Near‐Infrared (NIR) Organic Light‐Emitting Diodes (OLEDs): Challenges and Opportunities

The rapid development of the science and technology of organic semiconductors has already led to mass application of organic light‐emitting diodes (OLEDs) in television monitors of outstanding quality as well as in a large variety of smaller displays found in smartphones, tablets, and other gadgets, while introduction of the technology to the illumination sector is imminent. Notably, the requirements of all such applications for emission in the visible range of the electromagnetic spectrum are well tuned to the optical and electronic properties of typical organic semiconductors, thereby representing relatively “low‐hanging fruits,” in terms of material development and exploitation. However, the question arises as to whether developing materials suited for efficient near‐infrared (NIR, 700–1000 nm) emission is possible, and, crucially, desirable to enable new classes of applications spanning from through‐space, short‐range communications to biomedical sensors, night vision, and more generally security applications to name but a few. Here, the major fundamental hurdles to be overcome to achieve efficient NIR emission from organic π‐conjugated systems are discussed, recent progress is reviewed, and an outlook for further development of both materials and applications is provided.     

基于时空的弱点状运动目标检测技术

如何在复杂背景下持续有效地检测目标位置,一直是研究者们需要面对的主要挑战。本文在研究红外点状移动目标特征的基础上,根据目标无纹理,无形状的特性,提出一种改进的形态学目标增强算法,并利用目标连续时空不变性检测目标。首先,建立多尺度的图像金字塔,在每层上采用改进的形态学算法快速、粗糙定位小目标。然后进一步的根据目标在时空上的位置相关性,提出基于目标运动特征分析的精确检测方法。得到精确稳定的检测结果。最后实验结果表明与经典的形态学检测算法及其他算法相比,该技术能更有效地检测弱小目标,具有更高的鲁棒性。     

基于MF-R和AWS密钥管理机制的物联网健康监测大数据分析系统

带有传感器的可穿戴式医疗设备不断生成大量数据，由于数据的复杂性，难以通过处理和分析大数据来找到有价值的决策信息。为了解决这个问题，提出了一种新的物联网体系结构，用于存储和处理医疗应用的可扩展传感器数据（大数据）。所提出的架构主要由两个子架构组成：Meta Fog重定向（MF-R）架构和AWS密钥管理机制。MF-R架构使用Apache Pig和Apache HBase等大数据技术来收集和存储不同传感器设备生成的传感器数据，并利用卡尔曼滤波消除噪声。AWS密钥管理机制使用密钥管理方案，目的是保护云中的数据，防止未经授权的访问。当数据存储在云中时，所提出的系统能够使用随机梯度下降算法和逻辑回归来开发心脏病的预测模型。仿真实验表明，和其他几种算法相比，提出的算法具有更小的误差，且在吞吐量、准确度等方面具有一定的优越性。     

The Spatial LASSO With Applications to Unmixing Hyperspectral Biomedical Images

Hyperspectral imaging (HSI) is a spectroscopic method that uses densely sampled measurements along the electromagnetic spectrum to identify the unique molecular composition of an object. Traditionally HSI has been associated with remote sensing-type applications, but recently has found increased use in biomedicine, from investigations at the cellular to the tissue level. One of the main challenges in the analysis of HSI is estimating the proportions, also called abundance fractions of each of the molecular signatures. While there is great promise for HSI in the area of biomedicine, large variability in the measurements and artifacts related to the instrumentation has slow adoption into more widespread practice. In this article, we propose a novel regularization and variable selection method called the spatial LASSO (SPLASSO). The SPLASSO incorporates spatial information via a graph Laplacian-based penalty to help improve the model estimation process for multivariate response data. We show the strong performance of this approach on a benchmark HSI dataset with considerable improvement in predictive accuracy over the standard LASSO. Supplementary materials for this article are available online.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Characterizing physical habitat preferences and thermal refuge occupancy of brook trout (Salvelinus fontinalis) and Atlantic salmon (Salmo salar) at high river temperatures

