This paper proposes a novel computing architecture for high-precision, low-latency, low-power, and cost-effective computation of complex numberNth roots andNth powers. By integrating the high-precision properties of the coordinate rotation digital computer (CORDIC) algorithm with the low-latency benefits of piecewise linear (PWL) approximation, the architecture leverages the binary logarithm-antilogarithm relationship to compute roots and powers of arbitrary complex numbers. Specifically, TheNth roots andNth powers of the modulus of the input complex number are computed using normalization preprocessing and PWL, and the conversion between the plane coordinate and polar coordinate forms of the complex number is achieved using the CORDIC algorithm. The design is implemented in Verilog HDL and synthesized using 40nm CMOS technology at a frequency of 1GHz. The synthesis results show that the area consumption for the complexNth root computation is$24193.92\mu $m2, with a power consumption of 2.1352mW. The area consumption for the complexNth power computation is$20836.83\mu $m2, with a power consumption of 1.8658mW. Compared to the latest complexNth root design, the proposed architecture reduces the area by 11.67% and power consumption by 9.33%. The average accuracy exhibits only a slight reduction compared to the state-of-the-art design while remaining at the same order of magnitude. Furthermore, the computation delay for theNth root architecture is only 58.46% of the delay of the latest complexNth root design, while the delay for theNth power architecture is 94.87% of the delay of the existing real-valuedNth power design.

As unmanned robotic operations become prevalent in various fields, the presence of moving objects in complex scenes poses challenges to key technologies, such as environment mapping, obstacle avoidance, and trajectory prediction. In this article, we propose FP-MOS, a LiDAR-based dynamic object segmentation framework that integrates prediction information to continuously capture dynamic object information under constrained data conditions. First, a prediction module (PM) is employed to obtain predicted range images, enhancing the continuity of temporal information in the point cloud within the segmentation network. This improved continuity helps mitigate misjudgment issues caused by dynamic object turns and retrieves previously missed moving objects. Additionally, a motion attention weight guidance module is introduced, which accurately captures dynamic point cloud features through the transmission of point cloud weights between adjacent frames. Finally, we apply an improved adaptive local outlier factor (ALOF) method to filter out outliers in the segmentation results. Experimental results on the SemanticKITTI-MOS and Apollo datasets show that our method achieves leading Intersection-over-Union (IoU) scores of 79.3% and 79.0%, demonstrating the effectiveness of the FP-MOS method in network design and data optimization.

Precise positioning is a key factor and enabler technology for many use cases on intelligent transportation systems (ITS) and connected and automated vehicles (CAVs). Recently, the quantum positioning system (QPS) based on quantum ranging has emerged as a novel way to improve security and precision. As a key process of QPS, the entangled photons based ranging technology has picosecond-level clock synchronization, and the ranging accuracy can reach the Heisenberg limit. If promising QPS is deployed in the ITS and CAVs, it will cause a profound change. However, the existing QPS still lacks accuracy and robustness in different scenarios. To solve this problem, we proposed an efficient and robust fusion positioning system based on entangled photons. In this system, we derive the ranging accuracy limit with many factors and propose a fast data grouping and selection algorithm to improve real-time performance. Furthermore, we propose a fusion extended fingerprint localization method for robust positioning in the dynamic environment. The effectiveness and robustness of the system are verified by extensive experiments. When the range is 15m, the ranging accuracy can be limited to 0.0018m. The proposed system achieves the probability of positioning errors 90% within 0.13m with only two APs.

With the advent of the new era of 6G, new applications of smart factories and intelligent transportation systems based on real-time wireless sensing technology will confront great demands and challenges. In the intelligent transportation system, it is essential to realize services such as localization and intrusion detection for intelligent vehicles. To build a wide range of positioning network based on large-scale wireless networks, it is of great challenge to simultaneously solve the problem of unacceptable delay and bandwidth requirements caused by a large number of channel state information (CSI) data transmission. Therefore, we propose a novel algorithm, named PAOFIT, where a projection transformation aided CSI curve fitting compression algorithm is firstly proposed to decrease data distortions by improving the orthogonality of signal subspace and noise subspace, and an adaptive weighted average fitting order judgment algorithm is proposed to calculate the fitting order needed in the curve fitting process. Then, localization parameter, time of flight (ToF) are estimated by CSI reconstruction and parameter estimation. Finally, the location of the target is obtained by substituting these parameters into time difference of arrival (TDoA) wireless localization technology. Extensive experimental results verify that, compared with the existing compression algorithms, the proposed PAOFIT has a better performance in terms of compression ratio, median positioning error, residual and execution time.

With the development of the radar sensing technology, hand gesture sensing and recognition has attracted much attention. This letter adopts a frequency-modulated continuous wave (FMCW) radar to achieve short-range hand gesture sensing and recognition. Specifically, the range, Doppler, and angle parameters of hand gestures are measured by fast Fourier transformation (FFT) and multiple signal classification (MUSIC) algorithm, respectively. The mixup (MP) algorithm combined with augmentation (AU) algorithm using a weight factor is applied to expand the hand gesture data. Then, a complementary multidimensional feature fusion network-based hand gesture recognition (CMFF-HGR) is designed to extract the features and achieve HGR. Finally, a series of experiments are carried out to verify the effectiveness of the proposed approach, and the results show that the recognition accuracy is higher than the existing alternatives with low computational complexity.

Efficient and effective objects identification using radio frequency identification (RFID) is always a challenge in large-scale industrial and commercial applications. Among existing solutions, the tree-based splitting scheme has attracted increasing attention because of its high extendibility and feasibility. However, the conventional tree splitting algorithms can only solve tag collision with counter value equals to zero and usually result in performance degradation when the number of tags is large. To overcome such drawbacks, we propose a novel tree-based method called fast splitting algorithm based on consecutive slot status detection (FSA-CSS), which includes a fast splitting (FS) mechanism and a shrink mechanism. Specifically, the FS mechanism is used to reduce collisions by increasing commands when the number of consecutive collision is above a threshold, whereas the shrink mechanism is used to reduce extra idle slots introduced by the FS. Simulation results supplemented by prototyping tests show that the proposed FSA-CSS achieves a system throughput of 0.41, outperforming the existing ultra high frequency RFID solutions.

The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely.