Signal Processing Implementation Research Articles

Signal processing implementation of low-cost target speed detection of CW radar using FPGA

AbstractThe low-cost Continuous Wave (CW) radar architecture for object speed detection is presented in this research. An FPGA (Field-Programmable Gate Array) processor is utilized to implement the CW radar dependably and optimally. The suggested approach uses a bank of filters made expressly to extract velocity information from radar signals to compute the target velocity. Depending on the needs of the particular application, the acquired data has either undergone additional processing or been presented in an approachable manner. The implemented design is checked in real-time with a chip scope, functionally simulated, and then inspected with lab instruments to compare all simulated and implemented outcomes. Numerous applications, such as industrial automation, surveillance, and automotive systems, where accurate velocity detection is crucial and low-cost design is required, could benefit from this concept.

Biological neuron modeling based on bifunctional memristor and its application in secure communication

In recent years, memristors have shown great potential in building artificial neural networks. Many researchers have worked on improving and innovating the design of memristors. The bifunctional memristor proposed in this paper provides new ideas for establishing artificial neural networks. And a novel second-order memristor Hindmarsh–Rose neuron (SOM-HR) is constructed on this basis. The SOM-HR model can well simulate the dynamical behavior of the memristive autapse HR neuron under electromagnetic radiation (EMR), and many types of firing patterns, coexisting firing patterns and state transition are found. In addition, the correctness of the numerical simulation is verified by circuit simulation and digital signal processor (DSP) implementation. Finally, the chaotic sequences generated by this model are used for image encryption, which guarantees the security of the encryption scheme. The results of the study provide a reference for construction and application of complex neural network.

Machine learning evaluation in the Global Event Processor FPGA for the ATLAS trigger upgrade

The Global Event Processor (GEP) FPGA is an area-constrained, performance-critical element of the Large Hadron Collider's (LHC) ATLAS experiment. It needs to very quickly determine which small fraction of detected events should be retained for further processing, and which other events will be discarded. This system involves a large number of individual processing tasks, brought together within the overall Algorithm Processing Platform (APP), to make filtering decisions at an overall latency of no more than 8ms. Currently, such filtering tasks are hand-coded implementations of standard deterministic signal processing tasks.In this paper we present methods to automatically create machine learning based algorithms for use within the APP framework, and demonstrate several successful such deployments. We leverage existing machine learning to FPGA flows such as hls4ml and fwX to significantly reduce the complexity of algorithm design. These have resulted in implementations of various machine learning algorithms with latencies of 1.2 μs and less than 5% resource utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms into the GEP system and present their actual performance.Our work shows the potential of using machine learning in the GEP for high-energy physics applications. This can significantly improve the performance of the trigger system and enable the ATLAS experiment to collect more data and make more discoveries. The architecture and approach presented in this paper can also be applied to other applications that require real-time processing of large volumes of data.

Signal Processing Techniques of Lamb Waves for Structural Health Monitoring System - A Review

The structural health monitoring (SHM) system using Lamb wave approach has drawn a lot of interest because of its outstanding performance in terms of accuracy and adaptability. The implemented signal processing techniques in the diagnostic analysis are specifically reviewed in this work. The primary factors of the wide range of applied signal processing techniques are due to the multimode and dispersive behaviour of the Lamb waves. Several Lamb modes occur simultaneously, and because they have different dispersive characteristics, they can produce complicated superimposed signals. To effectively diagnose the observed signals, several mode separations approaches have been proposed. Generally, time-frequency representation is applied for signal processing techniques. The findings demonstrated that the proposed procedures were successful in decomposing the superimposed mode into individual modes for further analysis. All these works have shown how SHM systems based on Lamb waves have evolved over time to control and monitor the condition of the structure.

