Target tracking is an important field of investigation in wireless sensor networks. When multiple targets are closely spaced, their measurement points are mixed together due to the insufficient accuracy of the sensor. This may bring some difficulties in determining the positions of observation points required by subsequent algorithms and applications. Most of the known tracking algorithms are derived from the Kalman filter, extended Kalman filter, and particles filter. In this paper, a novel measurement data classification algorithm based on support vector machine (SVM) is provided. SVM and Kalman filter are combined to obtain the updated classification line at each sampling period, and the sampling points would be classified by the updated classification line to calculate the coordinates of the corresponding observation points, which are then used to estimate the precise positions of two targets. A series of simulations and experiments are carried out to validate the presented algorithm on classifying and tracking two targets moving closely. Simulation results for a maneuvering targets classification example illustrate the feasibility of the new algorithm, as well as experimental and quantitative results from the practical data validate the effectiveness and stability of our proposal in contrast with existing methods.

In the domain of autonomous driving systems, vehicle trajectory prediction represents a critical aspect, as it significantly contributes to the safe maneuvering of vehicles within intricate traffic environments. Nevertheless, a preponderance of extant research efforts have been chiefly centered on the spatio-temporal relationships intrinsic to the vehicle itself, thereby exhibiting deficiencies in the dynamic perception of and interaction capabilities with adjacent vehicles. In light of this limitation, we propose a vehicle trajectory prediction algorithm predicated on a hybrid prediction model. Initially, the algorithm extracts pertinent context information pertaining to the target vehicle and its neighboring vehicles through the application of a two-layer long short-term memory network. Subsequently, a fusion module is deployed to assimilate the characteristics of the temporal influence, spatial influence, and interactive influence of the surrounding vehicles, followed by the integration of these attributes. Ultimately, the prediction module is engaged to yield the predicted movement positions of the vehicles, expressed in coordinate form. The proposed algorithm was trained and validated using the publicly accessible datasets I-80 and US-101. The experimental results demonstrate that our proposed algorithm is capable of generating more precise prediction results.

Image colorization has a widespread application in video and image restoration in the past few years. Recently, automatic colorization methods based on deep learning have shown impressive performance. However, these methods map grayscale image input into multi-channel output directly. In the process, it usually loses detailed information during feature extraction, resulting in abnormal colors in local areas of the colorization image. To overcome abnormal colors and improve colorization quality, we propose a novel Double-Channel Guided Generative Adversarial Network (DCGGAN). It includes two modules: a reference component matching module and a double-channel guided colorization module. The reference component matching module is introduced to select suitable reference color components as auxiliary information of the input. The double-channel guided colorization module is designed to learn the mapping relationship from the grayscale to each color channel with the assistance of reference color components. Experimental results show that the proposed DCGGAN outperforms existing methods on different quality metrics and achieves state-of-the-art performance.

With the emergence of the multicore architecture and the increase of chip operating frequency, power optimization has become a key step of circuit logic synthesis. Aiming at the XNOR/OR circuits, with the goal of minimizing power, construct the optimal polarity fixed-polarity Reed–Muller (FPRM) circuits power optimization scheme. However, the power optimization for FPRM circuits is a multipeak combinatorial optimization problem, we first propose a metaheuristic optimization algorithm (MOA), which includes the global exploration optimizer, local deep exploitation optimizer, and initial population and uses the proposed differential evolution optimization, fierce wolf siege algorithm-based tabu search, and improved skew tent map to make the population evolve. Based on the proposed Huffman tree construction algorithm and MOA, we propose an efficient power optimization approach (EPOA) to find the minimum power FPRM circuit. Experimental results on the benchmark circuits confirm the effectiveness of EPOA.

In intelligent traffic monitoring, speed measuring millimeter waves (MMW) radar is one of the most commonly used tools for traffic enforcement. In traffic enforcement field, the radar must provide the evidence of each vehicle belongs to which lane. In this paper, we propose a novel kernel line segment adaptive possibilistic c-means clustering algorithm (KLSAPCM) for lane determination of vehicles. Firstly, the raw measurement data is preprocessed using the extracting method of data adjacent lane centerlines. Secondly, according to the improved minimum radius data search method, outliers are removed and the proposed KLSAPCM algorithm is initialized. Finally, the accuracy of lane determination has been improved by the proposed KLSAPCM clustering algorithm based on adaptive kernel line segment that conforms to the shape features of the measurement data in the actual scene. The experiment results for multiple scenes were: the KLSAPCM algorithm is compared with the DBSCAN, the k-means, the FCM, the PCM, the AMPCM, and the APCM algorithms on real measurement datasets, and the results highlight the classification rate of the proposed algorithm. Meanwhile, the proposed algorithm gets a good real-time performance and strong robustness for some sparse moving vehicle scene applications.