Hardware Configuration Research Articles

Design of Control System for Industrial Defective Product Sorting Station Based on IOT2050

Aiming at the problems of many types of abnormal states in industrial defective sorting stations, long time consuming analysis of abnormal causes, and untimely equipment monitoring, the control system of industrial defective sorting stations based on IOT2050 is designed. With Siemens PLC-1200 as the main body, combined with TIA Portal and IOT2050, it realizes the design of software and hardware configuration, programming, WinCC screen and industrial cloud platform of industrial defective sorting station control system. The simulation results show that the system runs stably, realizes automatic production operation and data monitoring, and is of great significance to ensure the reliable operation of equipment.

Passive IR-UWB Localization System for UAV-Based Electric Facility Inspection During GPS Outage

A practical localization system is proposed for a substation inspection unmanned aerial vehicle (UAV) performing its mission under global positioning system (GPS) signal blockage. The sensor hardware consists of a single transmitter and a cruciform receiver array, which produces the range difference (RD) information used for UAV positioning. The use of impulse-radio ultra wide-band (IR-UWB) devices secures the robustness against RF interference often caused by an exogenous electromagnetic field. With this hardware configuration, the UAV localization can be formulated as the state estimation problem for an uncertain linear state space model and be solved by using the computationally efficient robust weighted least squares (RWLS) estimator. Since the standard RWLS estimator could be sensitive to the imperfectness of prior knowledge on the noise statistic, a geometric constraint expressed in terms of a UAV position is exploited to secure the reliable localization performance in practice. Through experimental results, it is verified that the proposed solution provides reliable UAV positioning performance despite using the imperfect measurement noise statistics for the localization system.

DESIGN OF AUTONOMOUS ROBOTIC PLATFORMS BASED ON MODEL DATA

The article presents a method for selecting the configuration of the shooting equipment of a robotic platform, associated with the use of virtual digital models of sensors and a scene. The topic is really actual, since a number of complex technical and interesting scientific problems of computer vision are currently associated with the use of autonomous mobile systems. Technical difficulties are determined, first of all, by the fact that such complexes are equipped with many different sensors, the data from which must be processed in a single coordinate system with high accuracy. To do this, it is necessary to calibrate the system using an object with a special markup, the use of which allows you to effectively use computer vision algorithms and make the process fully automatic. The accuracy of the relative orientation of the sensors, which can be achieved in this case, can be considered a quality criterion when choosing survey equipment. In the process of designing a platform, it is rather difficult to determine in advance how many sensors and their placement is optimal in terms of the result - obtaining a high quality digital scene model. In this situation, it is natural to compare different hardware configurations and choose from them. However, to obtain the required amount of real data, a significant amount of time is required to work with complex expensive equipment. The key to solving the problem may be the use of virtual data - a realistic digital model of the sensors and the scene, synthesized using the graphics engine. This paper presents the process of designing a promising vehicle, including the selection and placement of shooting equipment on the platform – cameras and lidars. The best configuration is selected from the three proposed according to the criterion of the accuracy of restoring the positions of the sensors during the calibration process. The application software was developed on the basis of the Unreal Engine 4 graphics engine. Digital models of the vehicle, sensors and scene were created in accordance with the parameters of the real prototype. Taking into account the technical characteristics of the sensors, three options for their placement have been created. For each variant, s

Effective Educational Measures for Kindergarten School Readiness

School readiness not only affects children's school adaptation, but also leads to social and economic stratification to a certain extent. School readiness is not only the individual preparation of young children, but also the preparation of the entire education ecosystem, that is, kindergarten, primary school, family and community should be prepared for school entrance. Many foreign countries have taken interventions to prepare children for school, but high-quality preschool education is the most effective measure. It includes: first, providing courses suitable for early childhood development. The curriculum suitable for children's development has the following characteristics: point to the key experience of children's development, accumulate key experience in practical operation, and promote the continuous development of children's key experience. Second, provide high-quality teacher-child interaction. The teacher-child interaction that helps to improve the level of children's school readiness is shown as: establishing a friendly teacher-child interaction relationship, providing emotional support to children who are assessed to have problematic behaviors in participating activities, interacting with children with low family socio-economic status, and creating a language communication environment. Third, recruit more high-level teachers. Fourth, provide adequate and appropriate hardware configuration. Fifthly, scientific childcare guidance should be given to parents.

