Signal Processing Systems Research Articles

COSMIC’s Large-scale Search for Technosignatures during the VLA Sky Survey: Survey Description and First Results

Abstract Developing algorithms to search through data efficiently is a challenging part of searching for signs of technology beyond our solar system. We have built a digital signal processing system and computer cluster on the backend of the Karl G. Jansky Very Large Array (VLA) in New Mexico in order to search for signals throughout the Galaxy consistent with our understanding of artificial radio emissions. In our first paper, we described the system design and software pipelines. In this paper, we describe a postprocessing pipeline to identify persistent sources of interference, filter out false positives, and search for signals not immediately identifiable as anthropogenic radio frequency interference during the VLA Sky Survey. As of 2024 September 1, the Commensal Open-source Multi-mode Interferometric Cluster had observed more than 950,000 unique pointings. This paper presents the strategy we employ when commensally observing during the VLA Sky Survey and a postprocessing strategy for the data collected during the survey. To test this postprocessing pipeline, we searched toward 511 stars from the Gaia catalog with coherent beams. This represents about 30 minutes of observation during the VLA Sky Survey, where we typically observe about 2000 sources hr–1 in the coherent beamforming mode. We did not detect any unidentifiable signals, setting isotropic power limits ranging from 1011 to 1016 W.

Programming universal unitary transformations on a general-purpose silicon photonic platform

General-purpose programmable photonic processors provide a versatile platform for integrating diverse functionalities on a single chip. Leveraging a two-dimensional hexagonal waveguide mesh of Mach–Zehnder interferometers, these systems have demonstrated significant potential in microwave photonic applications. Additionally, they are a promising platform for creating unitary linear transformations, which are key elements in quantum computing and photonic neural networks. However, a general procedure for implementing these transformations on such systems has not been established yet. This work demonstrates the programming of universal unitary transformations on a general-purpose programmable photonic circuit with a hexagonal topology. We detail the steps to split the light on-chip, demonstrate that an equivalent structure to the Mach–Zehnder interferometer with one internal and one external phase shifter can be built in the hexagonal mesh, and program both the triangular and rectangular architectures for matrix multiplication. We recalibrate the system to account for passive phase deviations. Experimental programming of 3 × 3 and 4 × 4 random unitary matrices yields fidelities >98% and bit precisions over five bits. To the best of our knowledge, this is the first time that random unitary matrices are demonstrated on a general-purpose photonic processor and pave the way for the implementation of programmable photonic circuits in optical computing and signal processing systems.

A New Low-Power Hybrid Full Adder Using CCGDI Technique for a Portable MAC Unit

Full adder circuits are basic digital used in almost all multiplier architectures. Consequently, they serve as the fundamental building blocks in many digital signal or image processing systems. Different logic styles are employed in the design of full adder circuits, with the hybrid logic style being the most recent method for implementing low-power full adders. A hybrid full adder primarily relies on the XOR-XNOR module. This module greatly affects the performance of any hybrid full adder in terms of driving capability, power consumption, delay, and area. This paper proposes a new design of a hybrid full adder by introducing the CCGDI technique-based XOR-XNOR module. Simulation results demonstrate that the proposed CCGDI-based hybrid full adder architecture, constructed with a ratioless 12 transistors, consumes 9.48 µw of power and has a latency of 34.23 ps. A comparison with other state-of-the-art structures of hybrid full adders reveals a significant improvement in PDP (power-delay product) and driving capability.

