Explore the vast range of titles available.

\"Fully updated coverage of PCB design and construction with EAGLE. This thoroughly revised, easy-to-follow guide shows, step-by-step, how to create your own professional-quality PCBs using the latest versions of EAGLE. Make your own PCBs with Eagle: from schematic designs to finished boards, Second Edition, guides you through the process of a developing a schematic, transforming it into a PCB layout, and submitting Gerber files to a manufacturing service to fabricate your finished board. Four brand-new chapters contain advanced techniques, tips, and features. Downloadable DIY projects include a sound level meter, Arduino shield, Raspberry Pi expansion board, and more!\"--Page 4 of cover.

A measurement and development platform for collecting water quality data (the WaterWatcher) was developed. The platform includes sensors to measure turbidity, total dissolved solids (TDS), and water temperature as variables that are often collected to assess water quality. The design is extensible for research and monitoring purposes, and all the design files are provided under open-source permissive licenses for further development. System design and operation are discussed for illustrative purposes. A block diagram indicates elements of mechanical, electrical, and software design for this system. The mechanical assembly used to house circuit boards and sensors is designed using 3D printing for rapid prototyping. The electronic circuit board acts as a carrier for an Arduino 32-bit microcontroller board and an associated cellular module along with a GPS for geolocation of water quality measurements. The cellular module permits data transfer for Internet of Things (IoT) functionality. System operation is set up using a command line interface (CLI) and C + + code that allows for calibration coefficients and human-readable transfer functions to be defined so that sensor voltages are related to physical quantities. Data are cached on a secure digital (SD) card for backup. The circuit was calibrated, and system operation assessed by deployment on an urban reservoir. Biogeochemical cycles were identified in the collected data using spectrogram and semivariogram analyses to validate system operation. As a system with hardware and software released under an open source license, the WaterWatcher platform reduces the time and effort required to build and deploy low-cost water quality measurement sensors and provides an example of the basic hardware design that can be used for measurements of water quality.

This paper presents the development and application of an intelligent system based on computer vision and artificial intelligence for monitoring and automatic correction of the adhesive application process on printed circuit boards (PCB) in the electronics industry. The adhesive application process is essential for the precise fixing of components, and eventual failures can compromise the quality and performance of the final products. To automate visual inspection and reduce the occurrence of human errors, a convolutional neural network (CNN) model trained with real images of the production line was developed, capable of identifying correct patterns and failures in the application of the adhesive. The system integrates high-resolution cameras, image processing software and a control interface, enabling real-time monitoring and the execution of automatic corrective actions. The results obtained demonstrate the effectiveness of the proposed system, with a high level of accuracy in detecting faults, contributing to improving the quality of the production process and aligning with the principles of Industry 4.0. The research concludes that the adoption of intelligent systems based on computer vision represents a significant advance for quality control in the manufacturing of electronic devices. Keywords: Computer Vision. Artificial Intelligence. PCB Assembly. Industry 4.0. Este artigo apresenta o desenvolvimento e aplicacao de um sistema inteligente baseado em visao computacional e inteligencia artificial para monitorizacao e correcao automatica do processo de aplicacao de adesivos em placas de circuito impresso (PCB) na industria eletronica. O processo de aplicacao de adesivos e essencial para a fixacao precisa de componentes, e eventuais falhas podem comprometer a qualidade e o desempenho dos produtos finais. Para automatizar a inspecao visual e reduzir a ocorrencia de erros humanos, foi desenvolvido um modelo de rede neural convolucional (CNN) treinado com imagens reais da linha de producao, capaz de identificar padroes corretos e falhas na aplicacao do adesivo. O sistema integra camaras de alta resolucao, software de processamento de imagem e uma interface de controlo, permitindo a monitorizacao em tempo real e a execucao de accoes corretivas automaticas. Os resultados obtidos demonstram a eficacia do sistema proposto, com um elevado nivel de precisao na detecao de falhas, contribuindo para a melhoria da qualidade do processo produtivo e alinhando com os principios da Industria 4.0. A investigacao conclui que a adocao de sistemas inteligentes baseados em visao computacional representa um avanco significativo para o controlo de qualidade no fabrico de dispositivos eletronicos. Palavras-chave: Visao Computacional. Inteligencia Artificial. Montagem de PCBs. Industria 4.0. Este trabajo presenta el desarrollo y aplicacion de un sistema inteligente basado en vision por ordenador e inteligencia artificial para la monitorizacion y correccion automatica de el proceso de aplicacion de adhesivo en placas de circuito impreso (PCB) en la industria electronica. El proceso de aplicacion de adhesivo es esencial para la fijacion precisa de los componentes, y eventuales fallos pueden comprometer la calidad y el rendimiento de los productos finales. Para automatizar la inspeccion visual y reducir la aparicion de errores humanos, se desarrollo un modelo de red neuronal convolucional (CNN) entrenado con imagenes reales de la linea de produccion, capaz de identificar patrones correctos y fallos en la aplicacion del adhesivo. El sistema integra camaras de alta resolucion, software de procesamiento de imagenes y una interfaz de control, permitiendo la monitorizacion en tiempo real y la ejecucion de acciones correctivas automaticas. Los resultados obtenidos demuestran la eficacia del sistema propuesto, con un alto nivel de precision en la deteccion de fallos, contribuyendo a mejorar la calidad del proceso productivo y alineandose con los principios de la Industria 4.0. La investigacion concluye que la adopcion de sistemas inteligentes basados en vision por computador representa un avance significativo para el control de calidad en la fabricacion de dispositivos electronicos. Palabras clave: Vision por computador. Inteligencia Artificial. Montaje de PCB. Industria 4.0.

