Back-end Computer Research Articles

Building Foods Data Automation Platform Using Cloud Computing Type PaaS

Sources of information on the availability and prices of basic foods are spread on URLs, HTTP(s) on various websites in the Medan City government. Data synchronization and data integration between websites can produce quality information sourced from various public IP Address protocols. In a cloud-based Data Center, the Front End and Back End computer network devices are interconnected via the Internet. Data is stored as JSON and synced in realtime to every connected client. Platform metrics and Activity logs are collected automatically, routed to other locations using diagnostic settings. Cloud service solutions include IaaS (Infrastructure as a Service), SaaS (Software as a Service), and PaaS (Platform as a Service). PaaS integrates various operating systems and user applications. Secondary data collection takes public data that can be accessed using Web Crawling and Web Scraping techniques. Web API software components automatically update data between the client and server. Firebase Realtime Database can store and synchronize data in real time. The configuration of data requests that can be served by the food data cloud platform is 85% with a configuration runtime of 80%-95%.

Enhancing Card Transaction’s Security through Cyber Security

This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.

Enhancing Card Transaction Security through Cyber Security

This paper highlights the security issues associated with credit cards and underscores the crucial role of encryption in mitigating the risk of credit or debit card data theft. Credit card encryption encompasses safeguarding the card itself, securing the terminal used for card scanning, and ensuring the protection of card information during transmission between the terminal and a backend computer system. The encryption mechanism is specifically engineered to validate and limit access to card security features. In our project, we developed a web application using VS Code, employing HTML for the frontend and PHP for the backend, and implemented AES encryption as a robust security measure.

A Wireless Intelligent Motion Correction System for Skating Monitoring Based on a Triboelectric Nanogenerator

Smart sport and big data have become inextricably linked with new technologies and devices to monitor sport-related information in real time. In this paper, a lightweight, portable and self-powered triboelectric nanogenerator (LPS-TENG) has been developed to monitor the frequency and force of skaters’ pedaling. Friction layers are formed of polytetrafluoroethylene (PTFE) and nylon films. Based on the triboelectric effect, LPS-TENG does not require an external power supply, and it can be used to monitor biomechanical motion independently. Under the conditions of 1 Hz and 17.19 N, the outputting voltage of LPS-TENG is stabilized at 14 V. Wireless data transmission is achieved with the help of the LPS-TENG and AD module. Visual feedback is provided by the upper computer system in the process of processing data. The wireless intelligent motion correction system is composed of an LPS-TENG, an AD module and a back-end computer. It can clearly analyze the changes between different frequencies and forces during skating. Results showed that the signal of tester’s high-frequency and great-force motion, was transmitted to the computer, and its feedback was given after analysis and processing successfully. The system may help coaches develop training methods, means and tactics to increase athletes’ performance and competitive level in athletic sport. The purpose of this study is to provide new ideas for monitoring skaters’ sport techniques, promote the use of force sensors in the monitoring of sport and develop intelligent assistant training systems.

DeepImageTranslator V2: Analysis of Multimodal Medical Images using Semantic Segmentation Maps Generated through Deep Learning

Introduction: Analysis of multimodal medical images often requires the selection of one or many anatomical regions of interest (ROIs) for extraction of useful statistics. This task can prove laborious when a manual approach is used. We have previously developed a user-friendly software tool for image-to-image translation using deep learning. Therefore, we present herein an update to the DeepImageTranslator V2 software with the addition of a tool for multimodal medical image segmentation analysis (hereby referred to as the MMMISA). Methods: The MMMISA was implemented using the Tkinter library; backend computations were implemented using the Pydicom, Numpy, and OpenCV libraries. We tested our software using 4188 slices from whole-body axial 2-deoxy-2-[18F]-fluoroglucose-position emission tomography/ computed tomography scans ([¹⁸F]-FDG-PET/CT) of 10 patients from the American College of Radiology Imaging Network-Head and Neck Squamous Cell Carcinoma (ACRIN-HNSCC) database. Using the deep learning software DeepImageTranslator, a model was trained with 36 randomly selected CT slices and manually labelled semantic segmentation maps. Utilizing the trained model, all the CT scans of the 10 HNSCC patients were segmented with high accuracy. Segmentation maps generated using the deep convolutional network were then used to measure organ specific [¹⁸F]-FDG uptake. We also compared measurements performed using the MMMISA and those made with manually selected ROIs. Results: The MMMISA is a tool that allows user to select ROIs based on deep learning-generated segmentation maps and to compute accurate statistics for these ROIs based on coregistered multimodal images. We found that organ-specific [¹⁸F]-FDG uptake measured using multiple manually selected ROIs is concordant with whole-tissue measurements made with segmentation maps using the MMMISA tool. Doi: 10.28991/HIJ-2022-03-03-07 Full Text: PDF

DeepImageTranslator: A free, user-friendly graphical interface for image translation using deep-learning and its applications in 3D CT image analysis.

