Digital Signal Processing Research Articles

Newest Methods and Approaches to Enhance the Performance of Optical Frequency-Domain Reflectometers.

In this review, we summarize the latest advances in the design of optical frequency-domain reflectometers (OFDRs), digital signal processing, and sensors based on special optical fibers. We discuss state-of-the-art approaches to improving metrological characteristics, such as spatial resolution, SNR, dynamic range, and the accuracy of determining back reflection coefficients. We also analyze the latest achievements in the OFDR-based sensors: the accuracy of spatial localization of the impact, the error in detecting temperatures, deformation, and other quantities, and the features of separate measurement of various physical quantities. We also pay attention to the trend of mutual integration of frequency-domain optical reflectometry methods with time-domain optical reflectometry, which provides completely new sensing possibilities. We believe that this review may be useful to engineers and scientists focused on developing a lab setup, complete measurement instrument, or sensing system with specific requirements.

Disturbance observer‐based finite‐time control of a photovoltaic‐battery hybrid power system

AbstractHerein, the load power control of the stand‐alone photovoltaic‐battery hybrid power system (HPS) has been investigated. The underlying HPS consists of a boost DC‐DC converter, a non‐isolated bidirectional half‐bridge converter, a photovoltaic (PV) panel, and a battery pack. On the PV side, a disturbance observer‐based finite‐time terminal sliding mode control (FTSMC) is used to regulate the DC bus to the desired voltage, in the presence of irradiation variation and load changes. On the battery side, the load power control system is constructed, based on a model predictive control (MPC) algorithm, with constraints on state‐of‐charge (SOC) and maximum current value of the battery to improve the battery life cycle and high reliability of the system. To highlight the benefits of the closed‐loop system, the analytical proofs and numerical analysis are presented from a comparative viewpoint. The experimentally derived results, by implementation on TMS320F28335 digital signal processing (DSP), are also presented and discussed for practical justification.

Short-Circuit Fault Diagnosis on the Windings of Three-Phase Induction Motors through Phasor Analysis and Fuzzy Logic

An induction motor is an electric machine widely used in various industrial and commercial applications due to its efficiency and simple design. In this regard, a methodology based on the electric phasor analysis of line currents and the variations in the phase angles among these line currents is proposed. The values in degrees of the angles between every pair of line currents were introduced to a fuzzy logic algorithm based on the Mamdani model, developed using the Matlab toolbox for detection and isolation of the inter-turn short-circuit faults on the windings of an induction motor. To carry out the analysis, the induction motor was modified in its stator windings to artificially induce short-circuit faults of different magnitudes. The current signals are acquired in real time using a digital platform developed in the Delphi 7 high-level language communicating with a float point unit Digital Signal Processor (DSP) TMS320F28335 by Texas Instruments. The proposed method not only detects the short circuit faults but also isolates the faulty winding.

Non-interference detection of digital signals in transmission line utilizing quantum sensor of nitrogen-vacancy centers

Digital signals play an extremely important role in the integration and multi-functionality of industrial instrumentation. With the expansion of industrial application scenarios, non-interference detection of digital signals in certain extreme environments has become the need of the hour. This paper demonstrates a method for non-interference Detection of transmission line digital signals based on nitrogen-vacancy (NV) centers in diamond. The optical readability and the sensitivity to magnetic fields of the NV centers are utilized to characterize the current-induced weak magnetic field signals by fluorescence signals in a confined space. By combining quantum sensing technology, quantum imaging technology and electromagnetic induction technology through NV centers, it is able to realize high sensitivity and high precision multi-point measurement and dynamic monitoring of electrical signals. The temporal resolution of the digital signal was evaluated by regulating the duration of the input current in the transmission line, and the lowest transmission current in a single transmission line and two transmission lines in a 1.2 mm × 1.2 mm imaging field of view was explored. The experimental results show that the time resolution of the detected digital signals is up to 30 μs. This research highlights the potential value of NV centers in digital signal processing and provides new research directions for detecting digital signals in extreme environments.

