Hardware Techniques Research Articles

Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Automatic Speech Recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. These systems used data from both male and female speakers. The majority of commercial ASR systems available on adult speech are working efficiently. Speech data collected from both male and female speakers were used in these systems. In recent decades, ASR systems for children have emerged, such as reading tutors, aids for foreign language learning, and computer games. Child ASR systems are essential but poorly understood in the field of computer speech recognition. The child data collection is a very complex task. Child corpus is not available publicly, and variability of children speakers and ASR developed for a particular age group is not suitable for other age groups. These are some of the reasons for less and ineffective child ASR systems. However, the non-availability of child corpus publicly is a primary reason for ineffective child ASR. Designing and developing child corpus is a very tedious task. Therefore, the primary focus of this state-of-the-art review is to discuss various challenges encountered while designing and developing child corpus.

Concurrent TMS-fMRI: Technical Challenges, Developments, and Overview of Previous Studies.

Transcranial magnetic stimulation (TMS) is a promising treatment modality for psychiatric and neurological disorders. Repetitive TMS (rTMS) is widely used for the treatment of psychiatric and neurological diseases, such as depression, motor stroke, and neuropathic pain. However, the underlying mechanisms of rTMS-mediated neuronal modulation are not fully understood. In this respect, concurrent or simultaneous TMS-fMRI, in which TMS is applied during functional magnetic resonance imaging (fMRI), is a viable tool to gain insights, as it enables an investigation of the immediate effects of TMS. Concurrent application of TMS during neuroimaging usually causes severe artifacts due to magnetic field inhomogeneities induced by TMS. However, by carefully interleaving the TMS pulses with MR signal acquisition in the way that these are far enough apart, we can avoid any image distortions. While the very first feasibility studies date back to the 1990s, recent developments in coil hardware and acquisition techniques have boosted the number of TMS-fMRI applications. As such, a concurrent application requires expertise in both TMS and MRI mechanisms and sequencing, and the hurdle of initial technical set up and maintenance remains high. This review gives a comprehensive overview of concurrent TMS-fMRI techniques by collecting (1) basic information, (2) technical challenges and developments, (3) an overview of findings reported so far using concurrent TMS-fMRI, and (4) current limitations and our suggestions for improvement. By sharing this review, we hope to attract the interest of researchers from various backgrounds and create an educational knowledge base.

MetaSys: A Practical Open-source Metadata Management System to Implement and Evaluate Cross-layer Optimizations

This article introduces the first open-source FPGA-based infrastructure, MetaSys, with a prototype in a RISC-V system, to enable the rapid implementation and evaluation of a wide range of cross-layer techniques in real hardware. Hardware-software cooperative techniques are powerful approaches to improving the performance, quality of service, and security of general-purpose processors. They are, however, typically challenging to rapidly implement and evaluate in real hardware as they require full-stack changes to the hardware, system software, and instruction-set architecture (ISA). MetaSys implements a rich hardware-software interface and lightweight metadata support that can be used as a common basis to rapidly implement and evaluate new cross-layer techniques. We demonstrate MetaSys’s versatility and ease-of-use by implementing and evaluating three cross-layer techniques for: (i) prefetching in graph analytics; (ii) bounds checking in memory unsafe languages, and (iii) return address protection in stack frames; each technique requiring only ~100 lines of Chisel code over MetaSys. Using MetaSys, we perform the first detailed experimental study to quantify the performance overheads of using a single metadata management system to enable multiple cross-layer optimizations in CPUs. We identify the key sources of bottlenecks and system inefficiency of a general metadata management system. We design MetaSys to minimize these inefficiencies and provide increased versatility compared to previously proposed metadata systems. Using three use cases and a detailed characterization, we demonstrate that a common metadata management system can be used to efficiently support diverse cross-layer techniques in CPUs. MetaSys is completely and freely available at https://github.com/CMU-SAFARI/MetaSys .

Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands

Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.

CeMux: Maximizing the Accuracy of Stochastic Mux Adders and an Application to Filter Design

Stochastic computing (SC) is a low-cost computational paradigm that has promising applications in digital filter design, image processing, and neural networks. Fundamental to these applications is the weighted addition operation, which is most often implemented by a multiplexer (mux) tree. Mux-based adders have very low area but typically require long bitstreams to reach practical accuracy thresholds when the number of summands is large. In this work, we first identify the main contributors to mux adder error. We then demonstrate with analysis and experiment that two new techniques, precise sampling and full correlation, can target and mitigate these error sources. Implementing these techniques in hardware leads to the design of CeMux (Correlation-enhanced Multiplexer), a stochastic mux adder that is significantly more accurate and uses much less area than traditional weighted adders. We compare CeMux to other SC and hybrid designs for an electrocardiogram filtering case study that employs a large digital filter. One major result is that CeMux is shown to be accurate even for large input sizes. CeMux's higher accuracy leads to a latency reduction of 4× to 16× over other designs. Furthermore, CeMux uses about 35% less area than existing designs, and we demonstrate that a small amount of accuracy can be traded for a further 50% reduction in area. Finally, we compare CeMux to a conventional binary design and we show that CeMux can achieve a 50% to 73% area reduction for similar power and latency as the conventional design but at a slightly higher level of error.

