Classical Signal Processing Methods Research Articles

A Survey on Interference Mitigation for Wireless Body Area Networks

Wireless body area networks (WBANs) are noteworthy, dependable, and most advantageous development in many health directed applications. WBANs in health system connect tiny sensors to invasive or non-invasive medical equipment for patient examinations utilizing low power wireless technology. When WBANs are utilized in crowded areas or in conjunction with other wireless sensor networks, communication interference can arise. This can lead to unstable signal integrity, which can impair system performance. Thus, interference mitigation needs to be taken into account when designing. In this paper we survey interference mitigation methods used in WBANs, classify them, and finally point to some future research directions in this area. In particular, we start by reviewing sample papers that tackle the interference management problem through classical signal processing and shaping methods. Then we review some works on cooperative communications approaches to encounter this problem. After that we consider approaches that are not centralized through the use of game theoretic formulations. We close our discussion by surveying model free algorithm through learning based approaches.

Knowledge-informed FIR-based cross-category filtering framework for interpretable machinery fault diagnosis under small samples

Knowledge-informed FIR-based cross-category filtering framework for interpretable machinery fault diagnosis under small samples

Triple-0: Zero-shot denoising and dereverberation on an end-to-end frozen anechoic speech separation network.

Speech enhancement is crucial both for human and machine listening applications. Over the last decade, the use of deep learning for speech enhancement has resulted in tremendous improvement over the classical signal processing and machine learning methods. However, training a deep neural network is not only time-consuming; it also requires extensive computational resources and a large training dataset. Transfer learning, i.e. using a pretrained network for a new task, comes to the rescue by reducing the amount of training time, computational resources, and the required dataset, but the network still needs to be fine-tuned for the new task. This paper presents a novel method of speech denoising and dereverberation (SD&D) on an end-to-end frozen binaural anechoic speech separation network. The frozen network requires neither any architectural change nor any fine-tuning for the new task, as is usually required for transfer learning. The interaural cues of a source placed inside noisy and echoic surroundings are given as input to this pretrained network to extract the target speech from noise and reverberation. Although the pretrained model used in this paper has never seen noisy reverberant conditions during its training, it performs satisfactorily for zero-shot testing (ZST) under these conditions. It is because the pretrained model used here has been trained on the direct-path interaural cues of an active source and so it can recognize them even in the presence of echoes and noise. ZST on the same dataset on which the pretrained network was trained (homo-corpus) for the unseen class of interference, has shown considerable improvement over the weighted prediction error (WPE) algorithm in terms of four objective speech quality and intelligibility metrics. Also, the proposed model offers similar performance provided by a deep learning SD&D algorithm for this dataset under varying conditions of noise and reverberations. Similarly, ZST on a different dataset has provided an improvement in intelligibility and almost equivalent quality as provided by the WPE algorithm.

An improved multiple discrete time system for operational modal analysis of a slanted rotational blade under natural excitation via a long-range tracking continuously scanning laser Doppler vibrometer system

An improved multiple discrete time system for operational modal analysis of a slanted rotational blade under natural excitation via a long-range tracking continuously scanning laser Doppler vibrometer system

Improving deep models of protein-coding potential with a Fourier-transform architecture and machine translation task.

Ribosomes are information-processing macromolecular machines that integrate complex sequence patterns in messenger RNA (mRNA) transcripts to synthesize proteins. Studies of the sequence features that distinguish mRNAs from long noncoding RNAs (lncRNAs) may yield insight into the information that directs and regulates translation. Computational methods for calculating protein-coding potential are important for distinguishing mRNAs from lncRNAs during genome annotation, but most machine learning methods for this task rely on previously known rules to define features. Sequence-to-sequence (seq2seq) models, particularly ones using transformer networks, have proven capable of learning complex grammatical relationships between words to perform natural language translation. Seeking to leverage these advancements in the biological domain, we present a seq2seq formulation for predicting protein-coding potential with deep neural networks and demonstrate that simultaneously learning translation from RNA to protein improves classification performance relative to a classification-only training objective. Inspired by classical signal processing methods for gene discovery and Fourier-based image-processing neural networks, we introduce LocalFilterNet (LFNet). LFNet is a network architecture with an inductive bias for modeling the three-nucleotide periodicity apparent in coding sequences. We incorporate LFNet within an encoder-decoder framework to test whether the translation task improves the classification of transcripts and the interpretation of their sequence features. We use the resulting model to compute nucleotide-resolution importance scores, revealing sequence patterns that could assist the cellular machinery in distinguishing mRNAs and lncRNAs. Finally, we develop a novel approach for estimating mutation effects from Integrated Gradients, a backpropagation-based feature attribution, and characterize the difficulty of efficient approximations in this setting.