Implementation of Digital Signal Processor for Pulse Doppler Radar

This paper presents the design and implementation of a digital signal processor (DSP) board, utilizing a TMS320C50 family DSP chip, for pulse-Doppler radar systems. Pulse-Doppler radar systems often encounter challenges such as strong clutter, noise, and jamming in dealing with echoes. To overcome these challenges, advanced digital signal processing techniques are employed. The main objective of this paper is to introduce a cost-effective signal processing solution that significantly enhances the performance of the radar system and brings it up to speed with modern radar technologies. The hardware described in this work can also be effectively utilized for implementing various types of signal processing algorithms. Additionally, as a secondary objective, the paper presents the digital realization of a radar detector. Traditionally, this detector was constructed using an analog Doppler filter bank. However, in this work, it is digitally implemented using N digital filters in place of the analog bank. By utilizing the designed DSP board and implementing the radar detector digitally, this paper demonstrates the potential for improved performance and efficiency in pulse-Doppler radar systems. The advancements made in this work contribute to the development of cost-effective and technologically advanced radar systems. The research results are presented for four different wind conditions, showcasing the effectiveness of the proposed approach. Furthermore, the paper suggests an algorithm that combines parametric and non-parametric techniques and provides a detailed explanation of its implementation. Finally, using the non-parametric technique, the probability of detection curves (PD) are simulated with respect to the signal-to-clutter ratio (SCR) for each wind condition, and the simulation results are depicted and compared. The achievements of this paper include the proposal of an efficient approach for clutter suppression in ground surveillance pulse Doppler radar, the comparison of different clutter suppression algorithms, and the development of an algorithm that combines parametric and non-parametric techniques. The simulation results provide valuable insights into the performance of the proposed algorithms under different wind conditions.

Small RCS targets detection based on pulse compression radar signal generator and processor

In the last two decades, field programmable gate arrays (FPGAs) have become the leading technology for implementing real time systems, specially radars. The most important advantage of FPGA is its parallel nature of executing complicated algorithms. As a result, the received data flow will be processed in a real time manner, which is suitable for radar applications. This paper attempts to show the design and the FPGA implementation of a binary phase-coded pulse compression radar signal generator and processor, that are intended to determine the ranges, and the speeds of targets with small radar cross-section area like drones. The system operating waveform is a bi-phase sequence, that is known to have the best known peak sidelobe level compared to compound Barker codes of the same length. In addition, the design incorporates a range sidelobes cancellation method to remove the time sidelobes in the matched filter output. Also, we are going to represent and investigate the FPGA implementing steps of the radar waveform generator and processor according to the required parameters. Finally, the implementation results of the proposed system will be represented.

Exploring Gen in MaxMSP: Approaches to virtual analog and wavetable synthesis

The proposed poster presentation will explore using the Gen environment in MaxMSP to develop software that implements virtual analog and wavetable synthesis. Gen is a powerful tool that enables one to approach sound synthesis on an elemental level by implementing signal processing on the sample level instead of dealing with audio frames. The talk will explore both graphical patching and using the embedded text-based language GenExpr using the codebox operator. Focus will be placed on developing design patterns for developing efficient sound synthesis routines and learning to experiment in realtime with Gen.

Simulation and Implementation of Signal Processing for LFM Radar Using DSK 6713

This research aims to propose a comprehensive simulation and implementation methodology for LFM (Linear Frequency Modulated) Radar Signal Processing and its application, using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board. The motivation behind this study is to develop control software based on MATLAB R14 and SIMULINK to model various system software tasks, including detection, A/D conversion, Fast Fourier Transform (FFT), modulation, accumulation, decision-making, and target detection. The simulations are categorized into two groups: ideal beat frequency and parameterized beat frequency. We introduce several important terminologies for consideration, including pulse compression, SNR, matched filter, Doppler effect, and more. The use of real-time data exchange (RTDX) will facilitate the generation of input data and enable real-time calculations for outputs, leading to the creation of machine code for the DSP chip. This process aims to ensure data verification calculations and enhance the credibility and performance of the proposed methodology. By conducting thorough simulations, verification, and practical testing, the study demonstrates the satisfactory credibility and performance of the developed method. Using this research, we aim to contribute to the advancement of LFM Radar Signal Processing and enable its efficient implementation using digital signal processing techniques on the DSP Starter Kit (DSK) 6713 board.

