Hardware Components Research Articles

An IoT Based Smart Dustbin System

All human being are throws waste in dustbin or some other different places. The waste are plastics, degradable and non-degradable. All people are trying to put the waste in dustbin or garbage bin only. In cities, there are many public places where we see that dustbin or garbage bin are placed but there are overflowing. This create unhygienic condition in the surrounding. And it also create some serious diseases. At a same time, an odor extends throughout the city, and degrading the environment. Recycling bin is really a waste management processing, but they are limited space in a garbage bin, it does not require extra waste. Waste disposal is an efficient method of eliminating garbage disposed in commercial settings such as businesses, classrooms, colleges, shopping centers, and other public areas. We have to design the project, where the dustbin is fulled or not and the waste level of the trash bin is measured. The NodeMCU and the ultrasonic sensor is a hardware component for measuring the garbage bin. The software component is an IFTTT Webhook, and it is used to receiving a notification.The main concept of this project is when more than 70 percent of the garbage bin is filled, the IFTTT Webhook sends the notification and we receive the email.

DESIGN OF A CAMERA-ENABLED MOBILE ROBOT FOR IN-PIPE INSPECTION

The role of pipeline infrastructure is crucial in modern society, as it transports necessary resources such as water, gas, and oil. It is essential to prioritize the maintenance of these pipelines to prevent leaks, environmental harm, and economic losses. Traditional inspection techniques, which involve manual inspection, can be inefficient, laborious, and susceptible to mistakes. To overcome these obstacles, this article introduces a camera-equipped mobile robot specifically created for automated crack detection in pipelines. The robot, which is equipped with a high-resolution camera and advanced image processing algorithms, navigates inside pipelines on its own to capture detailed images of the pipe walls. These images are then processed using computer vision techniques to identify and categorize potential cracks. The algorithms utilize machine learning models that have been trained on a dataset of labelled crack images, enabling accurate and efficient crack detection. This paper provides a comprehensive overview of the robot system's design, development, and validation, detailing the critical hardware components such as the mobility platform, camera system, and sensor integration, as well as the software architecture, which encompasses image processing and machine learning frameworks. Field studies demonstrate the robot's ability to effectively detect cracks across various pipe materials and under diverse environmental conditions. Results indicate that the automated system not only improves inspection efficiency and accuracy but also significantly reduces the need for human intervention, thereby enhancing overall pipeline safety Keywords: pipeline inspection, crack detection, mobile robot, image processing, computer vision.

The Efficiency Assessment of the Blockchain Networks Adaptive Algorithm as Part of a Multi-Consensus System on Communication Networks

The relevance of the approaches and solutions proposed in the work is due to the desire for distributed computing and decentralization of system modules using blockchain technology in order to increase the autonomy, security and independence of system components. The implementation of blockchain technology for decentralization is hampered by the lack of flexibility in terms of the algorithm for making a single decision in the asynchronous system ‒ consensus, which affects the amount of generated network traffic and the requirements for the hardware component. Due to the changing nature of the transmitted traffic, the load on the communication channel, the time of day of traffic transmission, the equipment, and the network protocols used, the development of a single universal consensus algorithm is not possible, since it depends on a very wide range of variable and specific parameters characteristic of different tasks. The introduction of adaptation mechanisms to current network conditions and a change in the consensus algorithm without losing information and with a satisfactory level of delays would provide sufficient flexibility for further implementation in existing telecommunication systems. The purpose of this paper is to evaluate the effectiveness of the proposed adaptive algorithm for choosing a consensus for blockchain networks.The essence of the presented algorithm is to change the consensus algorithm on a section of the blockchain network upon reaching certain network conditions, which allows regulating the amount of generated load on the communication network to reduce the loss of transaction blocks and the subsequent delay in their processing. The proposed efficiency assessment model is based on mathematical modeling methods, differential equation analysis, queueing theory and network graphs. The analysis of the results showed the efficiency of the proposed model when comparing the results of the analytical calculation and the experiment. The scientific novelty of the proposed approach is to developing an adaptive consensus selection algorithm, as opposed to developing a universal consensus algorithm, and the efficiency assessment model of the proposed algorithm on the communication network, taking into account a number of parameters characterizing the devices and the section of the communication network.The theoretical significance is in the universality of the proposed efficiency assessment model for calculating the synchronization speed of all nodes of the blockchain network with specified network parameters.The practical significance of the proposed algorithm and efficiency assessment method lies in the formation of new approaches and opportunities for integrating blockchain technology into modern communication networks, abstracting from the problem of consensus selection in changing conditions of the communication network section.