A ceramic-based wideband capacitive-fed patch-loaded multi-layered rectangular dielectric resonator antenna (CFPL-ML-RDRA) with a compact form factor is proposed in this paper. The proposed antenna is composed of two ceramic substrates and a polymer as an adhesive. A capacitive-fed metallic patch structure is located on the top side of the bottom ceramic substrate. This novel structure generates two distinct resonant modes: the fundamental resonant mode of the RDRA and a hybrid resonant mode, which was confirmed through electric field (E-field) analysis and parametric studies. By merging these two resonant modes, the proposed antenna achieves a wide impedance bandwidth of 5.5 GHz, sufficient to cover the fifth-generation (5G) millimeter-wave (mmWave) frequency bands n257, n258, and n261 (5.25 GHz), while reducing the height of the DRA by 38.5% compared to the conventional probe-fed RDRA (PF-RDRA). Additionally, the 4 dBi realized gain bandwidth of the proposed CFPL-ML-RDRA is 5.4 GHz, which is 28.6% broader than that of the conventional PF-RDRA. To experimentally verify the antenna’s performance, the CFPL-ML-RDRA mounted on a test printed circuit board with a small ground size of 3.2 × 3.2 mm2 was fabricated and characterized. The measured data align well with the simulated data. Furthermore, excellent antenna array performance was achieved based on array simulations. Therefore, the proposed antenna structure is well-suited for 5G mmWave mobile applications.

The steering gear device includes two parts, a steering gear control circuit and a transmission component. The transmission component includes a ball screw and a motor. During the operation of the steering gear, due to the presence of the steering gear ball screw motor and friction, a certain amount of heat will be generated, which will affect the steering gear control circuit in a confined space. At the same time, the steering gear is inevitable in the actual working process, and will experience a high temperature environment, which will increase the temperature of the internal structure of the steering gear, and due to the difference in thermal expansion coefficients between various materials, stress and strain will occur in the structure, which may cause mismatch or even cracks in the system structure, and the steering gear system cannot work normally. It is necessary to analyze the thermal characteristics of the overall steering gear under multiple factors. Based on this, this paper uses COMSOL three-dimensional simulation software to conduct thermal simulation analysis on the shell of the steering gear containing the control circuit board. The temperature distribution and stress-strain response law of the control circuit board in the box, and the influence of different materials and thickness of the box heat insulation layer on the thermal characteristics of the control circuit are discussed, and then a reasonable thickness and material of the heat insulation layer are obtained for the design of the rudder chassis for reference.