Remote-Control Sound Pattern Recognition for Auditory Assessment and Therapy in Smart Healthcare

A remote-control method is proposed for healthcare services involving auditory assessment and therapy through video-conferencing software. Different video-conferencing software platforms have different sound responses, and the user computer also has variations in sound performance because of its hardware and software configuration. These restrictions affect the ability of video-conferencing software to deliver health services that require accurate, reliable, and repeatable sound. In this paper, a remote-control system board is developed wherein sound for auditory assessment or therapy is stored. A low-cost processor is used to remotely deliver sound by sound pattern recognition. Service providers play a sound pattern locally to remotely control the system board. The desired audio files can be delivered to the client side without suffering from sound loss in terms of frequency range and loudness. Experiments were also conducted to verify the performance of the proposed method. The results show that the remote-control method for audio playback can be realized with a low-cost processor, such as a microcontroller unit for sound pattern recognition and that it accurately delivers audio files in terms of frequency and amplitude.

INVESTIGATING THE PERFORMANCE INDICES OF YOLO MODELS IMPLEMENTED ON A DPU: A COMPARATIVE ANALYSIS

A comprehensive analysis of the performance indices achieved by implementing various “You Only Look Once” dataset (YOLO) models on a dedicated Deep Learning Processing Unit (DPU). YOLO models are renowned for their real-time object detection capabilities, making them a good choice for a range of applications including autonomous vehicles, surveillance systems, and robotics. In this study, the YOLO models are deployed onto a specialized hardware accelerator, specifically a DPU, to assess their inference speed, accuracy, and power efficiency. By conducting an in-depth comparative evaluation of multiple YOLO variants, including YOLOv3, YOLOv4, YOLOv5, YOLOv6 insights into how each model interacts with the DPU architecture are revealed. The experiments involve benchmarking these models across diverse datasets and varying hardware configurations. The results not only highlight the advantages and limitations of employing DPUs for YOLO-based applications but also help in choosing the most suitable model-DPU combination based on specific performance requirements. This study contributes to the optimization of real-time object detection systems and assists practitioners in making informed decisions regarding the model and hardware selection.

A Comparative Study on Consumer Preference and Satisfaction with Special Reference to the Usage of DELL and HP Laptops in Chennai City

Today technology has brought the world in own hands human being very much to the complicated work in to easier. Gradually the invention and discoveries are made from time to time has head us to a comfortable world. One such significant is the laptop. The objective of the study is to know access the cost and performance of HP laptop and evaluate satisfaction level of the customers towards HP laptop. The sample size of the study was conducted in Chennai city. Majority of the members use the laptop for studies. Majority of the respondents’ awareness are through friends & relatives. Majority of the respondents are aware of the hardware configuration in laptop computers. Michael Dell founded Dell computer corporation, PCs limited, in 1984 while a student at the University of Texas at Austin. Operating from Michael Dell’s off- campus dormitory room at Dobie Centre, the startup aimed to sell IBM PC-compatible computers built from stock components. Laptop once considered a luxury has now become an important gadget in the present era of technology rich world.

Plug-and-Play Typed Polarization Direction Indicator by Azimuthal Polarizer

This work is on polarization direction visualization of linearly-polarized light. Different from using conventional rotation or multiple beam-path approaches, a more direct and popular solution is to generate the so-called “Malus pattern” and visualize the polarization direction directly by an azimuthal polarizer (AP). Several experimental configurations on the basic principle have been reported. However, there has been no proper solution to plug-and-play the AP for the measurement directly since their high-performance hardware suffered from complex coupling relations, and their algorithms relied on filtering and manual operations. The present work is for the first time to: 1) point out the problems and propose the integration of the AP as a “plug-and-play” module; 2) propose a high-performance hardware configuration without coupling relation; and 3) propose a frequency-domain algorithm with high anti-noise ability and automatic identification. All the involved issues are experimentally verified. Besides, detailed discussions on the strong anti-noise ability and the factors which might influence the measurement accuracy are also elaborated.