The advent of deep-learning has set new standards in an array of image translation applications. At present, the use of these methods often requires computer programming experience. Non-commercial programs with graphical interface usually do not allow users to fully customize their deep-learning pipeline. Therefore, our primary objective is to provide a simple graphical interface that allows researchers with no programming experience to easily create, train, and evaluate custom deep-learning models for image translation. We also aimed to test the applicability of our tool in CT image semantic segmentation and noise reduction. DeepImageTranslator was implemented using the Tkinter library, the standard Python interface to the Tk graphical user interface toolkit; backend computations were implemented using data augmentation packages such as Pillow, Numpy, OpenCV, Augmentor, Tensorflow, and Keras libraries. Convolutional neural networks (CNNs) were trained using DeepImageTranslator. The effects of data augmentation, deep-supervision, and sample size on model accuracy were also systematically assessed. The DeepImageTranslator a simple tool that allows users to customize all aspects of their deep-learning pipeline, including the CNN, training optimizer, loss function, and the types of training image augmentation scheme. We showed that DeepImageTranslator can be used to achieve state-of-the-art accuracy and generalizability in semantic segmentation and noise reduction. Highly accurate 3D segmentation models for body composition can be obtained using training sample sizes as small as 17 images. In conclusion, an open-source deep-learning tool for accurate image translation with a user-friendly graphical interface was presented and evaluated. This standalone software can be downloaded at: https://sourceforge.net/projects/deepimagetranslator/.

Online Platform for Matching and Trading Shares of Autonomous Vehicles

In this paper, an online platform for a system of shared autonomous vehicles (AVs) is designed and tested. A model of fractional ownership among different households is presented, and a one-sided many-to-many matching problem between households and vehicles is solved. Preferences over vehicles, demographics of other co-owners, and weekly activity patterns of households are key factors taken into account in the matching process. In the designed platform, co-owners of each vehicle can reserve the vehicle in advance and can trade the reserved time with the other co-owners if the vehicle is no longer needed. Real-time submission of requests can also be accommodated through a trading mechanism among co-owners. The proposed architecture of the platform consists of three main components: ( i) a web-based user interface, ( ii) database, and ( iii) backend computation. The platform was tested using data collected from 15 households, who entered their trips and trading requests into the system.

Aquarium Monitoring System Based on Internet of Things

With the ever-increasing richness of social resources, the number of devices using the Internet of Things is also increasing. Currently, many people keep pets such as fish in their homes, and they need to be carefully taken care of. In particular, it is necessary to create a safe and comfortable environment for them and to maintain this environment continuously. An adverse environment can affect the growth of fish and may even result in their death. This study used the LinkIt 7697 module and the BlocklyDuino editor to produce a control system for a smart aquarium. The purpose of this system is to monitor the temperature, light intensity, and water level in an aquarium, as well as to provide alerts to presence of intruders; therefore, temperature, light, ultrasonic, and infrared sensing modules are used. The system has set aquarium environment thresholds, and it processes the signals obtained by the sensors to control and optimize the outputs to loads using data fusion calculations so that the aquarium has the most comfortable environment for the fish. An automatic feeder is also included in the system, and this uses a servo motor. The data from the system is uploaded to a back-end computer through the built-in Wi-Fi system of the LinkIt 7697 module. The Cloud Sandbox platform is used to display the results in real time, achieving the purpose of remote network monitoring.

Fall Detection System With Artificial Intelligence-Based Edge Computing

Falls are the second leading cause of death from unintentional injuries in older adults. Although many systems have been used to detect falls, they are limited by the computational complexity of the algorithm. The images taken by the camera must be transmitted through a network to the back-end server for calculation. As the demand for Internet of Things increases, this architecture faces such problems as high bandwidth costs and server computing overload. Emerging methods reduce the workload of servers by transferring certain computing tasks from cloud servers to edge computing platforms. To this end, this study developed a fall detection system based on neuromorphic computing hardware, which streamlines and transplants the neural network model of the back-end computer to the edge computing platform. Through the neural network model with integer 8 bit precision deployed on the edge computing platform, the object photos obtained by the camera are converted into human motion features, and a support vector machine is then used for classification. After experimental evaluation, an accuracy of 96% was reached, the detection speed of the overall system was 11.5 frames per second, and the power consumption was 0.3 W. This system can monitor the fall events of older adults in real time and over a long period. All data were calculated on the edge computing platform. The system only reports fall events via Wi-Fi, thereby protecting the privacy of the user.