Modular shadowgraph imaging for zooplankton ecological studies in diverse field and mesocosm settings

AbstractVarious iterations of shadowgraph imaging have been used to quantify zooplankton in situ with high spatial resolution. Because these systems can image relatively large volumes of water, they are especially useful for resolving less common meso‐ or macrozooplankton taxa (< 50 ind. m−3), such as larval fishes and gelatinous animals. However, larger volume imagers are typically integrated with heavy towed vehicles and deployed from research vessels, which introduces high costs and limits sampling approaches. Here we demonstrate that versatile configurations of shadowgraph imaging, including modular benchtop, handheld, and towed, compact vehicle systems (along with customizable software), allow for tailoring sampling to a variety of marine and freshwater settings (including mesocosms). These systems encompass a suite of possible architectures, designed for adapting the imaging depth of field, acquisition rates, sensor configuration, and deployment method to fit a wide range of sampling protocols, with high vertical resolution (~ 5 cm) and adequate taxonomic capabilities for > 0.5 mm organisms. The benchtop system facilitates an interactive approach to observe and quantify zooplankton behaviors and optical properties. Video footage from the benchtop system generates thousands of regions of interest min−1 for target organisms with variable orientations and swimming postures. When used in conjunction with in situ imaging, the benchtop system can build large machine learning training libraries targeted toward rare or morphologically diverse zooplankton, which often includes the larval stages of economically valuable taxa. These modular hardware and software components increase affordability and versatility while broadening the scope of scientific questions addressed by plankton imaging systems.

Analysing the radiation reliability, performance and energy consumption of low-power SoC through heterogeneous parallelism

This study focuses on the low-power Tegra X1 System-on-Chip (SoC) from the Jetson Nano Developer Kit, which is increasingly used in various environments and tasks. As these SoCs grow in prevalence, it becomes crucial to analyse their computational performance, energy consumption, and reliability, especially for safety-critical applications. A key factor examined in this paper is the SoC’s neutron radiation tolerance. This is explored by subjecting a parallel version of matrix multiplication, which has been offloaded to various hardware components via OpenMP, to neutron irradiation. Through this approach, this researcher establishes a correlation between the SoC’s reliability and its computational and energy performance. The analysis enables the identification of an optimal workload distribution strategy, considering factors such as execution time, energy efficiency, and system reliability. Experimental results reveal that, while the GPU executes matrix multiplication tasks more rapidly and efficiently than the CPU, using both components only marginally reduces execution time. Interestingly, GPU usage significantly increases the SoC’s critical section, leading to an escalated error rate for both Detected Unrecoverable Errors (DUE) and Silent Data Corruptions (SDC), with the CPU showing a higher average number of affected elements per SDC.

Mobile-Controlled River Cleaning Robot: A Review

This project focuses more on "Remote Controlled Unmanned River Cleaning Bot". In India water pollution is increasing day by day, Water pollution is a significant environmental issue, with rivers often acting as the primary carriers of waste materials such as plastics, debris, and other pollutants. Current manual cleaning methods are inefficient, costly, and other pollutants. Current manual cleaning methods are inefficient, costly, and unable to access certain areas of the water. This paper presents the development of a mobile-controlled river cleaning robot designed to tackle these challenges. The robot can be remotely operated via a mobile application, allowing for precise control of its navigation and cleaning operations. It is equipped with waste collection mechanisms and sensors to detect and remove debris from the river’s surface. The study covers the design, hardware and software components, and testing of the robot in real-world conditions. Results indicate that the mobile-controlled robot offers an effective, cost-effcient, and environmentally sustainable solution for maintaining cleaner waterways. This innovation has the potential to revolutionize river cleaning processes and significantly reduce water pollution.

