Due to the low complexity and cost characteristics of ultra-wideband (UWB) systems, a weighted acquisition algorithm based on energy detection is proposed in this paper. This method is divided into two steps to acquire the direct path (DP) component. Firstly, weighted energy detection is applied to determine which energy block the DP lies in by generalized likelihood ratio test (GLRT). A sub-optimal weighted vector is obtained, by which the closed form of detection performance is proposed. In the second step, the precise position of DP within the detected energy block is obtained by the statistical characteristics of the channel energy distributions. Key parameters that affect acquisition performance are studied by analytical and numerical methods. Simulations and experiments are carried out for performance and complexity comparison with traditional ones. The results show that weighted acquisition achieves better performance under relative low complexity conditions.

With the development of Internet technology, the demand for signing electronic contracts has been greatly increased. The electronic contract generated by the participants in an online way enjoys the same legal effect as paper contract. The fairness is the key issue in jointly signing electronic contracts by the involved participants, so that all participants can either get the same copy of the contract or nothing. Most existing solutions only focus on the fairness of electronic contract generation between two participants, where the digital signature can effectively guarantee the fairness of the exchange of electronic contracts and becomes the conventional technology in designing the contract signing protocol. In this paper, an efficient blockchain-based multi-party electronic contract signing (MECS) protocol is presented, which not only offers the fairness of electronic contract generation for multiple participants, but also allows each participant to aggregate validate the signed copy of others. Security analysis shows that the proposed MECS protocol enjoys unforgeability, non-repudiation and fairness of electronic contracts, and performance analysis demonstrates the high efficiency of our construction.

We modified the adaptive fuzzy classification algorithm (AFC), which allows fuzzy clusters to grow to meet the demands of a given task during training. Every fuzzy cluster is defined by a reference vector and a fuzzy cluster radius, and it is represented as a shape of hypersphere in pattern space. Any pattern class is identified by overlapping plural hyperspherical fuzzy clusters so that it is possible to approximate complex decision boundaries among pattern classes. The modified AFC was applied to recognize handwritten digits, and performances were shown compared with other neural networks.

A new approach of data clustering which is capable of detecting linked or crossed clusters, is proposed. In conventional clustering approaches, it is a hard work to separate linked or crossed clusters if the cluster prototypes are difficult to be represented by a mathematical formula. In this paper, we extract the force information from data points using the concept of psychological potential field, and utilize the information to measure the similarity between data points. Through several experiments, the force shows its effectiveness in diiscriminating different clusters even if they are linked or corssed.