Cooperative Multiband Spectrum Sensing Using Radio Environment Maps and Neural Networks.

Cogitive radio networks (CRNs) require high capacity and accuracy to detect the presence of licensed or primary users (PUs) in the sensed spectrum. In addition, they must correctly locate the spectral opportunities (holes) in order to be available to nonlicensed or secondary users (SUs). In this research, a centralized network of cognitive radios for monitoring a multiband spectrum in real time is proposed and implemented in a real wireless communication environment through generic communication devices such as software-defined radios (SDRs). Locally, each SU uses a monitoring technique based on sample entropy to determine spectrum occupancy. The determined features (power, bandwidth, and central frequency) of detected PUs are uploaded to a database. The uploaded data are then processed by a central entity. The objective of this work was to determine the number of PUs, their carrier frequency, bandwidth, and the spectral gaps in the sensed spectrum in a specific area through the construction of radioelectric environment maps (REMs). To this end, we compared the results of classical digital signal processing methods and neural networks performed by the central entity. Results show that both proposed cognitive networks (one working with a central entity using typical signal processing and one performing with neural networks) accurately locate PUs and give information to SUs to transmit, avoiding the hidden terminal problem. However, the best-performing cognitive radio network was the one working with neural networks to accurately detect PUs on both carrier frequency and bandwidth.

Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Going beyond RF: A survey on how AI-enabled multimodal beamforming will shape the NextG standard

Parallel Graph Signal Processing: Sampling and Reconstruction

Graph signal processing (GSP) extends classical signal processing methods to analyzing signals supported over irregular grids represented by graphs. Within the scope of GSP, sampling and reconstruction represent fundamental tools that have received considerable attention. For very large graphs, however, many of the current methods struggle with the computational and memory requirements. Vertex domain and randomized sampling strategies somewhat ameliorate the computational requirements, but these algorithms perform poorly at preserving signal fidelity for graphs with hundreds of thousands of vertices. To address these shortcomings, this paper introduces a new and scalable approach that can be easily parallelized. This new approach uses existing graph partitioning algorithms in concert with vertex-domain blue-noise sampling and reconstruction, performed independently across partitions. In the reconstruction, some degree of overlapping is added to the partitions to induce trans-partition smoothness in the recovered signal. We also propose two sampling schemes based on the spatial characteristic of the graph that minimizes the recovery error. The first combines graph partitioning with the Void-and-Cluster algorithm, while the second approach uses Error Diffusion. We conclude this paper with experiments on synthetic and real data that show the effectiveness of these new approaches on very large graphs.

Spectral estimation for Hamiltonians: a comparison between classical imaginary-time evolution and quantum real-time evolution

We consider the task of spectral estimation of local quantum Hamiltonians. The spectral estimation is performed by estimating the oscillation frequencies or decay rates of signals representing the time evolution of states. We present a classical Monte Carlo (MC) scheme which efficiently estimates an imaginary-time, decaying signal for stoquastic (i.e. sign-problem-free) local Hamiltonians. The decay rates in this signal correspond to Hamiltonian eigenvalues (with associated eigenstates present in an input state) and can be extracted using a classical signal processing method like ESPRIT. We compare the efficiency of this MC scheme to its quantum counterpart in which one extracts eigenvalues of a general local Hamiltonian from a real-time, oscillatory signal obtained through quantum phase estimation circuits, again using the ESPRIT method. We prove that the ESPRIT method can resolve S = poly(n) eigenvalues, assuming a 1/poly(n) gap between them, with poly(n) quantum and classical effort through the quantum phase estimation (QPE) circuits, assuming efficient preparation of the input state. We prove that our MC scheme plus the ESPRIT method can resolve S = O(1) eigenvalues, assuming a 1/poly(n) gap between them, with poly(n) purely classical effort for stoquastic Hamiltonians, requiring some access structure to the input state. However, we also show that under these assumptions, i.e. S = O(1) eigenvalues, assuming a 1/poly(n) gap between them and some access structure to the input state, one can achieve this with poly(n) purely classical effort for general local Hamiltonians. These results thus quantify some opportunities and limitations of MC methods for spectral estimation of Hamiltonians. We numerically compare the MC eigenvalue estimation scheme (for stoquastic Hamiltonians) and the quantum-phase-estimation-based eigenvalue estimation scheme by implementing them for an archetypal stoquastic Hamiltonian system: the transverse field Ising chain.