An X-Band CMOS Digital Phased Array Radar from Hardware to Software.

Phased array technology features rapid and directional scanning and has become a promising approach for remote sensing and wireless communication. In addition, element-level digitization has increased the feasibility of complicated signal processing and simultaneous multi-beamforming processes. However, the high cost and bulky characteristics of beam-steering systems have prevented their extensive application. In this paper, an X-band element-level digital phased array radar utilizing fully integrated complementary metal-oxide-semiconductor (CMOS) transceivers is proposed for achieving a low-cost and compact-size digital beamforming system. An 8–10 GHz transceiver system-on-chip (SoC) fabricated in 65 nm CMOS technology offers baseband filtering, frequency translation, and global clock synchronization through the proposed periodic pulse injection technique. A 16-element subarray module with an SoC integration, antenna-in-package, and tile array configuration achieves digital beamforming, back-end computing, and dc–dc conversion with a size of 317 × 149 × 74.6 mm3. A radar demonstrator with scalable subarray modules simultaneously realizes range sensing and azimuth recognition for pulsed radar configurations. Captured by the suggested software-defined pulsed radar, a complete range–azimuth figure with a 1 km maximum observation range can be displayed within 150 ms under the current implementation.

ІСТОРІЯ НИЗЬКОЧАСТОТНИХ ДОСЛІДЖЕНЬ ПУЛЬСАРІВ

Purpose: The main most pronounced events, which occurred in the initial period of the pulsars’ study at the decameter wavelength range, are presented. The example of the main scientific problems, which were formulated at the very beginning of pulsar research, shows how the emphasis and priorities of these studies have been changing over time, which tasks have finally been solved, and which are still waiting to be solved. It is shown how the ongoing modernization of the UTR-2 radio telescope have allowed to acquire new qualities in astrophysical research being made with this radio telescope and to identify new scientific directions. The example of the cited references shows how the pulsar research efforts in Ukraine have been developed and how they were integrated into the world astrophysical research of these unique objects. The purpose of this work is to show the relationship between the past and the present on the example of pulsars for longer than a semi-centennial period and to show how the scientific problems that were formulated in the past, and which could not be solved under the then-existing technical conditions, were solved by the subsequent generations of researchers. Design/methodology/approach: The methods of comparison and historical parallels show how the low-frequency studies of pulsars have been developed and evolved almost from their discovery until now. Findings: It is shown how quantitative transformations and technical development, as well as non-standard scientific approaches, unhackneyed thought and international cooperation allow to solve complex radio astronomical problems related to the low-frequency studies of pulsars. Conclusions: The paper provides a historical overview of more than half a century-long radio astronomical studies of pulsars, having been and still being made at the decameter band using the UTR-2 radio telescope. The “old” and current priorities in pulsar research are given, and it is shown how qualitatively the technical parameters of back end facility and computer performance have been changed in studying the coherent pulsar radio emission nature. Key words: aberration; frequency band; pulse; interpulse; dispersion measure; rotation measure; plasma; pulsar; radio telescope

Secured 6-Digit OTP Generation using B-Exponential Chaotic Map

Today, the traditional username and password sys-tems are becoming less popular on the internet due to their vulnerabilities. These systems are prone to replay attacks and eavesdropping. During the Coronavirus pandemic, most of the important transactions take place online. Hence we require a more secure method like one-time password generation to avoid any online frauds. one-time password generation has multiple techniques. With one-time password generation it has become possible to overcome the drawbacks posed by the traditional username and password systems. The one-time password is a two-way authentication technique and hence secure one-time password generation is very important. The current method of one-time password generation is time-consuming and consumes a lot of memory on backend servers. The 4-digit one-time password system limits its uses to 9999 users and with advance deep learning approaches and faster computing it is possible to break through the existing one-time password generation method. Hence we need a system that is not vulnerable to predictive learning algorithms. We propose a 6-digit one-time password generation technique based on a B-exponential chaotic map. The proposed 24-bit (6-digit) long one-time password system offers 120 times higher security as compared traditional 4-digit systems, with a faster backend computing system that selects 24-bits out of 10 8 bits in 89 seconds at 1.09 Kilo-bits per milliseconds. The proposed method can be used for online transactions, online banking, and even automated teller machines.

