结合深度学习与相关反馈的遥感图像检索

目的 针对基于内容的图像检索存在低层视觉特征与用户对图像理解的高层语义不一致、图像检索的精度较低以及传统的分类方法准确度低等问题，提出一种基于卷积神经网络和相关反馈支持向量机的遥感图像检索方法。方法 通过对比度受限直方图均衡化算法对遥感图像进行预处理，限制遥感图像噪声的放大，采用自学习能力良好的卷积神经网络对遥感图像进行多层神经网络的监督学习提取丰富的图像特征，并将支持向量机作为基分类器，根据测试样本数据到分类超平面的距离进行排序得到检索结果，最后采用相关反馈策略对检索结果进行重新调整。结果 在UC Merced Land-Use遥感图像数据集上进行图像检索实验，在mAP（mean average precision）精度指标上，当检索返回图像数为100时，本文方法比LSH（locality sensitive Hashing）方法提高了29.4%，比DSH（density sensitive Hashing）方法提高了37.2%，比EMR（efficient manifold ranking）方法提高了68.8%，比未添加反馈和训练集筛选的SVM（support vector machine）方法提高了3.5%，对于平均检索速度，本文方法比对比方法中mAP精度最高的方法提高了4倍，针对复杂的遥感图像数据，本文方法的检索效果较其他方法表现出色。结论 本文提出了一种以距离评价标准为核心的反馈策略，以提高检索精度，并采用多距离结合的Top-k排序方法合理筛选训练集，以提高检索速度，本文方法可以广泛应用于人脸识别和目标跟踪等领域，对提升检索性能具有重要意义。     

Dual-activator luminescence of LuAG:Mn4+/Tb3+ phosphor for optical thermometry

Mn⁴⁺ and Tb³⁺ singly doped and Mn⁴⁺/Tb³⁺ codoped lutetium aluminum garnet (Lu₃Al₅O₁₂, or simply LuAG) phosphors were synthesized and investigated for the application of optical thermometry. X-ray powder diffraction and luminescence spectroscopy measurements were performed on all samples to analyze their crystal phases and optical properties. In particular, temperature-dependent luminescence of the LuAG:Mn⁴⁺/Tb³⁺ sample was measured at the temperature range of 270–420 K. The results showed that the luminescence intensity of Mn⁴⁺ has gone through a remarkable decline while the luminescence of Tb³⁺ has an only insignificant change with the rise of temperature which leads to a dramatic decrease in the fluorescence intensity ratio (FIR) between the two activator Mn⁴⁺ and Tb³⁺. Further analysis showed that the LuAG:Mn⁴⁺/Tb³⁺ sample used for temperature sensing has a high relative sensitivity with maximum value of 4.3% K⁻¹ at 333 K. Our research indicated that this LuAG:Mn⁴⁺/Tb³⁺ material is a promising candidate for FIR-type optical temperature sensing.     

Evaluation of physiological workload assessment methods using heart rate and accelerometry for a smart wearable system

Work metabolism (WM) can be accurately estimated by oxygen consumption (VO₂), which is commonly assessed by heart rate (HR) in field studies. However, the VO₂–HR relationship is influenced by individual capacity and activity characteristics. The purpose of this study was to evaluate three models for estimating WM compared with indirect calorimetry, during simulated work activities. The techniques were: the HR-Flex model; HR branched model, combining HR with hip-worn accelerometers (ACC); and HR?+?arm-leg ACC model, combining HR with wrist- and thigh-worn ACC. Twelve participants performed five simulated work activities and three submaximal tests. The HR?+?arm-leg ACC model had the overall best performance with limits of agreement (LoA) of ?3.94 and 2.00?mL/min/kg, while the HR-Flex model had ?5.01 and 5.36?mL/min/kg and the branched model, ?6.71 and 1.52?mL/min/kg. In conclusion, the HR?+?arm-leg ACC model should, when feasible, be preferred in wearable systems for WM estimation.

Practitioner Summary: Work with high energy demand can impair employees’ health and life quality. Three models were evaluated for estimating work metabolism during simulated tasks. The model combining heart rate, wrist- and thigh-worn accelerometers showed the best accuracy. This is, when feasible, suggested for wearable systems to assess work metabolism.     

