Hardware Techniques Research Articles

A Reconfigurable Hardware Realization for Real-Time Simulation of Cardiac Excitation and Conduction

Dynamic simulation of complex cardiac excitation and conduction requires high computational time. Thus, the hardware techniques that can run in real-time simulation were introduced. However, according to existing studies, the hardware simulation requires high power consumption and involves a large size of the physical parts. Due to the drawbacks, this research presents the adaptation of nonlinear Ordinary Differential Equation (ODE)-based cardiac excitable models of Luo-Rudy Phase I (LR-I) and FitzHugh-Nagumo (FHN) in a reconfigurable hardware of field-programmable gate array (FPGA). FPGA rapid prototyping using MATLAB Hardware Description Language (HDL) Coder was used to convert a fixed-point MATLAB Simulink blocks design of the cardiac models into a synthesizable VHSIC Hardware Description Language (VHDL) code and verified using the FPGA-In-the Loop (FIL) Co-simulator. The Xilinx FPGA Virtex-6 XC6VLX240T ML605 evaluation board was chosen as the FPGA platform. The cardiac excitation response characteristics using the LR-I model and the simulations of reentrant initiation and annihilation using the FHN model were verified in Virtex-6 FPGA. This means a new real-time simulation-based analysis technique of cardiac electrical excitation and conduction wassuccessfully developed using the reconfigurable hardware.

Portable Air Quality Detector Using DSM501A Dust Sensor and Arduino Uno

Aims: The primary feature of this research is to design a circuit that can analyze the air around the system and determine the quality of the air.
 Study Design: This paper reports on the design and implementation of a portable air pollution analyzer using Arduino Uno, DSM501A dust sensor, LCD, switches, buzzer, and multiple LEDs. The air pollution analyzer system was designed using a combination of hardware and software techniques to measure air pollution properly.
 Place and Period of Study: The research was accomplished by the authors in a group of two students under the grasp of a professor as a part of one of his course capstone projects for the Bachelor of Science in Electrical and Electronic Engineering degree at the American International University Bangladesh (AIUB), Dhaka, Bangladesh. The authors performed their investigative tasks at AIUB from September 2023 to February 2024.
 Methodology: The air pollution analyzer was implemented using an Arduino Uno and dust sensor to generate and determine accurate air quality based on specific air contents. The system that was developed used a PM10 detection system that identified the amount of dust particles in the area. Based on PM10 readings, the air quality was identified. Moreover, the Air Quality Index (AQI) was determined using the PM10 readings. The LCD, buzzer, and multiple LEDs were used to display and indicate the air quality condition and levels.
 Results: The portable air pollution analyzer was successfully tested and found to be accurate and reliable in determining air quality. To indicate different hazardous states of air, red, green, and yellow color LEDs are used. The test outcomes were very satisfactory.
 Conclusion: This system demonstrates the use of a microcontroller in building a simple yet effective portable air quality detector. It can be scaled up for commercial production.

A comprehensive review of advanced trends: from artificial synapses to neuromorphic systems with consideration of non-ideal effects.

A neuromorphic system is composed of hardware-based artificial neurons and synaptic devices, designed to improve the efficiency of neural computations inspired by energy-efficient and parallel operations of the biological nervous system. A synaptic device-based array can compute vector-matrix multiplication (VMM) with given input voltage signals, as a non-volatile memory device stores the weight information of the neural network in the form of conductance or capacitance. However, unlike software-based neural networks, the neuromorphic system unavoidably exhibits non-ideal characteristics that can have an adverse impact on overall system performance. In this study, the characteristics required for synaptic devices and their importance are discussed, depending on the targeted application. We categorize synaptic devices into two types: conductance-based and capacitance-based, and thoroughly explore the operations and characteristics of each device. The array structure according to the device structure and the VMM operation mechanism of each structure are analyzed, including recent advances in array-level implementation of synaptic devices. Furthermore, we reviewed studies to minimize the effect of hardware non-idealities, which degrades the performance of hardware neural networks. These studies introduce techniques in hardware and signal engineering, as well as software-hardware co-optimization, to address these non-idealities through compensation approaches.

