Advances In Hardware Research Articles

Deep Photonic Reservoir Computer for Speech Recognition.

Speech recognition is a critical task in the field of artificial intelligence (AI) and has witnessed remarkable advancements thanks to large and complex neural networks, whose training process typically requires massive amounts of labeled data and computationally intensive operations. An alternative paradigm, reservoir computing (RC), is energy efficient and is well adapted to implementation in physical substrates, but exhibits limitations in performance when compared with more resource-intensive machine learning algorithms. In this work, we address this challenge by investigating different architectures of interconnected reservoirs, all falling under the umbrella of deep RC (DRC). We propose a photonic-based deep reservoir computer and evaluate its effectiveness on different speech recognition tasks. We show specific design choices that aim to simplify the practical implementation of a reservoir computer while simultaneously achieving high-speed processing of high-dimensional audio signals. Overall, with the present work, we hope to help the advancement of low-power and high-performance neuromorphic hardware.

Precision Gait Stability Analysis Combining Hardware and Software

Maintaining stable gait and avoiding falls require effective control of the body’s center of mass (CoM) relative to the base of support (BoS), defined by the feet's contact points with the floor. Quadrupeds have an advantage over bipeds but share common spinal neural control mechanisms. Human walking poses stability challenges since the CoM often extends beyond the BoS. This project aims to monitor gait stability by integrating advanced hardware and software solutions. Machine learning algorithms, such as Random Forest, will be employed to predict gait instability by capturing foot positions and sensor data. Inertial measurement sensors will track stability changes, and hardware outputs will be validated against software predictions for accuracy. A mobile app developed using React Native will display predicted parameters and indicate gait instability (yes/no). In case of detected instability, a wearable buzzer will alert the user. This system promises a more accurate solution for monitoring gait stability compared to existing models, by combining real-time data collection and sophisticated algorithms

Speeding up the Training of Neural Networks with the One-Step Procedure

In the last decade, research and corporate have shown a dramatically growing interest in the field of machine learning, mostly due to the performances of deep neural networks. These increasingly complex architectures solved a wide range of problems. However, training these sophisticated architectures require many computation on advanced hardware. With this paper, we introduce a new approach based on the One-Step procedure that may fasten their training. In this procedure, an initial guess estimator is computed on a subsample that is then improved with only one step of the Newton gradient descent on the whole dataset. To show the efficiency of this framework, we consider regression and classification tasks using simulated and real datasets. We consider classic architectures, namely multi-layer perceptrons and show, on our examples, that the One-Step procedure is often halving the computation time to train the neural networks while preserving the performances.

Remote operation of the DIII-D National Fusion Facility

Full remote scientific operation of the DIII-D National Fusion Facility is now possible through significant advances in the computer science hardware and software infrastructure made over the last decade. Capabilities around information visualization, data movement, and communication have all been enhanced. The level of capability deployed to remotely operate DIII-D required an infrastructure advancement over what had previously been achieved in the fusion community. The large quantity of real-time data that is automatically displayed on DIII-D’s control room screens can now be visualized by remote participants via web-based applications. New audio/video solutions using the VoIP and instant messaging application Discord have been implemented to mimic the dynamic and ad-hoc scientific conversations that are critical in successfully operating an experimental campaign. Discord’s ability for a user to rapidly move between audio channels, text with images, and share screens is a significant enhancement over traditional videoconferencing tools. In addition, multiple combinations of broadcast audio are made available via a web-based application to allow remote participants to simultaneously listen to general announcements/sounds while conducting their own specific conversations. Secure methodologies have been put into place to allow remote control of hardware including DIII-D’s plasma control system application. Secure methods also included the ability of the on-site team to closely coordinate their work with remote team members which has been enhanced through extensions to the wireless network and the use of tablet computers for audio/video/screen sharing. However, no amount of software can fully replace the need for ‘hands on hardware.’ This infrastructure was severely stress tested during the COVID-19 pandemic where occupancy of the DIII-D control room was restricted. Operational efficiency during the pandemic, measured in discharges per hour, remained high (3.8 ± 0.8) compared to values obtained pre-pandemic (3.7 ± 0.8).

