Local Measurement and Reconstruction for Noisy Graph Signals

The emerging field of signal processing on graph plays a more and more important role in processing signals and information related to networks. Existing works have shown that under certain conditions a smooth graph signal can be uniquely reconstructed from its decimation, i.e., data associated with a subset of vertices. However, in some potential applications (e.g., sensor networks with clustering structure), the obtained data may be a combination of signals associated with several vertices, rather than the decimation. In this paper, we propose a new concept of local measurement, which is a generalization of decimation. Using the local measurements, a local-set-based method named iterative local measurement reconstruction (ILMR) is proposed to reconstruct bandlimited graph signals. It is proved that ILMR can reconstruct the original signal perfectly under certain conditions. The performance of ILMR against noise is theoretically analyzed. The optimal choice of local weights and a greedy algorithm of local set partition are given in the sense of minimizing the expected reconstruction error. Compared with decimation, the proposed local measurement sampling and reconstruction scheme is more robust in noise existing scenarios.Comment: 24 pages, 6 figures, 2 tables, journal manuscrip

Occluded Person Re-identification

Person re-identification (re-id) suffers from a serious occlusion problem when applied to crowded public places. In this paper, we propose to retrieve a full-body person image by using a person image with occlusions. This differs significantly from the conventional person re-id problem where it is assumed that person images are detected without any occlusion. We thus call this new problem the occluded person re-identitification. To address this new problem, we propose a novel Attention Framework of Person Body (AFPB) based on deep learning, consisting of 1) an Occlusion Simulator (OS) which automatically generates artificial occlusions for full-body person images, and 2) multi-task losses that force the neural network not only to discriminate a person's identity but also to determine whether a sample is from the occluded data distribution or the full-body data distribution. Experiments on a new occluded person re-id dataset and three existing benchmarks modified to include full-body person images and occluded person images show the superiority of the proposed method.Comment: 6 pages, 7 figures, IEEE International Conference of Multimedia and Expo 201

Improving the Performance and Stability of Flexible Pressure Sensors with an Air Gap Structure

A highly sensitive flexible resistive pressure sensor based on an air gap structure was presented. The flexible pressure sensor consists of two face to face polydimethylsiloxane (PDMS) films covered with carbon nanotubes (CNTs). The pressure sensor with a 230 μm thickness air gap has relatively high sensitivity (58.9 kPa−1 in the range of 1–5 Pa, 0.66 kPa−1 in the range of 5–100 Pa), low detectable pressure limit (1 Pa), and a short response time (less than 1 s). The test results showed that the pressure sensor with an appropriate air gap has excellent pressure sensitive performance and application potential

InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition

In the field of face recognition, it is always a hot research topic to improve the loss solution to make the face features extracted by the network have greater discriminative power. Research works in recent years has improved the discriminative power of the face model by normalizing softmax to the cosine space step by step and then adding a fixed penalty margin to reduce the intra-class distance to increase the inter-class distance. Although a great deal of previous work has been done to optimize the boundary penalty to improve the discriminative power of the model, adding a fixed margin penalty to the depth feature and the corresponding weight is not consistent with the pattern of data in the real scenario. To address this issue, in this paper, we propose a novel loss function, InterFace, releasing the constraint of adding a margin penalty only between the depth feature and the corresponding weight to push the separability of classes by adding corresponding margin penalties between the depth features and all weights. To illustrate the advantages of InterFace over a fixed penalty margin, we explained geometrically and comparisons on a set of mainstream benchmarks. From a wider perspective, our InterFace has advanced the state-of-the-art face recognition performance on five out of thirteen mainstream benchmarks. All training codes, pre-trained models, and training logs, are publicly released \footnote{$https://github.com/iamsangmeng/InterFace$}.Comment: arXiv admin note: text overlap with arXiv:2109.09416 by other author

The movement-rotation (MR) correlation function and coherence distance of VLC channels

Adaptive transmission based on instantaneous channel state information is an important methodology to improve data rates of mobile users, which requires the periodic update of channel variations. Different from radio frequency (RF) channels, whose variations are governed by Doppler and multi-path effects, visible light communication (VLC) channel variations are mainly related to receiver movements and rotations. In this article, a movement-rotation (MR) correlation function is proposed to measure VLC channel variations with the changes in receiver location and orientation. The correlation function of VLC channel gain in the time domain can then be approximated by the MR correlation function, which is an important criterion for the design of data transmission frames. It is verified that the approximation by MR correlation function can approach the actual simulation and experiment results of VLC channel gain correlation function in the time domain. In addition, experiment and simulation results are presented to investigate variation characteristics of VLC channels in different scenarios. It is shown that a receiver movement of several decimeters or a change of 10-20 degrees in the inclined angle of the receiver is required in a typical scenario in order to observe a distinguishable change of VLC channel gain

Effects of Simulated Nitrogen Deposition on the Bacterial Community of Urban Green Spaces

Continuing nitrogen (N) deposition has a wide-ranging impact on terrestrial ecosystems. To test the hypothesis that, under N deposition, bacterial communities could suffer a negative impact, and in a relatively short timeframe, an experiment was carried out for a year in an urban area featuring a cover of Bermuda grass (Cynodon dactylon) and simulating environmental N deposition. NH4NO3 was added as external N source, with four dosages (N0 = 0 kg N ha−2 y−1, N1 = 50 kg N ha−2 y−1, N2 = 100 kg N ha−2 y−1, N3 = 150 kg N ha−2 y−1). We analyzed the bacterial community composition after soil DNA extraction through the pyrosequencing of the 16S rRNA gene amplicons. N deposition resulted in soil bacterial community changes at a clear dosage-dependent rate. Soil bacterial diversity and evenness showed a clear trend of time-dependent decline under repeated N application. Ammonium nitrogen enrichment, either directly or in relation to pH decrease, resulted in the main environmental factor related to the shift of taxa proportions within the urban green space soil bacterial community and qualified as a putative important driver of bacterial diversity abatement. Such an impact on soil life induced by N deposition may pose a serious threat to urban soil ecosystem stability and surrounding areas

Risk-averse stochastic dynamic power dispatch based on deep reinforcement learning with risk-oriented Graph-Gan sampling

The increasing penetration of renewable energy sources (RES) brings volatile stochasticity, which significantly challenge the optimal dispatch of power systems. This paper aims at developing a cost-effective and robust policy for stochastic dynamic optimization of power systems, which improves the economy as well as avoiding the risk of high costs in some critical scenarios with small probability. However, it is hard for existing risk-neutral methods to incorporate risk measure since most samples are normal. For this regard, a novel risk-averse policy learning approach based on deep reinforcement learning with risk-oriented sampling is proposed. Firstly, a generative adversarial network (GAN) with graph convolutional neural network (GCN) is proposed to learn from historical data and achieve risk-oriented sampling. Specifically, system state is modelled as graph data and GCN is employed to capture the underlying correlation of the uncertainty corresponding to the system topology. Risk knowledge is the embedded to encourage more critical scenarios are sampled while aligning with historical data distributions. Secondly, a modified deep reinforcement learning (DRL) with risk-measure under soft actor critic framework is proposed to learn the optimal dispatch policy from sampling data. Compared with the traditional deep reinforcement learning which is risk-neutral, the proposed method is more robust and adaptable to uncertainties. Comparative simulations verify the effectiveness of the proposed method