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Road information such as road profile has been widely used in intelligent vehicle systems to improve road safety, ride comfort, and fuel economy. However, practical challenges, such as vehicle heterogeneity, parameter uncertainty, and measurement reliability, make it extremely difficult for a single vehicle to accurately and reliably estimate such information. To overcome these limitations, we propose a new learning-based collaborative estimation approach by fusing information from a fleet of networked vehicles. However, information exchange among these vehicles necessary for collaborative estimation may disclose sensitive information such as individual vehicle’s identity, which poses serious privacy threats. To address this issue, we propose a unified privacy-preserving collaborative estimation framework which allows connected vehicles to iteratively refine estimation results through exploiting sequential measurements made by multiple vehicles traversing the same road segment. The collaborative estimation approach systematically incorporates privacy-protection schemes into the estimation design and exploits estimation dynamics to obscure exchanged information. Different from patching conventional privacy mechanisms like differential privacy that will compromise algorithmic accuracy or homomorphic encryption that will incur heavy communication/computation overhead, the dynamics enabled privacy protection does not sacrifice accuracy or significantly increase communication/computation overhead. Numerical simulations confirm the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

Periodic frequency-modulated (FM) interference signals, commonly employed by jammers and radars, may lead to the performance degradation or even failure of global navigation satellite systems and wireless communications. Time–frequency (TF) representations play an essential role in FM signal analysis and instantaneous frequency (IF) estimation, which is the basis of many nonstationary interference suppression algorithms. This article provides a TF representation approach for signals with multiple periodic FM components in the presence of sample loss. The proposed approach utilizes the periodicity and the sparsity of signal components in the TF domain to suppress cross-terms and artifacts. We reconstruct TF distributions of each component by time-averaging Wigner–Ville distribution. Then, the residual artifacts are eliminated by a low-complexity sparse-based adaptive directional TF filtering method. Simulation results confirm that the proposed approach provides an outstanding suppression of cross-terms and artifacts. The proposed TF representation approach can effectively improve the accuracy of the IF estimation of each signal component. [ABSTRACT FROM AUTHOR]

The spatial–temporal Savitzky–Golay (STSG) method for noise reduction can address the problem of tempor- ally continuous normalized difference vegetation index (NDVI) gaps and effectively increase local low NDVI values without overcorrection. However, STSG largely depends on the quality flags of the NDVI time-series data, and inaccurate quality flags yield misleading final results. STSG also requires extensive computing time when used in large-scale applications. This study proposes an enhanced method, called compute unified device architecture (CUDA)-based STSG (cuSTSG), to address the aforementioned limitations of STSG. First, cosine similarities between the annual NDVI time series were used to identify and exclude the NDVI values with inaccurate quality flags from the NDVI seasonal growth trajectory. Second, computational performance was improved by reducing redundant computations and parallelizing computationally intensive procedures using the CUDA on graphics processing units (GPUs). Experiments on four MODIS NDVI time-series datasets of various sizes and regions showed that compared with the original STSG, cuSTSG reduced the mean absolute errors of the final products by 4.90%, 7.77%, 11.76%, and 2.06%, respectively. The results also showed that cuSTSG on a GPU achieved more than 75 speed-up compared with the Interactive Data Language-implemented STSG, and more than 30 speed-up compared with the C++-implemented STSG. cuSTSG can effectively mitigate the impacts of inaccurate quality flags on final products and generate high-quality NDVI time series at large scales with high accuracy and performance. The source code of cuSTSG is available at https://github.com/HPSCIL/cuSTSG. [ABSTRACT FROM AUTHOR]

