Performance Assessment of Hybrid and Digital Irregular Array Configurations for Beyond 100-GHz Multi-User MIMO Systems

The performance of irregular phased array architectures is assessed in the context of multi-user multiple-input multiple-output (MU-MIMO) communications operating beyond 100 GHz. Realizing half-wavelength spaced planar phased arrays is challenging due to wavelength-integrated circuit (IC) size conflict at those frequencies where the antenna dimensions are comparable to IC size. Therefore, irregular array architectures such as thinned and clustered arrays are developed to mitigate the wavelength-IC size conflict. In the thinned arrays, radiating elements are permanently deactivated, while in clustered arrays, neighboring elements are grouped into subarrays. Furthermore, irregular arrays are integrated with hybrid beamforming architectures to manage the complexity introduced by full digital beamforming, where a single radio frequency chain is connected per power amplifier. An optimization problem is formulated to determine the optimal arrangement of antenna elements where the trade-off between spectral efficiency (SE) and sidelobe levels (SLL) can be tuned. Clustered array configurations are optimized by genetic algorithm and Algorithm-X based methodologies, where the former relies on a randomized search and the latter exploits brute-force search, respectively. Furthermore, a prototype array is designed on a printed circuit board (PCB) to verify the proposed methodologies through full-wave simulations. To have a fair comparison, clustered arrays with a grouping of two and four elements are compared with thinned arrays with half and quarter thinning ratios, respectively. The combination of hybrid and irregular array architectures leads to minimal or no performance degradation in the case of hybrid fully connected architectures but severe SE and SLL degradation in the case of hybrid partially connected architectures, respectively.

Scalable Multiport Antenna Array Characterization with PCB-Realized Tunable Load Network Providing Additional "Virtual" VNA Ports

We prototype a PCB-realized tunable load network whose ports serve as additional "virtual" VNA ports in a "Virtual VNA" measurement setup. The latter enables the estimation of a many-port antenna array's scattering matrix with a few-port VNA, without any reconnections. We experimentally validate the approach for various eight-element antenna arrays in an anechoic chamber in the 700-900 MHz regime. We also improve the noise robustness of a step of the "Virtual VNA" post-processing algorithms by leveraging spectral correlations. Altogether, our PCB-realized VNA Extension Kit offers a scalable solution to characterize very large antenna arrays because of its low cost, small footprint, fully automated operation, and modular nature.

Inductive Position Sensors based on Coupling of Coils on Printed Circuit Boards for Demanding Automotive Applications

Rotor position feedback is required in many industrial and automotive applications, e.g. for field-oriented control of brushless motors. Traditionally, magnetic sensors, resolvers or optical encoders are used to measure the rotor position. However, advances in inductive sensing concepts enable a low-cost, high-precision position measurement principle which is robust against magnetic stray fields exceeding 4000 A/m. The operating principle is based on the coupling of a transmitter coil with several receiver coils in the megahertz frequency range. The coils are part of a printed circuit board (PCB) which also comprises circuitry for demodulation and signal processing. The transmitter coil induces eddy currents in an electrically conductive passive coupling element, which provides position-dependent amplitude modulation. The voltage induced in the receiver coils encodes the rotor angle information, typically in quadrature signals. The coupling element requires no rare-earth materials and can be made of stainless steel, for instance. The PCB-based design of the sensor offers considerable flexibility in optimizing its performance. By tailoring the coil geometry and arrangement, accuracy, air gap and overall sensor dimensions can be adjusted to meet a broad range of application-specific requirements. A sensor design sample exhibits a mechanical angle error less than 0.02{\deg} (0.1{\deg} electrical) in both, finite-element simulation and test bench measurement, with good agreement.

