Reconfigurable scan networks (RSNs), like IEEE Std. 1687 networks, offer flexible and scalable access to embedded (on-chip) instruments. These networks are typically accessed from the outside via a dedicated test port, like the test access port (TAP) of IEEE Std. 1149.1. As not all integrated circuits have a dedicated test port, the IEEE Std. P1687.1 working group is exploring how existing functional ports can be used. Fundamental challenges are to determine what hardware to include in the component translating information between a functional port and an IEEE Std. 1687 network and to describe a protocol for the data transported over a functional interface. We have previously shown hardware and protocol to access a limited type of IEEE Std. 1687 networks, known as flat segment insertion bit (SIB)-based networks. In this paper, we present a solution to handle general IEEE Std. 1687 networks. We have made a number of implementations with various benchmarks on an FPGA to evaluate the data overhead and the area usage.

Correspondence identification is an essential problem for collaborative multi-robot perception, with the objective of deciding the correspondence of objects that are observed in the field of view of each robot. In this paper, we introduce a novel maximin hypergraph matching approach that formulates correspondence identification as a hypergraph matching problem. The proposed approach incorporates both spatial relationships and appearance features of objects to improve representation capabilities. It also integrates the maximin theorem to optimize the worst-case scenario in order to address distractions caused by non-covisible objects. In addition, we design an optimization algorithm to address the formulated non-convex non-continuous optimization problem. We evaluate our approach and compare it with seven previous techniques in two application scenarios, including multi-robot coordination on real robots and connected autonomous driving in simulations. Experimental results have validated the effectiveness of our approach in identifying object correspondence from partially overlapped views in collaborative perception, and have shown that the proposed maximin hypergraph matching approach outperforms previous techniques and obtains state-of-the-art performance.

Speaker verification has played a significant role in authentication with the booming development of smartphones and intelligent terminals in recent years. However, most speaker verification systems directly store the users original voiceprint template data (or called acoustic features). In this paper, we reveal the insecurity and sensitiveness of voiceprint template data by carrying out spoofing attacks on speaker verification systems using genetic algorithm. Meanwhile, multiple generation models based on different genetic algorithms (standard genetic algorithm, multiple population genetic algorithm) are proposed, but also the effects of these generation models are compared. Moreover, experimental results on state-of-the-art text-independent speaker verification techniques (such as i-vector, GMM-UBM) clearly demonstrate that our generated attack voice with leaked voiceprint template data can completely imitate users and pass the speaker verification.

Localizing occluded object is a long-term challenge in Advanced Driving Assistant System (ADAS) and autonomous driving research. In this paper, we propose a novel graph-matching based approach that leverages the challenge by adopting the deep learning and multiple-view geometry analysis. Specifically, the 3D scene reconstruction is firstly built by associating the comprehensive graph representations of the multiple-view observations, incorporated with the spatial relationship of the co-visible objects so as their discriminant appearance features. Followed by, the localization for occluded object is achieved by inferring from the reconstructed 3D geometry. We conduct experiments to validate the system in connected vehicular platform in the advanced traffic simulation dataset. The experimental results convincingly indicate the effectiveness of the proposed system in real- time object detection, graph generation, matching and location inference for occluded objects.