Fundamentals of deep brain stimulation for Parkinson's disease in clinical practice: part 2.

The field of neuromodulation has evolved significantly over the past decade. Developments include novel indications and innovations of hardware, software, and stimulation techniques leading to an expansion in scope and role of these techniques as powerful therapeutic interventions. In this review, which is the second part of an effort to document and integrate the basic fundamentals and recent successful developments in the field, we will focus on classic paradigms for electrode placement as well as new exploratory targets, mechanisms of neuromodulation using this technique and new developments, including focused ultrasound driven ablative procedures.

Efficient bead-on-plate weld model for parameter estimation towards effective wire arc additive manufacturing simulation

AbstractDespite the advances in hardware and software techniques, standard numerical methods fail in providing real-time simulations, especially for complex processes such as additive manufacturing applications. A real-time simulation enables process control through the combination of process monitoring and automated feedback, which increases the flexibility and quality of a process. Typically, before producing a whole additive manufacturing structure, a simplified experiment in the form of a bead-on-plate experiment is performed to get a first insight into the process and to set parameters suitably. In this work, a reduced order model for the transient thermal problem of the bead-on-plate weld simulation is developed, allowing an efficient model calibration and control of the process. The proposed approach applies the proper generalized decomposition (PGD) method, a popular model order reduction technique, to decrease the computational effort of each model evaluation required multiple times in parameter estimation, control, and optimization. The welding torch is modeled by a moving heat source, which leads to difficulties separating space and time, a key ingredient in PGD simulations. A novel approach for separating space and time is applied and extended to 3D problems allowing the derivation of an efficient separated representation of the temperature. The results are verified against a standard finite element model showing excellent agreement. The reduced order model is also leveraged in a Bayesian model parameter estimation setup, speeding up calibrations and ultimately leading to an optimized real-time simulation approach for welding experiment using synthetic as well as real measurement data.

A compressive seeding algorithm in conjunction with reordering-based compression.

Seeding is a rate-limiting stage in sequence alignment for next-generation sequencing reads. The existing optimization algorithms typically utilize hardware and machine-learning techniques to accelerate seeding. However, an efficient solution provided by professional next-generation sequencing compressors has been largely overlooked by far. In addition to achieving remarkable compression ratios by reordering reads, these compressors provide valuable insights for downstream alignment that reveal the repetitive computations accounting for more than 50% of seeding procedure in commonly used short read aligner BWA-MEM at typical sequencing coverage. Nevertheless, the exploited redundancy information is not fully realized or utilized. In this study, we present a compressive seeding algorithm, named CompSeed, to fill the gap. CompSeed, in collaboration with the existing reordering-based compression tools, finishes the BWA-MEM seeding process in about half the time by caching all intermediate seeding results in compact trie structures to directly answer repetitive inquiries that frequently cause random memory accesses. Furthermore, CompSeed demonstrates better performance as sequencing coverage increases, as it focuses solely on the small informative portion of sequencing reads after compression. The innovative strategy highlights the promising potential of integrating sequence compression and alignment to tackle the ever-growing volume of sequencing data. CompSeed is available at https://github.com/i-xiaohu/CompSeed.

Experimental DTC of an Induction Motor Applied to Optimize a Tracking System

In recent years, the areas of industrial application of high performance AC drives, especially the induction motor (IM), based on Direct Torque Controller (DTC) technique has gradually increased due to its advantages over the other techniques of control. Among these applications are cited, propulsion and tracking systems. This work presents the experimental part of a research project conducted by the authors where they have implemented an algorithm for a DTC in order to achieve a tracking system designed for a series of solar panels to maximize its overall efficiency. In this paper, three different switching tables are presented for choosing the best between them for a less fluctuation in torque. This study is justified by reduced cost of implementation compared to other methods that require the use of expensive hardware and complicated control techniques. The tests were carried out under the same conditions with the same equipment of the testbed, to get a good comparative study. Three variants were tested: simple switching table, table of sectors shifted by 30 ° and a table with 12 sectors. The results show an improvement, but a dilemma between the torque ripple and those of the stator flux.