Edge memory effect for hidden object tracking

Tracking objects through a scattering medium is highly desirable. As many recent advances have shown, non-invasive imaging and tracking in real-time can be achieved by utilizing optical memory effect for speckle correlations. Limited by the memory effect range, this method however works only for tiny objects, which reduces its popularity in tracking applications where the object is sizable but only its trajectory, not its image, is important. In this work, we show that the difference of speckle images is still correlated even when the moving object’s size is beyond the memory effect range. We have experimentally demonstrated the successful tracking of the hidden moving object, with its sizes exceeded the memory effect range. Our proposed technique requires a standard camera only, with no need to adopt other hardware techniques such as wavefront shaping, laser illuminated detection, or interferometric detection, hence is appealing for hidden object tracking in real-world applications.

Types of Lightweight Cryptographies in Current Developments for Resource Constrained Machine Type Communication Devices: Challenges and Opportunities

Machine-type communication devices have become a vital part of the autonomous industrial internet of things and industry 4.0. These autonomous resource-constrained devices share sensitive data, and are primarily acquired for automation and to operate consistently in remote environments under severe conditions. The requirements to secure the sensitive data shared between these devices consist of a resilient encryption technique with affordable operational costs. Consequently, devices, data, and networks are made secure by adopting a lightweight cryptosystem that should achieve robust security with sufficient computational and communication costs and counter modern security threats. This paper offers in-depth studies on different types and techniques of hardware and software-based lightweight cryptographies for machine-type communication devices in machine-to-machine communication networks.

Robust 2-D DOA Estimation in a Polarization Sensitive Single User Environment

Apart from the conventional parameters (such as signal-to-noise ratio (SNR), array geometry and size, and the number of samples), several other factors (e.g., alignment of the antenna elements, polarization parameters) influence the performance of direction of arrival (DOA) estimation algorithms. When all the antenna elements are aligned in the same direction, the polarization parameters uniformly affect the steering vectors, which is the underlying assumption of almost all conventional DOA algorithms. Unfortunately, in this case, for a given set of DOA angles, there exists a range of polarization parameters resulting in a very low SNR across all the antenna elements in the array and vice versa. To avoid this type of unwanted event, different antenna elements must be aligned differently. However, this fact will make almost all commonly used DOA estimation algorithms inoperable since the steering vectors are contaminated unevenly by the polarization parameters. To the best of our knowledge, no work in the literature handles this issue using simple hardware and signal processing techniques even for a single user environment. In this paper, that line of inquiry is pursued. We consider a circular array with the minimum number (i.e., 4) of short dipole antenna elements and propose an antenna alignment scheme. This ensures that at any given point no more than one element will suffer significantly from low SNR due to the contribution of polarization. A thresholding technique to isolate the antenna element after being seriously contaminated by the polarization parameters is developed and analyzed. Two algorithms that are suited for operating reliably in all possible DOA and polarization environments are addressed. The first algorithm follows the working principle of the popular MUSIC algorithm after cleaning the polarization contributions from the non-signal subspace. The other one, which is found as a byproduct of the process of cleaning the non-signal subspace, can estimate the DOA angles in a closed form manner. The implementations of the above algorithms for an arbitrary number of antenna elements greater than or equal to 4 are also presented. Finally, a thorough performance and complexity analysis are illustrated for those two algorithms considering various polarization and DOA scenarios.

