Digital Signal Processing Approach Research Articles

Применение машинного обучения и интеллектуального анализа данных в автоматизированной системе неразрушающего вихретокового контроля поверхностного слоя деталей подшипников

Changing the properties of the material during physical and mechanical processing can significantly reduce the working life of the manufactured product, therefore it is important to control the quality of the surface layer of the parts. To solve this problem, non-destructive testing techniques such as etching, visual, capillary, magnetic powder, ultrasonic, vibration, eddy current methods are used at bearing enterprises. The article discusses the physical foundations of the presented techniques and provides their comparative analysis. Machine vision and digital signal processing approaches can be used to automate the processing of the results of non-destructive testing of the surface layer of bearing parts within the framework of the Industry 4.0 concept. From the point of view of productivity and the possibility of integration into the production system, the eddy current method is the most promising, the result of surface control in this way is an array of digital values. The development of modern methods of information analysis makes it possible to efficiently process a large amount of data, and machine learning makes it possible to solve problems of classification, regression, etc. This article provides methodological support for the development and application of an automated eddy current control system using machine learning and data mining methods. The works of scientists devoted to the processing of the results of the shock control of various objects, including bearing parts, are considered, it is noted that previously attention had not been paid to the issue of a reasonable choice of a machine learning model for recognizing defects on the surface of parts. The possibility of using the median polishing method to transform the eddy current signal is shown. The development and implementation of a bearing defect recognition system based on the methodological support presented in this paper can significantly improve the efficiency of product quality control and optimize the technological process.

Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Digital Signal Processing Approaches in the field of Genomics: A Recent Trend

Digital signal processing (DSP) techniques have emerged as powerful tools in the field of genomics, enabling researchers to extract valuable insights from complex genetic data. This research paper presents a comprehensive analysis of the recent trends and advance- ments in applying DSP approaches to genomics. The objective is to provide an overview of the transformative role of DSP in genomic data analysis, variant calling, and interpretation. By leveraging DSP methods such as filtering, feature extraction, time-frequency analysis, and machine learning algorithms, researchers can enhance the quality of genetic signals, identify genetic variants, and gain a deeper understanding of genomic processes. The paper highlights key applications of DSP in genomics, including DNA sequence analysis, RNA expression pro- filing, epigenetics, and genome-wide association studies. Additionally, the challenges associated with applying DSP techniques in genomics, such as signal noise, data in- tegration, and computational complexity, are discussed. This research paper serves as a valuable resource for researchers, bioinformaticians, and geneticists seeking to harness the power of DSP in genomics, advancing our knowledge of genetic diseases and paving the way for personalized medicine and precision healthcare. Keywords: Digital signal processing, Genome analysis, Feature extraction, DNA sequence analysis, RNA expression profiling.

A quantum approach for digital signal processing

A quantum approach for digital signal processing

Genomic Signal Processing Methods in DNA Mapping Schemes for Prediction of Exon in a Gene Using Digital Filters

Genomic signal processing (GSP) is an engineering domain involved with the analysis of genomic data using digital signal processing (DSP) approaches after transformation of the sequence of genome to numerical sequence. One challenge of GSP is how to minimize the error of detection of the protein coding region in a specified deoxyribonucleic acid (DNA) sequence with a minimum processing time. Since the type of numerical representation of a DNA sequence extremely affects the prediction accuracy and precision. The impact of different DNA statistical representations on the identification of coding sequences (exons) was researched. In this study using the IIR inverse Chepyshev filter for twenty benchmark human genes. In order to accomplish this, the sensitivity, specificity, and correlation coefficient of the four most modern DNA numerical representation schemes GCC, FNO, atomic number, and 2-bit binary were measured and contrasted with those of EIIP, the most used technique for locating protein-coding regions.

Overview of high-speed TDM-PON beyond 50 Gbps per wavelength using digital signal processing [Invited Tutorial

The recent evolution of passive optical network standards and related research activities for physical layer solutions that achieve bit rates well above 10 Gbps per wavelength ( λ ) is discussed. We show that the advancement toward 50, 100, and 200 G b p s / λ will certainly require a strong introduction of advanced digital signal processing (DSP) technologies for linear, and maybe nonlinear, equalization and for forward error correction. We start by reviewing in detail the current standardization activities in the International Telecommunication Union and the Institute of Electrical and Electronics Engineers, and then we present a comparison of the DSP approaches for traditional direct detection solutions and for future coherent detection approaches.

An Adaptive Mapping Method Using Spectral Envelope Approach for DNA Spectral Analysis.