Lake Altimetry Using Long Coherent Integration Carrier Phase Measurements from Airborne GNSS Reflectometry

Today, land and ocean observations are crucial for monitoring climate change. The method of GNSS reflectometry is an opportunistic way to provide low-cost observations of many geophysical parameters. However, although this method has been the subject of numerous research studies, work is still in progress to improve its possibilities and fields of application. This paper focuses on GNSS reflectometry using carrier phase measurements for water altimetry measurements. The difficulties in implementing such a method lie in the need to collect a coherent signal and to solve the integer ambiguity value. In this context, the implementation of innovative signal processing is described, including the correlation of the reflected signals in dedicated software and the prolongation of the coherent integration time to enhance signal coherency. These processes were applied to data collected over Carcans-Hourtin Lake in France to compute the height of the reflection surface which was then compared to in situ GNSS buoy height measurements. The results show that at 300 ft (91.44 m), the differences between the lake heights measured with the buoy and with the reflectometry data can reach less than 1 cm for the L1, E1 and E5 GNSS signals. In addition, the slope of the geoid estimated with the reflectometry data is very consistent with that of the RAF20 geoid model, with a difference of up to less than 2 mm/km.

Readout Firmware of the Readout System for the MWDC at the ETF in HIRFL-CSR

Three multiwire drift chamber (MWDC) detectors and readout electrical equipment had been installed in 2018 as part of the cooler storage ring external target facility (ETF) project to gather track data in the ETF. In addition to its high efficiency and adaptability, the electronic device requires high precision in terms of position and energy resolution, as well as good flexibility and efficiency of the electronics. In this paper, we propose an electronic readout technique for MWDC detectors and show the implementation of a 32-channel signal processing in a single Xilinx Kintex-7 Field-Programmable Gate Array. Furthermore, we also present a thorough description of the readout firmware, with a focus on the deserialization mechanism and data processing algorithms. Additionally, we conducted a series of initial tests is conducted. The results show that the proposed technique can meet the application requirement.

Photon-counting-based underwater wireless optical communication employing orbital angular momentum multiplexing.

Underwater wireless optical communication (UWOC) is a critical technology for underwater communication, providing high speed, low latency, and security advantages. However, the strong attenuation in the water channel still limits the UWOC systems and their performances require further improvement. In this study, an orbital angular momentum (OAM) multiplexing UWOC system that uses photon-counting detection is experimentally demonstrated. By employing a single-photon counting module to receive photon signals, we analyze the bit error rate (BER) and photon-counting statistics by building a theoretical model that fits the real system, and demodulate the OAM states in single photon level and implement signal processing using field programmable gate array (FPGA) programming. Based on these modules, a 2-OAM multiplexed UWOC link is established over a water channel of 9 m. By using on-off keying modulation and 2-pulse position modulation, we achieve a BER of 1.26×10-3 with data rate of 20Mbps and 3.17×10-4 with data rate of 10Mbps respectively, which below the forward error correction (FEC) threshold of 3.8×10-3. The total transmission loss is 37 dB under an emission power of 0.5 mW, which is equivalent to the attenuation of 283 m Jerlov I type seawater from the perspective of energy loss. Our verified communication scheme will benefit the development of long-range and high-capacity UWOC.