Deep Learning-Based Posture Recognition for Motion-Assisted Evaluation

With the development of computer vision technology, human action pose recognition has gradually become a popular research direction, but there are still some problems in the application research based on pose recognition in sports action assisted evaluation. In this paper, the human motion pose recognition technology based on deep learning is introduced into this field to realize the intelligence of sports-assisted training. Firstly, we analyze the advantages and limitations of the state-of-the-art human motion pose recognition algorithms in computer vision in specific fields. On this basis, a human motion space recognition method based on periscope neural network is proposed. Firstly, the classical radar signal processing method is used to preprocess the echo signal of human spatial position and generate the frequency image in the process of human spatial position. Then, the periscope neural network (CNN) is constructed, and the time-frequency image is used as the input data of CNN to train the network parameters. Finally, the method is tested by using the open dataset in the network. The experimental results show that the designed CNN can accurately identify four different types of physical motion, and the accuracy coefficient is at least 97%.

Novel sparse representation degradation modeling for locating informative frequency bands for Machine performance degradation assessment

Performance degradation assessment (PDA) is an intersection technology of machine condition monitoring, degradation evaluation, and fault diagnosis in condition-based maintenance (CBM). A commonly used strategy of PDA is to use a health index (HI) to detect an incipient fault for machine condition monitoring and subsequently to track monotonic performance degradation trajectories. Sparse representation, as a classical signal processing method, has been widely studied for machine fault diagnosis while its extension to PDA is seldom reported. Based on the sparsity property of fault signals in the frequency domain, a novel sparse representation degradation modeling methodology that integrates sparse representation and monotonic degradation modeling is proposed in this paper to enrich PDA. At first, the sparsity property of square envelope spectra is thoroughly investigated. Based on the sparse representation theory, a HI is designed as the sum of weighted atoms. Besides, the amplitudes of square envelope spectra are used as a dictionary. Then, a sparse representation degradation model is proposed to realize the monotonicity property of a HI and the sparsity of weight coefficients. Experimental verification shows that HIs constructed from the proposed methodology are more suitable to PDA compared to existing HIs. Moreover, a prominent advantage of the proposed sparse degradation modeling is that no prior knowledge and complicated signal processing algorithms are required. Moreover, optimized weights are sparsely located at informative frequency bands for subsequent fault diagnosis, which provides informative frequencies for many blind fault diagnostic scenarios.

A VME method based on the convergent tendency of VMD and its application in multi-fault diagnosis of rolling bearings

Variational mode extraction (VME) is a novel fault diagnosis technology, which can effectively extract a narrow-band mode from the multi-component signal. However, its performance is seriously affected by two initial parameters: the initial guess of center frequency ωd and the balance factor α. To determine these two parameters adaptively, a VME method based on the convergence property of variational mode decomposition (VMD) is proposed for the multi-fault diagnosis of rolling bearings. Firstly, we study the influence of the balance factor on the convergence characteristic of VMD, the VMD convergence tendency chart is established. Secondly, the initial center frequencies of all latent modes in the faulty signal are adaptively determined by the VMD convergence tendency chart; taking kurtosis as the evaluation index, the corresponding balance factor is optimized separately. Finally, VME is used to extract all modes one by one. The experimental analog signal and engineering application signal are analyzed by this new method and compared with ensemble empirical mode decomposition (EEMD), fast kurtogram, and single-mode VMD. The results display that the new method can accurately extract multiple fault features from the bearing compound fault signal, and the fault feature extraction accuracy is better than the above three classical signal processing methods.

Signal Processing Methodology of Response Data from a Historical Arch Bridge toward Reliable Modal Identification

The paper is part of a case study concerning the structural assessment of a historical infrastructure in the local territory, a road three-span reinforced concrete arch bridge over a river, built by the end of World War I (1917). The purpose of the paper is twofold: first, in-situ acquired response data are systematically analysed by specific signal processing techniques, to form a devoted methodological procedure and to extract useful information toward possible interpretation of the current structural conditions; second, the deciphered information is elaborated, in view of obtaining peculiar conceptualisations of detailed features of the structural response, as meant to achieve quantitative descriptions and modelling, for final Structural Health Monitoring (SHM) and intervention purposes. The proposed methodology, integrating self-implemented and adapted classical signal processing methods, and refined techniques, such as Wavelet analysis and ARMA models, assembles a rather general, systematic methodological approach to signal processing, highlighting the capability to extract useful and fundamental information from acquired response data, also endowed of a non-stationary character, toward final structural interpretation, identification and modelling, thus enabling for developing a reliable and effective SHM platform, on strategic ageing infrastructures. For the present case study, non-stationary characteristics of the response signals are revealed and flattened out, to identify the underlying fundamental frequencies of the infrastructure and to advance particular interpretations of its current structural behaviour, in forming an enlarging structural consciousness of the bridge at hand.

