Reliable self-localization of unmanned aerial vehicles (UAVs) in dense urban environments remains a major challenge due to the frequent unavailability or degradation of Global Navigation Satellite Systems (GNSS) and other radio signals. This paper presents a robust and cost-effective method for UAV self-localization by using vision and millimeter-wave (mmWave) radar data in GNSS-denied environments. The approach generates an initial dense point cloud through depth estimation and semantic segmentation, which is then geometrically refined using sparse mmWave radar point cloud. A semantic-guided clustering method is applied to the mmWave radar point cloud to remove noise and extract key structural elements such as walls, which are later fused with vision-based depth information. For positioning, image matching algorithm provides coarse localization, followed by fine registration that leverages geometric features of windows to enhance precision. Experimental results demonstrate that the proposed method can achieve self-localization accuracy within 0.4 m, while maintaining low system complexity and deployment cost, offering a practical solution for UAV self-localization in GNSSdenied urban scenarios.

Defects in photovoltaic (PV) modules, including hotspots, shading, and diode failures, significantly reduce power-generation efficiency and pose safety risks. This study proposes a real-time detection and localisation framework for PV defects based on infrared images acquired by unmanned aerial vehicles (UAVs). A dedicated dataset of 5583 infrared/visible images was constructed under standardised acquisition conditions. An improved rotating-bounding-box detector, termed YOLO-CLO, was developed upon YOLOv8-OBB by introducing a lightweight C3m module and a shared-convolution LSCD-OBB detection head. The proposed detector attains 99.1% mAP@0.5, 96.7% mAP@0.5:0.95, and 59.88 FPS with only 8.52 M parameters and 23.6 GFLOPs, outperforming the baseline in both accuracy and efficiency. A multi-feature image-processing pipeline combining gradient, grayscale, temperature, and morphological cues identifies hotspots, diode failures, and obstructions with detection accuracies of 96.97%, 100%, and 88.89%, respectively. A component-level localisation strategy integrating GNSS metadata, the Hough transform, and an improved K-means clustering algorithm accurately recovers the row-column index of each defective module within an array. Comparative experiments against YOLOv5 and Faster R-CNN confirm the superiority of the proposed framework. The method offers low hardware dependency and is suitable for engineering deployment in large-scale PV power stations.

Drones are extensively utilized in both military and social development processes. Eliminating the reliance of drone positioning systems on GNSS and enhancing the accuracy of the positioning systems is of significant research value. This paper presents a novel approach that employs a real-scene 3D model and image point cloud reconstruction technology for the autonomous positioning of drones and attains high positioning accuracy. Firstly, the real-scene 3D model constructed in this paper is segmented in accordance with the predetermined format to obtain the image dataset and the 3D point cloud dataset. Subsequently, real-time image capture is performed using the monocular camera mounted on the drone, followed by a preliminary position estimation conducted through image matching algorithms and subsequent 3D point cloud reconstruction utilizing the acquired images. Next, the corresponding real-scene 3D point cloud data within the point cloud dataset is extracted in accordance with the image-matching results. Finally, the point cloud data obtained through image reconstruction is matched with the 3D point cloud of the real scene, and the positioning coordinates of the drone are acquired by applying the pose estimation algorithm. The experimental results demonstrate that the proposed approach in this paper enables precise autonomous positioning of drones in complex urban environments, achieving a remarkable positioning accuracy of up to 0.4 m.

Battery free Internet of Things (BF IoT), which is realized by harvesting ambient energy to power the IoT devices, has many advantages such as low power, low cost, free-maintenance, easy deployment, and environmental protection. As the two key enabling technologies of BF IoT, the integration of energy harvesting, management and backscatter communication (BackCom) is expected to meet practical applications. However, several fundamental issues need to be addressed to fully develop the potential of such integration. In this paper, we propose an ultra-low power energy harvesting and management scheme that effectively reduces the startup and quiescent power consumption. In the case of extremely limited energy harvested, the Long Range (LoRa) BackCom digital waveform generation algorithm with ultra-low power consumption and low complexity is studied, which can support low-power and low-cost micro-controllers (MCUs). Then, a radio frequency (RF) front-end scheme that is compatible with RF energy harvesting (RFEH), BackCom, and On-Off-Keying (OOK) receiving is proposed. On this basis, we design and implement a fully functional BF LoRa Tag, which integrates tag antennas, LoRa BackCom, low-power OOK receiving, multi-parameter real-time sensing, RFEH and management, at a cost of only 6. Our test results show that the BF LoRa Tag can be self-powered by harvesting the RF energy as low as -19 dBm, with the cold-start power as low as 280 nW, the quiescent power as low as 49 nW, the OOK receiving power consumption as low as 1.74 mu W, the LoRa BackCom power consumption of only 251 mu W, and the communication distance up to 445 meters. Compared with the prototypes in existing literature, the BF LoRa Tag not only has lower power consumption, wider coverage, and the lowest cost, but also has complete system functions. Finally, we discuss the reciprocity between stations and receivers, as well as the BF LoRa cellular communication network, which provides useful references for the practical application and large-scale deployment of BF IoT in the future.