Design and implementation of a least-mean-square adaptive notch filter

The task of removing sinusoidal components from observed signals can be accomplished by using a notch filter with a specific attenuation at a particular frequency. In some applications, however, such as acoustic feedback control, the frequency at which attenuation is required is unknown and possibly time-varying, and hence an adaptive notch filter is a more appropriate solution. Transitioning from a fixed notch filter to an adaptive one is by no means trivial and involves the understanding of a range of digital signal processing (DSP) topics from pole-zero placement techniques for designing infinite impulse response filters to optimal and adaptive filtering algorithms. In the signal processing algorithms and implementation graduate course taught at KU Leuven (Belgium), we study the design of an adaptive notch filter, which is based on a constrained biquadratic IIR representation, andwhose parameters are updated using a least-mean-square algorithm. Students also have to implement the algorithm on a 16-bit DSP TMS320C5515. In this presentation, we will discuss the design and implementation challenges of this adaptive notch filter and how it serves as an illustrative example/homework problem where several aspects of DSP are interwoven.

Programmable Metasurface Hybrid MIMO Beamforming: Channel Estimation, Data Transmission, and System Implementations at 28 GHz

With the development of wireless communication generations, multiple-input and multiple-output (MIMO) architectures have proven to be a solution to the higher required data rates for the increasing number of mobile users. As a matter of fact, better earnings can be gathered from a low-cost system with effective functions, which results in the hybrid beamforming architecture combining digital chains with large-scale antenna arrays. The programmable metasurface (PM) is a promising antenna array concept that realizes reconfigurable beamforming. It is raised to be an attractive antenna array architecture due to its low price and power consumption. However, experimental investigations combining PMs with modern wireless communication systems have not been intensively studied. Its challenges and difficulties of signal processing and system implementation remain uncertainties to be discovered. In this article, a PM hybrid MIMO beamforming system including the aforementioned important topics is presented as follows. First of all, the hybrid beamforming channel estimation algorithm adapted for PM is created by merging analog beam training and digital interleaved orthogonal frequency division multiplexing. Afterward, data transmissions based on the estimated channel state information utilizes variant signal recovery methods to examine the channel estimation accuracy and system feasibilities. To practically analyze the proposed system, the aforementioned aspects are implemented into system-level experiments using three PMs operating at 28 GHz for downlink wireless communication in both single-user and multiuser scenarios. Proper results are delivered, which successfully prove the PM hybrid MIMO beamforming system functionalities.

The Edge Application of Machine Learning Techniques for Fault Diagnosis in Electrical Machines.

The advent of digitization has brought about new technologies that enable advanced condition monitoring and fault diagnosis under the Industry 4.0 paradigm. While vibration signal analysis is a commonly used method for fault detection in literature, it often involves the use of expensive equipment in difficult-to-reach locations. This paper presents a solution for fault diagnosis of electrical machines by utilizing machine learning techniques on the edge, classifying information coming from motor current signature analysis (MCSA) for broken rotor bar detection. The paper covers the process of feature extraction, classification, and model training and testing for three different machine learning methods using a public dataset to then export the results to diagnose a different machine. An edge computing approach is adopted for the data acquisition, signal processing and model implementation on an affordable platform, the Arduino. This makes it accessible for small and medium-sized companies, albeit with the limitations of a resource-constrained platform. The proposed solution has been tested on electrical machines in the Mining and Industrial Engineering School of Almadén (UCLM) with positive results.

From playground swings to sway control of cranes: An active pendulum experiment

Dynamics is a core discipline in Mechanical and Aerospace Engineering programs and with the ubiquitous nature of control in modern-day applications, the field of mechatronics has gained popularity. Mechatronics refers to the field of engineering which integrates the engineering disciplines of mechanical, control, electronics, and computing. To create a testbed to illustrate a tabletop mechatronics system, the paper details the design, and fabrication of an active pendulum whose length can be changed in real-time using solenoids. This permits illustrating two concepts: (1) damping of pendulum oscillations which emulates the sway of a crane and (2) amplification of the oscillations which emulates the pumping of a playground swing. The paper describes the steps prior to experimental validation which includes: modeling, system identification, signal processing, and controller implementation. Numerical simulations are used to prototype the controller and eventually to compare the simulation results to the experimental ones. The results of all the experiments illustrate a close match between the simulated and experimental results. To permit reproduction of the experiment, the design details and code to implement the controllers are posted in a public repository.