The Case for FPGA-Based Edge Computing

Edge Computing has emerged as a new computing paradigm dedicated for mobile performance enhancement and energy efficiency purposes. Specifically, it benefits today’s interactive applications on power-constrained devices by offloading compute-intensive tasks to the edge nodes in close proximity. Meanwhile, FPGA is well known for its excellence in accelerating (domain-specific) compute-intensive tasks such as deep learning algorithms in a high performance and energy-efficient manner due to its hardware-customizable nature. In this paper, we make the first attempt to leverage and combine the advantages of these two, and proposed a new network-assisted computing model, namely FPGA-based edge computing. As a case study, we choose three computer vision (CV)-based mobile interactive applications, and implement their back-end computation engines on FPGA. By deploying such application-customized accelerator modules for computation offloading at the network edge, we experimentally demonstrate that this approach can effectively reduce response time for the applications and energy consumption for the entire system in comparison with traditional CPU-based edge/cloud offloading approach.

A Mm-Wave Wideband MIMO RX With Instinctual Array-Based Blocker/Signal Management for Ultralow-Latency Communication

This article demonstrates a wideband mm-Wave multiple-input–multiple-output (MIMO) receiver (RX) system with cascadable array-based high-order autonomous spatial filters (ASFs) for closed-loop frontend beamforming, achieving “instinctual” (“Instinctual” is defined as an autonomous operation for sensing, controlling, processing signals with ultralow-latency and no backend digital signal processing assist.) management of multiblockers/signals with ultrafast response time. Over a full field-of-view (FoV), the ASFs achieve automatic blocker suppression and desired signal beamforming without prior knowledge on the angle-of-arrival/frequency/modulation and without beam-searching using backend computations. In particular, the ASFs realize frontend blocker suppression and substantially relax the dynamic range requirement of down-stream analog-to-digital converters (ADCs) in digital arrays. Moreover, cascading multiple ASFs can sequentially suppress multiple blockers, essentially achieving the “iterative source localization” DSP algorithm by the frontend hardware. A proof-of-concept 27–41-GHz four-element MIMO RX array is implemented in a 45-nm CMOS silicon-on-insulator (SOI) process to support multistandard 5G links in the future congested/contested electromagnetic (EM) environments. With autonomous cancellation of multiple co-channel wideband blockers, a wideband desired signal can be received with high signal-to-interference-plus-noise ratio (SINR), achieving −28.64-dB error vector magnitude (EVM) for 3-Gb/s 64QAM and −32.73-dB EVM for 0.8-Gb/s 256QAM. The measured MIMO beamforming response time for beam scanning, localization, and computation is $\mu \text{s}$ /stage, enabling 5G and beyond-5G ultralow-latency mm-Wave links.

T-REST: An Open-Enabled Architectural Style for the Internet of Things

Computing offloading is a key challenge of new rising computing paradigms of the Internet of Things (IoT) like edge computing, which shifts computations to data sources as near as possible to gain the benefits, such as low latency and energy efficiency. However, the fragmentation problem of IoT devices results in a heterogeneous and disordered ecosystem, hindering the interoperating demands of computing offloading. What we need is an open-enabled ecosystem which allows third-party developers to create and update functions of deployed devices dynamically. We propose Things-representational state transfer (T-REST) which is an extension of the representational state transfer (REST) architectural style to address this problem. It integrates contents and computations together to inherit the uniform interface principle from REST. Three architectural constraints are added to REST: 1) reusable remote evaluation; 2) dynamic time series representation; and 3) computational hypertext. A novel event triggering mechanism is designed to decouple the tight coupling of front-end content accesses and back-end computations for resources. A reference prototype, named T-REST engine, is implemented to verify the proposed architecture with the open-enabled style, distributed semantics, and computing offloading features. Discussions show that T-REST preserves the benefits of REST. In addition, it achieves extremely lightweight footprints and can perform computing offloading through open-enabled architectures.

RFID – Making It So…With Some Help From the University of Florida

Recent advances in passive radio frequency identification (RFID) technologies have inspired a revolution in thinking about supply chain and inventory management. Briefly, modern passive RFID involves “smart” labels, readers and associated transmitting and receiving antennas, and back-end computer control and database management systems. Figure 1 gives examples of typical RFID components. This document is Circular 1465, one of a series from the Department of Agricultural & Biological Engineering, UF/IFAS Extension. First published: March 2005.

