Current Issue : April - June Volume : 2017 Issue Number : 2 Articles : 5 Articles
The paper identifies the need for human robot\ncollaboration for conventional light weight and heavy\npayload robots in future manufacturing environment. An\noverview of state of the art for these types of robots shows\nthat there exists no solution for human robot collaboration.\nHere, we consider cyber physical systems, which are based\non human worker participation as an integrated role in\naddition to its basic components. First, the paper identifies\nthe collaborative schemes and a formal grading system is\nformulated based on four performance indicators. A\ndetailed sensor catalog is established for one of the collaboration\nschemes, and performance indices are computed\nwith various sensors. This study reveals an assessment of\nbest and worst possible ranges of performance indices that\nare useful in the categorization of collaboration levels. To\nillustrate a possible solution, a hypothetical industrial\nscenario is discussed in a production environment. Generalizing\nthis approach, a design methodology is developed\nfor such human robot collaborative environments for various\nindustrial scenarios to enable solution implementation....
Human activity recognition, tracking and classification is an essential trend in assisted\nliving systems that can help support elderly people with their daily activities. Traditional activity\nrecognition approaches depend on vision-based or sensor-based techniques. Nowadays, a novel\npromising technique has obtained more attention, namely device-free human activity recognition\nthat neither requires the target object to wear or carry a device nor install cameras in a perceived\narea. The device-free technique for activity recognition uses only the signals of common wireless\nlocal area network (WLAN) devices available everywhere. In this paper, we present a novel elderly\nactivities recognition system by leveraging the fluctuation of the wireless signals caused by human\nmotion. We present an efficient method to select the correct data from the Channel State Information\n(CSI) streams that were neglected in previous approaches. We apply a Principle Component Analysis\nmethod that exposes the useful information from raw CSI. Thereafter, Forest Decision (FD) is adopted\nto classify the proposed activities and has gained a high accuracy rate. Extensive experiments have\nbeen conducted in an indoor environment to test the feasibility of the proposed system with a total\nof five volunteer users. The evaluation shows that the proposed system is applicable and robust to\nelectromagnetic noise....
The paper describes research results in the domain of cooperative intelligent transport systems.\nThe requirements for human-machine interface considering safety issue of for intelligent transport systems\n(ITS)are analyzed. Profiling of the requirements to cooperative human-machine interface (CHMI) for such\nsystems including requirements to usability and safety is based on a set of standards for ITSs. An approach\nand design technique of cooperative human-machine interface for ITSs are suggested. The architecture of\ncloud-based CHMI for intelligent transport systems has been developed. The prototype of software system\nCHMI 4ITS is described....
We investigate improvements to authentication on mobile touchscreen phones and present a novel extension to the widely used\ntouchscreen pattern lock mechanism. Our solution allows including nodes in the grid multiple times, which enhances the resilience\nto smudge and other forms of attack. For example, for a smudge pattern covering 7 nodes, our approach increases the amount of\npossible lock patterns by a factor of 15 times.Our concept was implemented and evaluated in a laboratory user test (...
The common spatial pattern (CSP) and other spatiospectral feature extraction methods have become the most effective and\nsuccessful approaches to solve the problem of motor imagery electroencephalography (MI-EEG) pattern recognition from\nmultichannel neural activity in recent years. However, these methods need a lot of preprocessing and postprocessing such as\nfiltering, demean, and spatiospectral feature fusion, which influence the classification accuracy easily. In this paper, we utilize\nlinear dynamical systems (LDSs) for EEG signals feature extraction and classification. LDSs model has lots of advantages such as\nsimultaneous spatial and temporal feature matrix generation, free of preprocessing or postprocessing, and low cost. Furthermore,\na low-rank matrix decomposition approach is introduced to get rid of noise and resting state component in order to improve\nthe robustness of the system. Then, we propose a low-rank LDSs algorithm to decompose feature subspace of LDSs on finite\nGrassmannian and obtain a better performance. Extensive experiments are carried out on public dataset from ââ?¬Å?BCI Competition\nIII Dataset IVaââ?¬Â and ââ?¬Å?BCI Competition IV Database 2a.ââ?¬Â The results show that our proposed three methods yield higher accuracies\ncompared with prevailing approaches such as CSP and CSSP....
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