A Smart Tactile Sensing System Based on Carbon Nanotube/Polypropylene Composites for Wearable Applications

Recent advances in nanomaterials render the possibility of fabricating tactile sensors suitable for electronic skin, haptic interfaces, and biomedical applications. Furthermore, the problem of performing complex inertial measurements generated during the tactile process of polymer composites can be tackled using artificial intelligence (AI). However, the implementation of AI-based signal processing on embedded devices still represents an open area of opportunity for the design of the next generation of smart wearable sensor systems. A novel nanostructured smart tactile sensing system for wearable applications is proposed, using a 3-D-printed structure with embedded electronic devices. The highly sensitive piezoresistive tactile sensor is based on multiwall carbon nanotube/polypropylene (MWCNT/PP) composites. The integration of electronic circuits for signal processing of an artificial neural network (NN) on a digital controller unit improves the tactile interpretation in the wearable embedded device. Experiments show that the pressure classification results on MWCNT/PP composites with 98% accuracy.

VLSI architecture and implementation of HDR camera signal processor

VLSI architecture and implementation of HDR camera signal processor

Optimal Signal Processing for Steady Control of a Robotic Arm Suppressing Hand Tremors for EOD Applications

Teleoperated robotics in recent years has proven to be valuable support in EOD tasks; a remarkable improvement in the systems that control these robots has been the Natural User Interfaces (NUI); however, the research that implements this type of system does not focus on the stability of the robotic arm movements, necessary for this type of applications due to the danger of working with explosives. In this paper, we propose the implementation of an Optimal Signal Processing for a NUI interface based on the Leap Motion (LM) controller. The main objective of this research is to correctly identify the intentional movements of the operator, achieve high stability of the robotic gripper and suppress the physiological tremors from the hand of the operator, considering not to increase the mental workload and not decrease the usability of the system. The signal processing proposed in this paper is composed of three filtering algorithms: Kalman, FIR, and moving average with a threshold. In addition, the obtained results are compared with the most representative processing of recent research using LM for robotic arm control. To evaluate and validate the proposed signal processing, a target path tracking test, a stability analysis of the robotic gripper, and a performance analysis in the execution of Pick and Place tasks, NASA-TLX and SUS questionnaires are developed. Finally, the proposed Optimal Signal processing is implemented in the DOBOT-MAGICIAN and tested by police officers of the EOD Unit-Arequipa (UDEX-AQP); the results indicate a reduction of the average Vibration of 31.61% and the Target Path Tracking error of 67.57%.

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many internet of things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.

Tracking Small Wildlife With Minimal-Complexity Radio Frequency Transmitters: Near-Optimal Detection

In a range of IoT applications, edge nodes require minimal size, mass, and power/energy use. When node complexity is severely constrained, the burden of detection falls to the receiving system. In this paper we present a method of detection for RF signals motivated by wildlife radio telemetry, where miniature beacons emit VHF pulses that are used to detect and localize animals in the field. We address the challenges of limited transmit power and path losses due to terrain that lead to low received SNR. In addition, the pulse train structure requires joint frequency acquisition, time synchronization, and detection; our approach addresses the key challenge of large uncertainty in signal parameters. Our algorithm explicitly integrates frequency acquisition and time synchronization, using nearly phase-coherent matched filtering followed by non-coherent pulse combining and a multiple hypotheses test to provide near-optimal detection performance in low SNR scenarios. We provide quantitative results showing performance as a function of SNR and signal uncertainty. When compared to the single pulse detection case as used in existing systems, our multipulse combining approach pulses improves detection performance by 4 dB, even under the conditions of temporal and frequency/phase uncertainty resulting from beacon design and manufacturing. We also describe an efficient real-time signal processing implementation of the algorithm, including signal tracking that supports localization after initial signal detection.