In the current chip quality detection industry, detecting missing pins in chips is a critical task, but current methods often rely on inefficient manual screening or machine vision algorithms deployed in power-hungry computers that can only identify one chip at a time. To address this issue, we propose a fast and low-power multi-object detection system based on the YOLOv4-tiny algorithm and a small-size AXU2CGB platform that utilizes a low-power FPGA for hardware acceleration. By adopting loop tiling to cache feature map blocks, designing an FPGA accelerator structure with two-layer ping-pong optimization as well as multiplex parallel convolution kernels, enhancing the dataset, and optimizing network parameters, we achieve a 0.468 s per-image detection speed, 3.52 W power consumption, 89.33% mean average precision (mAP), and 100% missing pin recognition rate regardless of the number of missing pins. Our system reduces detection time by 73.27% and power consumption by 23.08% compared to a CPU, while delivering a more balanced boost in performance compared to other solutions.

Convolutional neural networks (CNNs) are widely used in geotechnical engineering. Real-time detection in complex geological environments, combined with the strict power constraints of embedded devices, makes Field-Programmable Gate Array (FPGA) platforms ideal for accelerating CNNs. Conventional parallelization strategies in FPGA-based accelerators often result in imbalanced resource utilization and computational inefficiency due to varying kernel sizes. To address this issue, we propose a customized heterogeneous hybrid parallel strategy and refine the bit-splitting approach for Digital Signal Processor (DSP) resources, improving timing performance and reducing Look-Up Table (LUT) consumption. Using this strategy, we deploy the lightweight YOLOv5n network on an FPGA platform, creating a high-speed, low-power subsurface geotechnical defect-detection system. A layer-wise quantization strategy reduces the model size with negligible mean average precision (mAP) loss. Operating at 300 MHz, the system reduces LUT usage by 33%, achieves a peak throughput of 328.25 GOPs in convolutional layers, and an overall throughput of 157.04 GOPs, with a power consumption of 9.4 W and energy efficiency of 16.7 GOPs/W. This implementation demonstrates more balanced performance improvements than existing solutions.

River pollution is becoming more and more serious. In order to promote the construction of ecological civilization, this paper proposes a river pollution source monitoring and positioning system based on IoT. The main body of the system is composed of eight detection nodes and one sink node. All eight nodes can collect PH and turbidity values in the river through sensors, then send data to the sink node through radio frequency wireless transmission, which will process and report the data. Finally, the applet can realize the function of receiving data and timely warning, besides, users can observe the dynamic changes of PH value and turbidity value.

It is of great practical significance to study intelligent robots for crack detection of urban underground pipes instead of manual detection, which not only can save time and cost, but also can improve the accuracy rate of identification. The system consists of two parts: mobile platform and system detection platform. The mobile platform is obstacle-avoiding by ultrasonic module, segmenting the pipeline color by camera and issuing platform steering command. The system detection platform adopts Raspberry Pi as the core control system, and collects the pipeline leakage audio through the pickups, and the collected audio is passed through Gaussian mixture model-Hidden Markov model (GMM -HMM) and Deep neural networks- Hidden Markov model (DNN -HMM) respectively in Raspberry Pi model (DNN -HMM) for the purpose of detection. The accuracy and running speed of the two methods were tested separately through simulation experiments. GMM -HMM achieves 74.1% accuracy and 14 (frame/s) running speed, while DNN -HMM achieves 80.6% accuracy and 9 (frame/s) running speed, DNN -HMM has higher accuracy and running speed to meet the real-time requirements for pipeline leakage audio detection.

To address the voltage regulation problem of the DC-DC buck converter under multi-source disturbances, this paper proposes a composite anti-disturbance control strategy integrating a Chebyshev-based self-evolving fuzzy neural network (SECFNN) and an arctangent super-twisting sliding mode control (ASTSMC). First, to construct the composite anti-disturbance framework, a load algebraic reconstruction compensator (LARC) is utilized to analytically estimate real-time load dynamics, providing active feedforward compensation for extreme load steps. Second, targeting the unmodeled nonlinearities and parameter uncertainties, the SECFNN is deeply integrated into the control loop. It employs a bidirectional structural learning mechanism—dynamically growing and pruning fuzzy rules—to achieve high-precision adaptive approximation and intelligent compensation. Furthermore, serving as the robust inner-loop core of this composite strategy, the ASTSMC is introduced. By replacing the traditional discontinuous sign function with a continuous arctangent operator, it effectively mitigates sliding mode chattering while ensuring the rapid finite-time convergence of the current tracking error. Ultimately, by synergistically fusing feedforward disturbance rejection (LARC), intelligent nonlinear approximation (SECFNN), and robust tracking (ASTSMC), the proposed strategy significantly reduces transient voltage drops and achieves smoother steady-state performance. Comparative simulation experiments demonstrate the superiority of the proposed method, achieving a rapid startup settling time of 6.5 ms, limiting the maximum transient voltage drop to 15 mV, and completing dynamic reference tracking in 1.2 ms. Furthermore, hardware experimental results confirm its practical engineering feasibility, demonstrating a fast startup of 8.3 ms with zero overshoot, effectively mitigating transient voltage drops during load step changes, and completing dynamic tracking in just 2.2 ms, which verifies its reliable dynamic agility and strong robustness under various test conditions.

In recent years, with the rapid development of society and technology, the size of teaching facilities in colleges and universities has continued to expand, and most of the energy management in teaching areas is still controlled by people, leading to problems such as low management efficiency and serious energy waste. To address these types of problems, an intelligent classroom management system is proposed in this paper, which is based on the efficient implementation of low-complexity convolutional neural networks in STM32H7 series microprocessors and the coordination of each sensing module. The system has the option to automatically detect changes in the classroom environment to control the switching of various energy sources, or to remotely control the switching of the energy system via Wi-Fi from a mobile phone terminal. After testing, the system can efficiently and automatically manage the classroom energy system.