Current Aspects of Medical Rehabilitation of Patients with Cancer Related Lymphedema of Extremities: a Narrative Review

INTRODUCTION. Over the past decades, in developed countries and in the Russian Federation, there has been an increase in the number of patients with secondary lymphedema who have undergone surgical treatment with dissection of regional lymph nodes and radiation therapy for various forms of gynecological cancer in women and prostate cancer in men, as well as breast cancer. AIM. Search and analysis of the results of previously published randomized controlled trials (RCTs) of the effectiveness of various non-drug rehabilitation methods in patients with lymphedema of the extremities who underwent radical treatment for cancer of various locations. MATERIALS AND METHODS. A search was conducted and studied publications in international scientific peer-reviewed publications (PEDro Database) on medical rehabilitation for cancer treatment-related lymphedema (LSPR) for the period from 1996 to December 2023 using the keywords “lymphedema”, “rehabilitation”. 145 RCTs focused on medical rehabilitation and conservative treatment of LSPR. DISCUSSION. Lifestyle correction and psychosocial interventions are recommended to improve the quality of life of patients. Studies have found that early activation, various physical exercises with a slow increase in loads under the supervision of a physical therapist are safe and help increase endurance, strength and range of motion in a limb with lymphatic edema. The clinical effectiveness of therapeutic exercises in the pool has been proven, manifested in the reduction of edema in patients with LSPR. The effectiveness of compression products with Velcro technology as an alternative to rigid low-stretch bandages in patients with LSPR has been confirmed. The anti-edematous and anti-inflammatory effects of low-intensity laser therapy and magnetic therapy in the rehabilitation of patients with LSLR have been proven. Alternating pneumatic compression (APC) is recommended as an adjuvant treatment in addition to comprehensive decongestant therapy. Preference should be given to the use of technologies of advanced hardware lymphatic drainage methods that imitate manual techniques. CONCLUSION. To improve the physical and socio-psychological functioning of patients with LSPR, an integrated approach is required, including lifestyle changes, psychological correction, compression therapy, modern exercise therapy techniques, and safe physiotherapeutic technologies. Conducted scientific research indicates the high effectiveness of the use of manual and hardware lymphatic drainage techniques, while the use of PPC is more economical and accessible, and does not require the involvement of specialists who know the expensive method of manual lymphatic drainage.

Speculative computing for AAFM solutions in large-scale product configurations.

Parallel computing is a current algorithmic approach to looking for efficient solutions; that is, to define a set of processes in charge of performing at the same time the same task. Advances in hardware permit the massification of accessibility to and applications of parallel computing. Nonetheless, some algorithms include steps that require or depend on the results of other steps that cannot be parallelized. Speculative computing allows parallelizing those tasks and reviewing different execution flows, which can involve executing invalid steps. Speculative computing solutions should reduce those invalid flows. Product configuration refers to selecting features from a set of available options respecting some configuration constraints; a not complex task for small configurations and models, but a complex one for large-scale scenarios. This article exemplifies a videogame product line feature model and a few configurations, valid and non-valid, respectively. Configuring products of large-scale feature models is a complex and time-demanding task requiring algorithmic solutions. Hence, parallel solutions are highly desired to assist the feature model product configuration tasks. Existing solutions follow a sequential computing approach and include steps that depend on others that cannot be parallelized at all, where the speculative computing approach is necessary. This article describes traditional sequential solutions for conflict detection and diagnosis, two relevant tasks in the automated analysis of feature models, and how to define their speculative parallel version, highlighting their computing improvements. Given the current parallel computing world, we remark on the advantages and current applicability of speculative computing for producing faster algorithmic solutions.