Clinical and morphological assessment of combined protocol for Radiesse in dilution and hardware techniques in patients with signs of chronological skin aging and photoaging of the face and décolleté

Clinical and morphological assessment of combined protocol for Radiesse in dilution and hardware techniques in patients with signs of chronological skin aging and photoaging of the face and décolleté

Listening to tropical forest soils

Acoustic monitoring has proven to be an effective tool for monitoring biotic soundscapes in the marine, terrestrial, and aquatic realms. Recently it has been suggested that it could also be an effective method for monitoring soil soundscapes, but has been used in very few studies, primarily in temperate and polar regions.We present the first study of soil soundscapes using passive acoustic monitoring in tropical forests, using a novel analytical pipeline allowing for the use of in-situ recording of soundscapes with minimal soil disturbance. We found significant differences in acoustic index values between burnt and unburnt forests and the first indications of a diel cycle in soil soundscapes.These promising results and methodological advances highlight the potential of passive acoustic monitoring for large-scale and long-term monitoring of soil biodiversity. We use the results to discuss research priorities, including relating soil biophony to community structure and ecosystem function, and the use of appropriate hardware and analytical techniques.

Secondary Displacement was Common in Healing Distal Femur Fractures in a Cohort of Elderly Patients.

Surgical treatment of distal femoral fractures (DFFs) in osteoporotic bone is challenging despite improvements in hardware and surgical techniques. The occurrence and degree of secondary displacement during healing after bridging plate fixation are still unknown. This study aimed to assess the occurrence and degree of secondary displacement in healing DFFs in elderly patients and correlate the secondary displacement to body mass index, bone density, and weight-bearing regimen. The study involved 32 patients, 65years or older, with a DFF of AO/OTA types 33 A2-3, B1-2, C1-2, and 32(c) A-C,1-3, including peri-prosthetic fractures with stable implants. Twenty-seven patients had at least 8weeks of follow-up, and 21 patients had a complete 1-year follow-up. Minimally invasive surgery was performed using a distal anatomical femoral plate as a long bridge-plating construct. Secondary displacement was assessed with computed tomography of the entire femur postoperatively and at 8, 16, and 52weeks. Femoral length, coronal angulation (varus/valgus), and subsidence as the change in distance between the distal joint surface and a specified locking screw were measured. There was a statistically significant mean femoral shortening at 52weeks of 4.7mm (SD 3.9, (95% CI 2.9-6.5), P < 0.001) mainly by subsidence of the distal fragment. Most patients experienced limited coronal angulation. There was no correlation between body mass index or bone density and secondary displacement. At the 1-year follow-up, no patient needed revision surgery for non-union or plate breakage. Restricted weight-bearing for 8weeks did not prevent secondary displacements or adverse events such as cut-outs. Modern dynamic plate osteosynthesis could not prevent commonly occurring fracture subsidence in DFF in an elderly cohort. Restricted weight-bearing for 8weeks did not prevent secondary displacements or mechanical adverse events.

Immediate Effects of the Self-Retaining Barbed Suture Material Application &#x0D; for Gastrojejunostomy During Mini Gastric Bypass Laparoscopic Surgery