A Robust Kalman Filter Based on the Pearson Type VII-Inverse Wishart Distribution: Symmetrical Treatment of Time-Varying Measurement Bias and Heavy-Tailed Noise

This paper introduces a novel robust Kalman filter designed to leverage symmetrical properties within the Pearson Type VII-Inverse Wishart (PVIW) distribution, enhancing state estimation accuracy in the presence of time-varying biases and non-stationary heavy-tailed (NSHT) noise. The filter includes a shape parameter from the normal distribution and an extra variable from the Gamma distribution, which are used to symmetrically adjust the average and variation measures of the data to fit better under difficult noise conditions. To deal with unknown noise that changes over time, the filter uses the Inverse Wishart distribution to model and estimate the scale matrix deviations, making it easier to adapt to changes. The filter also uses a technique called Variational Bayesian to estimate both the state and the parameters at the same time. The results from simulations show that this new filter greatly improves the accuracy and strength of the estimation compared to the usual Kalman filters that assume a normal distribution, especially when there is non-stationary heavy-tailed noise. The main objective is to improve estimation in signal processing and control systems where heavy-tailed noise is prevalent.

Low-Power, High-Speed Adder Circuit Utilizing Current-Starved Inverters in 22 nm FDSOI

A low-power, high-speed adder circuit topology based on current-starved inverters is presented to provide a basic arithmetic function for low-power, high-frequency signal processing systems. The adder is designed in 22 nm FDSOI (Fully-Depleted Silicon-on-Insulator) technology and is suitable for operation up to 5 GHz (Giga-Hertz). The proposed adder utilizes current-starved inverters to implement low-power operation while still maintaining signal integrity for high-frequency sine signals. The circuit uses a differential input and output structure to mitigate potential noise coupling onto any high-frequency signal pathways. The proposed solution differs from standard adder architectures by utilizing a fully analog signal processing design, accepting analog inputs while outputting an analog signal, and offering suitable functionality at Giga-Hertz level signals as compared to other relevant works. The simulated experimental results show the power consumption to be approximately 150 nW at 0.8 V supply with an input-referred noise of 6.091 nV/Hz at 5 GHz.

Flexible artificial vision computing system based on FeO x optomemristor for speech recognition

With the advancement of artificial intelligence, optic in-sensing reservoir computing based on emerging semiconductor devices is high desirable for real-time analog signal processing. Here, we disclose a flexible optomemristor based on C27H30O15/FeO x heterostructure that presents a highly sensitive to the light stimuli and artificial optic synaptic features such as short- and long-term plasticity (STP and LTP), enabling the developed optomemristor to implement complex analogy signal processing through building a real-physical dynamic-based in-sensing reservoir computing algorithm and yielding an accuracy of 94.88% for speech recognition. The charge trapping and detrapping mediated by the optic active layer of C27H30O15 that is extracted from the lotus flower is response for the positive photoconductance memory in the prepared optomemristor. This work provides a feasible organic−inorganic heterostructure as well as an optic in-sensing vision computing for an advanced optic computing system in future complex signal processing.

Research and Design of Sine Wave and Square Wave Signal Generator

In modern electronic systems, signal generators are widely used in fields such as communication, signal processing, and automatic control systems. High-quality signal sources are key to ensuring system stability and reliability, especially in testing and measurement processes. Traditional signal generators often use complex hardware designs and costly dedicated chips, making it difficult to meet the cost and size requirements for mid- to low-frequency applications. Therefore, it is important to design a simple, cost-effective, and high-performance signal generator based on general-purpose operational amplifiers. In response to this need, this paper designs and implements a sine wave and square wave signal generator based on the UA741 operational amplifier, with a focus on adjustable control of signal frequency and duty cycle. By optimizing the design of the RC oscillation circuit and feedback network, the sine wave generator can output high-quality signals with low distortion, while the square wave generator achieves flexible duty cycle adjustment via adjustable resistors. Simulation results show that these circuits exhibit good stability and flexibility, making them suitable for communication, signal processing, and automatic control systems. Despite achieving the desired results, further research is needed in component selection and circuit optimization to improve precision and adaptability. Future work will focus on the automation of circuit adjustment mechanisms and performance validation under extreme conditions to meet more demanding application needs.

