Signal Processing Algorithms Research Articles

Remote sensor measurement system for sub-centimeter structural change detection

This work presents an autonomous remote sensor measurement system to monitor structural change detection. We utilized a portable network analyzer as a core component of a ground-based synthetic aperture radar to achieve structural monitoring. First, we realized a 2-meter rail apparatus, where antennas and radio frequency components of the radar can move, to obtain a synthetic radar aperture. Then, we develop signal processing algorithms to obtain radar images and to perform change detection. Although the range resolution of our system is 15 cm due to the frequency bandwidth of the radar, we have successfully developed an interferometric change detection technique to achieve up to 440μm change detection of the monitored structure.

A crowdsensing-based framework for sound and vibration data analysis in smart urban environments

In this paper, we introduce a framework tailored to the precise analysis of sound and vibration data in the dynamic context of smart cities. By harnessing the power of crowdsensing data, our system offers a robust and highly adaptable solution for the real-time monitoring and comprehensive assessment of acoustic and vibrational parameters within urban environments. The rise of smart cities, fueled by sensor tech and data analysis, demands advanced tools to tackle urban environmental issues and improve residents’ quality of life. Our framework empowers city stakeholders, offering insights for informed decisions in urban planning, transportation, infrastructure maintenance, and public health. Key features of our system include data acquisition through mobile crowdsensing application, advanced signal processing algorithms for noise and vibration identification, and the integration of geospatial information to provide location-specific context. Furthermore, our framework supports scalable and adaptable data analytics, ensuring the efficient utilization of resources and the effective management of urban environments. The presented framework addresses the challenges of noise pollution, structural health monitoring, transportation optimization, and public safety in smart cities. By exploiting crowdsourced data, it promotes a collaborative approach to data collection, analysis, and decision-making, fostering an environment where cities can continuously evolve and adapt to the evolving needs of their residents. This paper offers a detailed exploration of the system’s architecture, and showcases its practical implementation, affirming its potential as a powerful starting point for advancing the science of smart cities.

Improving Utilization of Dataflow Unit for Multi-Batch Processing

Dataflow architectures can achieve much better performance and higher efficiency than general-purpose core, approaching the performance of a specialized design while retaining programmability. However, advanced application scenarios place higher demands on the hardware in terms of cross-domain and multi-batch processing. In this article, we propose a unified scale-vector architecture that can work in multiple modes and adapt to diverse algorithms and requirements efficiently. First, a novel reconfigurable interconnection structure is proposed, which can organize execution units into different cluster typologies as a way to accommodate different data-level parallelism. Second, we decouple threads within each DFG node into consecutive pipeline stages and provide architectural support. By time-multiplexing during these stages, dataflow hardware can achieve much higher utilization and performance. In addition, the task-based program model can also exploit multi-level parallelism and deploy applications efficiently. Evaluated in a wide range of benchmarks, including digital signal processing algorithms, CNNs, and scientific computing algorithms, our design attains up to 11.95× energy efficiency (performance-per-watt) improvement over GPU (V100), and 2.01× energy efficiency improvement over state-of-the-art dataflow architectures.

Evaluation on improved sparse signal reconstruction algorithm for trusted AI and DCS technology

The improved Sparse Signal Reconstruction (SR) algorithm for Trusted Artificial Intelligence (AI) and Distributed Compressed Sensing (DCS) technology was thoroughly investigated. The study verified its effectiveness and advantages in trusted AI and DCS systems, which have significant implications for enhancing the credibility, security, and performance of signal processing and AI algorithms. The reconstruction performance was evaluated using Orthogonal Matching Pursuit (OMP), Basis Pursuit (BP), and Least Absolute Shrinkage and Selection Operator (LASSO). The analysis primarily focused on runtime, refactoring errors, and the number of successful reconstruction attempts. When K = 4, K = 6, K = 8, and K = 10, OMP outperformed BP and LASSO in terms of successful reconstructions, demonstrating better performance and higher reconstruction precision.

Low-complexity and low-latency equalization technique - probabilistic noise cancellation.

