On-line test of NoC is essential for its reliability. This paper proposed an integral test solution for on-line test of NoC to reduce the test cost and improve the reliability of NOC. The test solution includes a new partitioning method, as well as a test method and a test schedule which are based on the proposed partitioning method. The new partitioning method partitions the NoC into a new type of basis unit under test (UUT) named as interdependent components based unit under test (iDC-UUT), which applies component test methods. The iDC-UUT have very low level of functional interdependency and simple physical connection, which results in small test overhead and high test coverage. The proposed test method consists of DFT architecture, test wrapper and test vectors, which can speed-up the test procedure and further improve the test coverage. The proposed test schedule reduces the blockage probability of data packets during testing by increasing the degree of test disorder, so as to further reduce the test cost. Experimental results show that the proposed test solution reduces power and area by 12.7% and 22.7% over an existing test solution. The average latency is reduced by 22.6% to 38.4% over the existing test solution.

Skeleton based human action recognition has recently attracted a lot of attention in the research community. 3D skeleton data is becoming easier to access due to the evolution of new depth sensors like Kinect v2. However, the performance of the depth sensors is subjected to viewpoint variations and occlusions. In this paper, we propose a novel distributed sensor data fusion method to address this problem. The information weighted consensus filter(ICF) is introduced to fuse the skeleton data, so as to get more precise joint positions. To demonstrate the proposed idea, we capture the human action sequences in different views and compare the recognition accuracy between the fused and the raw data, and prove that the fused data can help improve recognition performance.