Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Aggregation-based phase transition tailored heterophase junctions of AgInS2 for boosting photocatalytic H2 evolution.

Due to high defect tolerance and multiphase allowance, AgInS2 (AIS) quantum dots (QDs) provide chances for designing new type junctions via tailoring defects, size, or phase structure. These new type junctions potentially enhance photoelectric performance, such as photocatalytic H2 evolution (PHE). Here, ultra-small AIS QDs (∼1 nm) with well-defined exciton absorption were prepared aqueously via a reverse hot-injection procedure for the first time. A coalescence or fast aggregation-based growth was observed for coarsening at 95 or 135 ℃, respectively. XRD and TEM investigations revealed that the tetragonal-orthorhombic (t-o) phase transition occurred via aggregation-based growth. The studies on phase transition kinetics resulted in fine-tailoring on AIS polymorphs, favoring t-o AIS junctions. UV-vis absorption spectra confirm the double absorption edge of the t-o heterophase junction with enhanced visible absorption. Steady and transient PL spectra suggest improvements in carriers' separation/transfer in this t-o junction. As a result, the optimized t-o AIS shows superior photocatalytic H2 evolution rates of 1022 µmol. g-1. h-1, 51.1 times that of t-AIS or 3.8 times that of o-AIS. This work is expected to provide new insight for designing ternary alloyed QDs with strongly coupled interfaces for effective H2 generations.

A Low-Cost and Efficient Indoor Fusion Localization Method.

Accurate indoor location information has considerable social and economic value in applications, such as pedestrian heatmapping and indoor navigation. Ultrasonic-based approaches have received significant attention mainly since they have advantages in terms of positioning with temporal correlation. However, it is a great challenge to gain accurate indoor localization due to complex indoor environments such as non-uniform indoor facilities. To address this problem, we propose a fusion localization method in the indoor environment that integrates the localization information of inertial sensors and acoustic signals. Meanwhile, the threshold scheme is used to eliminate outliers during the positioning process. In this paper, the estimated location is fused by the adaptive distance weight for the time difference of arrival (TDOA) estimation and improved pedestrian dead reckoning (PDR) estimation. Three experimental scenes have been developed. The experimental results demonstrate that the proposed method has higher localization accuracy in determining the pedestrian location than the state-of-the-art methods. It resolves the problem of outliers in indoor acoustic signal localization and cumulative errors in inertial sensors. The proposed method achieves better performance in the trade-off between localization accuracy and low cost.

Engineering of bionic Fe/Mo bimetallene for boosting the photocatalytic nitrogen reduction performance.

The "FeMo cofactors" in biological nitrogenase play a decisive role in nitrogen reduction. Herein, a novel bionic Fe/Mo bimetallene was applied in photocatalytic nitrogen reduction. The surface coating Fe/Mo bimetallene of Bi2Mo0.3W0.7O6 (BMWO) nanocrystals could effectively promote the separation and transportation of photogenerated carriers by multi-electron redox reactions and deliver 2.8 times longer photo-carrier lifetime. Consequently, the nitrogen fixation activity of Fe/Mo bimetallene-coated BMWO nanocrystal photocatalyst was obviously enhanced (218.93 µmol g-1h-1), which was about 4.8 times that of unmodified BMWO nanocrystals. This work provides a novel approach to design bionic Fe/Mo bimetallene-modified inorganic semiconductor photocatalysts for nitrogen reduction.

Adaptive Correlation Model for Visual Tracking Using Keypoints Matching and Deep Convolutional Feature.

Although correlation filter (CF)-based visual tracking algorithms have achieved appealing results, there are still some problems to be solved. When the target object goes through long-term occlusions or scale variation, the correlation model used in existing CF-based algorithms will inevitably learn some non-target information or partial-target information. In order to avoid model contamination and enhance the adaptability of model updating, we introduce the keypoints matching strategy and adjust the model learning rate dynamically according to the matching score. Moreover, the proposed approach extracts convolutional features from a deep convolutional neural network (DCNN) to accurately estimate the position and scale of the target. Experimental results demonstrate that the proposed tracker has achieved satisfactory performance in a wide range of challenging tracking scenarios.