Both inside data centers (DCs) and in short optical links between data centers (DC campuses), intensity-modulation and direct-detection (IMDD) systems using four-level pulse amplitude modulation (PAM4) will dominate this decade due to low transceiver price and power consumption. The next DC transceiver generation based on 100 Gbaud PAM4 will require advanced digital signal processing (DSP) algorithms and more powerful forward error correction (FEC) codes. Because of bandwidth limitations, the conventional DC DSP based on a few-tap linear feed-forward equalizer (FFE) is likely to be upgraded to more complex but still low-complexity Volterra equalizers followed by a noise whitening filter and either a maximum likelihood sequence estimation (MLSE) or a maximum a posteriori probability (MAP) algorithm. However, stringent power consumption and latency requirements may limit the use of complex algorithms such as decision feedback equalizer (DFE) or MLSE/MAP in DC networks (DCN). In this paper, we introduce a low-complexity, low-latency algorithm based on a feedforward structure, yielding a performance between DFE and MLSE. We call the novel equalization algorithm probabilistic noise cancellation (PNC), since it weights noise patterns based on their probabilities in the presence of bandwidth limitations. The probabilistic weighting is efficiently exploited in correcting correlated errors caused by noise coloring in the FFE.

Design of Low Power Thinned Smart Antenna for 6G Sky Connection

To improve radio access capability, sky connections relying on satellites or unmanned aerial vehicles (UAV), as well as high-altitude platforms (HAP) will be exploited in 6G wireless communication systems, complementing terrestrial networks. For long-distance communication, a large smart antenna will be used that is characterized by high amounts of power consumed by digital beamformers. This paper focuses on reducing power consumption by relying on a thinned smart antenna (TSA). The performance of TSA is investigated in the sub-6 GHz band. The differential evolution (DE) algorithm is used to optimize excitation weights of the individual dipoles in the antenna array and these excitation weights are then used in TSA for beamforming, with signal processing algorithms deployed. The DE technique is used with the least mean square, recursive least square and sample matrix inversion algorithms. The proposed method offers almost the same directivity, simultaneously ensuring lower side lobes (SLL) and reduced power consumption. For a TSA of 20, 31, and 64 dipoles, the power savings are 20%, 19.4%, and 17.2%, respectively. SLL reductions achieved, in turn, vary from 5.2 dB to 8.1 dB.

SPIMulator: A Spintronic Processing-In-Memory Simulator for Racetracks

In-memory processing is becoming a popular method to alleviate the memory bottleneck of the von Neumann computing model. With the goal of improving both latency and energy cost associated with such in-memory processing, emerging non-volatile memory technologies, such as Spintronic magnetic memory, are of particular interest as they can provide a near-SRAM read/write performance and eliminate nearly all static energy without experiencing any endurance limitations. Spintronic Racetrack Memory (RM) further addresses density concerns of spin-transfer torque memory (STT-MRAM). Moreover, it has recently been demonstrated that portions of RM nanowires can function as a polymorphic gate, which can be leveraged to implement multi-operand bulk bitwise operations. With more complex control, they can also be leveraged to build arithmetic integer and floating point processing in memory (PIM) primitives. This paper proposes SPIMulator, a Spintronic PIM sim ulator that can simulate the storage and PIM architecture of executing PIM commands in Racetrack memory. SPIMulator functionally models the polymorphic gate properties recently proposed for Racetrack memory, which allows transverse access that determines the number of ‘1’s in a segment of each Racetrack nanowire. From this simulation, SPIMulator can report real-time performance statistics such as cycle count and energy. Thus, SPIMulator simulates the multi-operand bit-wise logic operations recently proposed and can be easily extended to implement new PIM operations as they are developed. Due to the functional nature of SPIMulator, it can serve as a programming environment that allows development of PIM-based codes for verification of new acceleration algorithms. We demonstrate the value of SPIMulator through the modeling and estimations of performance and energy consumption of a variety of example applications, including the Advanced Encryption Standard (AES) for encryption primarily based on logical and look-up operations; multiplication of matrices, a frequent requirement in scientific, signal processing, and machine learning algorithms; and bitmap indices a common search table employed for database lookups.

Enhancement of Vital Signals for UWB Through-Wall Radar Using Low-Rank and Block-Sparse Matrix Decomposition

Ultra-wideband (UWB) vital detection radar plays an important role in post-disaster search and rescue, but the vital signal acquired in practice is often submerged in noise. In this paper, an advanced signal processing algorithm based on low-rank block-sparse representation is proposed to enhance the vital signal in life detection radar applications. The preprocessed echo signal can be decomposed into low-rank and block-sparse parts. The alternate direction method (ADM) is employed to obtain the block-sparse part containing the desired vital signal. We solve the subproblems involved in the ADM method using the Douglas/Peaceman–Rachford (DR) monotone operator splitting method. The projection method is applied to accelerate the calculation. Simulation and experimental results show that the proposed algorithm outperforms existing methods in terms of output signal-to-noise ratio (SNR).