Subpixel Edge Localization Based on Converted Intensity Summation under Stable Edge Region

To satisfy the rigorous requirements of precise edge detection in critical high-accuracy measurements, this article proposes a series of efficient approaches for localizing subpixel edge. In contrast to the fitting based methods, which consider pixel intensity as a sample value derived from a specific model. We take an innovative perspective by assuming that the intensity at the pixel level can be interpreted as a local integral mapping in the intensity model for subpixel localization. Consequently, we propose a straightforward subpixel edge localization method called Converted Intensity Summation (CIS). To address the limited robustness associated with focusing solely on the localization of individual edge points, a Stable Edge Region (SER) based algorithm is presented to alleviate local interference near edges. Given the observation that the consistency of edge statistics exists in the local region, the algorithm seeks correlated stable regions in the vicinity of edges to facilitate the acquisition of robust parameters and achieve higher precision positioning. In addition, an edge complement method based on extension-adjustment is also introduced to rectify the irregular edges through the efficient migration of SERs. A large number of experiments are conducted on both synthetic and real image datasets which cover common edge patterns as well as various real scenarios such as industrial PCB images, remote sensing and medical images. It is verified that CIS can achieve higher accuracy than the state-of-the-art method, while requiring less execution time. Moreover, by integrating SER into CIS, the proposed algorithm demonstrates excellent performance in further improving the anti-interference capability and positioning accuracy.

PCB Renewal: Iterative Reuse of PCB Substrates for Sustainable Electronic Making

PCB (printed circuit board) substrates are often single-use, leading to material waste in electronics making. We introduce PCB Renewal, a novel technique that "erases" and "reconfigures" PCB traces by selectively depositing conductive epoxy onto outdated areas, transforming isolated paths into conductive planes that support new traces. We present the PCB Renewal workflow, evaluate its electrical performance and mechanical durability, and model its sustainability impact, including material usage, cost, energy consumption, and time savings. We develop a software plug-in that guides epoxy deposition, generates updated PCB profiles, and calculates resource usage. To demonstrate PCB Renewal's effectiveness and versatility, we repurpose a single PCB across four design iterations spanning three projects: a camera roller, a WiFi radio, and an ESPboy game console. We also show how an outsourced double-layer PCB can be reconfigured, transforming it from an LED watch to an interactive cat toy. The paper concludes with limitations and future directions.

Defect Detection Network In PCB Circuit Devices Based on GAN Enhanced YOLOv11

This study proposes an advanced method for surface defect detection in printed circuit boards (PCBs) using an improved YOLOv11 model enhanced with a generative adversarial network (GAN). The approach focuses on identifying six common defect types: missing hole, rat bite, open circuit, short circuit, burr, and virtual welding. By employing GAN to generate synthetic defect images, the dataset is augmented with diverse and realistic patterns, improving the model's ability to generalize, particularly for complex and infrequent defects like burrs. The enhanced YOLOv11 model is evaluated on a PCB defect dataset, demonstrating significant improvements in accuracy, recall, and robustness, especially when dealing with defects in complex environments or small targets. This research contributes to the broader field of electronic design automation (EDA), where efficient defect detection is a crucial step in ensuring high-quality PCB manufacturing. By integrating advanced deep learning techniques, this approach enhances the automation and precision of defect detection, reducing reliance on manual inspection and accelerating design-to-production workflows. The findings underscore the importance of incorporating GAN-based data augmentation and optimized detection architectures in EDA processes, providing valuable insights for improving reliability and efficiency in PCB defect detection within industrial applications.

Design and Implementation of an IoT-based Respiratory Motion Sensor

In the last few decades, several wearable devices have been designed to monitor respiration rate in an effort to capture pulmonary signals with higher accuracy and reduce patients' discomfort during use. In this article, we present the design and implementation of a device for real-time monitoring of respiratory system movements. When breathing, the circumference of the abdomen and thorax changes; therefore, we used a Force Sensing Resistor (FSR) attached to the Printed Circuit Board (PCB) to measure this variation as the patient inhales and exhales. The mechanical strain this causes changes the FSR electrical resistance accordingly. Also, for streaming this variable resistance on an Internet of Things (IoT) platform, Bluetooth Low Energy (BLE) 5 is utilized due to the adequate throughput, high accessibility, and possibility of power consumption reduction. Furthermore, this device presents features such as low power consumption (0.4 mW), high precision, and ease of use.