Cracking the Core: Hardware Vulnerabilities in Android Devices Unveiled

As Android devices become more prevalent, their security risks extend beyond software vulnerabilities to include critical hardware weaknesses. This paper provides a comprehensive and systematic review of hardware-related vulnerabilities in Android systems, which can bypass even the most sophisticated software defenses. We compile and analyze an extensive range of reported vulnerabilities, introducing a novel categorization framework to facilitate a deeper understanding of these risks, classified by affected hardware components, vulnerability type, and the potential impact on system security. The paper addresses key areas such as memory management flaws, side-channel attacks, insecure system-on-chip (SoC) resource allocation, and cryptographic vulnerabilities. In addition, it examines feasible countermeasures, including hardware-backed encryption, secure boot mechanisms, and trusted execution environments (TEEs), to mitigate the risks posed by these hardware threats. By contextualizing hardware vulnerabilities within the broader security architecture of Android devices, this review emphasizes the importance of hardware security in ensuring system integrity and resilience. The findings serve as a valuable resource for both researchers and security professionals, offering insights into the development of more robust defenses against the emerging hardware-based threats faced by Android devices.

Pattern-based Remote Switch for Domestic and Technological Operations

Abstract: This research explores the development of a remote-control system for domestic appliances using user-drawn patterns on a grid, aiming to provide an intuitive and accessible control method. The system is designed around a 3x3 grid of infrared (IR) LEDs and receivers that detect patterns drawn by the user with a finger or stylus. Each grid coordinate is mapped to a specific action, such as turning lights on or off or adjusting the speed of a fan. The detected patterns are transmitted to a NodeMCU ESP8266 microcontroller, which decodes the patterns and activates the corresponding appliance through Wi-Fi commands and relays. The innovative aspects of this system include its simple and intuitive interaction method, allowing users to define custom patterns for various actions, which enhances personalized control. The pattern-based interaction is particularly beneficial for individuals with limited dexterity or mobility, offering an accessible alternative to traditional remote controls. This project demonstrates the potential of combining hardware components, such as IR LEDs and receivers, with embedded C programming to create a user-friendly and efficient home automation solution. The proposed system not only simplifies the user experience but also provides a flexible and customizable interface for controlling domestic appliances.

Assessment of digital literacy and user experience in patients with cochlear implants

Abstract Introduction The cochlear implant (CI) is the treatment of choice for people affected by deafness or profound hearing loss. Patients with cochlear implants (PwCIs) can enhance their hearing outcomes using digital hearing training programs (dHTPs). Current technological trends leans towards personalized, AI-driven dHTPs, aiming to optimize user experience (UX) and adherence. This study aims to investigate the digital literacy skills and UX of PwCI before and after testing a prototype of a dHTP with an AI-driven design (TraiCI). Methods Two research-specific questionnaires developed by the Hearing Center Düsseldorf (QDL and QUX) were used to assess digital literacy skills and user experience in TraiCI. Additionally, the User Experience Questionnaire (UEQ) was employed to evaluate general user experience. The UEQ assesses software quality across dimensions such as transparency and stimulation using paired opposites. Results 22 outpatient PwCIs the Hearing Center Düsseldorf with unilateral or bilateral CI (mean age 54 years; SD = 15) were included. In QDL, PmCI demonstrated high levels of digital literacy skills. The majority of PmCI (80.6%) reported prior experience with dHTP. Usage of these programs occurred daily to multiple times per week within the first six months after CI treatment, with session durations predominantly ranging from 10 to 30 minutes. QUX data indicated a smooth initiation process (70.8%) and seamless login experience (95.5%). The UEQ addressed various aspects of user experience, with ‘transparency’ receiving the highest scores (MW = 1.9; SD = 0.6), while ‘stimulation’ received the least positive ratings (MW = 0.0; SD = 2.1). Conclusions The results show a high degree of digital literacy among PwCIs. They encountered minimal challenges in operating hardware and software components. Further refinement is necessary to align with PwCI’s expectations and maximize benefits. Early and iterative involvement of PwCIs in future projects could optimize outcomes. Key messages • The study highlights the proficient digital literacy skills of cochlear implant patients (PwCIs). • Further refinement of dHTPs is needed to better align with PwCIs’ expectations and maximize benefits, emphasizing the importance of iterative involvement for optimized outcomes.