Design of Novel Photodetector Array in Optical Communication Systems and Optimization of Signal Processing Algorithm

This study aims to enhance the high-speed and high-power performance of photodetectors, focusing on the epitaxial layer structure of photodetector arrays. The epitaxial structure of the photodetector involves a P–I–N structure with InAlAs/InGaAs heterostructures. InAlAs is used as the P-region material, and a dual-waveguide photodetector array is designed with a core diameter of 20 μm. The optical coupling is achieved through normal incident vertical illumination. Additionally, a novel structure of a dual-core symmetrically connected photodetector array is proposed, altering the electrode structure and core arrangement to maintain consistent distances for the output signals from the cores to the device’s output terminal. In order to address the issue of minimum bit error rate in optical communication systems under conditions of optical noise and limited energy efficiency of received optical signals, a photodetector array signal processing algorithm is proposed. The experimentally designed dual-core symmetrically connected P–I–N photodetector array achieves a 3 dB bandwidth of 15.37 GHz at an average photocurrent of 5 mA. Furthermore, it maintains a 3 dB bandwidth of 11.67 GHz at a high-power operating state of 50 mA, with an alternating current (AC) saturation current of 56 mA and a saturated RF output power of 1.11 dBm at 11.67 GHz. In comparison, the improved dual-core traveling-wave photodetector array at 11.67 GHz only achieves an AC saturation current of 38 mA and a saturated RF output power of 2.66 dBm. The signal processing algorithm for the selected dual-core symmetrically connected P–I–N photodetector array demonstrates that employing a method of merging array elements reduces the system’s computational complexity with minimal impact on the system’s power consumption.

Terahertz-Band Near-Space Communications: From a Physical-Layer Perspective

Facilitated by the rapid technological development of the near-space platform stations (NSPS), near-space communication (NS-COM) is envisioned to play a pivotal role in the space-air-ground integrated network (SAGIN) for the sixth generation (6G) communications and beyond. In NS-COM, ultrabroadband wireless connectivities between NSPSs and various airborne/spaceborne platforms are required for a plethora of bandwidth-consuming applications, such as NSPS-based ad hoc networking, the in-flight Internet and the relaying technology. However, such requirement seem to contradict the scarcity of spectrum resources at conventional microwave frequencies, which motivates the exploitation of terahertz (THz) band ranging from 0.1 to 10 THz. Due to huge available bandwidth, THz signals are capable of supporting ultra-high-rate data transmission for NS-COM over 100 Gb/s, which are naturally suitable for the near-space environment with marginal path loss. Against this background, this article provides an extensive investigation on THz-band NS-COM (THz-NS-COM) from a physical-layer perspective. Firstly, we summarize the potential applications of THz communications in the near-space environment, where the corresponding technical barriers are analyzed. Secondly, the channel characteristics of THz-NS-COM and the corresponding modeling strategies are discussed. Thirdly, three essential research directions are investigated to surpass the technical challenges of THz-NS-COM, i.e., robust beamforming for ultramassive antenna array, signal processing algorithms against hybrid distortions, and integrated sensing and communications. Several open problems are also provided to unleash the full potential of THz-NS-COM.

High key rate continuous-variable quantum key distribution using telecom optical components

Quantum key distribution (QKD) is one quantum technology that can provide secure encryption keys for data transmission. The secret key rate (SKR) is a core performance indicator in QKD, which directly determines the transmission rate of enciphered data. Here, for the first time, we demonstrate a high-key-rate Gaussian-modulated continuous-variable QKD (CV-QKD) using telecom optical components. The framework of CV-QKD over these components is constructed. Specifically, the high-rate low-noise Gaussian modulation of coherent states is realized by a classical optical IQ modulator. High-baud low-intensity quantum signals are received by an integrated coherent receiver under the shot-noise limit. A series of digital signal processing algorithms are proposed to achieve accurate signal recovery and key distillation. The system can yield a high asymptotic SKR of 10.37 Mbps within 20 km standard telecom fiber, and the secure distance can exceed 100 km. This result confirms the feasibility of CV-QKD with state-of-the-art performance using telecom optical components. Besides, due to the ease of integrating these discrete components, it provides a high-performance and miniaturized QKD solution for the metropolitan quantum network.

