Sensor management is a crucial research subject for multi-sensor multi-target tracking in wireless sensor networks (WSNs) with limited resources. Bearings-only tracking produces further challenges related to high nonlinearity and poor observability. Moreover, energy efficiency and energy balancing should be considered for sensor management in WSNs, which involves networking and transmission. This paper formulates the sensor management problem in the partially observable Markov decision process (POMDP) framework and uses the cardinality-balanced multi-target multi-Bernoulli (CBMeMBer) filter for tracking. A threshold control method is presented to reduce the impact on tracking accuracy when using bearings-only measurements for sequential update. Moreover, a Cauchy–Schwarz divergence center is defined to construct a new objective function for efficiently finding the optimal sensor subset via swarm intelligence optimization. This is also conducive to dynamic clustering for the energy efficiency and energy balancing of the network. The simulation results illustrate that the proposed solution can achieve good tracking performance with less energy, and especially that it can effectively balance network energy consumption and prolong network lifetime.

Rapid single-flux-quantum (RSFQ) circuit requires an accurate timing scheme to maintain correct function, especially under a very high clock frequency. However, the reported timing regulation schemes have issues, including the bias margin of the circuit being small and the bias margin decrement caused by imprecise timing constraints. This paper proposes a new timing regulation scheme based on clock-follow-data clocking that can solve the problems. The scheme uses the unique structure of the RSFQ circuit to eliminate the impact caused by different timing libraries and extend the biasing margin. The simulation results of the XOR gate under various timing regulation schemes demonstrated that the proposed scheme could obtain the maximum STA bias margin. Also, the STA bias margin of the 4-bit parallel arithmetic logic unit (ALU) designed using the timing regulation scheme proposed in this paper was increased by 5%, the PSCAN2 bias margin was increased by 7.4%, and 36 ps reduced latency. The ALU fabricated using the “SIMIT Nb03” process was successfully measured with correct operation at low frequency. It showed this scheme could provide accurate timing constraints range and enhances circuit bias margin.

A three-stage power amplifier (PA) for WLAN application in 2.4-2.5 GHz is presented. The proposed PA employs an adaptive bias circuit to adjust the operating point of the PA to improve the linearity of the PA. Two methods to short the 2nd harmonic circuit are compared in the area of efficiency and gain of the PA. The PA is taped out in the process of 2 μm InGaP/GaAs HBT and is tested by the evaluation board. The measured results show that 31.5 dB power gain and 29.3 dBm P_1dB with an associated 40.4% power added efficiency (PAE) under the single tone stimulus. Up to 26.5 dBm output power can be achieved with an error vector magnitude (EVM) of lower than 3% under the 64QAM/OFDM WLAN stimulus.

Small object detection in aerial remote sensing images remains a challenging task due to low resolution, dense object distribution, and complex backgrounds. In this paper, we enhance the YOLOv10 architecture by introducing a lightweight framework that combines multi-scale feature extraction in the spatial domain with high-frequency enhancement in the frequency domain to improve the extraction of fine details. The approach further incorporates an entropy-guided mechanism to strengthen foreground discrimination, a statistically constrained loss function to suppress background interference, and a shared detection head to reduce parameter redundancy and maintain scale consistency. Experiments on the VisDrone dataset show that the proposed method achieves improvements of 3.1% in mAP@0.5 and 3.5% in mAP@0.5:0.95 over strong baselines, while keeping computational overhead low. Evaluation on the NWPU VHR-10 dataset confirms the model’s robustness and generalization across varied remote sensing scenarios. These results demonstrate the effectiveness of the proposed method for accurate and real-time small object detection in complex aerial imagery.