Collaborative Deep Neural Networks (DNNs) inference has emerged as a promising paradigm for growing number of artificial intelligence-integrated maritime Internet of Things (IoT) devices in maritime edge intelligence networks. However, the resource constraints of devices, the delay-sensitive nature of tasks, and the dynamic environmental conditions present significant challenges. While Multi-Armed Bandit (MAB) algorithms have been explored for task offloading, their performance is often constrained in highly dynamic scenarios with complex, nonlinear utility dependencies. To address these challenges, we propose a Group Neural MAB (GN-MAB) approach that jointly optimizes idle device selection (i.e., arm groups) and DNN partitioning decisions (i.e., arms) for efficient collaborative inference. Building upon the neural upper confidence bound algorithm, GN-MAB dynamically balances the exploration and exploitation, enabling continuous adaptation of offloading strategies across sequential inference tasks. Extensive experimental results show that GN-MAB outperforms baseline approaches, achieving superior inference performance while exhibiting robust adaptability to the fluctuating conditions of maritime environments.

To address the challenge of collaborative nodes being unable to accurately perceive each other’s positions in global navigation satellite system (GNSS)-denied environments (such as after hostile interference or in urban canyons), we propose a GNSS-independent collaborative positioning radio frequency (RF) sensor. This sensor estimates inter-node distances and orientations using wireless measurements between nodes, without requiring pre-deployed anchor points. First, we designed a low-nanosecond latency ranging logic circuit on field-programmable gate array (FPGA) hardware, enabling relative distance estimation between nodes via a low-latency collaborative ranging (LLCR) algorithm without synchronization. Additionally, a synthetic aperture rotating antenna system was built to construct an echo space energy distribution matrix, based on dynamic–static dual-channel phase differences for high-precision, unambiguous azimuth measurement, followed by angle and distance data integration for localization. Then, a novel RF sensor hardware system was designed that was lightweight, low in cost, and high in performance. Finally, two generations of prototype models were developed and tested in both an anechoic chamber and mounted on unmanned vehicles outdoors in fields. The results demonstrate that the proposed sensor can achieve high-precision relative position estimation between collaborative nodes in the absence of GNSS, with a positioning error of within 0.4 m, indicating that it is suitable for mounting on unmanned vehicles and other autonomous systems for collaborative positioning.

The accurate assessment of athletes' tactical performance is crucial for success in highly technical and competitive sports. Comprehensive modeling of tennis players' tactical scoring ability has become a key direction of current academic research due to the continuous advancement of sports technology. This study implements a LightGBM-based method to analyse and predict the situation of tennis matches. Machine learning algorithms such as LGBM, XGBOOST, support vector machines, perceptron machine networks, and logistic regression were used to compare the model results. The LGBM method with the best performance was selected for modelling to achieve dynamic assessment of players' performance. The accuracy, precision, recall, F1 score, and AUC were 0.69, 0.69, 0.7, 0.69, and 0.77, respectively. This research method aims to provide a scientific basis for understanding and predicting the scoring ability of tennis players in matches, which is significant for the field of sports science and technology.

近年来,无人机由于其灵活的移动性,已广泛运用于多个领域;直接定位作为无源定位中的一项新兴技术,在无线通信、雷达、声纳等领域具有重要价值.本文中结合两个研究方向,对基于无人机平台的运动多站无源定位问题展开研究.首先,利用多普勒频率与目标和接收站之间相对速度的非线性关系,将多普勒频率作为中间变量,直接将多个接收站的接收信号进行联合处理;然后,基于极大似然原理建立代价函数,通过确定目标函数的极值点以实现对目标位置的估计.仿真结果表明,本文中提出的定位方法避免了在估计过程中引入误差的同时,提高了系统的抗噪性能.