A Greenhouse Gas Footprint Analysis of Advanced Hardware Technologies in Connected Autonomous Vehicles

Greenhouse gas emissions are a critical concern for China’s automotive industry, especially for passenger cars due to their high sales’ volume. Recently, the trend towards connected and autonomous driving vehicles has been significant in the passenger car market. However, the impact of these systems on the life cycle emissions of vehicles remains unclear. This paper focuses on system function levels from driver assistance to full driving automation and studies their life cycle greenhouse gas emissions. This research establishes a component list for the hardware system and a material inventory. Then, this paper reveals significant differences in total system emissions at these technology levels, 540.1 kg for primary, 1318.7 kg for medium, and 2279.2 kg for advanced systems. Despite this difference, the total is less than 7.23% of the total vehicle emissions. To further reduce this portion of GHG emissions, it is recommended that vehicles favor millimeter-wave radar over solid-state LiDAR in their sensing system hardware, coupled with cameras as the primary sensing element. In addition, Intelligent Hardware Systems are not recommended for internal combustion engine passenger cars for optimal balance between functionality and environmental impact.

A Novel Frame-Selection Metric for Video Inpainting to Enhance Urban Feature Extraction.

In our digitally driven society, advances in software and hardware to capture video data allow extensive gathering and analysis of large datasets. This has stimulated interest in extracting information from video data, such as buildings and urban streets, to enhance understanding of the environment. Urban buildings and streets, as essential parts of cities, carry valuable information relevant to daily life. Extracting features from these elements and integrating them with technologies such as VR and AR can contribute to more intelligent and personalized urban public services. Despite its potential benefits, collecting videos of urban environments introduces challenges because of the presence of dynamic objects. The varying shape of the target building in each frame necessitates careful selection to ensure the extraction of quality features. To address this problem, we propose a novel evaluation metric that considers the video-inpainting-restoration quality and the relevance of the target object, considering minimizing areas with cars, maximizing areas with the target building, and minimizing overlapping areas. This metric extends existing video-inpainting-evaluation metrics by considering the relevance of the target object and interconnectivity between objects. We conducted experiment to validate the proposed metrics using real-world datasets from Japanese cities Sapporo and Yokohama. The experiment results demonstrate feasibility of selecting video frames conducive to building feature extraction.

Global marine phytoplankton dynamics analysis with machine learning and reanalyzed remote sensing.

Phytoplankton are the world's largest oxygen producers found in oceans, seas and large water bodies, which play crucial roles in the marine food chain. Unbalanced biogeochemical features like salinity, pH, minerals, etc., can retard their growth. With advancements in better hardware, the usage of Artificial Intelligence techniques is rapidly increasing for creating an intelligent decision-making system. Therefore, we attempt to overcome this gap by using supervised regressions on reanalysis data targeting global phytoplankton levels in global waters. The presented experiment proposes the applications of different supervised machine learning regression techniques such as random forest, extra trees, bagging and histogram-based gradient boosting regressor on reanalysis data obtained from the Copernicus Global Ocean Biogeochemistry Hindcast dataset. Results obtained from the experiment have predicted the phytoplankton levels with a coefficient of determination score (R2) of up to 0.96. After further validation with larger datasets, the model can be deployed in a production environment in an attempt to complement in-situ measurement efforts.

Adaptive temporal compression for reduction of computational complexity in human behavior recognition

The research on video analytics especially in the area of human behavior recognition has become increasingly popular recently. It is widely applied in virtual reality, video surveillance, and video retrieval. With the advancement of deep learning algorithms and computer hardware, the conventional two-dimensional convolution technique for training video models has been replaced by three-dimensional convolution, which enables the extraction of spatio-temporal features. Specifically, the use of 3D convolution in human behavior recognition has been the subject of growing interest. However, the increased dimensionality has led to challenges such as the dramatic increase in the number of parameters, increased time complexity, and a strong dependence on GPUs for effective spatio-temporal feature extraction. The training speed can be considerably slow without the support of powerful GPU hardware. To address these issues, this study proposes an Adaptive Time Compression (ATC) module. Functioning as an independent component, ATC can be seamlessly integrated into existing architectures and achieves data compression by eliminating redundant frames within video data. The ATC module effectively reduces GPU computing load and time complexity with negligible loss of accuracy, thereby facilitating real-time human behavior recognition.