Investigation on the randomly-coupled unit grouping multi-core fibers with different unit arrangement

Randomly-coupled unit grouping multi-core fiber (RC-UG-MCF) is a new fiber structure that combines weakly and strongly coupled multi-core fiber technologies. Compared to the weakly-coupled MCF with a large cladding size, the RC-UG-MCF can shrink the cladding size by grouping the cores. On the other hand, the RC-UG-MCF only maintains a few cores in the strongly-coupled condition, which can reduce the multiple-input multiple- output digital signal processing (MIMO-DSP) complexity compared to the coupled MCFs with full MIMO-DSP for all the cores. In this paper, we intro- duce three types of randomly-coupled unit grouping 12-core fiber, which are randomly-coupled 2-core unit grouping MCF (RC-2CUG-MCF), RC-3CUG- MCF and RC-4CUG-MCF. Subsequently, we compare these three RC-UG- MCFs in terms of group delay spread, inter-unit crosstalk (XT), and cladding size. To achieve an inter-unit XT of −40 dB/100 km, RC-3CUG-MCF can provide a solution to maintain MIMO-DSP scale at the level of 6 × 6 with 180-µm cladding radius. However, the RC-2CUG-MCF has worse inter-unit XT, and the RC-4CUG-MCF has larger MIMO complexity. Based on the analysis, there is a trade-off relationship between the MIMO-DSP scale and the fiber cladding size, assuming that the target inter-unit XT is fixed.

Hybrid ALM-DSP TDC in Intel Arria 10 FPGA

The article presents the first implementation of a time-to-digital converter (TDC) using a digital signal processing (DSP) block from an Intel field-programmable gate array (FPGA) device. The design and configuration capabilities of the DSP blocks of the leading FPGA manufacturers, AMD and Intel, are compared in the context of time/digital conversion. Next, three variants of TDCs are presented. The problem of ultra-wide bins observed in both the classic version of the converter based on the Adaptive Logic Modules (ALM) and the version built using only DSP blocks has been mitigated in a hybrid variant, combining both solutions. The proposed variant of the hybrid ALM-DSP TDC implemented in Arria 10 FPGA from Intel (20 nm process node) achieved a resolution of 2.1 ps, a single-shot-precision better than 6.36 ps and reduced the maximum differential nonlinearity (DNL) error from 74.5 ps to 12.7 ps.

Single-carrier 220-Gbit/s sub-THz wireless transmission over 214 m using a photonics-based system.

We have demonstrated, for the first time to our knowledge, a single-carrier 220-Gbit/s wireless link over 214-m distance within hard-decision forward error correction (HD-FEC) limit using a 300-GHz-band photonics-based system incorporated with on-line digital signal processing (DSP). The approach to obtaining these results was to optimize the transmission system by using a low-noised two-tone laser system to the transmitter and receiver and to apply the unique Fresnel region of the antenna to achieve longer distances. These research results are expected to serve as a low-cost and basic communication system technology for promising backhaul wireless communications in the sixth-generation era.

Design of Infinite Impulse Response Filters Based on Multi-Objective Particle Swarm Optimization

The goal of this study is to explore the effectiveness of applying multi-objective particle swarm optimization (MOPSO) algorithms in the design of infinite impulse response (IIR) filters. Given the widespread application of IIR filters in digital signal processing, the precision of their design plays a significant role in the system’s performance. Traditional design methods often encounter the problem of local optima, which limits further enhancement of the filter’s performance. This research proposes a method based on multi-objective particle swarm optimization algorithms, aiming not just to find the local optima but to identify the optimal global design parameters for the filters. The design methodology section will provide a detailed introduction to the application of multi-objective particle swarm optimization algorithms in the IIR filter design process, including particle initialization, velocity and position updates, and the definition of objective functions. Through multiple experiments using Butterworth and Chebyshev Type I filters as prototypes, as well as examining the differences in the performance among these filters in low-pass, high-pass, and band-pass configurations, this study compares their efficiencies. The minimum mean square error (MMSE) of this study reached 1.83, the mean error (ME) reached 2.34, and the standard deviation (SD) reached 0.03, which is better than the references. In summary, this research demonstrates that multi-objective particle swarm optimization algorithms are an effective and practical approach in the design of IIR filters.