Although conductivity is prevalently used in water quality detection for inorganic ions, its utility could be weakened when various kinds of ions are involved as it merely embodies water bulk resistance indiscriminately. Aiming at detectability enhancement, the article proposes a novel measurement method utilizing interfacial impedance for further exploration of ions. Based on theoretical analysis and measuring model comparison, a current-controlled method with difference measurement was derived, as well as the equivalent circuit. Then, a novel three-electrode sensor with a planar structure was accordingly designed and fabricated. After measurement parameters optimizing by experiments of frequency response and amplitude response, the sensor was tested with a traditional two-electrode sensor and conductivity sensor. Experimental results not only testified the performance of the proposed one in interfacial impedance measurement but also revealed a reduction process of interfacial impedance with increasing conductivity. The influence of water temperature was tested, too. Impedance differences between anion and cation inspired further experiments involving more ion species, which demonstrated that diverse positive ions trended to have similar relationships between conductivity and interfacial impedance while the relationships differed due to types of negative ions. The relationships make the measurement a promising tool for ion detection in certain applications.

为了提高射频隐身通信的安全传输性能,在对物理层信号安全性进行分析的基础上,该文通过研究多参数加权类分数阶傅里叶变换(Multiple Parameters Weighted-type Fractional Fourier Transform,MPWFRFT)的特点,提出了 K段多层多参数加权类分数阶傅里叶变换(K-segment Multi-layer Multi-ple Parameters Weighted-type Fractional Fourier Transform,K-ML-MPWFRFT)的概念,并进一步提出了基于K-ML-MPWFRFT的混沌相位调制方法.通过对多种调制方式进行分析,使用正交相移键控(Quadrature Phase Shift Keying,QPSK)技术对信号进行基带映射,再将信号通过K-ML-MPWFRFT变换,改变信号的特征,然后利用混沌序列对信号相位进行调制,在不影响合作方正确解调的同时降低非合作方检测概率.仿真结果表明,在变换参数相当的条件下,该文方法使调制星座图不具备可检测特征,显著提升了对抗调制方式识别与盲解调的能力;与传统算法相比,该文方法使非合作接收机误码率增加了 20％以上,有效提升了系统的抗截获性能.

Due to the rapid development of deep learning, the performance of object detection has greatly improved. However, object detection in high-resolution Unmanned Aerial Vehicles images remains a challenging problem for three main reasons: (1) the objects in aerial images have different scales and are usually small; (2) the images are high-resolution but state-of-the-art object detection networks are of a fixed size; (3) the objects are not evenly distributed in aerial images. To this end, we propose a two-stage Adaptive Region Selection Detection framework in this paper. An Overall Region Detection Network is first applied to coarsely localize the object. A fixed points density-based targets clustering algorithm and an adaptive selection algorithm are then designed to select object-dense sub-regions. The object-dense sub-regions are sent to a Key Regions Detection Network where results are fused with the results at the first stage. Extensive experiments and comprehensive evaluations on the VisDrone2021-DET benchmark datasets demonstrate the effectiveness and adaptiveness of the proposed framework. Experimental results show that the proposed framework outperforms, in terms of mean average precision (mAP), the existing baseline methods by 2.1% without additional time consumption.

In the complex electromagnetic environment of cities, detection and positioning of slow and small unmanned aerial vehicles (UAVs) have always been a very challenging research topic. Usually, multiple radar observation stations are required to detect and track targets. Moreover, each radar observation station requires strict synchronisation, and the system is complex and costly. In view of this, under the condition of known prior information of urban geography, this study proposes a method to locate UAVs in complex electromagnetic environments, which use multiple virtual mirror synchronous radars constructed by multipaths and created by the complex buildings in urban environments. First, the simulation model is constructed, and the corresponding simulation parameters are given. Then, the algorithm for solving the single-station radar location is proposed, and the algorithm is verified by the simulation of a ray propagation model. Finally, the proposed algorithm is further verified based on the measured data captured by the radar system in Section 2. The results show that the proposed algorithm based on the singlestation radar is able to locate the UAV in both line-of-sight and non-line-of-sight site, and its positioning accuracy is better than 1 m.