Detection of Atrial Fibrillation with Custom Designed Wavelet-based Convolutional Autoencoder

Remote monitoring of patients is of great importance in terms of early diagnosis of diseases and improving people's quality of life. With the rapid development of deep learning techniques, wearable health technologies have leaped forward. This has made the automatic diagnosis even more important. In this study, we provide a deep learning approach for classifying Atrial Fibrillation (AF) arrhythmia that uses a customized wavelet-based convolutional autoencoder (WCAE) model. WCAE is employed as an anomaly detector, which combines the time-frequency domain examination ability of wavelet and the data-driven feature learning capability of convolutional autoencoders. The proposed approach received average scores of 95.45%, 99.99%, 90.90%, and 95.23% for accuracy, precision, recall, and F1, respectively, on a large selection of publicly available datasets. The outcomes of the experiments demonstrate the significance of using deep learning-based models in diagnosing AF. Moreover, it is observed that utilization of wavelet methods along with autoencoder model has a great potential for biomedical signal processing systems.

Analysis and Design of Low-Power Piezoelectric Energy Harvesting Circuit for Wearable Battery-Free Power Supply Devices

Improving microelectronic technologies has created various micro-power electronic devices with different practical applications, including wearable electronic modules and systems. Furthermore, the power sources for wearable electronic devices most often work with electrical energy obtained from the environment without using standard batteries. This paper presents the structure and electrical parameters of a circuit configuration realized as a prototype of a low-power AC-DC conversion circuit intended for use as a power supply device for signal processing systems that test various biomedical parameters of the human body. The proposed prototype has to work as a wearable self-powered system that transfers electrical energy obtained through mechanical vibrations in the piezoelectric generator. The obtained electrical energy is used to charge a single low-voltage supercapacitor, which is used as an energy storage element. The proposed circuit configuration is realized with discrete components consisting of a low-voltage bridge rectifier, a low-pass filter, a DC-DC step-down (buck) synchronous converter, a power-controlling system with an error amplifier, and a window detector that produces a “power-good” signal. The power-controlling system allows tuning the output voltage level to around 1.8 V, and the power dissipation for it is less than 0.03 mW. The coefficient of energy efficiency achieved up to 78% for output power levels up to 3.6 mW. Experimental testing was conducted to verify the proposed AC-DC conversion circuit’s effectiveness, as the results confirmed the preliminary theoretical analyses and the derived analytical expressions for the primary electrical parameters.

Enhancing Electric Bicycle Efficiency: Investigating Regenerative Braking and Pedal Charging Systems

Electric bicycles (e-bikes) are gaining global popularity as part of the broader shift towards electric vehicles (EVs). This research presents a regenerative electric bicycle designed to maximize energy efficiency, aiming for a top speed of 45 km/h and a range of 50 km. The bicycle incorporates two key energy recovery systems: a downhill regenerative system that captures braking energy and converts it into electricity, and a pedal charging system that converts pedaling kinetic energy to recharge the battery. A digital signal processing system seamlessly switches between charging and electric drive based on rider input. This study examines the effectiveness of these systems. The regenerative braking system achieved an impressive 30.9% efficiency in capturing energy during testing. The pedal charging system initially demonstrated an efficiency of 2.98%. MATLAB simulations revealed clear trends in force and power requirements across different grades (0°, 2.5°, 5°, 7.5°, and 10°), highlighting the crucial role of aerodynamic drag and gradient resistance in influencing performance and energy efficiency. While controlled testing environments require further real-world evaluation, these findings underscore the promising potential of regenerative braking and pedal charging for enhancing electric bicycle efficiency. This research paves the way for the development of more efficient and accessible electric bicycles, promoting sustainable transportation solutions through the selection of durable, efficient, and environmentally friendly components.

Design and FPGA Implementation of Relative Dynamics Measurement Techniques for Satellite Communication Systems

Performance of a satellite communication receiver is greatly affected by the relative dynamics scenario. Relative dynamics is related to the variable motion between communication source and destination systems. It encompasses relative acceleration and relative jerk. The paper presents the phase error-based measurement techniques for relative dynamics. Binary Phase Shift Keying (BPSK) demodulator is considered for development and verification of the techniques. Paper describes the requirement and design of 3rd order BPSK demodulator for measurement and handling relative jerk. System level simulation is carried out for different scenario. Design is coded in VHDL and realized on the Xilinx platform. Application of developed system in adaptive signal processing based BPSK demodulator is also dealt in detail.