Rancang Bangun Sistem Informasi Apotek Berbasis Website di Apotek Qta Farma Sumedang

The design of a website-based pharmacy information system is a system created to enter data on products sold in pharmacies so that inventory and drug expiration periods can be controlled. For system design using the OOAD (object Oriented Analysis and Design) method while the model used is RUP (Rational Unified Process) with the development tool in the form of UML (Unified Modeling Language). There is also an explanation of the information system design process. Website-based pharmacies to be built, starting from the design of use case diagrams, activity diagrams, sequence diagrams, Class diagrams, User interfaces, hardware design and in hardware design this requires hardware configuration, specifications. hardware and software specifications.

Boosting Speech-to-Text software potential

The article focuses on finding ways of boosting efficiency and accuracy of Speech-to-Text (STT)-powered input. The effort is triggered by the growing popularity of the software among professional translators, which is in line with the general trend of abandoning typing in favor of speech-to-text applications. Insisting that better effectiveness of such programs is contingent on their accuracy, the researchers analyze major factors, both linguistic and technical in nature, affecting the computer-assisted speech transcribing quality. This leads to an experiment, putting the hypothesis to a test. Based on numerical and performance data, errors and their breakdown into categories in an attempt to figure out their origins, it dwells on various approaches to dictation in a combination with several hardware options and configurations. These pave the way for recommendations on the improvement of STT performance based on the Dragon software. The authors arrive at a conclusion that it is possible to boost the STT accuracy up to 99 percent by adjusting the program profile to accommodate phonetic features of the speaker with due consideration of his accent, adding to the dictionary the most complex and rare vocabulary beforehand, and fine-tuning input hardware. Other noteworthy results include ways to overcome the most complex transcribing challenges, i.e. proper names, placenames, abbreviations, etc.

Automated power modeling of computing devices: Implementation and use case for Raspberry Pis

Monitoring the power consumption of smart and connected devices is a challenging task with heterogeneous devices and a variety of hardware and software configurations. In order to accurately monitor these devices and follow the speedy changes in their configuration, a new approach is needed. In this paper, we present an automated architecture and approach to empirically generate power models for a large set of devices. Our approach allows conducting automated benchmarks to collect power data and metrics, generating or updating accurate power models, and allowing software tools to query and retrieve the most accurate and up-to-date power model of a specific device configuration. We also present a proof-of-concept implementation for modeling the power consumption of Raspberry Pi devices. Finally, we conduct a comprehensive experiment, modeling the entire current lineup of Raspberry Pi devices with error margins as low as 0.3%, and then we discuss the impact of multiple device configurations on power consumption.

Machine vision system for real-time debris detection on mechanical wild blueberry harvesters