Power-Efficient Actuation for Insect-Scale Autonomous Underwater Vehicles

We present a new evolution of the Very Little Eel-Inspired roBot, the VLEIBot++, a 900-mg swimmer driven by two 10-mg bare high-work density (HWD) actuators, whose functionality is based on the use of shape-memory alloy (SMA) wires. An actuator of this type consumes an average power of about 40 mW during in-air operation. We integrated onboard power and computation into the VLEIBot++ using a custom-built printed circuit board (PCB) and an 11-mAh 3.7-V 507-mg single-cell lithium-ion (Li-Ion) battery, which in conjunction enable autonomous swimming for about 20 min on a single charge. This robot can swim at speeds of up to 18.7 mm/s (0.46 Bl/s) and is the first subgram microswimmer with onboard power, actuation, and computation developed to date. Unfortunately, the approach employed to actuate VLEIBot++ prototypes is infeasible for underwater applications because a typical 10-mg bare SMA-based microactuator requires an average power on the order of 800 mW when operating underwater. To address this issue, we introduce a new 13-mg power-efficient high-performance SMA-based microactuator that can function with similar power requirements (approx. 80 mW on average) and actuation performance (approx. 3 mm at low frequencies) in air and water. This design is based on the use of a sealed flexible air-capsule that encloses the SMA wires that drive the microactuator with the purpose of passively controlling the heat-transfer rate of the thermal system. Furthermore, this new power-efficient encapsulated actuator requires low voltages of excitation (3 to 4 V) and simple power electronics to function. The breakthroughs presented in this paper represent a path towards the creation of insect-scale autonomous underwater vehicles (AUVs).

Streamlined shape of cyborg cockroach promotes traversability in confined environments by gap negotiation

The centimeter-scale cyborg insects have a potential advantage for application in narrow environments where humans cannot operate. To realize such tasks, researchers have developed a small printed-circuit-board (PCB) which an insect can carry and control it. The electronic components usually remain bare on the board and the whole board is mounted on platform animals, resulting in uneven morphology of whole cyborg with sharp edges. It is well known that streamlined body shape in artificial vehicles or robots contributes to effective locomotion by reducing drag force in media. However, little is known how the entire body shape impacts on locomotor performance of cyborg insect. Here, we developed a 10 mm by 10 mm board which provided electrical stimulation via Sub-GHz communication and investigated the impact of physical arrangement of the board using Madagascar hissing cockroach. We compared the success rate of gap negotiation between the cyborg with mounted board and implanted board and found the latter outperformed the former. We demonstrated our cyborg cockroach with implanted board could follow faithfully to the locomotion command via antennal or cercal stimulation and traverse a narrow gap like air vent cover. In contrast to the conventional arrangement, our cyborg insects are suitable for application in a concealed environment.

Parameter Competition Balancing for Model Merging

While fine-tuning pretrained models has become common practice, these models often underperform outside their specific domains. Recently developed model merging techniques enable the direct integration of multiple models, each fine-tuned for distinct tasks, into a single model. This strategy promotes multitasking capabilities without requiring retraining on the original datasets. However, existing methods fall short in addressing potential conflicts and complex correlations between tasks, especially in parameter-level adjustments, posing a challenge in effectively balancing parameter competition across various tasks. This paper introduces an innovative technique named PCB-Merging (Parameter Competition Balancing), a lightweight and training-free technique that adjusts the coefficients of each parameter for effective model merging. PCB-Merging employs intra-balancing to gauge parameter significance within individual tasks and inter-balancing to assess parameter similarities across different tasks. Parameters with low importance scores are dropped, and the remaining ones are rescaled to form the final merged model. We assessed our approach in diverse merging scenarios, including cross-task, cross-domain, and cross-training configurations, as well as out-of-domain generalization. The experimental results reveal that our approach achieves substantial performance enhancements across multiple modalities, domains, model sizes, number of tasks, fine-tuning forms, and large language models, outperforming existing model merging methods. The code is publicly available at: \url{https://github.com/duguodong7/pcb-merging}.