Development of Micro/Nano Pattern Arrays with Grating-Based Periodic Structures using the Direct Laser Lithography System

Introduction: This research delves into utilizing the Direct Laser Lithography System to produce micro/nanopattern arrays with grating-based periodic structures. Initially, refining the variation in periodic structures within these arrays becomes a pivotal pursuit. This demands a deep comprehension of how structural variation aligns with specific applications, particularly in photonics and material science. Method: Advancements in hardware, software, or process optimization techniques hold potential for reaching this objective. Using an optical beam, this system enables the engraving of moderate periodic and quasi-periodic structures, enhancing pattern formation in a three-dimensional environment. Through cost-effective direct-beam interferometry systems utilizing 405 nm GaN and 290 to 780 nm AlInGaN semiconductor laser diodes, patterns ranging from in period were created, employing 300 nm gratings. Result: The system's cost-efficiency and ability to achieve high-resolution permit the creation of both regular and irregular grating designs. By employing an optical head assembly from a bluray disc recorder, housing a semiconductor laser diode and an objective lens with an NA of 0.85, this system displays promising potential in progressing the fabrication of micro/nanopattern arrays. Conclusion: Assessing their optical, mechanical, and electrical properties and exploring potential applications across varied fields like optoelectronics, photovoltaics, sensors, and biomedical devices represent critical strides for further exploration and advancement.

Quantum Fourier networks for solving parametric PDEs

Many real-world problems, like modelling environment dynamics, physical processes, time series etc involve solving partial differential equations (PDEs) parameterised by problem-specific conditions. Recently, a deep learning architecture called Fourier neural operator (FNO) proved to be capable of learning solutions of given PDE families for any initial conditions as input. However, it results in a time complexity linear in the number of evaluations of the PDEs while testing. Given the advancements in quantum hardware and the recent results in quantum machine learning methods, we exploit the running efficiency offered by these and propose quantum algorithms inspired by the classical FNO, which result in time complexity logarithmic in the number of evaluations and are expected to be substantially faster than their classical counterpart. At their core, we use the unary encoding paradigm and orthogonal quantum layers and introduce a new quantum Fourier transform in the unary basis. We propose three different quantum circuits to perform a quantum FNO. The proposals differ in their depth and their similarity to the classical FNO. We also benchmark our proposed algorithms on three PDE families, namely Burgers’ equation, Darcy’s flow equation and the Navier–Stokes equation. The results show that our quantum methods are comparable in performance to the classical FNO. We also perform an analysis on small-scale image classification tasks where our proposed algorithms are at par with the performance of classical convolutional neural networks, proving their applicability to other domains as well.

FLOATING DOCK ANALYSIS SUBJECT TO DYNAMIC WAVE LOADS USING COMPUTATIONAL FLUID DYNAMICS

Floating dock systems are widely used in marine and waterfront facilities. Environmental loading and movements applied on these systems can be challenging to predict due to the complex interaction between structures and fluids under dynamic wave loads. Design of floating dock systems requires analysis of wave induced forces, relative movements of the dock, effects of connections, and subsequent loads and deflections in piles or mooring line anchors. Supporting structures such as piles or mooring lines can have significant impacts on load distribution within a floating dock system and shall be analyzed carefully during design. Recent advances in computer hardware and multi-physics computational fluid dynamics (CFD) technology have permitted transient dynamic wave analysis of entire floating dock system, to explicitly consider fluid-structure interaction in the analysis and design of floating dock systems. In this study, a CFD technology is applied to investigate the dynamic system characteristics of a floating dock system supported by both piles and mooring lines. CFD analysis is based on software package FLOW-3D® Hydro. Floating dock, piles, and mooring lines are modeled, and analysis results are presented. It is shown to be useful for analyzing floating docks which are typically too complex to accurately evaluate using empirical methods.

