Multiobjective PSO based adaption of neural network topology for pixel classification in satellite imagery

The proposed work involves the multiobjective PSO based adaption of optimal neural network topology for the classification of multispectral satellite images. It is per pixel supervised classification using spectral bands (original feature space). This paper also presents a thorough experimental analysis to investigate the behavior of neural network classifier for given problem. Based on 1050 number of experiments, we conclude that following two critical issues needs to be addressed: (1) selection of most discriminative spectral bands and (2) determination of optimal number of nodes in hidden layer. We propose new methodology based on multiobjective particle swarm optimization (MOPSO) technique to determine discriminative spectral bands and the number of hidden layer node simultaneously. The accuracy with neural network structure thus obtained is compared with that of traditional classifiers like MLC and Euclidean classifier. The performance of proposed classifier is evaluated quantitatively using Xie-Beni and β indexes. The result shows the superiority of the proposed method to the conventional one.     

Optimal-correlation-based reconstruction for distributed compressed video sensing

Distributed compressed video sensing (DCVS) is a framework that integrates both compressed sensing and distributed video coding characteristics to achieve a low-complexity video coding. However, how to design an efficient joint reconstruction by leveraging more realistic signal models is still an open challenge. In this paper, we present a novel optimal-correlation-based reconstruction method for compressively sampled videos from multiple measurement vectors. In our method, the sparsity is mainly exploited through inter-signal correlations rather than the traditional frequency transform, wherein the optimization is not only over the signal space to satisfy data consistency but also over all possible linear correlation models to achieve minimum-l₁-norm correlation noise. Additionally, a two-phase Bregman iterative based algorithm is outlined for solving the optimization problem. Simulation results show that our proposal can achieve an improved reconstruction performance in comparison to the conventional approaches, and especially, offer a 0.7–9.9 dB gain in the average PSNR for DCVS.     

When sensing goes pervasive

In line with the pervasive vision, pervasive sensing allows the provision of ubiquitous and pervasive monitoring and heterogeneous data collection. In the past decade, two dominant pervasive sensing paradigms have emerged: a mostly human-free paradigm centered around wireless sensor networks and a human-centric paradigm fueled by the rise of personal smart devices (smartphones and wearables). In this paper, we review the key advances in these areas and outline our vision for future directions and developments.     

用于发光温度传感的金属有机框架材料

金属有机框架(Metal organic frameworks,MOFs)由于其显著的结构多样性和可调的发光性能,为制备不同种类的发光传感器提供了良好契机。近年来,利用发光MOFs探测温度传感技术受到了人们的广泛关注。结合对发光测温的描述后,总结了发光型MOF温度计的最新研究进展,重点介绍了双发射型MOF在温度传感领域中的广泛应用。     

Synthesis and Humidity Sensing Properties of NiO Intercalated Polyaniline Nanocomposite

The exercising cooperative and interfacial properties of metal oxide and conducting polymer as a sensing material for humidity detection was the focal point of this study. In this piece of work nano sized NiO and its composite with polyaniline has been prepared. The cooperative effects of NiO on stuructural, morphology, humidity sensing behaviour of PANI has been investigated. Prepared materials were characterized by infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscope (SEM),UV–VIS spectroscopy and Four probe techniques. The result reveals that the NiO strongly influences on polymer chain, crystallinity, stability, electrical and optical properties of PANI, which improves its viability in technology development. Finally, PANI/NiO was used for electrochemical humidity sensing of a closed atmosphere. The result reveals that 100 times increase in sensitivity of PANI due to the presence of NiO nano particles. Finally, the results indicate that the impact of NiO on PANI makes it promising perspective materials for humidity monitoring of closed chamber with improved sensing parameters over several method and materials.     

A Noise-Aware Real-Time Processing Approach for Electroencephalogram Signal Classification

Electroencephalogram (EEG) signal processing has emerged as a critical problem for biometric applications due to its real-time requirement. While compressive sensing is an efficient method for signal compression, its application in EEG signal processing is limited due to its noise unawareness during transmission and time-consuming reconstruction procedure. In this paper, we propose a noise-aware sparse Bayesian learning approach with block structure (NA-BSBL) to achieve higher efficiency on data compression, reconstruction and classification. By applying novel structure for parameter and introducing the Mahalanobis Distance, our approach achieves an almost 20% reconstruction performance lift and 10% accuracy lift under noise condition. For further application of reconstructed EEG signal, we extract both the spatial and frequency domain features for classification. Experimental results show that the proposed approach can achieve 94% classification accuracy with 16% speed up compared with the conventional approach.     

A novel multi-focus image fusion method based on distributed compressed sensing

Multi-focus image fusion aims to produce an all-in-focus image by merging multiple partially focused images of the same scene. The main work is identifying the focused region and then composing all the focused regions. In this paper, a novel efficient multi-focus image fusion method based on distributed compressed sensing (DCS) is proposed. Firstly, the low-frequency and high-frequency images are obtained by comparing the variance of the source images, which are further utilized to get the low-frequency and high-frequency dictionaries. Secondly, DCS using joint sparsity model-1 (JSM-1) is applied to reconstruct the precise high-frequency images. Thirdly, the decision map is obtained based on all the high-frequency images and then improved by the morphological processing. Finally, the focused pixels are chosen from the source images through the decision map. Experimental results indicate that the proposed DCS-based method can be competitive with or even outperform some state-of-the-art methods in terms of both visual and quantitative metric evaluations.     

纸基底印刷无芯片RFID湿度传感器* .txt

摘要：提出了一种基于纸基底的喷墨印刷无芯片RFID湿度传感器，通过遗传算法与射频仿真软件HFSS相结合，对常规开口环谐振器结构进行分布式加载，得到目标频率为245 GHz的谐振特性良好的传感器模型；采用DMP3000型材料打印机，在不同纸基底上喷墨印刷银墨水制作传感器，以纸张本身的吸水性实现湿度传感；研究不同纸基底对湿度传感器感湿灵敏度和恢复度的影响。结果表明，柯达相纸传感器的灵敏度最高，高湿灵敏度可达到8 MHz%RH，双铜纸湿度传感器恢复度高且恢复时间短，仅为2 min；两种基底湿度传感器皆有较好的一致性、中长期稳定性，且湿敏特性在20℃～30℃范围内受温度影响较小。对纸基湿度传感器的感湿机理进行了分析，纸主要成分纤维素表面的羟基与吸附的水分子作用形成氢键，改变了基底的介电参数，传感器的湿敏特性与纸的成分和结构有关。 .txt