Analysis of the Response Characteristics of Ultra-Wideband Radio Fuze to Interference Signals

Abstract The response characteristics of the fuze to the interference signal with reasonable characteristic parameters are similar to the real echo signal, which may lead to the confusion of signal processing and recognition system. To solve this problem, this paper analyzes the influence of characteristic parameters of interference signal on the response characteristics of fuze in detail from two perspectives of time domain and frequency domain, Based on the analysis of receiver principle, the amplitude-frequency response model of sampling integral differential circuit is established, and the response characteristics of interference signal are analyzed from the perspective of frequency domain. Based on the analysis of the time-frequency characteristics of the echo signal of the UWB fuze, a time-frequency function cutting method is proposed to evaluate the anti-jamming performance of the UWB fuze. This methodology was applied to the evaluation of the anti-jamming performance of the UWB fuze to sinusoidal amplitude-modulation interference signal and the results show that the theoretical analysis and simulation results are consistent, and the research provides theoretical guidance for the anti-interference of fuze.

Analysis of the implementation efficiency of digital signal processing systems on the technological platform SoC ZYNQ 7000

The subject of this paper is the analysis of DSP algorithm implementations based on HLS synthesis and SIMD instructions acceleration on the SoC hardware platform. The goal of this article is to analyze various FIR filter software and hardware implementations based on the technological platform SoC ZYNQ 7000 while obtaining metrics of hardware resource consumption, power efficiency, and execution performance. The tasks are as follows: determine the ways of implementing algorithms; choose the analysis criteria for multivariate experiment; implement algorithms using SIMD instructions on the ARM part of the given SoC; implement algorithms using High-Level Synthesis for the FPGA part; and measure and obtain the results for each signal topology. The used methods: High-Level Synthesis, optimization techniques based on vector instructions, and multivariate experiment analysis. The following results were obtained: for the given criteria and metrics. The FIR filter was implemented on the ZedBoard development platform with SoC ZYNQ 7000. The data were obtained from post-synthesis power analysis and dynamic SoC consumption using tools from Xilinx and Analog Devices. The corresponding IP blocks were implemented using High-Level Synthesis. The experiment was completed to obtain execution performance metrics. Conclusions. The scientific novelty of the obtained results is summarized as follows: the competitor analysis was performed for the set of implementations of the given algorithms deployed on the ZYNQ platform using both SIMD instructions and several HLS-based topologies for the FPGA-offload execution strategy. The analysis of the multivariate experiment was also completed for selected criteria, power consumption, filtering speed (inverse value – delay), and the amount of hardware costs as a percentage of the used resources.

On the theory of spectral compression-assisted optical temporal differentiation

Bandwidth limitation represents a significant factor that degrades the performance of optical devices. The dimensions, composition and configuration of optical devices impose intrinsic constraints on processing broadband optical pulse signals. The enhancement of the response bandwidth of optical devices represents a significant challenge. In this study, we put forward the theory of self-similar spectral compression (SSSC), which involves solving the nonlinear Schrödinger equation with variable coefficients by using the Taylor expansion and residual theorem. The spectral waveform can be precisely preserved in the process of SSSC, leading to a predictable compression factor without pedestals. To demonstrate the effectiveness of the proposed SSSC, we present a case study by designing an on-chip optical time-domain differentiator (OTD) system including a silicon-based tapered spiral waveguide. A 200-fs chirped pulse is well differentiated at multiple orders in the OTD system. Although the linear loss of spiral waveguide has a detrimental impact on SSSC, the broadband spectrum can still be self-similarly compressed, leading to a reduction of differentiation deviation of 22.5 times. The proposed SSSC theory offers valuable guidance for designing all-optical signal processing systems with high spectral resolution and low signal error.

