To address the problems of low network accuracy, slow speed, and a large number of model parameters in printed circuit board (PCB) defect detection, an improved detection algorithm of PCB surface defects based on YOLOv5 is proposed, named PCB-YOLO, in this paper. Based on the K-means++ algorithm, more suitable anchors for the dataset are obtained, and a small target detection layer is added to make the PCB-YOLO pay attention to more small target information. Swin transformer is embedded into the backbone network, and a united attention mechanism is constructed to reduce the interference between the background and defects in the image, and the analysis ability of the network is improved. Model volume compression is achieved by introducing depth-wise separable convolution. The EIoU loss function is used to optimize the regression process of the prediction frame and detection frame, which enhances the localization ability of small targets. The experimental results show that PCB-YOLO achieves a satisfactory balance between performance and consumption, reaching 95.97% mAP at 92.5 FPS, which is more accurate and faster than many other algorithms for real-time and high-precision detection of product surface defects.

This study investigates the humidity-sensing properties of two semiconductor metal oxide (SMO)-reduced graphene oxide (RGO) nanocomposites: TiO₂/RGO and α-Fe₂O₃/RGO, at room temperature. Both nanocomposites are synthesized via hydrothermal methods and coated onto printed circuit board (PCB) interdigital electrodes to construct humidity sensors. The surface morphology and crystallographic structure of the materials are characterized using field emission scanning electron microscopy (FESEM) and X-ray diffraction (XRD). The sensors are tested across a humidity range of 11%RH to 97%RH, and the impedance is measured over a frequency range of 1 Hz to 1 MHz. The results show that both TiO₂/RGO and α-Fe₂O₃/RGO exhibit favorable humidity-sensing performance at room temperature. The sensitivity and humidity hysteresis of TiO₂/RGO are 12.2 MΩ/%RH and 3.811%RH, respectively, while those of α-Fe₂O₃/RGO are 0.826 MΩ/%RH and 8.229%RH. The response and recovery times of TiO₂/RGO are 72 s and 99 s, respectively, while those of α-Fe₂O₃/RGO are 48 s and 54 s. Both sensors demonstrate good repeatability and stability. These findings suggest that SMO/RGO nanocomposites are promising materials for the development of low-cost, high-sensitivity, and stable humidity sensors.

The security and reliability of data transmission between IoT devices are considered to be major challenges in the development of IoT technology. This paper presents a low-power, low-cost RISC-V processor for IoT applications with an integrated hybrid encryption accelerator, which can achieve efficient and secure encryption and decryption of data transmitted between IoT devices. The hybrid encryption accelerator, which uses the SM3 and the SM4, respectively, as hash and symmetric encryption algorithms, achieves a balance between encryption security, high speed, and key-management convenience. Both the processor and encryption accelerator are designed using the Verilog HDL language and are subsequently implemented and evaluated on both FPGA and ASIC platforms. The performance of the proposed processor and that of the Hummingbird E203 and the XuanTie E902 are compared. It is shown that, on the FPGA platform, the total resource utilization rate is reduced by 39.1~66.2%. In a 90 nm CMOS process, it is shown that the power efficiency of the proposed processor is increased by 10~34.8% and the circuit area is reduced by 32.5~57.1%.

A novel self-calibration comparator for a 12-bit 2.5 MSPS successive approximation register analog-to-digital converter (SAR ADC) applied in a touch microcontroller unit (MCU) with small area, high precision, fast response speed, and low-voltage detection is proposed in this paper. A combination of input/output offset storage (IOS/OOS) and an offset trimming circuit was employed to reduce the offset of the cascade preamplifier and the operational transconductance amplifier (OTA), a novel offset trimming circuit with a 5-bit digital controller was designed to further reduce the residual offset voltage, and an improved self-calibration technology was also implemented to compensate the conversion error in SAR ADC system to a minimum. Simulation and measured results show that the input-referred offset calibrating range is ±9.15 mV at 0.61 mV/step, the low-voltage detection of SAR ADC is realized by compensating the conversion error to a minimum, and the effective number of bits (ENOB) and figure of merit (FoM) at 5 V supply and 2.5 M/s rate in the 12-bit SAR ADC with a 95 nm CMOS are 11.33 bits and 726.6 fJ/conversion-step, respectively. The proposed self-calibration comparator applied in the SAR ADC system can automatically eliminate the offset voltage caused by nonidealities and meet the requirements of the touch MCU.

Surface defects, which often occur during the production of aluminum profiles, can directly affect the quality of aluminum profiles, and should be monitored in real time. This paper proposes an effective, lightweight detection method for aluminum profiles to realize real-time surface defect detection with ensured detection accuracy. Based on the YOLOv5s framework, a lightweight network model is designed by adding the attention mechanism and depth-separable convolution for the detection of aluminum. The lightweight network model improves the limitations of the YOLOv5s framework regarding to its detection accuracy and detection speed. The backbone network GCANet is built based on the Ghost module, in which the Attention mechanism module is embedded in the AC3Ghost module. A compression of the backbone network is achieved, and more channel information is focused on. The model size is further reduced by compressing the Neck network using a deep separable convolution. The experimental results show that, compared to YOLOv5s, the proposed method improves the mAP by 1.76%, reduces the model size by 52.08%, and increases the detection speed by a factor of two. Furthermore, the detection speed can reach 17.4 FPS on Nvidia Jeston Nano’s edge test, which achieves real-time detection. It also provides the possibility of embedding devices for real-time industrial inspection.

This paper presents a compact equivalent lumped circuit model for on-chip helical transformers. From fundamental model, a simple coupled RL loop is added to account for the skin and proximity effect, and a series RLC branch is added to account for the parasitic coupling effects between inductors. Several helical transformers with various design configurations were fabricated using a standard 0.18 µm 1P6M CMOS process. It’s demonstrated that the proposed model shows good accuracy over two times self-resonance frequency, which is essential for the design of high-speed and radio-frequency circuit.