periods and interference to signal power ratios are applied. A set of measurements was performed to evaluate the performance of the communication network in the presence of different EM interferences. Bit rate measurements are presented and the impact of the interference characteristics on communication quality is analyzed, by highlighting the effect of the signal processing performed by the OFDM receiver.

In the inspection of concrete structures, the precise localization of rebar serves as the foundation for depth measurement and subsequent information analysis. Addressing the challenges of signal noise suppression and measurement accuracy enhancement in rebar detection, this paper proposes an unscented Kalman filter algorithm based on an adaptive forgetting factor. Conventional forgetting factor approaches exhibit poor adaptability and low filtering accuracy. When the noise is non-stationary or subject to abrupt disturbances, the accuracy becomes even less reliable, making it challenging to maintain stable filtering performance in complex environments. To overcome these shortcomings, this paper introduces an adaptive forgetting factor strategy that dynamically adjusts the noise covariance matrix based on real-time noise characteristics. This enables the filter to self-optimize its performance under complex conditions, thereby improving measurement accuracy. Furthermore, the algorithm incorporates a residual update mechanism, leveraging residuals to adjust the measurement noise covariance in real time, further enhancing the filtering efficacy. Simulation results demonstrate that the proposed algorithm significantly improves signal filtering accuracy in complex noise environments, outperforming traditional conventional forgetting factor approaches in detector position estimation and precise rebar localization.

The processing of radio frequency (RF) and microwave signals in the optical domain is an attractive approach to overcome the bottlenecks of bandwidth, power loss, and electromagnetic interference (EMI) encountered in conventional electronic signal processing systems. A wide range of emerging RF signal processing applications require specifically high resolution, ultra-wide bandwidth, wide-range tunability, and fast reconfigurability. While these requirements are difficult to achieve using conventional all-electronic processing, they are feasible with photonics-based signal processing. Holography is an historic technology that allows the light scattered from an object to be recorded and reconstructed so that the object can reappear when a reference optical beam illuminates a hologram used to record that object. Holography has a wide range of applications such as optical signal storage and retrieval and information processing. The application of reconfigurable phase holograms to realize optical beam steering is an attractive field for either optical engineering or fundamental research. The reconfigurable phase holograms are calculated from the targeted beam steering pattern and implemented using spatial light modulator. Opto-VLSI processors are one of these devices that can dynamically generate phase holograms and perform optical beam steering. Although full-electronic RF signal processing is very flexible and controllable, it is experiencing the bottlenecks of bandwidth and EMI. Processing microwave signals in the photonic domain can overcome the bottlenecks in the electronic signal processing. However the current technologies of microwave photonics have limited flexibility and reconfigurability. The Opto-VLSI technology is a novel and potential discipline that combine benefits of photonic devices and the intelligence plus processing capabilities of Very-LargeScale Integrated (VLSI) circuits. It integrates intelligence into photonic systems providing a new foundation for a future information processing and communication systems and networks. This book chapter will discuss a new methodology of expanding the use of OptoVLSI from the conventional paradigm of optical beam processing to a new frontier of

This paper discusses THz bandwidth information and signal processing based on RF-photonic technologies. In particular, we will emphasize integrated circuit approaches to RF-photonics where highly stable THz bandwidth information and signal processing can take place. We will demonstrate THz signal generation by optical arbitrary waveform generation (OAWG), and will describe its inverse process, optical arbitrary waveform measurement (OAWM). Further, we will discuss an RF-photonic lattice filter useful for optical equalization and other THz signal processing replacing traditional electronic digital signal processing which cannot currently scale to THz. The paper will also cover future prospects for the THz information and signal processing by integrated RF-photonic methods.

This paper presents a novel approach to Lyapunov stability theory-based adaptive filter (LAF) design. The proposed design is based on the minimization of the Euclidean norm of the difference weight vector under negative definiteness constraint defined over a novel linear Lyapunov function. The proposed fixed step size LAF (FSS-LAF) algorithm is first obtained by using the method of Lagrangian multipliers. The FSS-LAF satisfying asymptotic stability in the sense of Lyapunov provides a significant performance gain in the presence of a measurement noise. The stability of the FSS-LAF algorithm is also statistically analyzed in this study. Moreover, gradient variable step size (VSS) algorithms are adapted to the FSS-LAF algorithm to further enhance the performance for the first time in this paper. These VSS algorithms are Benveniste (BVSS), Mathews and Farhang–Ang (FVSS) algorithms. Simulation results on system identification problems show that the bounds of step size for the FSS-LAF algorithm are verified, and especially, the BVSS-LAF and FVSS-LAF algorithms provide a better trade-off between steady-state mean square deviation error and convergence rate than other proposed algorithms.