Improving fruit quality is important to wild blueberry producers. Plant debris like leaves and stems caused a reduction in fruit quality during mechanical harvesting. A debris detection system was developed using two Logitech C920 webcam cameras mounted before and after the blower fan on a commercial mechanical wild blueberry harvester. Images were collected from two commercially managed wild blueberry fields located in central Nova Scotia to develop a dataset of 1000 images. Two convolutional neural networks (CNNs), YOLOv3 and YOLOv3-SPP, were used and compared for performance analysis. The CNNs were trained using no augmented images, images with five augmentations (sharpening, brightness, contrast, gamma correction, and saturation), and images with five augmentations that were more heavily weighted towards gamma. Four different computer hardware packages (Hewlett-Packard, Shuttle XPC, Jetson TX2, and GPU-based Desktop) were used to determine the appropriate hardware and CNN model combination for real-time performance. YOLOv3 and YOLOv3-SPP were trained to mAP scores of 72.87 and 74.38% using the dataset with more gamma-augmented images. This was an improvement over the dataset with no augmentation (67.75; 68.49%) and equally numbered augmentations (71.26%; 73.03%). YOLOv3-SPP combined with an Intel® Core™ i9-7900X CPU and Nvidia GeForce RTX™ 2080 Ti GPU in a desktop computer achieved the fastest detection rate of 33.30 ms. The detection speed on all other hardware configurations exceeded 33.30 ms, indicating that a powerful desktop GPU is required for real-time performance of this task. With adequate processing hardware, the developed technology could be integrated into a control system to automatically adjust brushes based on conveyor information from mechanical wild blueberry harvesters to make cleaning berries easier and enhance fruit quality.

Performance Analysis of Unmanned Aerial Vehicle Enabled Wireless Power Transfer Considering Radio Frequency System Imperfections

A simple and energy-efficient communication system configuration is proposed for Unmanned Aerial Vehicle Energy Transmitters (UAV-ETs) for Wireless Power Transfer (WPT) applications. The results show that the proposed UAV-ET’s hardware configuration improves WPT time by 9.305%, and reduces UAV-ET’s power consumption by 7.47% compared to the UAV Base Stations (UAV-BSs). Also, in the prior works on UAV-enabled WPT the UAV-ET’s communication system configuration was not proposed, and UAV-ET’s radio frequency component’s real-world imperfections were ignored. Therefore, the work proposes a holistic energy efficiency optimization framework for UAV-ETs and hardware design parameters for qualifying the UAV-ET’s hardware components to maximize UAV-ET’s WPT time. Additionally, a three-dimensional UAV-ET placement optimization is proposed for maximizing WPT time, and results show that higher UAV-ET heights above the Wireless Energy-Receivers (WERs) decrease the WPT time by 4.75%. Besides, the results suggest that Antenna Array (AA) losses and the power amplifier’s power added efficiency variation reduce UAV-ET’s WPT time by 16.48%. Finally, a 115 g crossed-slotted waveguide AA for UAV-ET is manufactured using laser cutting, and the experiments confirm that the AA’s total loss is 0.72 dB, therefore, the AA qualifies requirements according to the proposed UAV-ET’s hardware component design optimization framework.

Improving scalability of Earth system models through coarse-grained component concurrency – a case study with the ICON v2.6.5 modelling system

Abstract. In the era of exascale computing, machines with unprecedented computing power are available. Making efficient use of these massively parallel machines, with millions of cores, presents a new challenge. Multi-level and multi-dimensional parallelism will be needed to meet this challenge. Coarse-grained component concurrency provides an additional parallelism dimension that complements typically used parallelization methods such as domain decomposition and loop-level shared-memory approaches. While these parallelization methods are data-parallel techniques, and they decompose the data space, component concurrency is a function-parallel technique, and it decomposes the algorithmic space. This additional dimension of parallelism allows us to extend scalability beyond the limits set by established parallelization techniques. It also offers a way to maintain performance (by using more compute power) when the model complexity is increased by adding components, such as biogeochemistry or ice sheet models. Furthermore, concurrency allows each component to run on different hardware, thus leveraging the usage of heterogeneous hardware configurations. In this work we study the characteristics of component concurrency and analyse its behaviour in a general context. The analysis shows that component concurrency increases the “parallel workload”, improving the scalability under certain conditions. These generic considerations are complemented by an analysis of a specific case, namely the coarse-grained concurrency in the multi-level parallelism context of two components of the ICON modelling system: the ICON ocean model ICON-O and the marine biogeochemistry model HAMOCC. The additional computational cost incurred by the biogeochemistry module is about 3 times that of the ICON-O ocean stand alone model, and data parallelization techniques (domain decomposition and loop-level shared-memory parallelization) present a scaling limit that impedes the computational performance of the combined ICON-O–HAMOCC model. Scaling experiments, with and without concurrency, show that component concurrency extends the scaling, in cases doubling the parallel efficiency. The experiments' scaling results are in agreement with the theoretical analysis.