Latency and throughput of online processing in Soft X-Ray GEM-based measurement system

The paper presents latency and performance study of the monitoring system for plasma impurities radiation in a tokamak. The developed solution measures radiation in Soft X-Ray range and can provide valuable information about the ongoing experiment with low latency. This work presents the test configuration with a single-dimensional pixel topology, with 64 channels, tested at photon rate about 100 kHz per channel. The hardware consists of the GEM-based detector, FPGAs, PCIe transmission system and the computer system with 64-bit x86 architecture. The software is the C/C++ code optimized for the execution time and working in the global trigger mode. The system uses raw data acquisition and the backend computations in CPU to provide high accuracy and high-quality results, not easily achievable otherwise. The research includes measurements of the throughput of the system and the latency of the whole data path - between the pulse detection in FPGA and storing of spectroscopy histograms in the file system in memory. The direction of improvements and further development were proposed.

Analyzing the audio signals of degenerative arthritis with an electronic stethoscope

The advance of modern medical technology has extended people's life and increased the average age of the society. Some chronic diseases are due to the aging of the population and knee joint aging is a common disease in the elderly. Common joint pathology contains degenerative arthritis, arthroncus of knees, and gouty arthritis. Knee joints are the largest and the most complicated joints in a human body as well as the joint bearing huge pressure. Wrong posture, overuse, or vigorous exercise often cause injuries to knee joints, and such injuries could easily result in joint pathology and patients falling and breaking bones due to pain and powerlessness. An acoustic wave technology, aiming at knee joints, is designed to examine a patient's current condition of joints. An electronic stethoscope or high-resolution recording equipment is utilized for collecting necessary signals, through which the wide-frequency audio signals of knee joints could be measured for the analyses and statistics in a back-end computer. Besides, it could classify the groups with healthy and degenerative knee joints to assist physicians in proceeding non-invasive joint degeneration examination clinically and doing the most suitable rehabilitation therapy.

Comparison of synthetic aperture architectures for miniaturised ultrasound imaging front-ends

BackgroundPoint of care ultrasonography has been the focus of extensive research over the past few decades. Miniaturised, wireless systems have been envisaged for new application areas, such as capsule endoscopy, implantable ultrasound and wearable ultrasound. The hardware constraints of such small-scale systems are severe, and tradeoffs between power consumption, size, data bandwidth and cost must be carefully balanced.MethodsIn this work, two receiver architectures are proposed and compared to address these challenges. Both architectures uniquely combine low-rate sampling with synthetic aperture beamforming to reduce the data bandwidth and system complexity. The first architecture involves the use of quadrature sampling to minimise the signal bandwidth and computational load. Synthetic aperture beamforming (SAB) is carried out using a single-channel, pipelined protocol suitable for implementation on an FPGA/ASIC. The second architecture employs compressive sensing within the finite rate of innovation framework to further reduce the bandwidth. Low-rate signals are transmitted to a computational back-end (computer), which sequentially reconstructs each signal and carries out beamforming.ResultsBoth architectures were tested using a custom hardware front-end and synthetic aperture database to yield B-mode images. The normalised root-mean-squared-error between the quadrature SAB image and the RF reference image was 13% while the compressive SAB error was 22% for the same degree of spatial compounding. The sampling rate is reduced by a factor of 2 (quadrature SAB) and 4.7 (compressive SAB), compared to the RF sampling rate. The quadrature method is implemented on FPGA, with a total power consumption of 4.1 mW, which is comparable to state-of-the-art hardware topologies, but with significantly reduced circuit area.ConclusionsThrough a novel combination of SAB and low-rate sampling techniques, the proposed architectures achieve a significant reduction in data transmission rate, system complexity and digital/analogue circuit area. This allows for aggressive miniaturisation of the imaging front-end in portable imaging applications.

Improved Duplicate Record Detection Using ASCII Code Q-gram Indexing Technique

With the aim of reducing duplicate records in databases, duplicate record detection (DRD) ensures the integrity of data. Its role is to identify records signifying same entities either in the same or in different compared to database. A diversity of indexing techniques has been proposed to support DRD. Q-gram is one of the common techniques used to index databases. This paper introduces modification to the Q-gram indexing technique. Such modification participates in improving the performance of the duplicate detection process and in reducing the time and number of comparisons. In the proposed work, in order to make the back-end computations easier, Q-gram strings are alternatively converted into numeric values using their corresponding ASCII code. Based on these numeric values, the indexing will decrease the complexity of Q-gram comparisons and speed up the DRD process as a whole. Unlike the existing approaches, the proposed technique is easier in implementation and requires less memory space. Two other variations of the proposed technique are introduced in this paper to decrease the matching process time; the first uses a range for matching, while the second sorts words alphabetically inside blocks. According to experimental results, the three proposed techniques perform much faster and are almost as accurate as the current Q-gram technique, meaning that they can be used in large-sized databases DRD.