Learnable digital signal processing: a new benchmark of linearity compensation for optical fiber communications

The surge in interest regarding the next generation of optical fiber transmission has stimulated the development of digital signal processing (DSP) schemes that are highly cost-effective with both high performance and low complexity. As benchmarks for nonlinear compensation methods, however, traditional DSP designed with block-by-block modules for linear compensations, could exhibit residual linear effects after compensation, limiting the nonlinear compensation performance. Here we propose a high-efficient design thought for DSP based on the learnable perspectivity, called learnable DSP (LDSP). LDSP reuses the traditional DSP modules, regarding the whole DSP as a deep learning framework and optimizing the DSP parameters adaptively based on backpropagation algorithm from a global scale. This method not only establishes new standards in linear DSP performance but also serves as a critical benchmark for nonlinear DSP designs. In comparison to traditional DSP with hyperparameter optimization, a notable enhancement of approximately 1.21 dB in the Q factor for 400 Gb/s signal after 1600 km fiber transmission is experimentally demonstrated by combining LDSP and perturbation-based nonlinear compensation algorithm. Benefiting from the learnable model, LDSP can learn the best configuration adaptively with low complexity, reducing dependence on initial parameters. The proposed approach implements a symbol-rate DSP with a small bit error rate (BER) cost in exchange for a 48% complexity reduction compared to the conventional 2 samples/symbol processing. We believe that LDSP represents a new and highly efficient paradigm for DSP design, which is poised to attract considerable attention across various domains of optical communications.

Design of Energy Efficient Arithmetic VLSI Circuits

Advanced electronics have become an integral part of human lives, from Smartphones in our pockets to the modern vehicles on the road and to the satellites that orbit the earth. Power consumption has become a major constraint in these applications, often employing data- intensive digital signal processing systems and architectures. While enormous transistor density at the nanoscale has been increased in the Very Large-Scale Integration (VLSI) multicore era comparable to Moore's law, improving the performance of computing systems has become extremely difficult. Sustaining technological advancements for such energy-constrained devices has paved the way for new areas in research. “Approximate Computing (AC)” has become one of the most promising paradigms, a field that has gainedsignificant attention in the quest for energy efficiency in recent years. AC algorithms are numerically approximate rather than accurate. Inherent error resilience is the primary source of motivation behind AC. Recent research demonstrates that error resilience is pervasive in cyber search, deep learning, multimedia, recognition and data mining. Arithmetic units are the fundamental building blocks in most of the data dominated applications at the micro- architectural level of abstraction. AC is optimal for power and area efficient arithmetic circuits.

Tunable active noise control circuit topology for multiple-feature applications

A tunable active-noise-control (ANC) circuit topology for headsets used in different applications is presented in this article. In the current consumer headset market, ANC is a mature technology that is commonly applied to wireless headsets connected to smartphones for listening to music and making phone calls. The development of ANC headsets has resulted in low-cost and simple devices due to the built-in ANC registers in the digital circuit. Digital circuit implementation is important to provide ultra-low latency processing for different algorithms compared with using digital signal processors (DSPs). However, a limitation associated with these built-in ANC filters is that the ANC digital circuit has been designed only for the wireless headset consumer market. Consequently, ANC headsets can only be used for designated features. ANC tuned for planes by eliminating low-frequency engine noise has been commonly used in the past twenty years. Pass-through (PT) amplification is also commonly tuned to allow users to hear external sounds and have conversations without having to remove their headset. In addition, ANC and PT amplification are also not allowed to operate simultaneously on the existing chipsets. In this paper, an ANC circuit topology with ultra-low latency processing is presented, where the ANC response of the headset can be tuned without any restriction imposed by the built-in ANC filters. The proposed ANC circuit topology consists of a commercial ANC chipset with an external audio codec to provide tunable characteristics. It is achieved by fixed maximum noise cancellation of ANC chipset being modified by summing with the signal from audio-codec path via an audio mixer. Acoustic performance for several headset applications implemented using the proposed circuit is validated. This simple circuit topology can be easily developed and adapted to the current headset consumer market for consumer-selective ANC in different environments.