METHODS AND PRACTICES OF TRAINING AND APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING VARIOUS DEFECTS ON THE SURFACES OF BUILDING STRUCTURES

The subject of research in this article is the practice of implementing and applying types of artificial intelligence – convolutional neural networks and data science - to detect various defects on the surfaces of building structures. The purpose of the study is to identify the advantages and disadvantages of implementing and using convolutional neural network technologies together with other software and hardware components to automate traditional methods of monitoring the technical condition of the external surfaces of buildings and structures. The article solves the following tasks: to substantiate the effectiveness of implementation of convolutional neural network technologies and data science methods; to apply them in practice with software and hardware technologies to automate traditional methods of performing work on detecting various defects on the surfaces of building structures in the construction and operation of buildings and structures; to show the problems and shortcomings of these methods and technologies. To solve the tasks, an integrated approach was used with the use of general scientific and special research methods (analysis, explanation, generalisation, comparison). The following results have been obtained: the features that affect the accuracy of the analysis of the collected data used by neural convolutional network technologies to detect various defects on the surfaces of building structures have been identified; practices and methods for more efficient and accurate application of this technology have been reflected. Conclusions: the study allowed to identify the practical opportunities and problems that exist in this technology; recommendations for the effective use of this technology were developed; factors influencing the more efficient use of this technology in industry were identified.

Robotic Assembly – Mobile Platform for Construction Field

"Construction sites often involve the manual transportation of heavy objects, posing risks to workers' safety and efficiency. In this paper, it was proposed a design, development, and evaluation of a service robot specifically created for carrying heavy objects in the construction field. The robot is equipped with robust locomotion capabilities, intelligent perception systems, and efficient control mechanisms to navigate through complex environments and handle various types of loads. It was presented the technical design of the robot, including its hardware components, sensing modalities, and software architecture. Furthermore, it was discussed the experimental validation of the robot's performance in real-world construction scenarios, highlighting its effectiveness in improving productivity, reducing labor costs, and enhancing workplace safety. Through this research, the aim is to demonstrate the potential of service robots as valuable assets in the construction industry, paving the way for future advancements in robotic assistance technologies. Keywords: human-robot collaboration, manipulation, autonomous systems, object transportation, mechanics."

Pi Class – A Revolutionary Step Forward in Hybrid Class Management System

The COVID-19 pandemic has caused widespread disruptions globally, resulting in a state of health emergency with numerous deaths reported and an overall implementation of quarantine and isolation. Strict lockdowns have been implemented to curb the spread of the virus, requiring social distancing and limiting physical interactions. These measures had far-reaching impacts on all aspects of life, including education. Education was one of the most affected sectors, with difficulty delivering quality education in schools, colleges, and universities. It was hard to provide quality education, a basic need of humanity. In this research paper, we propose an adequate solution to overcome the difficulties of maintaining educational quality during and after the COVID-19 pandemic and its multiple variants. The proposed solution is a hybrid model for an autonomous lecture recording system that facilitates students to attend physical classes and attend lectures virtually. The solution proposed is a cost-effective and convenient way for students to access lectures. The application involves hardware and software components that record and preserve lectures' audio and visual aspects. The system will allow lectures to be delivered directly to the students' devices. The major modules of the project include Python scripting, model training, UI/UX design, and app development.

ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ В ОБРАЗОВАНИИ

The article is devoted to the problem of using robots in the educational process in the training of specialists. The article describes training robots based on the Robot Operating System, i.e. based on a set of software libraries and tools that help create applications for robots. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Robot Integration with ROS (Robot Operating System) is the process of combining the hardware and software components of a robot with the ROS infrastructure. This allows the robot to interact with other devices and systems, as well as use a variety of ready-made tools and libraries available within the ecosystem. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry. Project Development: Using ROS, students can develop their own robotics projects, ranging from simple mobile robots for indoor navigation to complex manipulators for performing tasks in industry.

A hierarchical model of the information environment for railway power supply system critical objects control

It is proposed to consider the hierarchical model of the information environment as a structure according to its functions (implemented by the software part) and architecture (responsible for effective implementation of functional capabilities). It is proposed to improve the information management system of Ukrzaliznytsia’s critical power supply facilities, based on the developed hierarchical model of the information environment, which takes into account the presence of Ukrzaliznytsia’s own generation sources by integrating the appropriate control subsystem into the information system structure. The model emphasizes the integration of software and hardware components for data collection, pre-processing, and decision-making, contributing to a highly automated and intelligent control system. It proposes a multi-level structure incorporating artificial intelligence and advanced technologies to optimize electricity consumption and improve operational efficiency within the railway power supply system.

Transition and change: opportunities and challenges of CHW programme reform for community health systems and vertical disease programmes in Liberia

BackgroundGlobally, community health worker (CHW) programmes are critical to addressing health worker shortages and have been recognised as critical pillars within the drive towards universal health coverage (UHC). In 2016, the Liberian Ministry of Health launched the National Community Health Services Policy 2016–2021, which included significant CHW programme reform to address ongoing health workforce capacity gaps in the country. However, little consideration was given to the impact of such reforms on ongoing health interventions that rely heavily on the use of CHW cadres. Our study explores how CHW programme reform in Liberia influenced performance of CHWs involved in the delivery of Neglected Tropical Disease (NTD) programmes to elucidate how health systems reform can impact the delivery of routine health interventions and vice versa.MethodsWe used a qualitative case study approach conducted between March 2017 and August 2018. Our instrumental case study approach uses qualitative methods, including document review of five CHW and NTD program-related policy documents; 25 key informant interviews with facility, county, and national level decision-makers; and 42 life and job histories with CHWs in Liberia. Data were analysed using a thematic framework approach, guided by Kok et al. framework of CHW performance. Data were coded in QRS NVIVO 11 Pro.ResultsOur findings show that CHW programme reform provides opportunities and challenges for supporting enhanced CHW performance. In relation to health system hardware, we found that CHW programme reform provides better opportunities for: formal recognition of CHWs; strengthening capacity for effective healthcare delivery at the community level through improved and formalised training; a more formal supervision structure; and provision of monthly incentives of 70 US dollars. Efficiency gaps in routine intervention delivery can be mitigated through the strengthening of these hardware components. Conversely, supervision deficits in routine CHW functioning can be supported through health interventions. In relation to systems software, we emphasise the ongoing importance of community engagement in CHW selection that is responsive to gendered power hierarchies and accompanied by gendered transformative approaches to improving literacy.ConclusionsThis study shows how CHW programme reform provides opportunities and challenges for health system strengthening that can both positively and negatively impact the functioning of routine health interventions. By working together, CHW programmes and routine health interventions have the opportunity to leverage mutually beneficial support for CHWs, which can enhance overall systems functioning by enhancing CHW performance.

Cyber-Physical Perception Interface for Co-Simulation Applications.

Co-simulation can bring improvements to the development of cyber-physical perceptive systems (CPPS) in critical fields, allowing uninterrupted system operation and flexibility to use both real-time sensor data and non-real-time data. This paper proposes a co-simulation approach that integrates physical systems and communication systems, including both hardware and software components. This study demonstrates how systems of different natures with discrete or continuous events can be simulated using three methods: time stepped, global event driven, and variable stepped. Through two case studies from the medical and energy fields, CPPS and co-simulation reveal their importance for the future by improving precision and efficiency, which leads to more accurate diagnoses and personalized treatments in the medical field and increases the stability of energy networks.

