Small Computer Research Articles

A concise 40 T pulse magnet for condensed matter experiments

There is a growing interest in using pulsed high magnetic field as a controlling parameter of physical phenomena in various scientific disciplines, such as condensed matter physics, particle physics, plasma physics, chemistry, and biological studies. We devised a concise and portable pulsed magnetic field generator that produces a 40 T field with a pulse duration of 2 ms. It is assembled using only off-the-shelf components and a homemade coil that leverages small computers, Raspberry Pi, and Python codes. It allows for straightforward modification for general purposes. As working examples, we show representative applications in condensed matter experiments of magnetoresistance, magnetization, and magnetostriction measurements for graphite, NdNi2P2, and NdCo2P2, respectively, with the maximum magnetic field of 41 T and the lowest temperature of 4.2 K.

Pose Estimation of a Cobot Implemented on a Small AI-Powered Computing System and a Stereo Camera for Precision Evaluation.

The precision of robotic manipulators in the industrial or medical field is very important, especially when it comes to repetitive or exhaustive tasks. Geometric deformations are the most common in this field. For this reason, new robotic vision techniques have been proposed, including 3D methods that made it possible to determine the geometric distances between the parts of a robotic manipulator. The aim of this work is to measure the angular position of a robotic arm with six degrees of freedom. For this purpose, a stereo camera and a convolutional neural network algorithm are used to reduce the degradation of precision caused by geometric errors. This method is not intended to replace encoders, but to enhance accuracy by compensating for degradation through an intelligent visual measurement system. The camera is tested and the accuracy is about one millimeter. The implementation of this method leads to better results than traditional and simple neural network methods.

Deep Learning Method for Real-Time Fire Detection System for Urban Fire Monitoring and Control

During urban fire incidents, real-time videos and images are vital for emergency responders and decision-makers, facilitating efficient decision-making and resource allocation in smart city fire monitoring systems. However, real-time videos and images require simple and embeddable models in small computer systems with highly accurate fire detection ratios. YOLOv5s has a relatively small model size and fast processing time with limited accuracy. The aim of this study is to propose a method that employs a YOLOv5s network with a squeeze-and-excitation module for image filtering and classification to meet the urgent need for rapid and accurate real-time screening of irrelevant data. In this study, over 3000 internet images were used for crawling and annotating to construct a dataset. Furthermore, the YOLOv5, YOLOv5x and YOLOv5s models were developed to train and test the dataset. Comparative analysis revealed that the proposed YOLOv5s model achieved 98.2% accuracy, 92.5% recall, and 95.4% average accuracy, with a remarkable processing speed of 0.009 s per image and 0.19 s for a 35 frames-per-second video. This surpasses the performance of other models, demonstrating the efficacy of the proposed YOLOv5s for real-time screening and classification in smart city fire monitoring systems.

Dynamic Key Generation Using GWO for IoT System

One well-known technological advancement that significantly impacts many things is the Internet of Things (IoT). These include connectivity, work, healthcare, and the economy. IoT can improve life in many situations, including classrooms and smart cities, through work automation, increased output, and decreased worry. However, cyberattacks and other risks significantly impact intelligent Internet of Things applications. Key generation is essential in information security and the various applications that use a distributed system, networks, or Internet of Things (IoT) systems. Several algorithms have been developed to protect IoT applications from malicious attacks; since IoT devices usually have small memory resources and limited computing and power resources, traditional key generation methods are inappropriate because they require high computational power and memory usage. This paper proposes a method of Dynamic Key Generation Method (DKGM) to overcome the difficulty using a specific chaotic map called the Zaslavskii Map and a swarm intelligent algorithm for optimization called Grey Wolf Optimizer (GWO). DKGM's ability to generate several groups-seed numbers using the Zaslavskii map depends on various initial parameters. GWO selects strong generated numbers depending on the randomness test as a fitness function. Three wolfs GWα, GWβ, and GWΩ, are used to simulate the behavior of a pack of grey wolves when attacking prey. The speed and position of each wolf are updated depending on the best three wolves. Finally, use the sets GWα in the round, GWβ in the subkey, and GWΩ in shifting operations of the Chacha20 hash function. The dynamic procedure was used to improve the high-security analysis of the DKGM approach over earlier methods. Simulations show that the suggested method is preferable for IoT applications.