Unveiling a Multiplexed Sensing System for Advanced Biosensing Applications with Thread-Based Electrochemical Sensors

The miniaturization and portability of electrochemical sensing devices play a crucial role in a wide range of applications, including medical diagnostics, environmental monitoring, and point-of-care testing. Integrating a pseudo-reference electrode addresses challenges linked to traditional counterparts, ensuring enhanced portability and miniaturization without performance compromise. Our research delves into the significance of this innovation, emphasizing its impact on signal stability, reproducibility, and overall sensor reliability. The tailored pseudo-reference for compact electrochemical devices contributes to a highly efficient, cost-effective, and user-friendly design, ideal for point-of-care diagnostics and field applications. This work showcases the design, implementation, and performance evaluation of a portable electrochemical sensing system with a pseudo-reference-integrated readout circuit, improving reliability under diverse operating conditions. In the proposed research, we present the design and implementation of a dual-channel configurable multiplexed sensing platform utilizing thread-based electrochemical sensors for continuous potentiometric and amperometric measurements.For the pH sensor, the fabrication process initiates with the cleaning (using isopropyl alcohol) and subsequent plasma treatment of polysorb sutures. The cleaned sutures undergo multiple coatings (4 cycles) with conductive carbon ink, followed by baking at 60°C for 30 to 35 minutes to ensure uniform conductivity. Carbon-coated (CC) sutures undergo electrochemical pre-treatment in a solution of 0.50 M H2SO4 and 0.10 M KCl, followed by electrodeposition with polyaniline (PANI) using potentiometric deposition at 0.80 V in the presence of 0.50 M aniline in a solution containing 1.0 M H2SO4. An insulating polymer layer of 5% polyurethane (PU) is applied at the sensor's center, leaving ends exposed for electrical connections and subsequent hydrogel coating for real sample analysis. The O2 sensor fabrication involves cleaning silver-coated threads, coating with dielectric ink for a sensing region, and UV curing. Two identical threads serve as the cathode and anode, with an intentionally longer anode region as a common reference for the pH sensor. This comprehensive approach contributes to developing a versatile and efficient sensing system for multiplexed measurements, showcasing potential applications in diverse environments.The measurement system includes a signal conditioning circuit (denoted as analog front-end or AFE in Fig. 1) comprising a trans-impedance amplifier (TIA) and an op-amp in the unity-gain configuration: the former is used to convert the current into voltage which originates from the oxygen sensor whereas the latter is used to read out the open-circuit voltage (OCV) for the pH sensor. The outputs of the TIA and the voltage buffer are sequentially selected using a 2:1 multiplexer with a control bit (SEL) before digitizing using an analog-to-digital converter (ADC). There is also a digital-to-analog converter (DAC) generating a bias voltage (VBIAS) of 0.55V for the oxygen sensor as the oxygen electrode requires a certain potential to maximize the dynamic range of the current measurement. The microcontroller handles digital signal processing, which can be connected to a computer for data storage and visualization. The operation of the sensor multiplexing system is validated in standard buffer solutions and gel mediums with different pH and O2 concentrations. The proposed setup demonstrated a sensitivity of ~65 mV/pH for the pH sensor (n=3), and 0.6621 nA/mgL-1 for the O2 sensor (n=3) in a gel medium with common ions and electrolytes. The sensor results were validated against commercial oxygen and pH probes for the same solutions. The developed sensing platform and sensor can precisely monitor and track changes in pH and O2 levels within less than 2 minutes for repeated cycles. This architecture has the potential for extending the low-power multiplexed sensing to multiple biomarkers with an increased number of sensors for ingestible devices, wearable sensing, and cellular agriculture applications. KEYWORDS: Multiplexed sensing, pseudo-reference, pH and oxygen monitoring, miniaturized circuit, thread-based electrochemical sensor Figure 1

Comparison of milk-clotting activity measurement using the Berridge's operator-based approach and a proposed digital method

The milk clotting activity (MCA) analysis of coagulants used in the production of cheese is an important parameter used in the modern dairy industry. The MCA is calculated using the estimated milk-clotting time (MCT). The standard method used for estimation of MCT is presented in the ISO 23058/IDF 199 Standard using the Berridge's operator-based approach. In this paper a new method is presented to estimate the MCT using digital image acquisition and signal processing. The estimate is obtained by processing a sequence of images of the wall of a round bottom flask containing milk and the coagulant. Digital signal processing and filter techniques were used to remove interferences and artifacts and improve the clotting time estimation. A set of 28 samples of milk plus coagulant were processed. The method's accuracy is about 1/30 s and the average relative error, when compared to an experienced human operator, was με = 2.82%, according to the experimental results, with an associated standard deviation of σε = 3.07% and a median of mε = 1.92%. The method may be used to replace the human operator during the MCT estimation procedure.

