Toll-like receptors for pathogen detection in water: challenges and benefits

The need for effective and efficient methods for pathogen detection in water is as serious as ever due to the health risk posed to human population by the consumption of pathogen-contaminated water. One of the important research streams which have been focused on by researchers for development of novel techniques for this purpose is biosensor technologies. Using different bio-recognition elements and transduction methodologies, biosensors have the potential to detect their analyte of interest in a fast and highly specific manner. Different pathogenic agents can be recognised by toll-like receptors (TLRs). The innate immune system of higher organisms employs TLRs for triggering intracellular signalling and induction of the expression of immune response genes. In this report, we explore the challenges associated with employing TLRs for pathogen detection in water samples. Although methods using TLR expressing cells also have been discussed, the focus of this review is on using TLR proteins as the bio-recognition elements in biosensors.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.     

Helstrom Bound for Squeezed Coherent States in Binary Communication

In quantum information processing, using a receiver device to differentiate between two non-orthogonal states leads to a quantum error probability. The minimum possible error is known as the Helstrom bound. In this work, we study the conditions for state discrimination using an alphabet of squeezed coherent states and compare them with conditions using the Glauber-Sudarshan, i.e., standard, coherent states.     

Estimating Non-Gaussianity of a Quantum State by Measuring Orthogonal Quadratures

We derive the lower bounds for a non-Gaussianity measure based on quantum relative entropy (QRE). Our approach draws on the observation that the QRE-based non-Gaussianity measure of a single-mode quantum state is lower bounded by a function of the negentropies for quadrature distributions with maximum and minimum variances. We demonstrate that the lower bound can outperform the previously proposed bound by the negentropy of a quadrature distribution. Furthermore, we extend our method to establish lower bounds for the QRE-based non-Gaussianity measure of a multimode quantum state that can be measured by homodyne detection, with or without leveraging a Gaussian unitary operation. Finally, we explore how our lower bound finds application in non-Gaussian entanglement detection.     

A Hierarchical Scheme for Video-Based Person Re-identification Using Lightweight PCANet and Handcrafted LOMO Features

A two-level hierarchical scheme for video-based person re-identification (re-id) is presented, with the aim of learning a pedestrian appearance model through more complete walking cycle extraction. Specifically, given a video with consecutive frames, the objective of the first level is to detect the key frame with lightweight Convolutional neural network (CNN) of PCANet to reflect the summary of the video content. At the second level, on the basis of the detected key frame, the pedestrian walking cycle is extracted from the long video sequence. Moreover, local features of Local maximal occurrence (LOMO) of the walking cycle are extracted to represent the pedestrian' s appearance information. In contrast to the existing walking-cycle-based person re-id approaches, the proposed scheme relaxes the limit on step number for a walking cycle, thus making it flexible and less affected by noisy frames. Experiments are conducted on two benchmark datasets: PRID 2011 and iLIDS-VID. The experimental results demonstrate that our proposed scheme outperforms the six state-of-art video-based re-id methods, and is more robust to the severe video noises and variations in pose, lighting, and camera viewpoint.     

复杂背景下小目标恒虚警检测技术研究

为了实现复杂背景下的弱小目标恒虚警检测,提出了一种基于CEM算法的恒虚警探测算法.在分析CEM算法滤波处理结果分布规律的基础上,采用TM-CFAR方法选择恒虚警探测门限,并结合形态学方法克服了背景虚警概率曲线中的拖尾现象.以ETM数据为例进行了计算机模拟.结果表明,该方法在信干比为-5dB和虚警率为10-4的条件下,可以有效地检测出被探测目标.     

偏振探测的机理及应用

偏振探测技术已经成为一种重要的探测手段。本文探讨了偏振探测的机理,如物体的表面特性(粗糙度、色泽和电导率等)对光的偏振特性的影响,介绍了偏振探测技术在地物遥感探测、大气探测、水下探测、医学诊断、天文探测、目标检测、图像处理和军事等领域的应用。     

基于DPHP理论的海洋沉积物热导率检测系统的研究

为了高效精确地对海洋沉积物热导率进行探测,对热脉冲双探针测量理论-DPHP(dual-probe heat-pulse measurement theory)理论,以及基于该理论的测量系统的设计和检测技术进行了深入地研究.在此基础上,设计了一种方便灵活的微型热导率原位测量探头,该探头直径仅为2.5 cm,长20 cm.经过在实验室验证,其平均测量速度 40 s,热导率精度可以达到 0.05.测量速度和精度有了较大提高.