Accepted Papers
  • Mathematical Proof of the New proposed Non-Coprime moduli set using forward conversion in Residue Number System
    Mansour Bader,Al-Balqa'a Applied University,Jordan and Andraws Swidan,Jordan University,Jordan

    In this paper a mathematical proof of the new Binary-to-RNS Non-Coprime moduli set in RNS [1] of the form { 2n-2, 2n, 2n+2 } is presented. The modulies 2n-2, 2n+2 are known to be called conjugates of each other and has been discussed in previous literature [1 - 4]. Coprime moduli sets are known to offer these benefits: 1) Large dynamic ranges. II) Fast RNS arithmetic. III) Simple and efficient RNS processing hardware. IV) Efficient weighted-to-RNS and RNS-to-Weighted converters. When comparing the Non-Coprime ones to them the DR (Dynamic Range )is the dominant. The dynamic range achieved by the set above is defined by the least common multiple ( LCM ) of the moduli and the non-coprime set was carefully chosen to do the mathematical calculations upon. This new non-coprime moduli set is unique and the only one of its shape.

  • Acute Leukemia Classification Using Convolution Neural Network In Clinical Decision Support System
    Thanh.TTP,Ki-Ryong Kwon,Giao N. Pham,Jin-Hyeok Park,Dept. of IT Convergence and Applications Eng & Kwang-Seok Moon,Dept. Electronics Eng,Pukyong National University,Korea and Suk-Hwan Lee,Tongmyong University, Busan, Korea

    Leukemia induced death has been listed in the top ten most dangerous mortality basis for human being. Some of the reason is due to slow decision-making process which caused suitable medical treatment cannot be applied on time. Therefore, good clinical decision support for acute leukemia type classification has become a necessity. In this paper, the author proposed a novel approach to perform acute leukemia type classification using convolution neural network (CNN) classifier. Our experimental result only covers the first classification process which shows an excellent performance in differentiating normal and abnormal cells. Further development is needed to prove the effectiveness of second neural network classifier.

  • Multi-View Feature Fusion Network For Vehicle Re-Identification
    Haoran Wu, Dong Li, Yucan Zhou, Qinghua Hu,Tianjin University,China

    Identifying whether two vehicles in different images are same or not is called vehicle re-identification. In cities, there are lots of cameras, but cameras cannot cover all the areas. If we can re-identify a car disappearing from one camera and appearing in another in two adjacent regions, we can easily track the vehicle and use the information help with traffic management. In this paper, we propose a two-branch deep learning model. This model extracts two kinds of features for each vehicle. The first one is license plate feature and the other is the global feature of the vehicle. Then the two kinds of features are fused together with a weight learned by the network. After, the Euclidean distance is used to calculate the distance between features of different inputs. Finally, we can re-identify vehicles according to their distance. We conduct some experiments to validate the effectiveness of the proposed model.