In current robot calibration approaches, the error propagation and identification of serial and parallel robots fail to be solved intuitively and generically, resulting in an inefficient calibration implementation and a low accuracy improvement. In this article, we present a generic error modeling method of serial robots and extend to parallel robots by finite and instantaneous screw (FIS) theory. The differential map and the explicit description of FIS on the robot motions enable a concise error modeling of the serial robot. The identifiability of errors in serial robot is discussed. The maximum independent errors are proved to be 4r + 2p + 6, where r and p are the numbers of revolute and prismatic joints, respectively. Based on the error mapping of serial limbs, reciprocal twist and wrench are introduced to consider the interaction among limbs and reveal the error propagation of the parallel robot. Then, the identification algorithms with high robustness and efficiency are investigated for the serial and parallel robots. Specifically, the conventional ill-conditioning problems of parallel robots are addressed. Finally, the proposed kinematic calibration framework for both types of robots are compared with the existing methods, and verified by simulations and experiments. The results show that our calibration approach improves the robot accuracy in a robust and efficient manner.

In this paper, we consider the problem of “evil twin” attacks in wireless local area networks (WLANs). An evil twin is essentially a rogue (phishing) Wi-Fi access point (AP) that looks like a legitimate one (with the same SSID). It is set up by an adversary, who can eavesdrop on wireless communications of users' Internet access. Existing evil twin detection solutions are mostly for wireless network administrators to verify whether a given AP is in an authorized list or not, instead of for a wireless client to detect whether a given AP is authentic or evil. Such administrator-side solutions are limited, expensive, and not available for many scenarios. Thus, a lightweight, effective, and user-side solution is highly desired. In this work, we propose a novel user-side evil twin detection technique that outperforms traditional administrator-side detection methods in several aspects. Unlike previous approaches, our technique does not need a known authorized AP/host list, thus it is suitable for users to identify and avoid evil twins. Our technique does not strictly rely on training data of target wireless networks, nor depend on the types of wireless networks. We propose to exploit fundamental communication structures and properties of such evil twin attacks in wireless networks and to design new active, statistical and anomaly detection algorithms. Our preliminary evaluation in real-world widely deployed 802.11b and 802.11 g wireless networks shows very promising results. We can identify evil twins with a very high detection rate while maintaining a very low false positive rate.

Dynamic modeling and optimal design of a 3-DOF parallel robot with flexible links for high-speed pick-and-place operation is presented in this paper. The dynamic model is formulated using substructure displacement method. Meanwhile, the exactitude of the model is verified through the software ANSYS. A novel index, which is represented by the ratio of the sum of power consumption to the total movable mass, is proposed. The dynamic performance constraint is represented by minimal value of the first natural frequency. The appropriate design variables are selected through monotonic analysis. The effects of the constraints on the feasible domain of design variables are investigated in depth via an example, and a set of optimized design parameters are obtained for minimizing power consumption throughout the entire workspace.

In this paper, we consider the problem of “evil twin” attacks in wireless local area networks (WLANs). An evil twin is essentially a phishing (rogue) Wi-Fi access point (AP) that looks like a legitimate one (with the same SSID name). It is set up by an adversary, who can eavesdrop on wireless communications of users' Internet access. Existing evil twin detection solutions are mostly for wireless network administrators to verify whether a given AP is in an authorized list or not, instead of for a wireless client to detect whether a given AP is authentic or evil. Such administrator-side solutions are limited, expensive, and not available for many scenarios. For example, for traveling users who use wireless networks at airports, hotels, or cafes, they need to protect themselves from evil twin attacks (instead of relying on those wireless network providers, which typically may not provide strong security monitoring/management service). Thus, a lightweight and effective solution for these users is highly desired. In this work, we propose a novel user-side evil twin detection technique that outperforms traditional administrator-side detection methods in several aspects. Unlike previous approaches, our technique does not need a known authorized AP/host list, thus it is suitable for users to identify and avoid evil twins. Our technique does not strictly rely on training data of target wireless networks, nor depend on the types of wireless networks. We propose to exploit fundamental communication structures and properties of such evil twin attacks in wireless networks and to design new active, statistical and anomaly detection algorithms. Our preliminary evaluation in real-world widely deployed 802.11b and 802.11g wireless networks shows very promising results. We can identify evil twins with a very high detection rate while keeping a very low false positive rate.