ANCHOR: A New Proposed Stream Cipher for Smart Cards with Crypto Co-Processor

A smart card is a small pocket-sized computer with limited resources used for secure data processing and storage. The card consists of different software and hardware components, including a microprocessor, crypto co-processor, RAM, secure ROM, and operating system. Even though smart cards have a lot of limitations in terms of processing power and small-sized memory, smart cards are widely used in many applications that require a high degree of security such as e-passports, citizen cards, e- banking, etc... Basically, the security of a smart card depends mainly on the security strength of the cipher algorithm implemented inside. This paper presents a new lightweight, high-speed, and cryptographically strong stream cipher algorithm (ANCHOR) suitable for implementation on smart cards. The building blocks of the proposed cipher were carefully built/chosen with high-valued cryptographic properties. The randomness and linear complexity properties of the proposed cipher algorithm have been successfully tested with statistical tests of the NIST suite and the Berlekamp- Massey algorithm respectively. In order to test the performance of the proposed cipher algorithm, the algorithm was developed in C language and executed on a Linux machine.

Global challenges and the ‘farm to fork’ strategies of the European Green Deal: Blessing or curse

AbstractThe article evaluates how well the goals of the European Green Deal are justified, especially considering the risks to energy and food security arising from the conflict between Russia and Ukraine. We agree with the objectives of the European Green Agreement as a whole, but whether some of the objectives which feature in the EASAC study can be achieved by 2030 is questionable, and the description of the tools necessary to achieve the objectives is incomplete. Among other things, there is hardly any mention of the role played by precision farming with digitalization, which is a revolutionary change from an ecological and economic point of view, in reducing the use of synthetic inputs, in regenerating the original state of the soil, in reducing GHG emissions, thus in increasing biodiversity, and at the same time in intensifying production, and finally in expanding the application of biotechnology. We examine these areas in our analysis. Some of the objectives of the EASAC study to be achieved by 2030 are subject to debate, and the description of the information and communication conditions necessary to achieve the objectives is incomplete. The IoT (Internet of Things) responds to global and local challenges: it integrates the precision technologies, WSNs (Wireless Sensor Networks), artificial intelligence, mobile field (Smart Small Robots) and remote data loggers (UAVs: Unmanned Air Vehicles and satellites), Big Data, and cloud computing. Consequently, decision support is increasingly developing into unmanned decision making. IoT (Internet of Things) is the basis of “Farm to Fork” and “Lab to Field” monitoring approaches.This article evaluates the implementation of European Green Agreement objectives in light of energy and food security risks arising from the Russia-Ukraine conflict. While overall support for the agreement exists, the feasibility of certain EASAC study objectives by 2030 is called into question due to insufficient tools specifications. Notably absent is the emphasis on precision farming with digitalization, which is a transformative ecological and economic practice. Our analyses look into its function in reducing synthetic inputs, soil regeneration, GHG emission reduction, biodiversity enhancement, production intensification, and biotechnology development. Debates surround EASAC study objectives for 2030, despite limited information and communication restrictions. The Internet of Things (IoT) arises as a solution, combining precision technology, WSNs (wireless sensor networks), AI (artificial intelligence), smart small robots, UAVs (unmanned aerial vehicles), satellites, big data, and cloud computing. As a result, decision support turns toward unmanned decision-making, with IoT laying the groundwork for “Farm to Fork” and “Lab to Field” monitoring systems.

A Deep Learning Based Framework for Accurate Prediction of Disease of Crops and Nutrients in Smart Agriculture

Abstract: The health and production of crops have a major impact on societal well-being, and agriculture is essential to maintaining global food security. Crop output and quality are greatly impacted by nutrient shortages and crop diseases. We offer a unique approach to this problem that makes use of the Raspberry Pi, an inexpensive, small, and energy-efficient single-board computer, to forecast crop illnesses and identify nutrient deficits in real time. Our solution provides farmers and other agricultural stakeholders with fast and accurate information for efficient crop management by combining computer vision, machine learning, and sensor technologies. The Raspberry Pi has many sensors to assess ambient and soil conditions in addition to a high-resolution camera for taking pictures. Using a machine learning model trained on a variety of datasets pertaining to agricultural illnesses and nutritional deficits, the system takes pictures of crop leaves and soil conditions. Preprocessing images, feature extraction, and convolutional neural network (CNN)-based categorization are important parts of our approach. The system can recognize evidence of nutrient deficits, such as nitrogen, phosphorus, and other deficiencies, as well as symptoms of a variety of crop illnesses, including fungal infections, bacterial blights, and viral diseases.