Background. The safety of self-retaining barbed suture material application when closing wounds of hollow organs and forming anastomoses remains controversial. Data on the use of self-retaining barbed suture material at the stage of a single anastomosis formation during mini gastric bypass laparoscopic surgery (mini gastric bypass - MGB) in the treatment of morbid obesity is scarce and includes both examples of suturing a technological hole after the implementation of a hardware technique, and totally hand-sewn intracorporeal knotless formation of a gastrojejunostomy.The aim of the study was to evaluate the immediate effects of single-row continuous sutures performed with self-retaining barbed unidirectional suture material for intracorporeal hand-sewn gastrojejunostomy during MGB.Methods. The study included 116 patients with grade II-III obesity who underwent MGB. The total duration of operations and the duration of the gastrojejunostomy stage, the volume of intraoperative blood loss, the frequency and severity of intra- and postoperative complications were prospectively studied in accordance with the unified classifications of Satava-Kazaryan and Accordion, respectively. The first group consisted of 56 patients; a conventional synthetic (polydioxanone) monofilament suture material was used for hand-sewn gastrojejunostomy in patients of this group. The second group consisted of 60 patients; gastrojejunostomy was performed with single-needle unidirectional self-retaining barbed absorbable polyester monofilaments in patients of this group. The study groups did not differ significantly in demographic characteristics, body mass index (BMI), the nature of comorbid pathology and the frequency of previous operations.Results. The use of unidirectional self-retaining barbed suture material for a hand-sewn intracorporeal single-row gastrojejunostomy in MGB was accompanied by a significant reduction in the total duration of interventions due to a reduced gastrojejunostomy stage if compared with the use of conventional synthetic monofilaments. The median volume of blood loss during operations did not exceed 50 ml and had no significant differences between groups. Intraoperatively, in patients of the study groups there were registered only complications of the first degree of severity, according to the Satava-Kazaryan classification, with a frequency of 6.7-8.9% (p = 0.737). In the postoperative period, the development of minor complications (first severity of the Accordion system) occurred in 19.6% and 16.7% of patients of the first and second groups, respectively (p = 0.810). The duration of hospital stay was 3.0 (2.5; 3.0) and 2.7 (2.7; 3.0) days in the first and second groups, respectively (р=0,790).Conclusion. The achieved reduced duration of MGB due to the reduced stage of gastrojejunostomy with self-retaining barbed unidirectional suture material, and comparable immediate effects of surgical treatment in patients of the first and second groups demonstrate significant outcomes. Further study is necessary to investigate long-term effects of the knotless suture application for a single anastomosis formation during MGB surgery.

GPS Receiver Simplification for Low cost Applications and Multipath Mitigation Analysis on SDR based Re configurable Software Receiver

Many modern position-based applications rely heavily on the Global Navigation Satellite System (GNSS). Most applications require precise position data obtained through sophisticated hardware with a high computational capacity in the receiver. Some cost-effective applications may not require precise position data and require less complex signal processing. The use of efficient hardware and signal processing techniques to reduce the overall cost of a GNSS receiver is an active research topic. This paper considers Global Positioning System (GPS) constellation and proposes two factors to reduce the receiver complexity: sampling frequency and the number of tracking channels. A Keysight GNSS signal generator to record GPS signals, a Software Defined Radio board and a software-based GPS receiver are used in the experimentation. The sampling frequencies are 40, 20, 10 and 5 MHz considered, and tracking channels are reduced from 12 to 6 and then 4. The increase of error in the receiver position with 6 and 4 satellites is considerably small, but the number of tracking channels and signal processing requirements are reduced considerably. The GPS signals are affected by many errors; one of the significant sources of error is multipath propagation. Three distinct GPS multipath scenarios are generated for four satellite signal combinations with the GNSS simulator for the receiver performance analysis. Three multipath mitigation techniques, namely Early Minus Late (EML), Narrow correlator (NC) and strobe correlator (SC) methods, are considered because of their simple structure and fewer signal processing requirements. The error reductions of three multipath scenarios are compared, and the SC method performs better in all three multipath scenarios.

Functional Meta-Analysis of the Proteomic Responses of Arabidopsis Seedlings to the Spaceflight Environment Reveals Multi-Dimensional Sources of Variability across Spaceflight Experiments.