FLOATING DOCK ANALYSIS SUBJECT TO DYNAMIC WAVE LOADS USING COMPUTATIONAL FLUID DYNAMICS

Floating dock systems are widely used in marine and waterfront facilities. Environmental loading and movements applied on these systems can be challenging to predict due to the complex interaction between structures and fluids under dynamic wave loads. Design of floating dock systems requires analysis of wave induced forces, relative movements of the dock, effects of connections, and subsequent loads and deflections in piles or mooring line anchors. Supporting structures such as piles or mooring lines can have significant impacts on load distribution within a floating dock system and shall be analyzed carefully during design. Recent advances in computer hardware and multi-physics computational fluid dynamics (CFD) technology have permitted transient dynamic wave analysis of entire floating dock system, to explicitly consider fluid-structure interaction in the analysis and design of floating dock systems. In this study, a CFD technology is applied to investigate the dynamic system characteristics of a floating dock system supported by both piles and mooring lines. CFD analysis is based on software package FLOW-3D® Hydro. Floating dock, piles, and mooring lines are modeled, and analysis results are presented. It is shown to be useful for analyzing floating docks which are typically too complex to accurately evaluate using empirical methods.

IoT Based Smart Parking System

Abstract: The IoT-based smart parking system represents an innovative approach to alleviating the prevalent issues of parking shortages and traffic congestion in modern cities. By integrating advanced hardware components and software applications, this system aims to enhance transportation efficiency and sustainability while improving commuter experiences. At its core lies the ESP32 microcontroller, orchestrating data processing from various sensors including parking and traffic IR sensors. These sensors enable real-time monitoring of parking availability and traffic flow, with data seamlessly transmitted to an LCD display for immediate visualization. Additionally, commuters can access this information remotely through a mobile application or web portal, empowering them to make informed travel decisions. The system's standout feature, the smart parking allocation system (SPAS), allows users to locate and reserve parking spaces in advance, effectively reducing the time spent searching for spots and consequently mitigating traffic congestion. Overall, the integration of IoT technology into transportation infrastructure offers a promising solution to the challenges of urban mobility, fostering efficiency, sustainability, and improved commuter satisfaction.

Methodology for Robotic In Vitro Testing of the Knee.

The knee joint plays a pivotal role in mobility and stability during ambulatory and standing activities of daily living. Increased incidence of knee joint pathologies and resulting surgeries has led to a growing need to understand the kinematics and kinetics of the knee. In vivo, in silico, and in vitro testing domains provide researchers different avenues to explore the effects of surgical interactions on the knee. Recent hardware and software advancements have increased the flexibility of in vitro testing, opening further opportunities to answer clinical questions. This paper describes best practices for conducting in vitro knee biomechanical testing by providing guidelines for future research. Prior to beginning an in vitro knee study, the clinical question must be identified by the research and clinical teams to determine if in vitro testing is necessary to answer the question and serve as the gold standard for problem resolution. After determining the clinical question, a series of questions (What surgical or experimental conditions should be varied to answer the clinical question, What measurements are needed for each surgical or experimental condition, What loading conditions will generate the desired measurements, Do the loading conditions require muscle actuation) must be discussed to help dictate the type of hardware and software necessary to adequately answer the clinical question. Hardware (type of robot, load cell, actuators, fixtures, motion capture, ancillary sensors) and software (type of coordinate systems used for kinematics and kinetics, type of control) can then be acquired to create a testing system tailored to the desired testing conditions. Study design and verification steps should be decided upon prior to testing to maintain the accuracy of the collected data. Collected data should be reported with any supplementary metrics (RMS error, dynamic statistics) that help illuminate the reported results. An example study comparing two different ACL reconstruction techniques is provided to demonstrate the application of these guidelines. Adoption of these guidelines may allow for better interlaboratory result comparison to improve clinical outcomes.

The Burden for High-Quality Online Data Collection Lies With Researchers, Not Recruitment Platforms.

A recent article in Perspectives on Psychological Science (Webb & Tangney, 2022) reported a study in which just 2.6% of participants recruited on Amazon's Mechanical Turk (MTurk) were deemed "valid." The authors highlighted some well-established limitations of MTurk, but their central claims-that MTurk is "too good to be true" and that it captured "only 14 human beings . . . [out of] N = 529"-are radically misleading, yet have been repeated widely. This commentary aims to (a) correct the record (i.e., by showing that Webb and Tangney's approach to data collection led to unusually low data quality) and (b) offer a shift in perspective for running high-quality studies online. Negative attitudes toward MTurk sometimes reflect a fundamental misunderstanding of what the platform offers and how it should be used in research. Beyond pointing to research that details strategies for effective design and recruitment on MTurk, we stress that MTurk is not suitable for every study. Effective use requires specific expertise and design considerations. Like all tools used in research-from advanced hardware to specialist software-the tool itself places constraints on what one should use it for. Ultimately, high-quality data is the responsibility of the researcher, not the crowdsourcing platform.