A Robust Methodology for Dynamic Proximity Sensing of Vehicles Overtaking Micromobility Devices in a Noisy Environment

The safety of cyclists, e-scooters, and micromobility devices in urban environments remains a critical concern in sustainable urban planning. A primary factor affecting this safety is the lateral passing distance (LPD) or dynamic proximity of motor vehicles overtaking micromobility riders. Minimum passing distance laws, where motorists are required to maintain a minimum distance of 1.5 m when passing a cyclist, are difficult to enforce due to the difficulty in determining the exact distance between a moving vehicle and a cyclist. Existing systems reported in the literature are invariably used for research and require manual intervention to record passing vehicles. Further, due to the dynamic and noisy environment on the road, the collected data also need to be manually post-processed to remove errors and false positives, thus making such systems impractical for use by cyclists. This study aims to address these two concerns by providing an automated and robust framework, integrating a low-cost, small single-board computer with a range sensor and a camera, to measure and analyze vehicle–cyclist passing distance and speed. Preliminary deployments in Singapore have demonstrated the system’s efficacy in capturing high-resolution data under varied traffic conditions. Our setup, using a Raspberry Pi 4, LiDAR distance sensor, a small camera, and an automated data clustering technique, had a high success rate for correctly identifying the number of close vehicle passes for distances between 1 and 1.5 m. The insights garnered from this integrated setup promise not only a deeper understanding of interactions between motor vehicles and micromobility devices, but also a roadmap for data-driven urban safety interventions.

Smart Safety Belt

This project is all about creating a special Belt designed to enhance Smart's safety. In today's world, Smart Women often feel vulnerable and concerned about their well-being, especially when they are out alone or in unfamiliar surroundings. To address this issue, we have developed a smart Belt equipped with features like GPS, a distress signal, a camera. All these components are controlled by a small computer called ESP 32. The primary goal of this project is to make Women feel safer and more in control of their personal security. If a woman ever finds herself in an unsafe situation, she can use the Belt to send her location to the police, her family, or trusted contacts. The Belt also includes a notification to alert people. In case of a physical threat, the Belt can capture an image of the person for possible identification. Our project demonstrates that this innovative Belt can provide a practical and reliable solution for Smart's safety concerns. We envision a future where such technology can be further expanded to safeguard other vulnerable groups and individuals, making the world a safer place for everyone. Key Words: Smart's Safety, Smart Belt, GPS Technology, Distress Signal, Personal Security, ESP 32 Control

Real-Time Hardware Emulation of Microgrid Forming Wireless Power Transfer Systems

The prevalence of wireless charging approaches has manifested a grid-supporting potential which can be evaluated by hardware-based methodologies. In this work, real-time emulation of a bidirectional wireless power transfer (WPT) system capable of DC microgrid formation is investigated on the field-programmable gate array (FPGA). Following a detailed analysis of the transfer characteristics of the dual-active bridge with a series-compensated resonant tank in between, a unified control scheme utilizing a phase-shift strategy is proposed for a flexible voltage or current regulation, thereby enabling the DC microgrid-forming capability. Electromagnetic transient modeling is then carried out so that an accurate digital emulation platform is feasible for prototyping. The fact that the WPT has a high frequency compels a small computation step size, which poses a dramatic challenge to real-time execution. A partition-iteration approach is therefore proposed for matrix dimension reduction which ultimately results in an alleviated processing burden. In the meantime, the parallelism of configurable logic blocks and the pipelined architecture of the FPGA are explored to achieve a low hardware latency. The analytical models, as well as the proposed control method, are validated experimentally, and then real-time hardware emulation of a DC microgrid consisting of WPT systems is performed for an integration study.

Challenges in data representation for efficient execution of encryption operation

Big number operation has always been a bottleneck to computer system as it imposes high demand on computing power. With a limited power available, operations such as exponentiation and multiplication involving large integer belonging to encryption process requires grave scrutiny. One way to address this issue is by replacing an original complex computation into a sequence of small computations that in the end produces the same results. This paper takes an evolutionary approach to survey numerous articles that have contributed to the advancement of integer representation. Numerous representations were proposed, those that come into play concentrated on reducing non-zero digits and limiting non-zero spacing other than allowing subtraction operation. A comparison was made to distinguish the properties of each method from the others. This detailed outlook can be a guide for identifying the correct representation to be chosen for implementation within specific application.