The human quest for sustainable habitation of extraterrestrial environments necessitates a robust understanding of life's adaptability to the unique conditions of spaceflight. This study provides a comprehensive proteomic dissection of the Arabidopsis plant's responses to the spaceflight environment through a meta-analysis of proteomics data from four separate spaceflight experiments conducted on the International Space Station (ISS) in different hardware configurations. Raw proteomics LC/MS spectra were analyzed for differential expression in MaxQuant and Perseus software. The analysis of dissimilarities among the datasets reveals the multidimensional nature of plant proteomic responses to spaceflight, impacted by variables such as spaceflight hardware, seedling age, lighting conditions, and proteomic quantification techniques. By contrasting datasets that varied in light exposure, we elucidated proteins involved in photomorphogenesis and skotomorphogenesis in plant spaceflight responses. Additionally, with data from an onboard 1 g control experiment, we isolated proteins that specifically respond to the microgravity environment and those that respond to other spaceflight conditions. This study identified proteins and associated metabolic pathways that are consistently impacted across the datasets. Notably, these shared proteins were associated with critical metabolic functions, including carbon metabolism, glycolysis, gluconeogenesis, and amino acid biosynthesis, underscoring their potential significance in Arabidopsis' spaceflight adaptation mechanisms and informing strategies for successful space farming.

Suppression of cortical electrostimulation artifacts using pre-whitening and null projection

Objective. Invasive brain–computer interfaces (BCIs) have shown promise in restoring motor function to those paralyzed by neurological injuries. These systems also have the ability to restore sensation via cortical electrostimulation. Cortical stimulation produces strong artifacts that can obscure neural signals or saturate recording amplifiers. While front-end hardware techniques can alleviate this problem, residual artifacts generally persist and must be suppressed by back-end methods. Approach. We have developed a technique based on pre-whitening and null projection (PWNP) and tested its ability to suppress stimulation artifacts in electroencephalogram (EEG), electrocorticogram (ECoG) and microelectrode array (MEA) signals from five human subjects. Main results. In EEG signals contaminated by narrow-band stimulation artifacts, the PWNP method achieved average artifact suppression between 32 and 34 dB, as measured by an increase in signal-to-interference ratio. In ECoG and MEA signals contaminated by broadband stimulation artifacts, our method suppressed artifacts by 78%–80% and 85%, respectively, as measured by a reduction in interference index. When compared to independent component analysis, which is considered the state-of-the-art technique for artifact suppression, our method achieved superior results, while being significantly easier to implement. Significance. PWNP can potentially act as an efficient method of artifact suppression to enable simultaneous stimulation and recording in bi-directional BCIs to biomimetically restore motor function.

Approximate Computing: Hardware and Software Techniques, Tools and Their Applications

The limitations of scaling in CMOS technology pose challenges in meeting the requirements of future applications. To address these challenges, researchers are exploring various design techniques, including Approximate Computing (AC), which leverages the inherent error resilience of applications to achieve high performance and energy gains with desired quality. AC has gained popularity as a computer paradigm for error-resilient applications, and many researchers have studied AC across computing layers and developed tools for implementing these techniques. This paper provides a comprehensive survey of AC techniques at the abstraction levels of software and hardware and discusses the tools to implement AC in hardware and software, quality evaluation tools and comparison points. The paper also covers existing frameworks for AC, potential applications, future research directions, challenges and limitations. This information can guide researchers in identifying promising avenues for further advancements and innovations in this domain. Additionally, this paper compares state-of-the-art surveys of AC and highlights the unique features and contributions of this work that distinguish our work from previous surveys.

Technical Review of Radio Frequency Identification and Internet of Things Technologies in Business Operations and Automated Indoor Location

The technological diversity applied in practical examples entails the identification and the analysis of different implementation options, thus opening the way to a world of technology. The goal of this paper is to spot novel prospects for incorporating sensors and Big Data techniques for better property administration strategies, through an indoor localization and navigation architecture. The hereby analysis uses an approach that combines Radio Frequency Identification technology, performance efficient algorithms for calculating routes in very large datasets, methodologies for optimizing spatial representations and Internet of Things specific hardware distribution techniques to properly address challenging logistics issues and complex facilities management. The proposed solution is tailored for a large underground parking area in a commercial center, enabling easy car location identification, automatic parking time calculation and dynamically determined occupancy levels, along with descriptive statistics about busy time intervals or visiting patterns.