Agreement of anthropometric and body composition measures predicted from 2D smartphone images and body impedance scales with criterion methods

Background/objectivesBody composition and anthropometry assessment from two-dimensional smartphone images is possible through advancement of computational hardware and artificial intelligence (AI) techniques. This study established agreement of a novel smartphone assessment, compared with traditional bioelectrical impedance analysis (BIA), and criterion measures. Subjects/methodsBody composition of 929 adults was measured using DXA (GE lunar iDXA), a foot-to-foot BIA machine (TANITA BC-313), and predictions from two-dimensional smartphone images. Anthropometry measures were also collected. Body composition and anthropometry estimates were compared via concordance coefficient correlation (CCC), equivalence testing, Bland–Altman analysis, and root mean square error (RMSE). Results2D smartphone image predictions for percent body fat (%BF) (males: CCC = 0.90 and RMSE = 2.9, and females: CCC = 0.90 and RMSE = 2.8) reported greater agreement with DXA measures than the BIA measures (males: CCC = 0.66 and RMSE = 5.6, and females: CCC = 0.79 and RMSE = 4.6). All anthropometry 2D smartphone image predictions had a strong agreement with criterion measurements (CCC ≥ 0.84 and RMSE ≤ 3.3). Body composition and anthropometry measures predicted by the 2D smartphone images were clinically equivalent at ±2.5 and ±5.0% thresholds. BIA %BF was not equivalent at either threshold; with only female BIA fat-free mass equivalent at the ±5% threshold. ConclusionBody composition predictions from 2D smartphone application images provide a promising alternative to BIA scales for in-home body composition assessment. Future research should assess the validity of this method for longitudinally tracking body composition and indicating an individual’s potential risk of chronic diseases.

SDCBench: A Benchmark Suite for Workload Colocation and Evaluation in Datacenters

Colocating workloads are commonly used in datacenters to improve server utilization. However, the unpredictable application performance degradation caused by the contention for shared resources makes the problem difficult and limits the efficiency of this approach. This problem has sparked research in hardware and software techniques that focus on enhancing the datacenters’ isolation abilities. There is still lack of a comprehensive benchmark suite to evaluate such techniques. To address this problem, we present SDCBench, a new benchmark suite that is specifically designed for workload colocation and characterization in datacenters. SDCBench includes 16 applications that span a wide range of cloud scenarios, which are carefully selected from the existing benchmarks using the clustering analysis method. SDCBench implements a robust statistical methodology to support workload colocation and proposes a concept of latency entropy for measuring the isolation ability of cloud systems. It enables cloud tenants to understand the performance isolation ability in datacenters and choose their best-fitted cloud services. For cloud providers, it also helps them to improve the quality of service to increase their revenues. Experimental results show that SDCBench can simulate different workload colocation scenarios by generating pressures on multidimensional resources with simple configurations. We also use SDCBench to compare the latency entropies in public cloud platforms such as Huawei Cloud and AWS Cloud and a local prototype system FlameCluster-II; the evaluation results show FlameCluster-II has the best performance isolation ability over these three cloud systems, with 0.99 of experience availability and 0.29 of latency entropy.

Integrated Waveform Design for MIMO Radar and Communication via Spatio-Spectral Modulation

This paper considers the integrated waveform design to simultaneously achieve a desired radar beampattern and multi-users communication for a dual-function Multiple-Input Multiple-Output (MIMO) system. To this end, a spatio-spectral modulation strategy via shaping the spatial waveform Energy Spectral Density (ESD) in directions of communication is proposed for the communication function, while beampattern Integrated Sidelobe Level (ISL) is minimized to enhance radar detectability. Meanwhile, Peak-to-Average Ratio (PAR) and power restrictions to comply with the current hardware technique and the mainlobe width constraint to cohere the beampattern main energy on the spatial region of interest are forced, respectively. Exploiting an equivalent reformulation of the original non-convex optimization problem, a Sequential Block Enhancement (SBE) framework that alternately updates each waveform in each emitting antenna is developed to monotonically decrease ISL. Each block involves the Dinkelbach’s procedure, sequential convex approximation and Alternating Direction Method of Multipliers (ADMM) to obtain single waveform, while the analytic proof with the converged block being a Karush-Kuhn-Tucker (KKT) point is provided. Finally, numerical results highlight the effectiveness of both the proposed dual function scheme and the waveform synthesis technique in comparison with some counterparts.

Artificial intelligence techniques for implementation of intelligent machining

For the past few years, the rapid progress and development of artificial intelligence (AI) based technologies have been analyzed for the applications of the intelligent manufacturing industry, i.e., industry 4.0. this has triggered a valuable transformation in means, models, and ecosystems within the manufacturing industry and AI development. With the advancement in manufacturing technology, there is a need to execute these technologies and AI more efficiently and cost-effectively. It can be possible by combining traditional manufacturing and machining technologies with recently developed intelligent manufacturing technologies comprising hardware and software techniques. This review paper discusses various AI implementation-based intelligent manufacturing industries with their architecture and technology systems based on the integration of AI with manufacturing and information communication. Furthermore, AI-based manufacturing application, their implementation, and current development in intelligent manufacturing have also been discussed.