Evaluating the use of smartphones for processing computational tasks: "A case study for the annotation of prokaryotic genomes"

Currently, there is a growing dependence on smartphones in the daily lives of thousands of people, noting that the number of users continues to rise, surpassing the number of computers in use globally. In addition, the performance of smartphone hardware has become an essential factor when choosing these devices, along with price, brand and operating system. Constant innovations in smartphones have made these devices increasingly powerful, even rivaling the performance of many computers. The example of the Samsung S23 5G, which has features comparable to a low-performance notebook, illustrates the advancement of the hardware in these devices. Despite the growing demand for advanced hardware in smartphones, many users may not fully understand the technical specifications of the processors. The article proposes an evaluation of the use of this hardware in the execution of specific tasks, with a focus on the annotation of prokaryotic genomes. The methodology of the study involved selecting popular smartphones from Samsung and Motorola, choosing the Android operating system due to its efficiency in resource management, using the Prokka tool for genomic annotation due to its minimal resource requirements and using Termux, a terminal emulator that allows users to run a full Linux distribution on Android devices. To evaluate performance, the article uses the Linux 'time' command to measure execution time, memory consumption, CPU utilization and other relevant metrics

Fire Fighting Robot with SMS and Call Alert

In emergency situations such as fire outbreaks, timely intervention is crucial to minimize damage and ensure safety. Traditional fire-fighting methods often face challenges in reaching remote or hazardous locations promptly. To address this gap, we present a pioneering solution in the form of a "Fire Fighting Robot with SMS Alert." Our project integrates advanced hardware and software components to develop a robust system capable of autonomously detecting smoke and fire, and alerting the relevant authorities through SMS. The hardware design involves equipping a mobile platform with hit-sensing sensors for detecting impacts and smoke. Software development utilizes the Arduino IDE to program the Arduino UNO microcontroller, enabling efficient control and communication. Through meticulous implementation and rigorous testing, our project demonstrates promising results. The robot effectively senses fire and smoke, triggering immediate SMS alerts to designated recipients. Additionally, upon completion of its tasks, the robot initiates a call to the owner for further instructions, ensuring comprehensive response capabilities. This paper highlights the significance of our contribution to the field of emergency response systems, offering a practical and innovative solution to enhance safety and mitigate risks in fire incidents. The outcomes of our project provide valuable insights for future developments in Electronics and Telecommunication Engineering, with potential applications in various domains beyond fire-fighting scenarios. Key Words: Fire Fighting Robot, SMS Alert, Emergency Response, Robotics, Arduino, Hardware Design, Software Development, etc.

Business chatbots with deep learning technologies: state-of-the-art, taxonomies, and future research directions

With the support of advanced hardware and software technology, Artificial Intelligence (AI) techniques, especially the increasing number of deep learning algorithms, have spawned the popularization of online intelligent services and accelerated the contemporary development and applications of chatbot systems. The promise of providing 24/7 uninterrupted business services and minimizing workforce costs has made business chatbots a hot topic due to the impact of the pandemic. It has attracted considerable attention from academic researchers and business practitioners. However, a thorough technical review of advanced chatbot technologies and their relevance and applications to various business domains is rare in the literature. The main contribution of this review article is the critical analysis of various chatbot development approaches and the underlying deep learning computational methods in the context of some business applications. We first conceptualize current business chatbot architectures and illustrate the technical characteristics of two common structures. Next, we explore the mainstream deep learning technologies in chatbot design from the perspective of computational methods and usages. Then, we propose a new framework to classify chatbot construction architectures and differentiate the traditional retrieval-based and generation-based chatbots in terms of the modern pipeline and end-to-end structures. Finally, we highlight future research directions for business chatbots to enable researchers to devote their efforts to the most promising research topics and commercial scenarios and for practitioners to benefit from realizing the trend in business chatbot development and applications.