Abstract With the continuous development of brain network technology, it has become a hot area of neuroscience and information technology to research the human emotion changes, cognitive status and psychiatric disorders by means of brain network. In recent years, any smart device can be used as a terminal sensor in the Internet of Things for information interaction. It will be the... With the continuous development of brain network technology, it has become a hot area of neuroscience and information technology to research the human emotion changes, cognitive status and psychiatric disorders by means of brain network. In recent years, any smart device can be used as a terminal sensor in the Internet of Things for information interaction. It will be the new research aspect for brain-computer Interface (BCI) to regard the human brain (the most intelligent “device”) as a terminal sensor in the Internet of Things and to construct a network based on the human brains (we name it as Internet of Brains). In this paper, a causal connectivity brain network (CCBN) was firstly constructed based on multivariate autoregressive (MVAR) modeling, independent component analysis (ICA) and partial directed coherence (PDC). Then the different features of three emotional states (positive, neutral, negative) were respectively extracted by graph theoretical analysis based on the CCBN estimated by granger causality analysis. The relationship between characteristics of EEG pattern and emotional states was disclosed. Result suggested that a classification rate of about 88.75% was achieved in the human subject studied, thus it is feasible to discriminate emotional states with causal connectivity brain network and is a promising non-invasive approach for studying the model of affective computing. Furthermore the model of wearable affective computing was estimated based on above relationship with the portable EEG acquisition device, and prototype system of Internet of Brains would be achieved for BCI. Read More Read Less

Abstract Visual serving, also known as Vision-Based Robot Control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor to control the motion of a robot. In the recent past, research on imagebased visual serving has been concentrated on potential problems of stability and on robustness with respect to camera calibration errors and feature extracting errors.... Visual serving, also known as Vision-Based Robot Control and abbreviated VS, is a technique which uses feedback information extracted from a vision sensor to control the motion of a robot. In the recent past, research on imagebased visual serving has been concentrated on potential problems of stability and on robustness with respect to camera calibration errors and feature extracting errors. We present two image-based visual serving strategies for driving a robot equipped with a camera toward a desired configuration. Since the topic spans many disciplines our goal was to implement complete integrated robot by reviewing the prerequisite topics from robotics and computer vision. Since any visual servo system must be capable of tracking image features in a sequence of images, we also include an overview of feature-based and correlation-based methods for tracking without human intervention – only GUI for calibration purposes and manual control. Moreover we have done a complete control driver for the robot using embedded systems. Simulation and experimental results show the effectiveness of the proposed system. Read More Read Less

Abstract A novel channel estimator which performs channel estimation bit-wise instead of symbol-wise is proposed in this paper. Combined with superposition mapping (SM), the proposed algorithm is able to provide multiple channel estimates for a single channel coefficient. Numerical results indicate that bit-wise soft channel estimation (BWSCE) is able to outperform symbol-wise soft channel estimation (SWSCE) even at lower computational cost.... A novel channel estimator which performs channel estimation bit-wise instead of symbol-wise is proposed in this paper. Combined with superposition mapping (SM), the proposed algorithm is able to provide multiple channel estimates for a single channel coefficient. Numerical results indicate that bit-wise soft channel estimation (BWSCE) is able to outperform symbol-wise soft channel estimation (SWSCE) even at lower computational cost. Read More Read Less

Abstract The Motion JPEG 2000 (Part 3 of the JPEG 2000 standard) has a higher performance compared to traditional video compression standards [1], however the problem encountered in the transmission of Motion JPEG 2000 files is the loss of packets in the codestream Motion JPEG2000 files. Conditional Replenishment has been proposed [2], but controlling the movement is a major difficulty due... The Motion JPEG 2000 (Part 3 of the JPEG 2000 standard) has a higher performance compared to traditional video compression standards [1], however the problem encountered in the transmission of Motion JPEG 2000 files is the loss of packets in the codestream Motion JPEG2000 files. Conditional Replenishment has been proposed [2], but controlling the movement is a major difficulty due to decomposition algorithms used by the DWT (Discrete Wavelet Transform). In this paper, we propose the use of the method based on the principle of motion estimation to reliably reconstruct the lost packets in the transmission of Motion JPEG2000 codestream with a lot of movement. An objective and subjective assessment of the proposed method was performed in terms of measurement of PSNR (Peak Signal to Noise Peak) and SSIM (structural similarity). Read More Read Less

Abstract In this article, the specification of a histopathology decision making support system, based on Pawlak’s information system concept and Mamdani type fuzzy control is presented. The proposed system supports the recognition process of HER-2/neu histopathology preparations through microscopy image information analysis. We used Pawlak’s information system to identify the decisive set of features and the optimal set of decision rules... In this article, the specification of a histopathology decision making support system, based on Pawlak’s information system concept and Mamdani type fuzzy control is presented. The proposed system supports the recognition process of HER-2/neu histopathology preparations through microscopy image information analysis. We used Pawlak’s information system to identify the decisive set of features and the optimal set of decision rules under the considered histopathology problem. Then, the so generated decision rules were transformed into fuzzy rules and exploited in Mamdani reasoning. The proposed approach was tested over real clinical data of HER-2/neu breast cancer histopathology images. Read More Read Less

Abstract In this paper a new method for key distribution using non-linear codes in mobile ad hoc networks, is presented. This method, in addition to meeting CFF criteria, the same code word length, number of more nodes, more tolerance malicious nodes, less computational complexity and memory size, can also reduce the processing power. The main idea of this method, the use... In this paper a new method for key distribution using non-linear codes in mobile ad hoc networks, is presented. This method, in addition to meeting CFF criteria, the same code word length, number of more nodes, more tolerance malicious nodes, less computational complexity and memory size, can also reduce the processing power. The main idea of this method, the use of non-linear codes that produced using cyclic codes on Z4 for key predistribution in mobile ad hoc networks. In proposed scheme, the resulting codeword for each node, a string of binary presence or absence of the key can specify the location of that, Furthermore that the location of “1”, the number specified key is available. These codes in the same conditions (length and size) with a linear codes have a minimum distance. Read More Read Less