Design and optimisation of a two-stage amplifier based on a differential input stage and a common-source amplifier

Abstract. Since the emergence of integrated circuits, through the circuit structure, the production process of continuous iterative updating, so that its used in various types of electronic equipment more and more widely. Currently, operational amplifier circuits play an extremely important role in signal processing and communication systems. Amplifiers have different characteristics depending on the needs of different electronic devices. This thesis addresses the optimisation and design of the characteristics of the two-stages amplifier. Firstly, several classical amplifier structures are analysed. Select the appropriate structure and combine it into a two-stages amplifier. Simulation is carried out on cadence software and later the simulation results are analysed before parameter optimisation. On this basis, a two-stages amplifier based on a PMOS differential input stage with a common-source amplifier has been designed. It also improves the amplifier's gain, frequency range, gain bandwidth, and other performance metrics. Using PMOS self-bias circuit to improve power supply stability. Finally, based on the development hotspots of integrated circuits, the possibility of circuit optimization was analyzed. Discussions were conducted on negative feedback circuits, phase compensation methods, and other aspects.

Advanced Applications of Polymer Hydrogels in Electronics and Signal Processing.

The current state of affairs in the field of using polymer hydrogels for the creation of innovative systems for signal and image processing, of which computing is a special case, is analyzed. Both of these specific examples of systems capable of forming an alternative to the existing semiconductor-based computing technology, but assuming preservation of the used algorithmic basis, and non-trivial signal converters, the nature of which requires transition to fundamentally different algorithms of data processing, are considered. It is shown that the variability of currently developed information processing systems based on the use of polymers, including polymer hydrogels, leads to the need to search for complementary algorithms. Moreover, the well-known thesis that modern polymer science allows for the realization of functional materials with predetermined properties, at the present stage, receives a new sounding: it is acceptable to raise the question of creating systems built on a quasi-biological basis and realizing predetermined algorithms of information or image processing. Specific examples that meet this thesis are considered, in particular, promising information protection systems for UAV groups, as well as systems based on the coupling of neural networks with holograms that solve various applied problems. These and other case studies demonstrate the importance of interdisciplinary cooperation for solving problems arising from the need for further modernization of signal processing systems.

An automated AI-powered IoT algorithm with data processing and noise elimination for plant monitoring and actuating.

This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO2) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.

Design of a new fiber-optical transmission-based fast ion loss probe on Experimental Advanced Superconducting Tokamak

Design of a new fiber-optical transmission-based fast ion loss probe on Experimental Advanced Superconducting Tokamak

Research on Marine Environmental Noise Based on Acoustic Bait Domestication Technology

Acoustic domestication type of marine pastures are generally established in shallow waters with water depth not exceeding 200 m. In order to study the effect of acoustic domestication, this paper aims to investigate the environmental noise in the shallow waters of the sea. Unlike the stable deep sea environment, the environmental noise in the shallow sea is extremely complex, which has a direct impact on the effect of acoustic domestication. In this paper, in order to study the marine environmental noise, a hydroacoustic signal processing system is established, which is divided into two parts: the hardware part and the software part; the hardware part is mainly for signal sampling, and the software part is for signal processing. The hardware part of the hydroacoustic signal processing system includes three parts: acoustic-electric conversion, intermediate processing and signal recording. The study of ship noise showed that the fishing boat is about 5m long, with a power of 60hp, and passes about 10m away from the hydrophone, and it is more than 10dB higher than that without a ship, when there is a ship passing, in which the 400Hz is 17.82dB higher. Measurements of ocean ambient noise at different depths show that the noise decreases with depth, but when close to the seabed, the noise decreases with depth due to the reflection effect of the seabed on the sound and the biological noise of the seabed.

Localized surface plasmon resonance induced nonlinear absorption and optical limiting activity of gold decorated graphene/MoS2 hybrid

Localized surface plasmon resonance induced nonlinear absorption and optical limiting activity of gold decorated graphene/MoS2 hybrid