Cost-effective 3D documentation device in forensic medicine

3D documentation in forensics and forensic medicine is being introduced more frequently in various institutes around the world. However, several institutes lack capacity in finances as well as staff to perform 3D documentations regularly. This technical paper aims to present a 3D documentation device that is low cost and easy to use and is a viable entry level solution for forensic medical departments. For this the small single-board computer Raspberry Pi 4 was used in conjunction with its high quality (HQ) camera module to create the 3DLamp - a flexible, low cost and easy to use documentation device. Besides a detailed description of the device this paper also presents four case examples where a 3D documentation was performed and analyses the acquired data and the created 3D models. It was found that the device returns feasible 3D models that appear usable for forensic 3D reconstructions.

СУЧАСНІ РІШЕННЯ ТА НАПРЯМКИ РОЗВИТКУ ОСНОВНИХ ЕЛЕМЕНТІВ СИСТЕМИ ТОЧНОГО ЗЕМЛЕРОБСТВА

One of the most advanced directions in agriculture is precision agriculture, which is an integrated process of managing plant growth according to their needs. This technology has become possible due to the development of informatics, communication systems, and progress in the automation of agricultural machinery. A fundamental part of precision agriculture is the development and adaptation of strategies and practices for farming in modern conditions. The main focus is on measuring, understanding, and applying practical factors that affect plants, such as soil hydro-physical and chemical properties, landscape, seeds, applied technology, sowing and harvesting schedules, diseases and pests, weeds, and agro-climatic conditions. The purpose of the work is to explore innovative, modern solutions, and development directions of the basic elements of precision agriculture systems that will contribute to making informed management decisions to ensure food security in agricultural production. The work emphasizes that precision agriculture is considered an integral part of resource-saving ecological farming, involving the application of an integrated management system rather than individual separate elements. Thanks to the rapid development of information technologies based on innovative achievements in cybernetics, optics, sensor systems, laser and computer technology, satellite navigation systems, sensors of various purposes, and wireless communication means, solutions have been developed that integrate all elements of the agricultural machinery complex into a single whole. Among the modern solutions and development directions of precision agriculture systems are the application of satellite remote sensing with spatial resolution up to a few centimeters (GeoEye and WorldView); UAVs equipped with multispectral cameras, various sensors, satellite navigation systems, a small onboard computer, and equipment for chemical application; ground sensors designed for continuous (mobile) monitoring of plant and soil properties, such as nitrogen content, water stress, organic matter, and soil moisture (ISARIA, MiniVeg-N, and YARA N); and the use of systems such as Agronomic Decision-making Engine (ADE), See & Spray Ultimate, AiCPlus, SmartSprayer, etc. It is noted that with the development of electronics and information technologies, various sensor systems have emerged, widely used in agricultural production to control the production process. To successfully implement precision agriculture technologies, reliable information about the soil, plants, the surrounding environment, and their variability in both space (within a field) and time is necessary.

Adaptive multi-dimensional Taylor network tracking control of time-varying delay nonlinear systems subject to input saturation

For the tracking control problem of a class of nonlinear systems with input saturation constraint and time-varying delay, an adaptive tracking control scheme based on multi-dimensional Taylor network (MTN) is designed, and the proposed control scheme has the advantages of simple structure and small computation. A Gaussian error function is introduced to transform the input saturation into a linear model with bounded error. The Lyapunov-Krasovskii function is constructed, and the nonlinearity of the system is approximated using MTN, and an adaptive control strategy is constructed based on the backstepping method. The results show that the proposed control method can both ensure that all signals in the closed-loop system are bounded and achieve convergence of the tracking error to a small neighborhood near the origin. Numerical simulations verify the effectiveness of the method.

A low-cost open-source imaging platform reveals spatiotemporal insight into leaf elongation and movement.

Plant organs move throughout the diurnal cycle, changing leaf and petiole positions to balance light capture, leaf temperature, and water loss under dynamic environmental conditions. Upward movement of the petiole, called hyponasty, is one of several traits of the shade avoidance syndrome (SAS). SAS traits are elicited upon perception of vegetation shade signals such as far-red light (FR) and improve light capture in dense vegetation. Monitoring plant movement at a high temporal resolution allows studying functionality and molecular regulation of hyponasty. However, high temporal resolution imaging solutions are often very expensive, making this unavailable to many researchers. Here, we present a modular and low-cost imaging set-up, based on small Raspberry Pi computers, that can track leaf movements and elongation growth with high temporal resolution. We also developed an open-source, semi-automated image analysis pipeline. Using this setup we followed responses to FR enrichment, light intensity, and their interactions. Tracking both elongation and the angle of the petiole, lamina, and entire leaf in Arabidopsis (Arabidopsis thaliana) revealed insight into R:FR sensitivities of leaf growth and movement dynamics and the interactions of R:FR with background light intensity. The detailed imaging options of this system allowed us to identify spatially separate bending points for petiole and lamina positioning of the leaf.