Enhanced Sensitivity in Nanopore Sensing of Cancer Biomarkers in Human Blood via Click Chemistry

Cancer biomarkers are expected to be indicative of the occurrence of certain cancer diseases before the tumors form and metastasize. However, many biomarkers can only be acquired in extremely low concentrations, which are often beyond the limit of detection (LOD) of current instruments and technologies. A practical strategy for nanopore sensing of cancer biomarkers in raw human blood down to the femtomolar level is developed here. This strategy first converts the detection of cancer biomarkers to the quantification of copper ions by conducting a sandwich assay involving copper oxide nanoparticles. The released Cu²⁺ is then taken to catalyze the “click” reaction which ligates a host–guest modified DNA probe. Finally, this DNA probe is subjected to single‐channel recordings to afford the translocation events that can be used to derive the concentrations of the original biomarkers. Due to the amplification effects of nanoparticle loadings and the “click” reaction, the LOD of this strategy can be as low as the subfemtomolar level. Further, the acid treatment step could effectively eliminate the interferences from plasma proteins in raw human blood and make the strategy highly suitable for the detection of cancer biomarkers in clinical samples.     

一种基于压缩感知的运动目标检测技术

运动目标检测是计算机视觉、图像处理等相关领域的研究热点，其核心就是对视频图像中的每一帧图像进行相应的研究和处理。本文主要研究思路是从压缩感知技术采样信号的角度出发，将每一帧的二维图像压缩采样成具有少量信息的一维信号，再通过信号重构用少量数据量将图像重构出来，最后通过目标检测技术对每一帧的图像进行运动目标提取。仿真实验表明该方法是可行和有效的，同时可以大大减少目标检测中所记录的数据量，解决海量数据的存储与传输问题。     

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     

Fabrication of a novel ZnO–CoO/rGO nanocomposite for nonenzymatic detection of glucose and hydrogen peroxide

Herein, a novel nanocomposite of binary ZnO–CoO nanoparticles loaded on the graphene nanosheets (ZnO–CoO/rGO) has been successfully constructed via a facile, economical and two–step process. The obtained ZnO–CoO/rGO hybrids with high electrical conductivity and abundant active sites, could be modified on a glassy carbon electrode to detect glucose and H₂O₂ multi–functionally. The fabricated biosensor exhibits wide linear range for glucose (10 μM to 11.205 mM) and H₂O₂ (25 μM to 11.1 mM), and their corresponding sensitivity are 168.7 μA mM^?1 cm^?2 and 183.3 μA mM^?1 cm^?2. The limits of detection are 1.3 μM and 0.44 μM for the oxidation of glucose and the reduction of H₂O₂, respectively. Furthermore, remarkable selectivity, long–term stability and outstanding reproducibility of the non–enzyme biosensor prove that ZnO–CoO/rGO hybrids are the promising candidate in practical applications.     

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.     

Room temperature response and enhanced hydrogen sensing in size selected Pd-C core-shell nanoparticles: Role of carbon shell and Pd-C interface

In the present work, the effect of carbon shell around size selected palladium (Pd) nanoparticles on hydrogen (H₂) sensing has been studied by investigating the sensing response of Pd-C core-shell nanoparticles having a fixed core size and different shell thickness. The H₂ sensing response of sensors based on Pd and Pd-C nanoparticles deposited on SiO₂ and graphene substrate has been measured over a temperature range of 25 °C–150 °C. It is observed that Pd-C nanoparticle sensor shows higher sensitivity with increase in shell thickness and faster response/recovery in comparison to that of Pd nanoparticle samples. Pd-C nanoparticles show room temperature H₂ sensitivity in contrast to Pd nanoparticles which respond only at higher temperatures. Role of carbon shell is also understood by investigating H₂ sensing properties of Pd and Pd-C nanoparticles on graphene substrates. These results show that higher catalytic activity and electronic interaction at Pd-C interface, a complete coverage and protection of Pd surface by carbon and presence of structural defects in nanoparticle core are important for room temperature and higher sensing response.     

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.