Electron Density Specification in the Inner Magnetosphere From the Narrow Band Receiver Onboard DSX

AbstractElectron density plays an important role in the study of wave propagation and is known to be associated with the index of refraction and radiation belt diffusion coefficients. The primary objective of our investigation is to explore the possibility of implementing an onboard signal processing algorithm to automatically obtain electron densities from the upper hybrid resonance traces of wave spectrograms for future missions. U‐Net, developed for biomedical image segmentation, has been adapted as our deep learning architecture with results being compared with those extracted from a more traditional semi‐automated method. As a product, electron densities and cyclotron frequencies for the entire DSX mission between 2019 and 2021 are acquired for further analysis and applications. Due to limited space measurements, a synthetic image generator based on data statistics and randomization is proposed as an initial step toward the development of a generative adversarial network in hopes of providing unlimited realistic data sources for advanced machine learning.

Метод оценки отражательных характеристик объектов с использованием мульти-фокусных зондирующих полей

The possibility of using special (multi-focus) fields for solving problems related to the experimental evaluation of the reflective characteristics of objects has been considered. Regularities in the formation of multi-focus fields in three-dimensional cases have been specified. The advantages of the proposed approach have been demonstrated in terms of flexible adjustment of signal processing algorithms based on digital signal filtering methods. This ensures obtaining radar characteristics of objects, considering the peculiarities of constructing single-position, bi-static, and multi-static location systems.

An Acoustic Array Sensor Signal Recognition Algorithm for Low-Altitude Targets Using Multiple Five-Element Acoustic Positioning Systems with VMD

To solve the problem in target acoustic signal processing and recognition when the target flies at a low altitude based on the acoustic positioning system, which is often affected by external interference and brings false information, this paper proposes a target signal processing and recognition algorithm for low-altitude target acoustic positioning based on variational modal decomposition and the test method of multiple five-element acoustic arrays. This algorithm uses VMD to decompose the target signal into modal components with different central frequencies and then performs wavelet threshold processing on the low-frequency part of the signal. After determining the remaining signal components and the low-frequency part’s threshold, the residual component is reconstructed. Based on the test principle and calculation model of the five-element acoustic positioning system, following processing of the low-altitude target acoustic positioning signal using variational modal decomposition, the cross-correlation function method is introduced to perform correlation operations on the basic array of five acoustic sensors and then obtain the time value and time difference of the target acoustic information in each acoustic sensor, ultimately determining the spatial position of the target. Finally, we used the data fusion processing method for target coordinates in multi-acoustic basic arrays to determine the actual target position. By comparing the results obtained using the high-speed camera method with those of the proposed approach, it was found that the average error in the test area of 100 × 100 m was less than 1 m.

Terminal Phase Navigation for AUV Docking: An Innovative Electromagnetic Approach

This study introduces a groundbreaking approach for real-time 3D localization, specifically focusing on achieving seamless and precise localization during the terminal guidance phase of an autonomous underwater vehicle (AUV) as it approaches an omnidirectional docking component in an automated deployable launch and recovery system (LARS). Using the AUV’s magnetometer, an economical electromagnetic beacon embedded in the docking component, and an advanced signal processing algorithm, this novel approach ensures the accurate localization of the docking component in three dimensions without the need for direct line-of-sight contact. The method’s real-time capabilities were rigorously evaluated via simulations, prototype experiments in a controlled lab setting, and extensive full-scale pool experiments. These assessments consistently demonstrated an exceptional average positioning accuracy of under 3 cm, marking a significant advancement in AUV guidance systems.