A fabrication strategy for millimeter-scale, self-sensing soft-rigid hybrid robots.

Soft robots typically involve manual assembly of core hardware components like actuators, sensors, and controllers. This increases fabrication time and reduces consistency, especially in small-scale soft robots. We present a scalable monolithic fabrication method for millimeter-scale soft-rigid hybrid robots, simplifying the integration of core hardware components. Actuation is provided by soft-foldable polytetrafluoroethylene film-based actuators powered by ionic fluid injection. The desired motion is encoded by integrating a mechanical controller, comprised of rigid-flexible materials. The robot's motion can be self-sensed using an ionic resistive sensor by detecting electrical resistance changes across its body. Our approach is demonstrated by fabricating three distinct soft-rigid hybrid robotic modules, each with unique degrees of freedom: translational, bending, and roto-translational motions. These modules connect to form a soft-rigid hybrid continuum robot with real-time shape-sensing capabilities. We showcase the robot's capabilities by performing object pick-and-place, needle steering and tissue puncturing, and optical fiber steering tasks.

Single-molecule detection and super-resolution imaging with a portable and adaptable 3D-printed microscopy platform (Brick-MIC).

Over the past decades, single-molecule and super-resolution microscopy have advanced and represent essential tools for life science research. There is, however, a growing gap between the state of the art and what is accessible to biologists, biochemists, medical researchers, or labs with financial constraints. To bridge this gap, we introduce Brick-MIC, a versatile and affordable open-source 3D-printed microspectroscopy and imaging platform. Brick-MIC enables the integration of various fluorescence imaging techniques with single-molecule resolution within a single platform and exchange between different modalities within minutes. We here present variants of Brick-MIC that facilitate single-molecule fluorescence detection, fluorescence correlation spectroscopy, time-correlated single-photon counting and super-resolution imaging (STORM and PAINT). Detailed descriptions of the hardware and software components, as well as data analysis routines, are provided, to allow non-optics specialists to operate their own Brick-MIC with minimal effort and investments. We foresee that our affordable, flexible, and open-source Brick-MIC platform will be a valuable tool for many laboratories worldwide.

Heralding entangled optical photons from a microwave quantum processor

Exploiting the strengths of different quantum hardware components may increase the capabilities of emerging quantum processors. Here we propose and analyze a quantum architecture that leverages the nonlocal connectivity of optics, along with the exquisite quantum control offered by superconducting microwave circuits, to produce entangled optical resource states. In contrast to previous proposals on optically distributing entanglement between superconducting microwave processors, we use squeezing between microwaves and optics to produce microwave-optical Bell pairs in a dual-rail encoding from a single microwave quantum processor. Moreover, the microwave quantum processor allows us to deterministically entangle microwave-optical Bell pairs into larger cluster states, from which entangled optical photons can be extracted through microwave measurements. Our scheme paves the way for small microwave quantum processors to create heralded entangled optical resource states for optical quantum computation, communication, and sensing using imperfect microwave-optics transducers. We expect that improved isolation of the superconducting processor from stray optical fields will allow the scheme to be demonstrated with currently available hardware. Published by the American Physical Society 2024

Spatial impulse response analysis and ensemble learning for efficient precision level sensing

In this paper, we propose an innovative method for determining the fill level of containers, such as trash cans, addressing a critical aspect of waste management. The method combines spatial impulse response analysis with machine learning (ML) techniques, offering a unique and effective approach for sound-based classification that can be extended to various domains beyond waste management. By employing a buzzer-generated sine sweep signal, we create a distinctive signature specific to the fill level of the waste container. This signature, once accurately decoded, is then interpreted by a specially developed ensemble learning algorithm. Our approach achieves a classification accuracy of over 90% when implemented locally on a development board, optimizing operational efficiencies and eliminating the need to delegate complex classification tasks to external entities. Using low-cost and energy-efficient hardware components, our method offers a cost-effective approach that contributes to sustainable and efficient waste management practices, providing a reliable and locally deployable solution.