A circular TE0n mode content analysis technique based on the multi-mode coupler with high mode selectivity is proposed. To improve the mode selectivity, a deformed waveguide, entitled the quad-polar waveguide, which discriminates the azimuthally symmetric TE-mode on the waveguide wall, is introduced. The influence of deformation on the TE01 and TE02 modes’ field pattern, the corresponding transmission loss, and the power capacity in the quad-polar waveguide are analyzed. Based on the analysis, a dual-mode coupler of the circular TE01 and TE02 modes, consisting of an oversized deformed circular waveguide coupler and a pair of circular-to-quad-polar waveguide transitions, is designed for a high-power Ka-band gyrotron traveling-wave tube system. To verify the high mode selectivity of a quad-polar waveguide, a simple coupling with equal-radius and equal-interval holes are used. A prototype is fabricated and measured, and the results show that the coupling coefficient is −40 dB with the directivity of 27 dB for the TE01 mode, suppressing the unwanted mode TE02 mode with 53 dB. For the TE02 mode, the coupling coefficient is −40 dB with the directivity of 25 dB and suppresses the unwanted mode TE01 mode with 75 dB from 30 to 40 GHz. [ABSTRACT FROM AUTHOR]

Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multisource images. Using spectral unmixing analysis and spatial interpolation, the flexible spatiotemporal data fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of subdomains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements. First, the TPS interpolator is replaced by an accelerated inverse distance weighted (IDW) interpolator to reduce computational complexity. Second, the algorithm is parallelized based on the compute unified device architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). Third, an adaptive domain decomposition (ADD) method is proposed to improve the fusion accuracy at the edges of subdomains and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deep-learning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3–182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at https://github.com/HPSCIL/cuFSDAF. [ABSTRACT FROM AUTHOR]

Brain machine interfaces (BMIs) used for movement restoration primarily rely on studies of motor decoding. It has been proved that local field potentials (LFPs) from primary motor cortex and premotor cortex of normal rodents could be used for decoding motor signals. However, few studies have explored the decoding performance of these brain areas under motor cortex damage. In this work, we focus on force decoding performance of LFPs spectrum from both ipsilesional caudal forelimb area (CFA) and rostral forelimb area (RFA) of rodents with ischemia over CFA. After three months of ischemia induced by photothrombosis over CFA, the power of high-frequency bands (>120 Hz) from both CFA and RFA can decode force signals by Kalman filters. The fair performance of CFA indicates motor reorganization over penumbra. Further exploration of RFA decoding ability proves that at least four electrodes of RFA should be used on decoding and electrodes far from CFA of stroke rats could achieve almost as good results as those close to CFA of normal rats, which indicates the motor remapping. Experimental results show the long-term stability of PM LFPs decoding performance of stroke rats as the trained Kalman model could be used to accurately decode force some days later which provides a possibility for online decoding system. In conclusion, our work shows that even under CFA ischemia, high-frequency power of LFPs from RFA is still able to accurately decode force signals and has long stability, which provides the possibility of BMIs for motor function reconstruction of chronic stroke patients. [ABSTRACT FROM AUTHOR]

In order to reduce resistance loss due to skin effect and proximity effect in high-frequency inductive wireless transmission coils, an impedance calculation model is built in this paper for analyzing the electromagnetic field and resistance loss of the double-layer composite conductor in the frequency range of less than 100 kHz. The analytical formulas on calculating high-frequency resistance and power loss of the composite conductor are obtained. It is found that there is a critical frequency above which the resistance of the double-layer composite conductor is significantly lower than that of a pure copper conductor. The loss density within the aluminum layer is lower than that of a copper conductor, and will increase sharply at the interface between copper and aluminum layer. The wiring mode, coil shape factor, and the magnetic field angle determine the distribution of the magnetic field intensity around the composite conductor. When the angle of magnetic field vector varies from 0° to 360°, the magnetic field intensity will vary in a spiraling way. Finally, the high-frequency resistances of the two double-layer composite conductor coils are measured and are consistent with the analytical results, which is helpful to the high-frequency coil design in inductive wireless power transmission. [ABSTRACT FROM PUBLISHER]

Desynchronization is a powerful tool to achieve round-robin scheduling, which is crucial in applications as diverse as media access control of communication networks, realization of analog-to-digital converters, and scheduling of traffic flows in intersections. Due to the remarkable scalability and simplicity, pulse-coupled oscillators based desynchronization is receiving increased attention. In this paper, we systematically characterize pulse-coupled oscillators based decentralized phase desynchronization and propose an interaction function that is more general than the existing results. Numerical simulations show that the proposed pulse-based interaction function also has better robustness to pulse losses, time delays, and frequency errors than the existing results. [ABSTRACT FROM PUBLISHER]