The digital signal processing approaches were investigated as a preliminary indicator for discriminating between the protein coding and non-coding regions of DNA. This is because a three-base periodicity (TBP) has already been proven to exist in protein-coding regions arising from the length of codons (three nucleic acids). This demonstrates that there is a prominent peak in the energy spectrum of a DNA coding sequence at frequency rad/sample. However, because DNA sequences are symbolic sequences, these should be mapped into one or more signals such that the hidden information is highlighted. We propose, therefore, two new algorithms for computing adaptive mappings and, by using them, finding periodicities. Both such algorithms are based on the spectral envelope approach. This adaptive approach is essentially important since a single mapping for any DNA sequence may ignore its intrinsic properties. Finally, the improved performance of the new methods is verified by using them with synthetic and real DNA sequences as compared to the classical methods, especially the minimum entropy mapping (MEM) spectrum, which is also an adaptive method. We demonstrated that our method is both more accurate and more responsive than all its counterparts. This is especially important in this application since it reduces the risks of a coding sequence being missed.

Real-time reception of NHS-OFDM signal with SPA-enhanced channel estimation for intensity-modulated direct-detection systems

In this Letter, we have experimentally verified a low-complexity subcarrier pairwise-averaging (SPA)-enhanced channel estimation (CE) method for small-size fast Fourier transform (FFT) non-Hermitian symmetric orthogonal frequency-division multiplexing (NHS-OFDM) transceivers. Compared with intra-symbol frequency averaging (ISFA), more than 20% look-up tables and 10% logic power consumption can be saved. The least-square (LS), ISFA, and SPA CE methods are compared by offline and real-time digital signal processing approaches. The results show that the receiver sensitivity of the SPA NHS-OFDM transmission system with 64/128-point FFT can be improved by more than 1 dB at the bit error rate of 3.8 × 10-3 compared to the LS.

Systematic digital signal processing approach in various biometric identification

Biometrics are unique physical characteristics, such as fingerprints, that can be used for automatic recognition. Biometric identifiers are often classified as physiological characteristics associated with body shape. The goal is to capture a piece of biometric data from that person. It could be a photograph of their face, a recording of their voice, or a picture of their fingerprints. While there are numerous types of biometrics for authentication, the six most common are facial, voice, iris, near-field communication, palm or finger vein patterns, and Quick Response (QR) code. Biometrics is a subset of the larger field of human identification science. This paper explores computational approaches to speaker recognition, face recognition, speech recognition, and fingerprint recognition to assess the overall state of digital signal processing in biometrics.

A Parametric Network for the Global Compensation of Physical Layer Linear Impairments in Coherent Optical Communications

This paper proposes a parametric network for the joint compensation of multiple linear impairments in coherent optical communication systems. The considered linear impairments include both static and time-variant effects such as in-phase/quadrature (IQ) imbalance, laser phase noise (PN), chromatic dispersion (CD), polarization mode dispersion (PMD), and carrier frequency offset (CFO). To jointly compensate for these considered impairments, the proposed network is composed of parametric layers that exploit the particular signal model of each impairment. The layers’ parameters are jointly learned during a training stage. This stage uses a supervised step that exploits the knowledge of some transmitted data (preamble and/or pilots) and a self-labeling step that uses the knowledge of the symbol constellation. In addition, a new validation technique that does not require a different dataset is developed to avoid overfitting. The parametric network performance is compared to classical digital signal processing (DSP), and Deep Learning (DL) approaches using simulated data. Simulation results show that the proposed network outperforms the competing approaches in terms of Bit Error Rate (BER) while maintaining a relatively reduced computational complexity. In the scenarios considered, compared to the parametric network, the DSP approach introduces an OSNR penalty between 0.2 dB and 1.7 dB at a BER of 4×10-3. Furthermore, simulation results demonstrate that the proposed network is way more flexible than other approaches since it can easily be adapted to a different scenario and coupled with other techniques.

Equalization of Intensity-Modulated Fiber-Optic Voltage Sensors for Power Distribution Systems

We test fiber-optic voltage sensors based on optical reflection from a piezoelectric transducer. Our specific devices possess a 2 kHz fundamental resonance, and we verify a readily usable frequency band from approximately 10 Hz to 3 kHz, with a dynamic range of 60 dB for a detection system integrating over this entire band. Additionally, we demonstrate a digital signal processing approach to equalize the measured frequency response, enabling accurate retrieval of short-pulse inputs. Our results suggest the value and applicability of intensity-modulated fiber-optic voltage sensors for measuring both steady-state waveforms and broadband transients which, coupled with the straightforward and compact design of the sensors, should make them effective tools in electric grid monitoring.