Benchmarking a neutral-atom quantum computer

In this study, we simulated the algorithmic performance of a small neutral atom quantum computer and compared its performance when operating with all-to-all versus nearest-neighbor connectivity. This comparison was made using a suite of algorithmic benchmarks developed by the Quantum Economic Development Consortium. Circuits were simulated with a noise model consistent with experimental data from [Nature 604, 457 (2022)]. We find that all-to-all connectivity improves simulated circuit fidelity by [Formula: see text]–[Formula: see text], compared to nearest-neighbor connectivity.

A Lightweight Algorithm for Recognizing Pear Leaf Diseases in Natural Scenes Based on an Improved YOLOv5 Deep Learning Model

The precise detection of diseases is crucial for the effective treatment of pear trees and to improve their fruit yield and quality. Currently, recognizing plant diseases in complex backgrounds remains a significant challenge. Therefore, a lightweight CCG-YOLOv5n model was designed to efficiently recognize pear leaf diseases in complex backgrounds. The CCG-YOLOv5n model integrates a CA attention mechanism, CARAFE up-sampling operator, and GSConv into YOLOv5n. It was trained and validated using a self-constructed dataset of pear leaf diseases. The model size and FLOPs are only 3.49 M and 3.8 G, respectively. The mAP@0.5 is 92.4%, and the FPS is up to 129. Compared to other lightweight indicates that the models, the experimental results demonstrate that the CCG-YOLOv5n achieves higher average detection accuracy and faster detection speed with a smaller computation and model size. In addition, the robustness comparison test CCG-YOLOv5n model has strong robustness under various lighting and weather conditions, including frontlight, backlight, sidelight, tree shade, and rain. This study proposed a CCG-YOLOv5n model for accurately detecting pear leaf diseases in complex backgrounds. The model is suitable for use on mobile terminals or devices.

Brain tumor classification using fine-tuned transfer learning models on magnetic resonance imaging (MRI) images.

Brain tumors are a leading global cause of mortality, often leading to reduced life expectancy and challenging recovery. Early detection significantly improves survival rates. This paper introduces an efficient deep learning model to expedite brain tumor detection through timely and accurate identification using magnetic resonance imaging images. Our approach leverages deep transfer learning with six transfer learning algorithms: VGG16, ResNet50, MobileNetV2, DenseNet201, EfficientNetB3, and InceptionV3. We optimize data preprocessing, upsample data through augmentation, and train the models using two optimizers: Adam and AdaMax. We perform three experiments with binary and multi-class datasets, fine-tuning parameters to reduce overfitting. Model effectiveness is analyzed using various performance scores with and without cross-validation. With smaller datasets, the models achieve 100% accuracy in both training and testing without cross-validation. After applying cross-validation, the framework records an outstanding accuracy of 99.96% with a receiver operating characteristic of 100% on average across five tests. For larger datasets, accuracy ranges from 96.34% to 98.20% across different models. The methodology also demonstrates a small computation time, contributing to its reliability and speed. The study establishes a new standard for brain tumor classification, surpassing existing methods in accuracy and efficiency. Our deep learning approach, incorporating advanced transfer learning algorithms and optimized data processing, provides a robust and rapid solution for brain tumor detection.

Boosting RDataFrame performance with transparent bulk event processing

Boosting RDataFrame performance with transparent bulk event processing

OPTIMIZING LANTANA CLASSIFICATION: HIGH-ACCURACY MODEL UTILIZING FEATURE EXTRACTION

As an invasive and poisonous plant, Lantana has become a pest in the agricultural world. Still, on the other hand, it becomes an ornamental plant with different positive potentials. Lantana flower datasets are not yet widely available for open image classification research, given that the research needs are still broad in remote sensing. This study aims to provide a model with classifier accuracy that outperforms similar studies and Lantana datasets for classification needs using several algorithms that can be run on small source computers. This study used five types of lantana colors, red, white, yellow, purple, and orange, as the primary dataset, which had 411 instances. VGG16 assisted feature extraction in preparing datasets for the data training using three classifiers: decision tree, AdaBoost, and k-NN. 2-fold cross-validation, 5-fold cross-validation, and a self-organizing map are used to help validate each process. The experiment to measure the classifier's performance resulted in a good figure of 99.8% accuracy for 2-fold cross-validation, 100% for 5-fold cross-validation, and a primary dataset of lantana interest that can be accessed freely on the IEEE Data port. This study outperformed other related studies in terms of classifier accuracy.