An Ultra-Low-Cost Soft Error Protection Scheme Based on the Selection of Critical Variables

The exponentially increasing occurrence of soft errors makes the optimization of reliability, performance, hardware area, and power consumption one of the main concerns in modern embedded processors. Since the design cost of hardware techniques aimed at improving the reliability of microprocessors is quite expensive for resource-constrained embedded systems, software-level fault tolerance mechanisms have been proposed as an attractive solution for soft error threats. However, many software-level redundancy-based schemes are accompanied by considerable performance overhead, which is not acceptable for many embedded applications. In this work, we have introduced an ultra-low-cost soft error protection scheme for embedded applications, which works based on source-code analysis and identifying critical variables. After identification, these vital variables are adequately protected by placing runtime checks at critical points of execution. Our experimental results based on several applications demonstrate that the proposed scheme can mitigate the failure rate by 47% with negligible performance degradation.

Hardware realisation of nonlinear dynamical systems for and from biology

The focus of this thesis is on the applications of nonlinear dynamical systems in bioengineering which are mainly used in large-scale and generally categorised into two groups: (1) dynamical systems from biology (2) dynamical systems for biology. The mathematical models describing the dynamical systems used in the above systems can be simulated with the use of powerful software such as MATLAB, however, for large-scale simulations software begins to collapse. Besides, computer-based simulations are not always suitable for interfacing with biological/physical systems where continuous monitoring with low power and area consumption might be required. To alleviate these issues, a few novel hardware techniques for both aforementioned groups are proposed and their hardware results compared and validated by software simulations.

The Digital Forest: Mapping a Decade of Knowledge on Technological Applications for Forest Ecosystems

AbstractForest ecosystem resilience is of considerable interest worldwide, particularly given the climate crisis, biodiversity loss, and recent instances of zoonotic diseases linked to deforestation and forest loss. Novel, digital‐based technologies are also increasingly ubiquitous. We provide a more comprehensive understanding of how these new technologies are being used for forest management in different sectors and contexts, and discuss potential implications and future research needs for forestry researchers, managers, and policymakers. We carried out a literature database search and scoping review to collect peer‐reviewed articles from 2010 to 2020, and developed a forest‐technology classification to identify hardware and/or software technologies and techniques, methodology used, forest management application(s), spatial and temporal context, subsequent challenges and limitations, and opportunities. A qualitative analysis revealed a strong emphasis on remote sensing‐based innovations for forest monitoring, planning, and management, where machine‐learning techniques also play an important role in data collection, processing, and analysis. Data fusion approaches are also becoming more common, enabled by open‐source data sets and data sharing practices. More emerging technologies and applications include virtual/augmented environments for understanding human‐nature relationships and behavior patterns, automated workflows for forestry operations, and urban green infrastructure mapping and ecosystem services assessments via social media and mobile tracking applications. The continued adoption of digital‐based tools will likely bring about new research questions about forest ecosystems as dynamic social, ecological, and technological landscapes, and future work should more closely examine how forestry researchers, managers, and stakeholders can anticipate and adapt to both environmental and technological uncertainty change in a forest‐ecosystem context.

Internal mode control based coordinated controller for brushless doubly fed induction generator in wind turbines during fault conditions

Brushless double fed induction generator (BDFIG) based machines have gained popularity in wind turbine applications because of their easily accessible design. Low voltage ride through (LVRT) is critical for the reliable integration of renewable energy with the power grid. The refore, LVRT capability of brushless DFIGs makes them an attractive choice for maintaining voltage stability in grid. The existing works on BDFIG control suffer from two major drawbacks. Firstly, the methodology does not consider LVRT as a design metric, and secondly, these techniques do not have any means for coordinating between a machine side inverter (MSI) and grid side inverter (GSI). This results in sub-optimal controller design and eventually result in the violation of grid code requirements. To solve these issues, this paper proposes the use of brushless DFIGs in wind turbines using a control technique based on analytical modeling. Moreover, employing internal model control (IMC), the proposed technique can effectively coordinate the control between the MSI and GSI resulting in reduced oscillations, overshoots and improved stability under fault conditions. Furthermore, the simulation results for wind turbine generators show that the proposed scheme significantly improves the stability and compliance of grid codes ascompared to the existing hardware techniques.