Optimization of activity-driven event detection for long-term ambulatory urodynamics.

Lower urinary tract dysfunction (LUTD) is a debilitating condition that affects millions of individuals worldwide, greatly diminishing their quality of life. The use of wireless, catheter-free implantable devices for long-term ambulatory bladder monitoring, combined with a single-sensor system capable of detecting various bladder events, has the potential to significantly enhance the diagnosis and treatment of LUTD. However, these systems produce large amounts of bladder data that may contain physiological noise in the pressure signals caused by motion artifacts and sudden movements, such as coughing or laughing, potentially leading to false positives during bladder event classification and inaccurate diagnosis/treatment. Integration of activity recognition (AR) can improve classification accuracy, provide context regarding patient activity, and detect motion artifacts by identifying contractions that may result from patient movement. This work investigates the utility of including data from inertial measurement units (IMUs) in the classification pipeline, and considers various digital signal processing (DSP) and machine learning (ML) techniques for optimization and activity classification. In a case study, we analyze simultaneous bladder pressure and IMU data collected from an ambulating female Yucatan minipig. We identified 10 important, yet relatively inexpensive to compute signal features, with which we achieve an average 91.5% activity classification accuracy. Moreover, when classified activities are included in the bladder event analysis pipeline, we observe an improvement in classification accuracy, from 81% to 89.0%. These results suggest that certain IMU features can improve bladder event classification accuracy with low computational overhead.Clinical Relevance: This work establishes that activity recognition may be used in conjunction with single-channel bladder event detection systems to distinguish between contractions and motion artifacts for reducing the incorrect classification of bladder events. This is relevant for emerging sensors that measure intravesical pressure alone or for data analysis of bladder pressure in ambulatory subjects that contain significant abdominal pressure artifacts.

High-Definition Dynamic Voltage Restorer Systems Using Equivalent Time Sampling Techniques and Circular Structural Memory Filters

Due to advances in power electronics technology and the evolution of automation devices, the number of electrical devices that are sensitive to power quality is rapidly increasing, and for this reason, users are increasing their demand for high quality. To meet power quality demands, many power conversion devices are used, including dynamic voltage restorers (DVRs). DVRs are recognized as devices that can effectively manage problems such as voltage segments, swells, and harmonics. DVR control requires many samples for harmonic compensation, which has the disadvantage of being complicated to implement due to fast digital signal processing computation and the application of the cyclic discrete Fourier transform. In this paper, a high-precision DVR system configuration is proposed that compensates for harmonics using a periodically equivalent time-interval sampling technique and a novel circular-structured memory filter. The proposed circular-structured multi-pointer memory filter is an effective filter algorithm for high-precision input voltage measurement because it can remove noise and compensate for the delay of the phase angle of the filter in voltage measurement. A simulation and DVR prototype system were built, and the feasibility and effectiveness of the phase angle multi-filter voltage detection method and the compensation method were verified by experiments.

Studies on Noise Reduction of Optically Pumped Magnetometers by Digital Signal Processing

Studies on Noise Reduction of Optically Pumped Magnetometers by Digital Signal Processing

Range-Doppler image classification for interference detection in over-the-horizon radar

Sky-wave over-the-horizon radar (OTHR), as a useful tool for beyond-line-of-sight target detection, is inevitably affected by radio frequency interference (RFI) and transient interference (TSI). Conventional interference detection is usually based on radar signal processing. This paper proposes to detect the interference by classifying the range-Doppler (RD) image into three categories, i.e. RFI, TSI, and NoI (No Interference), according to the texture differences. Unlike the conventional way, RD image classification has direct reflection of the interference's influence on target detection. To design the RD image classifier, we develop signal models to construct the simulated database, use real data to construct the real database, and employ various classification theories, e.g. deep learning, traditional machine learning, feature fusion, and ensemble learning. Experimental results show that the RD image classifier is an effective and promising tool for interference detection. Strong interference can be accurately detected, while weak interference needs more study in the future.