Classification of Distal Growth Plate Ossification States of the Radius Bone Using a Dedicated Ultrasound Device and Machine Learning Techniques for Bone Age Assessments

X-ray imaging, based on ionizing radiation, can be used to determine bone age by examining distal growth plate fusion in the ulna and radius bones. Legal age determination approaches based on ultrasound signals exist but are unsuitable to reliably determine bone age. We present a low-cost, mobile system that uses one-dimensional ultrasound radio frequency signals to obtain a robust binary classifier enabling the determination of bone age from data of girls and women aged 9 to 24 years. These data were acquired as part of a clinical study conducted with 148 subjects. Our system detects the presence or absence of the epiphyseal plate by moving ultrasound array transducers along the forearm, measuring reflection and transmission signals. Even though classical digital signal processing methods did not achieve a robust classifier, we achieved an F1 score of approximately 87% for binary classification of completed bone growth with machine learning approaches, such as the gradient boosting machine method CatBoost. We demonstrate that our ultrasound system can classify the fusion of the distal growth plate of the radius bone and the completion of bone growth with high accuracy. We propose a non-ionizing alternative to established X-ray imaging methods for this purpose.

Photoplethysmography and Deep Learning: Enhancing Hypertension Risk Stratification.

Blood pressure is a basic physiological parameter in the cardiovascular circulatory system. Long-term abnormal blood pressure will lead to various cardiovascular diseases, making the early detection and assessment of hypertension profoundly significant for the prevention and treatment of cardiovascular diseases. In this paper, we investigate whether or not deep learning can provide better results for hypertension risk stratification when compared to the classical signal processing and feature extraction methods. We tested a deep learning method for the classification and evaluation of hypertension using photoplethysmography (PPG) signals based on the continuous wavelet transform (using Morse) and pretrained convolutional neural network (using GoogLeNet). We collected 121 data recordings from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) Database, each containing arterial blood pressure (ABP) and photoplethysmography (PPG) signals. The ABP signals were utilized to extract blood pressure category labels, and the PPG signals were used to train and test the model. According to the seventh report of the Joint National Committee, blood pressure levels are categorized as normotension (NT), prehypertension (PHT), and hypertension (HT). For the early diagnosis and assessment of HT, the timely detection of PHT and the accurate diagnosis of HT are significant. Therefore, three HT classification trials were set: NT vs. PHT, NT vs. HT, and (NT + PHT) vs. HT. The F-scores of these three classification trials were 80.52%, 92.55%, and 82.95%, respectively. The tested deep method achieved higher accuracy for hypertension risk stratification when compared to the classical signal processing and feature extraction method. Additionally, the method achieved comparable results to another approach that requires electrocardiogram and PPG signals.

Automated recognition of cardiac arrhythmias using sparse decomposition over composite dictionary

Cardiovascular diseases (CVDs) are the leading cause of deaths worldwide. Due to an increase in the rate of global mortalities, biopathological signal processing and evaluation are widely used in the ambulatory situations for healthcare applications. For decades, the processing of pathological electrocardiogram (ECG) signals for arrhythmia detection has been thoroughly studied for diagnosis of various cardiovascular diseases. Apart from these studies, efficient diagnosis of ECG signals remains a challenge in the clinical cardiovascular domain due to its non-stationary nature. The classical signal processing methods are widely employed to analyze the ECG signals, but they exhibit certain limitations and hence, are insufficient to achieve higher accuracy. This study presents a novel technique for an efficient representation of electrocardiogram (ECG) signals using sparse decomposition using composite dictionary (CD). The dictionary consists of the stockwell, sine and cosine analytical functions. The technique decomposes an input ECG signal into stationary and non-stationary components or atoms. For each of these atoms, five features i.e., permutation entropy, energy, RR-interval, standard deviation and kurtosis are extracted to determine the feature sets representing the heartbeats that are classified into different categories using the multi-class least-square twin support vector machines. The artificial bee colony (ABC) technique is used to determine the optimal classifier parameters. The proposed method is evaluated under category and personalized schemes and its validation is performed on MIT-BIH data. The experimental results reported a higher overall accuracy of 99.21% and 90.08% in category and personalized schemes respectively than the existing techniques reported in the literature. Further a sensitivity, positive predictivity and F-score of 99.21% each in the category based scheme and 90.08% each in the personalized schemes respectively. The proposed methodology can be utilized in computerized decision support systems to monitor different classes of cardiac arrhythmias with higher accuracy for early detection and treatment of cardiovascular diseases.