PSYCHOLOGICAL COMPETENCE OF MILITARY LEADERS IN COMBAT CONDITIONS

The article examines military leaders’ psychological competence in combat. The peculiarities of psychological support for military-professional activities are determined. The psychological support of combat activities includes: mobile groups for psychological support and rehabilitation, educational work (psychological education), debriefings, mini-trainings, interactive communication in recreation camps, identification of service members affected by stress factors and timely first psychological aid, detection of service members with deviant behavior to send them for an extraordinary examination by psychiatrists, recommendations for evacuation from the zones of extreme (combat) conditions. To ensure psychological services for service members, organizational and legal efforts should be applied, but methodological support is also important, in particular available hardware, writing, computerized methods (questionnaires, tests, etc.) and various techniques, first of all - trainings, helping to solve professional and psychological problems. Depending on the tasks of psychological counseling, there are several psychological regulatory programs: trainings for self-regulation, targeted program relieving mental stress or fatigue, mobilizing the body’s reserve capabilities, general health program to normalize service members’ states and recover their body. Service members’ professional competence is considered by scientists as an integral characteristic that determines their ability to solve professional problems and fulfill typical professional tasks that arise in real professional situations, using knowledge, professional and life experience, values ​​and inclinations. Combat mission efficiency is determined by a person’s intellectual and physical capabilities, as well as their responsibility, independence, emotional and volitional hardiness, flexibility, ability to make decisions and interact with other military members under stress.

Neuromodulation Approaches in Parkinson's Disease Using Deep Brain Stimulation and Transcranial Magnetic Stimulation.

Parkinson's Disease (PD) is the second most common neurodegenerative disease, characterized by progressive motor (such as resting tremor, hypokinesia, postural instability) and non-motor symptoms (such as neuropsychiatric decline and autonomic dysfunction). Since its introduction in the late 1980s, deep brain stimulation (DBS) has revolutionized the treatment of PD. Initially used in patients' with advanced PD with either medically refractory motor symptoms or medication intolerance, DBS typically provides excellent improvement in motor symptoms. Indications for DBS have continued to expand, with demonstrated efficacy in early PD and essential tremor, and promising preliminary results in the treatment of epilepsy, psychiatric disease, and depression. Advancements in DBS hardware, programming, neuroimaging, and surgical techniques have led to progressive improvement in efficacy and safety profiles. Thanks to ongoing research into remote programming, adaptive DBS, new targets, and alternative interventions, such as transcranial magnetic stimulation, the opportunities for further improvements in DBS and neuromodulation are bright.

GSM Based Automatic Irrigation System

Agriculture plays a significant part in India's food production development. Agriculture in our nation is reliant on the monsoons, which provide little water. As a result, irrigation is utilised in agriculture. Water is given to plants in an irrigation system based on the soil type. An automated irrigation system based on ARMs and a GSM module is presented in this article. The proposed system is a machine-based system that automates land irrigation by integrating multiple software and hardware techniques to locate precise field information and offer instantaneous watering across the area. Sensors, an LCD display, GSM, and an ARM7LPC2148 CPU are all included. The LCD will be used for field display. A Subscriber Identity Module (SIM) will be included in the GSM module, and users will be able to interact using this SIM number. When the user activates or gives a command, the appropriate sensor activates and reads the current reading, sending the data to the same user's mobile phone and displaying them on the field's LCD panel. If necessary, the user will immediately take the appropriate action. We're utilising sensors to keep track of the field's condition. Temperature and level sensors are what those are. The ARM CPU is linked to all of these devices. GSM is utilised for communication, and we may communicate with the components via AT (attention) commands. We use motors for level sensing applications.

A variable neighborhood search algorithm for energy conscious task scheduling in heterogeneous computing systems

SummaryEnergy efficiency in heterogeneous computing systems has attracted increasing interests due to its economic and environmental impacts during recent decades. Based on power‐aware hardware techniques, such as dynamic voltage frequency scaling, efforts have been made through task scheduling to reduce the total energy consumption for executing a parallel application while maintaining its time efficiency. In this case, energy conscious task scheduling refers to a bi‐objective optimization that aims to minimize the overall completion time (makespan) and the total energy consumption, simultaneously. Existing energy conscious scheduling algorithms conduct energy optimization by means of slack reclamation or a trade‐off function. However, the performance of slack reclamation has been proved to be upper‐bounded and methods relying on trade‐off functions cannot guarantee bi‐objective optimization. In this article, an energy conscious task scheduling algorithm is proposed to tackle the above issues based on the framework of variable neighborhood search. Two neighborhood structures are designed to reduce makespan and the total energy consumption, respectively. Furthermore, a pruning technique is incorporated into the algorithm to accelerate the searching process. Extensive experimental results on both randomly generated and real‐world applications demonstrate that the proposed algorithm improves the time‐efficient schedules on average by 22.4% for the energy consumption and 1.2% for the makespan.