Fault-Tolerant Hardware Acceleration for High-Performance Edge-Computing Nodes

High-performance embedded systems with powerful processors, specialized hardware accelerators, and advanced software techniques are all key technologies driving the growth of the IoT. By combining hardware and software techniques, it is possible to increase the overall reliability and safety of these systems by designing embedded architectures that can continue to function correctly in the event of a failure or malfunction. In this work, we fully investigate the integration of a configurable hardware vector acceleration unit in the fault-tolerant RISC-V Klessydra-fT03 soft core, introducing two different redundant vector co-processors coupled with the Interleaved-Multi-Threading paradigm on which the microprocessor is based. We then illustrate the pros and cons of both approaches, comparing their impacts on performance and hardware utilization with their vulnerability, presenting a quantitative large-fault-injection simulation analysis on typical vector computing benchmarks, and comparing and classifying the obtained results. The results demonstrate, under specific conditions, that it is possible to add a hardware co-processor to a fault-tolerant microprocessor, improving performance without degrading safety and reliability.

A comprehensive survey on hardware-assisted malware analysis and primitive techniques

Malware poses an extremely dangerous threat to the digital world, significantly impacting various domains such as smart cities, intelligent transportation, wireless sensor networks (WSNs), and the Internet of Vehicles (IoV). In the Internet of Things (IoT) context, which includes various applications, security threats, and vulnerabilities are high, especially for resource-constrained single-board devices. One of the critical challenges is the presence of unauthorised access to IoT devices that attempt to replicate authorized devices by mimicking their hardware and software specifications. This review paper provides a comprehensive overview of the security issues related to various hardware-level attacks, memory attacks, hardware Trojans, and their countermeasures in different domains. In addition, the paper addresses the study of hardware malware and explores their detection techniques for hardware-based malware, including side-channel and chip-based attacks using machine learning. Using primitive hardware techniques, such as Physically Unclonable Functions (PUFs), to generate the unique key for the device by preventing unauthorised access to the hardware during authentication for different domains is thoroughly investigated with different security features. This review analyzes the different types of PUFs, their characteristics, and their use for various security tasks, including device authentication and the key generation process. It critically evaluates the strengths and limitations of the PUF-based approach and identifies potential research directions to overcome the existing challenges.

Fish Monitoring from Low-Contrast Underwater Images

A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions.

HMT: A Hardware-centric Hybrid Bonsai Merkle Tree Algorithm for High-performance Authentication

The Bonsai Merkle tree (BMT) is a widely used tree structure for authentication of metadata such as encryption counters in a secure computing system. Common BMT algorithms were designed for traditional Von Neumann architectures with a software-centric implementation in mind and as such, they are predominantly recursive and sequential in nature. However, the modern heterogeneous computing platforms employing Field-Programmable Gate Array (FPGA) devices require concurrency-focused algorithms to fully utilize the versatility and parallel nature of such systems. The recursive nature of traditional BMT algorithms makes them challenging to implement in such hardware-based setups. Our goal for this work is to introduce HMT, a hardware-friendly BMT algorithm that enables the verification and update processes to function independently and provides the benefits of relaxed update while being comparable to the eager update in terms of update complexity. The methodology of HMT contributes both novel algorithmic revisions and innovative hardware techniques to implementing BMT. We mathematically demonstrate the challenges of potentially unbounded recursions in relaxed BMT updates. To solve this problem, we use a partitioned BMT caching scheme that allocates a separate write-back cache for each BMT level—thus allowing for low and fixed upper bounds for dirty evictions compared to the traditional BMT caches. Then we introduce the aforementioned hybrid BMT algorithm that is hardware-targeted, parallel, and relaxes the update depending on BMT cache hit but makes the update conditions more flexible compared to lazy update to save additional write-backs. Deploying this new algorithm, we have designed a new BMT controller with a dataflow architecture including speculative buffers and parallel write-back engines to facilitate performance-enhancing mechanisms (like multiple concurrent authentication and independent updates) that were not possible with the conventional lazy algorithm. Our empirical performance measurements on a Xilinx U200 accelerator FPGA have demonstrated that HMT can achieve up to 7× improvement in bandwidth and 4.5× reduction in latency over lazy-update BMT baseline and up to 14% faster execution in standard benchmarks compared to a state-of-the-art, eager-update BMT solution.