Radio-frequency (RF)-powered backscattering communication (BC) has many advantages, such as low-power consumption, battery-free, small size, low cost, full sealing, manual maintenance-free, and easy mass deployment. However, the weak RF energy harvested and the short BC distance have become the two critical obstacles in the real-world implementation of this potential technology. In this article, we propose a high-sensitivity RF energy harvesting (RFEH) and long-distance BC method, and for the first time, we demonstrate the RF-powered self-sustainable long-distance BC device named RFEH LoRa. In order to improve RFEH sensitivity, an ultralow-power (ULP) consumption voltage monitoring and startup control circuit topology is designed to reduce the power consumption to 245 nanowatt (nW) during the accumulation of RF energy. While in order to increase the BC distance, a direct digital frequency synthesis technology is used to generate a linear frequency modulation square wave sequence conforming to the LoRa specifications based on the ULP microcontroller unit, and the square wave is used to drive an RF switch to achieve long-distance BC of hundreds of meters. Test results show that the instantaneous BC current is as low as 130 microampere (mu A), and the standby current is only 52 nanoampere (nA). RFEH LoRa is implemented with commercial off-the-shelf components. It can self-startup by harvesting RF energy as low as -22.5 dBm, and send its sensing data to the receiver of 381-m away.

通信链路层特征盲识别是智能通信和通信对抗领域关键技术.为提高基于IEEE 802.11协议的无线(局域)网/无线保真(wireless fidelity,Wi-Fi)信号的编码参数盲识别精度,提出了一种基于深度学习的低密度奇偶校验码(low density parity check code,LDPC)编码参数盲识别算法,可准确盲识别信道编码算法的信息位码长和码率.算法以解调后的比特流为训练数据集,搭建多层深度神经网络模型,经过多次调参和迁移训练,最终得到了能够准确预测编码参数的网络模型.实验结果表明,该网络模型能够在高达10％误码条件下得到优于91％的编码参数盲预测率,在无误码的条件下,编码参数盲预测准确度高达95.32％,为智能通信和通信对抗的研究提供了一定参考价值.

The application of Wireless Power Transmission (WPT) has attracted more and more attention, but the energy efficiency cannot be high due to the inevitable loss. Energy efficient WPT is increasingly demanded especially in implantable devices due to their wireless inefficiency nature. In addition, many implantable devices include digital circuits such as a Microcontroller Unit (MCU) in which the supply voltage may fluctuate severely with the driving clock of digital circuits switches between ground (Gnd) and supply voltage (Vdd), since the power transmitted wirelessly is limited, and the system may even cut off under wireless charging conditions. Therefore, the load regulation is also required in a WPT system. This paper presents a WPT system with load regulation and optimized antenna design for implantable devices, for which measurement results show that the maximum transmission efficiency can reach 79.3% and it can still necessitate a large dynamic load current range. When the load current is switched between 12 uA and 5 mA, the overshoot and undershoot of the output voltage are 150 mV and 80 mV, respectively. Finally, several state-of-the-art WPT applications in biomedical devices are introduced.

为了满足电子信息类大学生对机器视觉与自动控制系统强化学习的需求,克服传统实验装置集成度低、枯燥机械的缺点,设计并实现了一种以高性能微控制器为控制核心的基于机器视觉的板球自动控制实验装置.实验装置具有平台新颖、实验综合性强、理论联系紧密的特点,并且系统集成度高、控制快速精确,有利于开展从易到难的递进式实验,兼具实用性与先进性.

Conductivity is a crucial parameter in water quality detection, which can roughly represent overall concentration of various inorganic ions. However, traditional conductivity sensors can only afford high performance measurement in a relatively low range while the concentration may vary much more in realworld water environment. This paper proposes a high-precision and wide-range measurement method based on a novel planar interdigital electrode sensor array and a self-adaptive algorithm. The array is composed of 3 pairs of planar electrodes with various cell constants aiming at different subdivided conductivity sections. The follow-up circuit and the self-adaptive algorithm keep the optimal electrode pair dominates the output of the array. Numerical simulations were utilized to optimize sensor parameters before fabrication. PCB manufacturing technique was used which guaranteed a relatively low manufacturing cost and stable performance. Experiments were conducted to verify the sensing performance and results showed that the array can maintain precise measurement from 0.5μs/cm to 500ms/cm.

We present a complete low-power Wi-Fi backscatter communication solution which based on a single microcontroller, and for the first time we demonstrate the battery-free Wi-Fi device prototype which is built using commercial-off-the-shelf components on a printed circuit board. It can operate on power that is harvested from ambient light, and its power consumption is 2-3 orders of magnitude lower than that of traditional active Wi-Fi chipset. The instantaneous communication current is only 900uA, the standby current is 1.25uA, and the communication distance is 50 feet. The solution we proposed not only has the advantages of system simplification, low cost, and no need for battery, but also enables the rapid development of Wi-Fi IoT products with existing commercial microcontrollers.