For high-speed communication services such as 5G technology, the use of millimeter-wave (mmWave) components substantially increases in mobile applications. The interconnect based on a substrate-integrated waveguide (SIW) is an efficient solution for connecting these devices. However, the SIW characteristics in the mmWave frequency range are not sufficiently presented from the practical viewpoint. In this paper, the experimental characterization of mmWave SIWs in flexible printed circuit boards (FPCBs) and their simulation results are presented. A practical method using balanced/single slot transition is proposed for microstrip-to-SIW transition. Using a full-wave simulation and genetic algorithm, the proposed slot technique is optimized. It is experimentally demonstrated that the cutoff frequency affects the operating band of the SIW differently. The per-unit-length losses of the full-mode and half-mode SIWs are obtained as 0.0375 dB/mm and 0.0609 dB/mm, respectively. Using the measurements, the SIW type effect on the transmission loss is quantitatively analyzed, and the loss is increased up to 62.4% at 39 GHz.

A compact isolated low-power 32-channel 16-bit data-logging system around an NXP ARM processor (LPC1768) and four of the linear technology octal analogue-to-digital converters (ADCs) LTC1857/58/59 is designed. The system requires only 250 mA when powered at 5 V to run at full power (including a capacitive 2.8 inch touch-screen display and 32 Gb SHDC SD memory card). The sample rate is configurable up to 1 k SPS per channel as well as voltage dynamic input up to ±10 V; additionally, 32 chip select lines (SPI protocol) individually addressable and controllable while sampling to configure user-designed programmable gain amplifiers (PGAs) are available. Collaboration is being sought to improve the software capabilities, particularly to enrich the very basic user interface and to add wireless connectivity. The code is available (under the GPL licence) at our repository, the gerber file to reproduce the PCB is available (on the As-Is basis) on request. The galvanic isolation between the power supply data connection and ADC channels makes the data-logger also compatible with the main powered PCs, hence it is suitable for the implementation of medical devices at least for the prototyping and initial testing stages.

The piezoelectric needle selector is a crucial component of computerized dobby weft knitting machines. With the continuous development of weft knitting machine technology, enhancing the accuracy of piezoelectric needle selector control is essential. Accurate determination of whether the blades are in the correct position would significantly improve the precision of piezoelectric needle selector control. In this study, piezoelectric ceramic sensors were used to collect impact vibration signals when the blades struck the damper baffle. Key hardware circuits were designed for this purpose. A self-learning algorithm was employed to capture the highest point and the time it takes to reach the highest point in the impact vibration signal. A fault detection algorithm was used to implement closed-loop fault detection for piezoelectric needle selectors. Experimental results and practical applications have demonstrated that this research effectively addresses the accurate determination of whether the piezoelectric needle selector blades are in the correct position. It has reduced the defect rate of fabric production in weft knitting, thereby improving the overall efficiency and profitability of businesses.

This study addresses the complexities of carbon quota trading markets amidst global warming concerns, proposing a deep reinforcement learning (DRL)-based decision-making model to enhance trading strategies. Acknowledging the limitations of conventional methods in navigating volatile carbon prices, policy shifts, and informational disparities, the research integrates DRL's advanced capabilities. It commences with an overview of DRL principles and its successful applications, followed by an analysis of market dynamics and trading nuances. A DRL model is then formulated, delineating state-action spaces and a tailored reward function for optimized learning within the carbon trading context. Model refinement involves hyperparameter tuning for superior performance. The summary concludes with an evaluation of the model's efficacy, highlighting its adaptability and computational demands, while outlining avenues for further enhancement and real-world implementation to combat climate change through improved carbon market operations.