A New Digital Sense Multiple Access (DSMA) Protocol for High-Speed Wireless Networks

In this paper, we propose a new digital sensemultiple access with delayed transmission (DSMA/DT)protocol for reverse channel in high-speed wirelessnetworks. The new protocol is motivated by theobservation that the existing DSMA protocol does not yieldsatisfactory throughput for long round-trip propagationand processing delay, which occurs in outdoor high-speedenvironments or when the receiver hardware requires long signal processing time. The newDSMA/DT protocol is intended to reduce the performanceimpacts of the round-trip delay. Look-ahead busy/idleflag, seizure queueing, and reserved time slots are also devised for the new protocol. Whilerequiring at most two additional status bits on theforward channel and no additional hardware capability,these features further enhance the protocol performance and enable constant-bit-rate service withlittle added complexity in control. The channelthroughput of the DSMA/DT protocol and the optionalfeatures are analyzed. Closed-form expressions for thethroughput are obtained. For non-negligible round-tripdelay relative to packet transmission time, ournumerical results show that the new protocol improvesthe throughput by as much as 60% when compared to theexisting DSMA protocol. For superior performance andsimplicity, the DSMA/DT protocol will be appropriate foruse in high-speed wireless networks.     

波长拓展型InGaAsBi近红外探测器

InGaAs光电探测器广泛应用于短波红外检测。在InGaAs中掺入Bi可以减小带隙,延长探测波长。通过控制In和Bi的组分可使In_yGa_1-yAs_1-xBi_x与InP晶格匹配,同时,扩展探测波长至3 μm以上。设计并研究了In_0.394Ga_0.606As_0.913Bi_0.087 p-i-n光电探测器的光电性能。计算了不同温度、吸收层厚度和p（n）区掺杂浓度下的暗电流和响应率特性。获得了3 μm的截止波长。该结构为拓展InP基晶格匹配的短波红外探测器的探测波长提供了一种可行的方法。     

Facial expression recognition based on dual-feature fusion and improved random forest classifier

Facial expression recognition (FER) is an important means for machines to understand the changes in the facial expression of human beings. Expression recognition using single-modal facial images, such as gray scale, may suffer from illumination changes and the lack of detailed expression-related information. In this study, multi-modal facial images, such as facial gray scale, depth, and local binary pattern (LBP), are used to recognize six basic facial expressions, namely, happiness, sadness, anger, disgust, fear, and surprise. Facial depth images are used for robust face detection initially. The deep geometric feature is represented by point displacement and angle variation in facial landmark points with the help of depth information. The local appearance feature, which is obtained by concatenating LBP histograms of expression-prominent patches, is utilized to recognize those expression changes that are difficult to capture by only the geometric changes. Thereafter, an improved random forest classifier based on feature selection is used to recognize different facial expressions. Results of comparative evaluations in benchmarking datasets show that the proposed method outperforms several state-of-the-art FER approaches that are based on hand-crafted features. The capability of the proposed method is comparable to that of the popular convolutional neural-network-based FER approach but with fewer demands for training data and a high-performance hardware platform.