Possibility of Monitoring the Dynamic Impact of the Rolling Stock on the Track Superstructure Using Strain Gauges and Vibrometry

One of the priority tasks in development of railway transport is to raise its efficiency by increasing the time between repairs for both the track superstructure and the rolling stock. Solving this problem is impossible without timely, complete, and reliable information about the dynamics of interaction of the rolling stock with the infrastructure. To organise monitoring of dynamic processes, completely different types of primary transducers are used: resistance strain gauges, fibre-optic sensors, dynamometers (force sensor pads), accelerometers, acoustic emission sensors. This implies relevance of the scientific and technical problem of comparative testing of sensors of different types to assess the information content of their signals and justify the criteria for choosing primary transducers when solving specific monitoring problems.The objective of the study is to comparatively test removable resistive strain sensors, optical polarisation strain sensors and accelerometers under a passing train and to evaluate their information content to control rail depression and detect defects on the running surface of wagon wheels.The study using finite element modelling and a physical model of a rail as of a beam on an elastic foundation, substantiates the relationship between longitudinal deformations and vertical accelerations of the rail foot. Comparative tests of removable strain-resistive and optical polarisation strain sensors and accelerometers were held on an experimental section of track under a passing train. A signal processing algorithm has been developed and the equivalence of strain gauges and vibrometers for determining the depression of a rail under a passing train has been proven. Acomparison has been made of the pulse components of the signals of deformation and vibration acceleration during movement of a wheel with a defect on the running surface, and the requirements for the frequency characteristics of the sensors and their mounting on the rail surface have been substantiated.

Music Signal Processing and Optimization Algorithm Based on Wavelet Neural Network Model

Music Signal Processing and Optimization Algorithm Based on Wavelet Neural Network Model

An Open CAPT System for Prosody Practice: Practical Steps towards Multilingual Setup

This paper discusses the challenges posed in creating a Computer-Assisted Pronunciation Training (CAPT) environment for multiple languages. By selecting one language from each of three different language families, we show that a single environment may be tailored to cater for different target languages. We detail the challenges faced during the development of a multimodal CAPT environment comprising a toolkit that manages mobile applications using speech signal processing, visualization, and estimation algorithms. Since the applied underlying mathematical and phonological models, as well as the feedback production algorithms, are based on sound signal processing and modeling rather than on particular languages, the system is language-agnostic and serves as an open toolkit for developing phrasal intonation training exercises for an open selection of languages. However, it was necessary to tailor the CAPT environment to the language-specific particularities in the multilingual setups, especially the additional requirements for adequate and consistent speech evaluation and feedback production. In our work, we describe our response to the challenges in visualizing and segmenting recorded pitch signals and modeling the language melody and rhythm necessary for such a multilingual adaptation, particularly for tonal syllable-timed and mora-timed languages.

Optical access networks for fixed and mobile applications [Invited Tutorial

Optical access networks are playing a more critical role in providing diverse emerging applications for human- and machine-type communications. With the ever-growing demand for higher capacity, higher fidelity, and massive connections, numerous innovative transmission schemes and digital signal processing algorithms have been proposed in a cost-effective way. Here, we focus on two main scenarios, namely, fixed and mobile access. For the fixed access network, the solutions are categorized into direct detection, simplified coherent detection, and self-coherent detection. For the mobile fronthaul, we classify the schemes according to the functional split, including the analog radio-over-fiber, common public radio interface (CPRI), enhanced CPRI (eCPRI), and integrated and hybrid digital/analog-over-fiber interfaces. This tutorial provides an overview of the enabling technologies for fixed and mobile applications in the fifth-generation (5G) era and beyond.

Full Mass Range ΦSDM Orbitrap Mass Spectrometry for DIA Proteome Analysis

Optimizing data-independent acquisition methods for proteomics applications often requires balancing spectral resolution and acquisition speed. Here, we describe a real-time full mass range implementation of the phase-constrained spectrum deconvolution method (ΦSDM) for Orbitrap mass spectrometry that increases mass resolving power without increasing scan time. Comparing its performance to the standard enhanced Fourier transformation signal processing revealed that the increased resolving power of ΦSDM is beneficial in areas of high peptide density and comes with a greater ability to resolve low-abundance signals. In a standard 2 h analysis of a 200ng HeLa digest, this resulted in an increase of 16% in the number of quantified peptides. As the acquisition speed becomes even more important when using fast chromatographic gradients, we further applied ΦSDM methods to a range of shorter gradient lengths (21, 12, and 5min). While ΦSDM improved identification rates and spectral quality in all tested gradients, it proved particularly advantageous for the 5min gradient. Here, the number of identified protein groups and peptides increased by >15% in comparison to enhanced Fourier transformation processing. In conclusion, ΦSDM is an alternative signal processing algorithm for processing Orbitrap data that can improve spectral quality and benefit quantitative accuracy in typical proteomics experiments, especially when using short gradients.