Summary form only given. The author discusses developments in optical signal processing and highlights the many ways in which the technology of optical and electronic signal processing is converging.

We present accurate signal processing techniques based on the continuous wavelet transform (CWT) for nonstationary optical coherence tomography (OCT) interference signals. OCT is attracting considerable attention in the clinical, scientific, and engineering fields. However, the temporal or spatial period of the detected interference signal is unequal because of the nonlinearity of the scanning device or the inverse proportionality of the phase to the wavelength. If these nonstationary interference signals are acquired at equal sampling intervals and processed using fast Fourier transform (FFT), measurement errors occur. We first propose techniques that compensate for the fringe intervals of the temporal interference signal based on the CWT. The accurate position measurement of a reflective surface is demonstrated. Next, we describe a novel signal processing technique that accurately determines the positions of the fringe peak and reference mirror for TD-OCT. An accurate step-height measurement is demonstrated in this study. Finally, we describe a threedimensional profile measurement method. The optical path difference can be determined through a simple relationship between the wavelength and the corresponding phase obtained by the CWT. Measurements of a step-height configured with a pair of gauge blocks and the thickness distribution of a thin glass plate are performed. In the latter case, we demonstrate that the accuracy is improved in the pulse-driven technique.

This innovative practice work-in-progress paper presents the outline of a bachelor level medical engineering lab that comprises electronical sensor signal processing. It explains how to teach and educate medical technology students the entire signal processing chain up to medical interpretation based on the practically relevant, widespread example of pulse oximetry. The various analog circuit blocks and the required post-processing with a microcontroller are discussed, as well as the interaction of all blocks to form the pulse oximeter system. For this purpose, the students are divided into small teams of three and receive a printed circuit board developed specifically for the experiment. With this board it is possible to extract certain parts of the signal processing measurement chain and analyze them with different tools, e.g. wave generator and oscilloscope. The accompanying handout guides and challenges students through the complex analysis of pulse oximetry, with subtasks to assist them. Tutors are available to support students with questions or if they get stuck. The aim of this practical exercise is for the students themselves to understand which individual steps are required and how they interact in order to obtain a valid measured value at the end. They learn the practical relevance and application of various electronic components and filter circuits, as well as the subsequent digitization and post-processing with the help of a microcontroller. In addition, the participants will familiarize themselves with the use of various measuring devices. The associated handout and its tasks do not provide step-by-step instructions. The students are encouraged to think creatively in their groups and to engage in group discussions so they understand the problem and find a solution for each subtask. After solving each subtask, the group receives feedback from a tutor who explains whether the solution to the task is right or wrong and whether there could have been smarter ways. By the end of the exercise, the students will have understood the complete signal path in depths from the sensor to the interpretation of the measured value and all the difficulties involved, based on a modern medical technology application.

The interferometric Fiber Bragg Grating (FBG) hydrophone has wide applications in ocean acoustic detection and seismic wave monitoring. The interference signal from external sources is routed to the elements through the link fiber connecting the dry and wet ends, which is a critical bottleneck that limits system performance. This paper presents a comprehensive model for link fiber-induced spurious signals, which demonstrates the physical processes and signal characteristics of the Doppler effect and the Rayleigh scattering effect, illustrating how they interact to generate the link interference signal. It is found that the dominant physical mechanism depends on the location of the link interference signal source. The interference signal source closer to the dry end mainly transmits signals to hydrophone elements through the Doppler effect, and the amplitude of the induced spurious signal depends on the frequency. When the link fiber is longer, the interference signal from the source near the wet end is simultaneously transmitted to hydrophone elements through the Doppler effect and Rayleigh scattering effect, during which the Rayleigh scattering effect leads to a spurious signal intensity increase of up to 10 dB. Additionally, the correlation with signal frequency decreases as instability rises. This thorough analysis of the physical process of link fiber-induced spurious signals will advance the development of interference suppression techniques, substantially boosting the sensitivity and reliability of interferometric FBG hydrophones for applications in harsh environments with disturbances.