Advanced Wireless Local Positioning via Compressed Sensing

Wireless local positioning systems are based on measurements of signal time of flights and/or bearing angles to determine the position of an active transmitter in 3-D space. Traditional time-of-flight and angle measurements with such positioning systems require many data samples at high rates to fulfill the Nyquist criterion. Moreover, in multipath channels the ability of classical signal processing methods to resolve the delay and bearing vectors of the multiple transmission paths is strictly limited by the radio frequency bandwidth and aperture size. In this paper, we show that the wireless channel is ideally suited for the application of compressed sensing. We can thus reduce the number of samples without major losses in localization performance, or we can improve the resolution. After introducing a general model of a localization task with multipath propagation, and following the definition of key parameters of existing systems, we review the necessary data processing stages with compressed sensing and verify its excellent applicability. Finally, measurements are presented to verify the theoretical predictions experimentally.

Extended FDD-WT method based on correcting the errors due to non-synchronous sensing of sensors

Abstract In this research, a combinational non-parametric method called frequency domain decomposition-wavelet transform (FDD-WT) that was recently presented by the authors, is extended for correction of the errors resulting from asynchronous sensing of sensors, in order to extend the application of the algorithm for different kinds of structures, especially for huge structures. Therefore, the analysis process is based on time-frequency domain decomposition and is performed with emphasis on correcting time delays between sensors. Time delay estimation (TDE) methods are investigated for their efficiency and accuracy for noisy environmental records and the Phase Transform – β (PHAT-β) technique was selected as an appropriate method to modify the operation of traditional FDD-WT in order to achieve the exact results. In this paper, a theoretical example (3DOF system) has been provided in order to indicate the non-synchronous sensing effects of the sensors on the modal parameters; moreover, the Pacoima dam subjected to 13 Jan 2001 earthquake excitation was selected as a case study. The modal parameters of the dam obtained from the extended FDD-WT method were compared with the output of the classical signal processing method, which is referred to as 4-Spectral method, as well as other literatures relating to the dynamic characteristics of Pacoima dam. The results comparison indicates that values are correct and reliable.

A Sinusoidal Frequency Modulation Fourier Transform for Radar-Based Vehicle Vibration Estimation

The intricate vibration of a working vehicle provides an important signature to the vehicle type. Small vibrations introduce phase modulation in radar echoes, which is referred to as micro-Doppler (m-D) phenomenon and can be modeled as sinusoidal frequency-modulated (SFM) signal. Such phase modulation induced by vibrations consists of multiple frequency components; moreover, the modulation is usually rather weak. Present parametric estimators are difficult to estimate so many parameters of every frequency component, while nonparametric approaches suffer from low precision. This paper considers the analysis of SFM signal with weak and multiple frequency components modulation on phase term. We first define the SFM signal space to bridge a gap between the SFM signal analysis and classical signal processing methods. Based on the defined signal space, a novel m-D analysis method, i.e., the sinusoidal frequency modulation Fourier transform (SFMFT), is presented. With the operations acting directly on the phase term of SFM signal, SFMFT gives the frequency spectrum of vibration traces. Unlike the existing methods, which apply a sliding short-time window to perform an instantaneous approximation, the proposed method makes use of the global data, which can provide a longer integral period gain, and consequently improves the estimation performance significantly. Simulation results indicate that the proposed method outperforms the existing methods in the spectrum accuracy, the range of estimable vibration amplitude/frequency, and the computation complexity.

Research Progress of The Algebraic and Geometric Signal Processing

The investigation of novel signal processing tools is one of the hottest research topics in modern signal processing community. Among them, the algebraic and geometric signal processing methods are shown to be one of the most powerful tools for the representation of the classical signal processing method. In this paper, we provide an overview of recent contributions pertaining to the algebraic and geometric signal processing. Specifically, the paper focuses on the mathematical structures behind the signal processing by emphasizing the algebraic and geometric structure of the signal processing. The two major topics were discussed. First, the classical signal processing concepts are related to the algebraic structures, and the recent results associated with the algebraic signal processing theory are introduced. Second, the recent progress of the geometric signal and information processing representations associated with the geometric structure are discussed. From these discussions, it is concluded that the research on the algebraic and geometric structure of signal processing can help the researchers to understand the signal processing tools deeply, and also help us to find novel signal processing methods in signal processing community. Its practical applications are expected to grow significantly in years to come, given that the algebraic and geometric structure of signal processing offer many advantages over the traditional signal processing.