Safety management of Internet of Things engineering construction based on GPU parallel computing

This article introduces the calculation method for continuous safety analysis of GPU speed. Combine the configuration of CPU-GPU software with hardware and software configuration, logically allocate computing resources, and use the DC flow system to diagnose whether there are errors in the entire network. Combining the characteristics of network connectivity after a failure, a true N-1 error analysis based on the same calculation strategy of data collection is proposed, and comparisons are made. Various levels of granularity are designed and calculations are performed. This method can create power lines for each group, and ensure fast, real-time safety analysis, and propose an improved method to analyze Web applications. In the multi-level parallel computing model, the key steps of multi-GPU integration are described. The virtual container technology detects applications that intensively interact between multiple GPU panels in managing security analysis and sensitivity analysis applications, as well as application design for security application analysis and sensitivity analysis. This paper selects several typical construction project cases through the research on the construction safety management of construction companies based on the Internet of Things, which mainly involve five issues of on-site safety inspection, staff safety education, safety conflict management and safety awareness, and the application of BIM technology. Highlights the company’s problems in the safety management of engineering construction; it also consults and integrates relevant construction team types and actual management conditions, and combines viewpoints and actual conditions to provide reasonable and contradictory suggestions from five perspectives, namely: completion Safe construction project system, using advanced technology to improve the efficiency of construction safety management.

Profiling the BLAST bioinformatics application for load balancing on high-performance computing clusters

BackgroundThe Basic Local Alignment Search Tool (BLAST) is a suite of commonly used algorithms for identifying matches between biological sequences. The user supplies a database file and query file of sequences for BLAST to find identical sequences between the two. The typical millions of database and query sequences make BLAST computationally challenging but also well suited for parallelization on high-performance computing clusters. The efficacy of parallelization depends on the data partitioning, where the optimal data partitioning relies on an accurate performance model. In previous studies, a BLAST job was sped up by 27 times by partitioning the database and query among thousands of processor nodes. However, the optimality of the partitioning method was not studied. Unlike BLAST performance models proposed in the literature that usually have problem size and hardware configuration as the only variables, the execution time of a BLAST job is a function of database size, query size, and hardware capability. In this work, the nucleotide BLAST application BLASTN was profiled using three methods: shell-level profiling with the Unix “time” command, code-level profiling with the built-in “profiler” module, and system-level profiling with the Unix “gprof” program. The runtimes were measured for six node types, using six different database files and 15 query files, on a heterogeneous HPC cluster with 500+ nodes. The empirical measurement data were fitted with quadratic functions to develop performance models that were used to guide the data parallelization for BLASTN jobs.ResultsProfiling results showed that BLASTN contains more than 34,500 different functions, but a single function, RunMTBySplitDB, takes 99.12% of the total runtime. Among its 53 child functions, five core functions were identified to make up 92.12% of the overall BLASTN runtime. Based on the performance models, static load balancing algorithms can be applied to the BLASTN input data to minimize the runtime of the longest job on an HPC cluster. Four test cases being run on homogeneous and heterogeneous clusters were tested. Experiment results showed that the runtime can be reduced by 81% on a homogeneous cluster and by 20% on a heterogeneous cluster by re-distributing the workload.DiscussionOptimal data partitioning can improve BLASTN’s overall runtime 5.4-fold in comparison with dividing the database and query into the same number of fragments. The proposed methodology can be used in the other applications in the BLAST+ suite or any other application as long as source code is available.

