Nonnegative Matrix Factorization (NMF) is a promising data-driven matrix decomposition method, and is becoming very active and attractive in machine learning and blind source separation areas. So far NMF algorithm has been widely used in diverse applications, including image processing, anti-collision for Radio Frequency Identification (RFID) systems and audio signal analysis, and so on. However the typical NMF algorithms cannot work well in underdetermined mixture, i.e., the number of observed signals is less than that of source signals. In practical applications, adding suitable constraints fused into NMF algorithm can achieve remarkable decomposition results. As a motivation, this paper proposes to add the minimum volume and minimum correlation constrains (MCV) to the NMF algorithm, which makes the new algorithm named MCV-NMF algorithm suitable for underdetermined scenarios where the source signals satisfy mutual independent assumption. Experimental simulation results validate that the MCV-NMF algorithm has a better performance improvement in solving RFID tag anti-collision problem than that of using the nearest typical NMF method.

Fast identification is an urgent demand for modern RFID systems. In this paper, we propose a novel algorithm, access probability adjustment based fine-grained Q-algorithm (APAFQ), to enhance the efficiency of RFID identification with low computation overhead. Specifically, instead of estimation accuracy, the target of most proposed anti-collision algorithms, the APAFQ scheme is driven by updating Q value with two different weights, slot by slot. To achieve higher identification efficiency, the reader adopts fine-grained access probability during the identification process. Moreover, based on the responses from tags, APAFQ adjusts the access probability adaptively. Simulations show the superiority of APAFQ over existing Aloha-based algorithms.

In recent years, the development of visual tracking is getting better and better, but some methods cannot overcome the problem of low accuracy and success rate of tracking. Although there are some trackers will be more accurate, they will cost more time. In order to solve the problem, we propose a reinforced tracker based on Hierarchical Convolutional Features (HCF for short). HOG, color-naming and grayscale features are used with different weights to supplement the convolution features, which can enhance the tracking robustness. At the same time, we improved the model update strategy to save the time costs. This tracker is called RHCF and the code is published on https://github.com/z15846/RHCF. Experiments on the OTB2013 dataset show that our tracker can validly achieve the promotion of the accuracy and success rate.