Classification of SARS-CoV-2 and non-SARS-CoV-2 using machine learning algorithms

Due to the continued evolution of the SARS-CoV-2 pandemic, researchers worldwide are working to mitigate, suppress its spread, and better understand it by deploying digital signal processing (DSP) and machine learning approaches. This study presents an alignment-free approach to classify the SARS-CoV-2 using complementary DNA, which is DNA synthesized from the single-stranded RNA virus. Herein, a total of 1582 samples, with different lengths of genome sequences from different regions, were collected from various data sources and divided into a SARS-CoV-2 and a non-SARS-CoV-2 group. We extracted eight biomarkers based on three-base periodicity, using DSP techniques, and ranked those based on a filter-based feature selection. The ranked biomarkers were fed into k-nearest neighbor, support vector machines, decision trees, and random forest classifiers for the classification of SARS-CoV-2 from other coronaviruses. The training dataset was used to test the performance of the classifiers based on accuracy and F-measure via 10-fold cross-validation. Kappa-scores were estimated to check the influence of unbalanced data. Further, 10 × 10 cross-validation paired t-test was utilized to test the best model with unseen data. Random forest was elected as the best model, differentiating the SARS-CoV-2 coronavirus from other coronaviruses and a control a group with an accuracy of 97.4 %, sensitivity of 96.2 %, and specificity of 98.2 %, when tested with unseen samples. Moreover, the proposed algorithm was computationally efficient, taking only 0.31 s to compute the genome biomarkers, outperforming previous studies.

Palliation of Four-Wave Mixing in Optical Fibers Using Improved DSP Receiver

A long haul optical communication system (LHOCS) is one of the key resources to fulfill the higher capacity requirements in future communication networks. To launch LHOCS, the system mainly faces high order nonlinear effects. The four-wave mixing (FWM) is one of the major nonlinear effects, which limits the transmission distance. Therefore, in this paper, an advanced duo-binary (DB) modulation scheme-based system is evaluated by employing an improved digital signal processing (IDSP) approach at the receiver side to suppress the FWM effect. In addition, an analytical analysis is also performed for the proposed system. To observe the difference between the IDSP and conventional digital signal processing (DSP), the various performance metrics such as bit error rate (BER), Q-factor, and optical signal-to-noise ratio (OSNR) parameters are evaluated. Variable channel spacing along with polarization mode dispersion (PMD) are analyzed at several ranges of input powers and fiber lengths. The analytical and simulation-based calculations exhibit the effectiveness of the proposed model and hence, FWM effect are compensated to achieve 500 km optical fiber propagation range with a BER below 10−6.

Digital synthesis of programmable photonic integrated circuits

Programmable photonic waveguide meshes can be programmed into many different circuit topologies and thereby provide a variety of functions. Due to the complexity of the signal routing in a general mesh, a particular synthesis algorithm often only accounts for a specific function with a specific cell configuration. In this paper, we try to synthesize the programmable waveguide mesh to support multiple configurations with a more general digital signal processing platform. To show the feasibility of this technique, photonic waveguide meshes in different configurations (square, triangular and hexagonal meshes) are designed to realize optical signal interleaving with arbitrary duty cycles. The digital signal processing (DSP) approach offers an effective pathway for the establishment of a general design platform for the software-defined programmable photonic integrated circuits. The use of well-developed DSP techniques and algorithms establishes a link between optical and electrical signals and makes it convenient to realize the computer-aided design of optics–electronics hybrid systems.

Pilot Tone Power Limits of Brillouin Amplified Carrier Recovery for Optical Communications

The benefit of Brillouin amplification for optical carrier recovery from pilot tones is evaluated in application to coherent signal detection without a conventional local oscillator (LO) laser. The focus being on the narrow gain bandwidth suppressing broadband spectral noise power relative to the pilot tone. Calculations indicate its use at the receiver with realistic 30 dB gain and 30 MHz gain bandwidth permit lowering the minimum pilot tone to signal power ratio (PSR) for low distortion signal demodulation by up to ≈24 dB in the case of a 10 GHz wide signal channel, and even larger for broader channels. The ultimate limit being its ratio to the narrowness of the Brillouin gain bandwidth. Low noise Brillouin gain is also accounted for from characterization by a coherent receiver and digital signal processing approach for an optical fiber used as the gain medium in experiments. Its use in optical carrier recovery from a 3 channel WDM 48 Gb/s 64-QAM signal with polarization multiplexed pilot tones demonstrates improvement consistent with predictions in reducing the impact of pilot tone noise to near that for a conventional LO. The estimated low PSR limit for a bit error rate below the hard decision FEC limit was extrapolated as ≈-38 dB. This rose to ≈-30 dB with inclusion of 80 km link transmission due to the restricted signal launch power for avoiding nonlinear distortion. The insight highlights the wide-ranging capabilities for enhancing performance in advanced coherent communications.