Color indicators as parameters for predicting the content of organic carbon in soils

The study of chemical, physical and biological properties of soil is important for theoretical and practical soil science. The study of soil properties, in particular the determination of organic carbon content, requires a lot of time and money. Effective measurement of soil organic carbon is necessary to manage the functioning of ecosystems. Organic carbon has an important effect on soil properties and also plays a key role in mitigating the effects of climate change, as carbon dioxide (CO2) can be removed from the atmosphere and stored in the soil, thereby reducing the warming effect. Soil color is one of the key soil characteristics that can be quickly analyzed and is closely related to a number of other physical and chemical soil parameters. According to the researches of domestic and foreign scientists, soils are the main storage place for soil carbon, which plays an important role in the global carbon cycle. With technological progress and the development of imaging systems, image-based soil characterization methods have attracted considerable attention from the global soil science community. Unlike soil spectroscopy, imaging devices such as digital cameras or phone cameras are readily available. Traditionally, soil color is quantified using Munsell’s color system, which requires subjective visual comparison, but recently foreign scientists have begun to use RGB, CMYK, HSB color schemes, the definition of which is available in almost all computer graphics editors. With the increase in the use of smartphones and the gradual improvement of the quality of photo images, the use of smartphone photos to predict organic carbon content shows great potential for development. Unlike face recognition and other recognition methods that mainly rely on object profiles, color is one of the most important factors in determining the accuracy of organic carbon prediction. Different hardware configurations and hardware software settings may cause differences in photo colors.

METROLOGICAL CHARACTERIZATION OF OPTICAL 3D COORDINATE MEASUREMENT SYSTEMS – COMPARISON OF ALTERNATIVE HARDWARE DESIGNS AS PER ISO 10360

Abstract. This research focuses on the characterization of the metrology of Optical 3D Coordinate Measurement Systems (O3DCMS). The focus is set on the identification and execution of the procedure indicated by the currently active technical standards related to industrial O3DCMS, for their metrological assessment, objective comparison, and performance tracking. This work leads to the implementation of an ad hoc software for the execution of the standard tests by the ISO 10360-13 standard. The implemented software application is employed in a real-case scenario for evaluating the performances of an industrial 3D scanner based on structured light. The specific hardware components to be assessed are two light sources of the active stereoscopic vision system, named Digital Light Projectors (DLP). The case study applies the procedures and metrics indicated by the active standards to objectively compare two alternative hardware design of the system under test. This results in the identification of the most performing hardware configuration, allowing the selection of the best system design, basing on objective metrological parameters.

A Programmable On-Chip Hopf Bifurcation Circuit

This effort discusses a programmable Hopf bifurcation element based on Transconductance Amplifiers (TA) that can be directly implemented in configurable hardware (e.g. an SoC FPAA). The TA Hopf bifurcation circuit shows the complete dynamics of a Van der Pol circuit, a Hopf bifurcation prototype. This Low-Pass Filter (LPF) TA-implemented ODE shows the necessary linear dynamics, as well as stable and oscillatory nonlinear dynamics expected near a Hopf bifurcation. This component directly uses the dynamics of the TA devices not translating equations, reducing the implementation complexity.

Comparative study on the performance of deep learning implementation in the edge computing: Case study on the plant leaf disease identification

This research examines and explores four different pre-trained CNN deep learning models (AlexNet, VGG16, ResNet50, and DenseNet121) to be adopted as edge solution. The model is developed and evaluated using the PlantVillage dataset. Image transformation techniques and down sampling were carried out to mitigate the unbalanced class distribution problem. From the preliminary works, DenseNet121 was selected as the implementation model since it had the best accuracy (96.4%) in comparison with other models. The model is then tested on different endpoint devices (CPU, GPU and VPU) in different programming environments (standard PyTorch and OpenVINO) to test the consistency of models that work across different hardware and software configurations. Results show that the adoption of model on the edge (VPU) is able to maintain relatively high recall values, precision and F1 scores. The successful application of the model on VPU device shows the potential of the model to be implemented for the detection of plant diseases as edge solution/embedded application. • Plant disease identification deep learning model has been developed. • Model performance in different hardware and environment settings has been compared. • The model in edge setting with VPU has a better accuracy and inference time.