Anthropogenic influences, including climate change, are increasing river temperatures in northern and temperate regions and threatening the thermal habitats of native salmonids. When river temperatures exceed the tolerance levels of brook trout and Atlantic salmon, individuals exhibit behavioural thermoregulation by seeking out cold‐water refugia – often created by tributaries and groundwater discharge. Thermal infrared (TIR) imagery was used to map cold‐water anomalies along a 53 km reach of the Cains River, New Brunswick. Trout and salmon parr did not use all identified thermal anomalies as refugia during higher river temperature periods (>21°C). Most small‐bodied trout (8–30 cm) were observed in 80% of the thermal anomalies sampled. Large‐bodied trout (>35 cm) required a more specific set of physical habitat conditions for suitable refugia, that is, 100% of observed large trout used 30% of the anomalies sampled and required water depths >65 cm within or adjacent to the anomaly. Densities of trout were significantly higher within anomalies compared with areas of ambient river temperature. Salmon parr were less aligned with thermal anomalies at the observed temperatures, that is, 59% were found in 65% of the sampled anomalies; and densities were not significantly different within/ outside anomalies. Salmon parr appeared to aggregate at 27°C, and after several events over 27°C variability in aggregation behaviour was observed – some fish aggregated at 25°C, others did not. We stipulate this is due to variances of thermal fatigue. Habitat suitability curves were developed for velocity, temperature, depth, substrate, and deep water availability to characterize conditions preferred by fish during high‐temperature events. These findings are useful for managers as our climate warms, and can potentially be used as a tool to help conserve and enhance thermal refugia for brook trout and Atlantic salmon in similar systems.     

Development of LTCC micro-hotplate with PTC temperature sensor for gas-sensing applications

Low temperature co-fired ceramic (LTCC) micro-hotplates show wide applications in gas sensors and micro-fluidic devices. It is easily structured in three-dimensional structures. This paper presents the low power consumption micro-hotplates which were developed with PTC (positive temperature coefficient) temperature sensor and inter-digitated electrodes. The paper presents two different structures for micro-hotplate with platinum as a heating element. The PTC temperature sensor using two different materials viz. PdAg and platinum paste are developed with micro-hotplates. The simulation has been achieved through COMSOL for LTCC and alumina micro-hotplates. The temperature variation with power consumption has been measured for the developed LTCC micro-hotplates. The change in resistance of PTC temperature sensors was measured with micro-hotplate temperature. The aim of this study was to place a temperature sensor with the gas sensor module to measure and control the temperature of micro-hotplate. A SnO₂ sensing layer is coated on LTCC micro-hotplate using screen printing and characterized for the sensing of carbon monoxide gas (CO). This study will be beneficial for designing hotplates based on LTCC technology with low power consumption and better stability of temperature for gas-sensing applications.     

太赫兹波计算鬼成像:原理和展望

本文立足于太赫兹波成像领域近年来备受关注的研究热点—太赫兹波计算鬼成像,首先回顾了鬼成像从量子到经典再到计算的历史过程,然后阐述了计算鬼成像的数学原理,随后综述了计算鬼成像在太赫兹波段的发展历程,及其在超衍射分辨成像、石墨烯光电导成像、太赫兹光谱成像等方面的应用,并在最后展望了太赫兹波计算鬼成像的发展前景:计算鬼成像作为一种成像手段,可以绕开在太赫兹频段缺乏经济高效的焦面阵列式探测器的难题,但目前的成像帧率还难以满足快速成像的应用需求,相信在未来随着器件性能的提升和成像算法的优化,其成像帧率可以得到大幅提升。     

太赫兹脉冲焦平面成像技术的发展与应用

作为太赫兹技术中的重要组成部分,太赫兹脉冲焦平面成像一经问世就引起了行业内的广泛关注,人们引入了各种方法去提升此成像技术的测量性能,同时也尝试将此成像技术应用于不同的工业和基础研究领域。本文综述了近年来人们对太赫兹脉冲焦平面成像的技术改良和应用研究,包括提升成像系统的空间分辨率、信噪比、信息获取能力,以及将此成像技术应用于光谱识别检测、超表面器件功能验证、太赫兹特殊光束测量、太赫兹表面波观测等,希望该综述能够推动太赫兹脉冲焦平面成像的进一步技术革新和应用拓展。