An Echo Suppression Delay Estimator for Angle-of-Arrival Ultrasonic Indoor Localization

In this article, we propose a new robust, stable, accurate, and efficient time-delay estimation scheme for the angle-of-arrival (AoA) ultrasonic indoor single-source localization systems. The AoA technique employing microphone arrays and ultrasonic signal emitting sources has turned out to be a promising alternative to the conventional distance-based ultrawideband systems for indoor localization. The crux of AoA estimation lies in the derivation of time-delay estimates, which is typically handled by advanced digital signal processing approaches based on cross-correlation and matching algorithms. The calculation of the channel response for each received signal and the conversion from the time to the time-delay domain consent our echo suppression delay estimator (ESDE) to apply echo suppression, in order to overcome the typical challenges introduced by the channel in indoor environments. Moreover, the optimization of the cross-correlation algorithm through oversampling the cross power spectrum and the curve-fitting algorithm allows to ensure subsample precision results. The performances of our developed AoA estimation system are evaluated by carrying out real-world experiments and compared with the state-of-the-art generalized cross correlators. Our ESDE algorithm is shown to outperform several generalized solutions.

A Survey of FIR Filter Design Techniques: Low-complexity, Narrow Transition-band and Variable Bandwidth

In the present century, digital signal processing (DSP) approaches are considered to be one of the most powerful technologies which may shape the science and technology in coming decades. From 1970 onwards, a drastic revolution took place in a wider domain of DSP which has made it popular in several studies such as radar and sonar signal processing, digital televisions, wireless communication scenarios and other multimedia setup etc. Digital filters form the backbone of this DSP architecture and in point of fact the field of digital filter design has drawn justified recognition from the researchers throughout the world for the last 50 years. In connection to this, thousands of research articles may be found from the literature which had extensively addressed on the design of such filters. In order to meet the requirement of narrow transition-band, finite impulse response (FIR) filters are commonly assumed to be of higher order and accordingly it significantly enhances computational complexity. In regard to this, construction of hardware efficient digital filters had drawn significant consideration which aims to include minimum hardware elements during its application and consequently consumes less power. This review paper illustrates various techniques for the implementation of hardware efficient narrow transition-band FIR filter and investigates a number of favourable attributes which are capable in sustaining the stringent requirements of communication standards. A good number of relevant articles are taken from the literature so as to make a robust and complete review.

Design of Area Efficient Filter Bank for Digital Hearing Aids

The objective of the work scheduled is to design an area-efficient filter bank in a digital hearing aid by making use of Verilog HDL for functional verification, Synthesis & Physical Design in Cadence- Genus & Innovus respectively using ASIC design flow. Hypoacusis is a medical term which is well referred to as hearing impairment is a total or partial incompetence to hear. Around 466 million people live with Hypoacusis worldwide this is over 5% of the world’s populace. Hearing Aid is an Acoustoelectric Transducer which selectively enhances signal to ear to reimburse hearing loss. As technology is advancing day by day digital hearing aids are more adaptive, small and lightweight. The main block of the hearing aid is a filter bank which performs sound decomposition. To design a filter bank, it requires a precise intelligence of distinct digital signal processing approaches used in hearing aids among them the most important is to design a filter in which FIR, IIR, IFIR are crucial, which extracts signal within frequency bands. The proposed design area is minimized by 70.53%. thereby the size of the device is drastically reduced. The proposed design has an added advantage of lower power and delay.

An efficient hardware logarithm generator with modified quasi-symmetrical approach for digital signal processing

This paper presents a low-error, low-area FPGA-based hardware logarithm generator for digital signal processing systems which require high-speed, real time logarithm operations. The proposed logarithm generator employs the modified quasi-symmetrical approach for an efficient hardware implementation. The error analysis and implementation results are also presented and discussed. The achieved results show that the proposed approach can reduce the approximation error and hardware area compared with traditional methods.

DSP for High-Speed Short-Reach IM/DD Systems Using PAM

Intensity modulation and direct detection (IM/DD) together with pulse amplitude modulation (PAM) is a cost-efficient solution for intra-datacenter links. However, sophisticated digital signal processing (DSP) is inevitable to reach the demanded high data rates for these short-reach applications. In this article, we compare different DSP schemes that enable rates of 200 Gb/s/λ and beyond. Those schemes include Tomlinson-Harashima precoding, partial response signaling, Viterbi equalization and post-filtering together with Volterra nonlinear equalization. Extensive experimental measurements are shown in which the DSP approaches are compared at gross rates between 180 Gb/s and 300 Gb/s. The experiments are done in C-band for fiber links of up to 2 km length. As modulation formats, PAM4, PAM6, and PAM8 are considered. Net bit rates well over 200 Gb/s are shown for all formats at a range of 1 km single mode fiber using a transmission system consisting of currently available commercial components. The optimum DSP configuration as well as the best modulation format varies along the investigated data rates. The best solution is shown to change from partial response PAM4 for lower rates to PAM6 and PAM8 at high bit rates.