Symmetrical components‐based robust stabilizing control of a grid‐connected inverter under unbalanced voltage sag

AbstractThis study presents an approach for robust current and power control for a three‐phase grid‐connected inverter set up with an L‐filter that operates under an unbalanced AC grid‐voltage source. This control method consists of two paralleled current control for the positive and negative sequences to accomplish one of three selective control targets: a) supplying balanced three‐phase current; b) supplying non‐oscillated real power; and c) supplying non‐oscillated imaginary power to the AC grid for a given reference value utilizing the identical control gains. The real and imaginary power references, together with the measured grid voltages, are used in the computation of the reference current for each control target. In addition, the proposed dual‐current control system has a control law that comprises state feedback to offer stability and an integral effect to get rid of the offset error in the dq‐frame. To reduce settling time under uncertain system conditions, an optimization problem based on linear matrix inequalities is solved to determine a set of robust stabilizing gains. Simulation and experimental results using a TMS320F28377D digital signal processor validate the proposed control performance under three control targets.

An FPGA-based parallel deconvolution application envisaging high-energy calorimeters operating under severe pile-up conditions

Signal pileup may arise when the system response time is larger than what would be required by the event rate in the application. In this condition, information superposition occurs, and signal estimation is deteriorated due to such information distortion. For online applications, mitigating such a pileup distortion usually requires embedded digital signal processing, which often involves FPGA-based designs when high event rates are a concern. Recently, high-energy particle experiments started facing an unprecedented increase in pileup conditions, which demanded new approaches for signal estimation. This work proposes a dedicated parallel signal processing system embedded on an FPGA platform that implements different filtering techniques for online energy reconstruction in calorimeters (energy measurement) operating under severe pileup conditions. Each designed filter is an inverse approximation of the calorimeter readout channel's impulse response, performing deconvolution of the incoming signals. Such a deconvolution approach for energy estimation was recently introduced, and the proposed filters were implemented in this work. Evaluation of the digital system design was performed using a data set that considers a typical high-energy calorimeter front-end response, as high-luminosity particle collider experiments produce high pileup distortions in calorimeter signals when processing the subproducts of the collisions. As the Large Hadron Collider (LHC) provides the most demanding conditions in terms of signal pileup, the acquisition system of the proposed solution is synchronous with its collision rate (40 MHz). The results showed that the proposed implementation may be applied as a preprocessing (pileup reduction) step in calorimeter instrumentation and could reduce the signal pileup within the fast response time required by such experiments.

Low-modal-crosstalk doped-fiber amplifiers in few-mode-fiber-based systems

Independent light propagation through one or multiple modes is commonly considered as a basic demand for mode manipulation in few-mode fiber (FMF)- or multimode fiber (MMF)-based optical systems such as transmission links, optical fiber lasers, or distributed optical fiber sensors. However, the insertion of doped-fiber amplifiers always kills the entire effort by inducing significant modal crosstalk. In this paper, we propose the design of doped-fiber amplifiers in FMF-based systems adopting identical multiple-ring-core (MRC) index profiles for both passive and doped fibers to achieve low modal crosstalk. We develop the direct-glass-transition (DGT) modified chemical vapor deposition (MCVD) processing for precise fabrication of few-mode erbium-doped fibers (FM-EDFs) with MRC profiles of both refractive index and erbium-ion doping distribution. Then, a few-mode erbium-doped-fiber amplifier (FM-EDFA) with a maximum gain of 26.08 dB and differential modal gain (DMG) of 2.3 dB is realized based on fabricated FM-EDF matched with a transmission FMF supporting four linearly polarized (LP) modes. With the insertion of the FM-EDFA, 60 + 60 km simultaneous LP01/LP11/LP21/LP02 transmission without inter-modal multiple-input multiple-output digital signal processing (MIMO-DSP) is successfully demonstrated. The proposed design of low-modal-crosstalk doped-fiber amplifiers provides, to our knowledge, new insights into mode manipulation methods in various applications.