There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

A scheme is developed for classifying the types of motion perceived by a humanlike robot. It is assumed that the robot receives visual images of the scene using a perspective system model. Equations, theorems, concepts, clues, etc., relating the objects, their positions, and their motion to their images on the focal plane are presented. >

http://ieeexplore.ieee.org/iel2/815/3148/00100651.pdf

Robot vision

Ethnologue: Languages of the World

Pattern recognition

Software-defined network (SDN) has become one of the most important architectures for the management of largescale complex networks, which may require repolicing or reconfigurations from time to time. SDN achieves easy repolicing by decoupling the control plane from data plane. Thus, the network routers/switches just simply forward packets by following the flow table rules set by the control plane. Currently, OpenFlow is the most popular SDN protocol/standard and has a set of design specifications. Although SDN/OpenFlow is a relatively new area, it has attracted much attention from both academia and industry. In this paper, we will conduct a comprehensive survey of the important topics in SDN/OpenFlow implementation, including the basic concept, applications, language abstraction, controller, virtualization, quality of service, security, and its integration with wireless and optical networks. We will compare the pros and cons of different schemes and discuss the future research trends in this exciting area. This survey can help both industry and academia R&D people to understand the latest progress of SDN/OpenFlow designs.

/pdf/a-survey-on-software-defined-network-and-openflow-from-2w15cg0qir.pdf

A Survey on Software-Defined Network and OpenFlow: From Concept to Implementation

With the ever increasing role of computerized machines in society, Human Computer Interaction (HCI) system has become an increasingly important part of our daily lives. HCI determines the effective utilization of the available information flow of the computing, communication, and display technologies. In recent years, there has been a
tremendous interest in introducing intuitive interfaces that can recognize the user's body movements and translate them into machine commands. For the neural linkage with
computers, various biomedical signals (biosignals) can be used, which can be acquired from a specialized tissue, organ, or cell system like the nervous system. Examples include Electro-Encephalogram (EEG), Electrooculogram (EOG), and Electromyogram (EMG). Such approaches are extremely valuable to physically disabled persons. Many attempts have been made to use EMG signal from gesture for developing HCI. EMG signal processing and controller work is currently proceeding in various direction including the
development of continuous EMG signal classification for graphical controller, that enables the physically disabled to use word processing programs and other personal computer
software, internet. It also enable manipulation of robotic devices, prosthesis limb, I/O for virtual reality games, physical exercise equipments etc. Most of the developmental area is based on pattern recognition using neural networks. The EMG controller can be programmed to perform gesture recognition based on signal analysis of groups of muscles
action potential. This review paper is to discuss the various methodologies and algorithms used for EMG signal classification for the purpose of interpreting the EMG signal into computer command.

EMG signal classification for human computer interaction: a review

An increasing safety and reducing road accidents, thereby saving lives are one of great interest in the context of Advanced Driver Assistance Systems. Apparently, among the complex and challenging tasks of future road vehicles is road lane detection or road boundaries detection. It is based on lane detection (which includes the localization of the road, the determination of the relative position between vehicle and road, and the analysis of the vehiclepsilas heading direction). One of the principal approaches to detect road boundaries and lanes using vision system on the vehicle. However, lane detection is a difficult problem because of the varying road conditions that one can encounter while driving. In this paper, a vision-based lane detection approach capable of reaching real time operation with robustness to lighting change and shadows is presented. The system acquires the front view using a camera mounted on the vehicle then applying few processes in order to detect the lanes. Using a pair of hyperbolas which are fitting to the edges of the lane, those lanes are extracted using Hough transform. The proposed lane detection system can be applied on both painted and unpainted road as well as curved and straight road in different weather conditions. This approach was tested and the experimental results show that the proposed scheme was robust and fast enough for real time requirements. Eventually, a critical overview of the methods were discussed, their potential for future deployment were assist.

/pdf/real-time-lane-detection-for-autonomous-vehicles-1g1pzwwuqf.pdf

Real time lane detection for autonomous vehicles

Electromyography (EMG) signal is a measure of muscles' electrical activity and usually represented as a function of time, defined in terms of amplitude, frequency and phase. This biosignal can be employed in various applications including diagnoses of neuromuscular diseases, controlling assistive devices like prosthetic/orthotic devices, controlling machines, robots, computer etc. EMG signal based reliable and efficient hand gesture identification can help to develop good human computer interface which in turn will increase the quality of life of the disabled or aged people. The purpose of this paper is to describe the process of detecting different predefined hand gestures (left, right, up and down) using artificial neural network (ANN). ANNs are particularly useful for complex pattern recognition and classification tasks. The capability of learning from examples, the ability to reproduce arbitrary non-linear functions of input, and the highly parallel and regular structure of ANNs make them especially suitable for pattern recognition tasks. The EMG pattern signatures are extracted from the signals for each movement and then ANN utilized to classify the EMG signals based on features. A back-propagation (BP) network with Levenberg-Marquardt training algorithm has been used for the detection of gesture. The conventional and most effective time and time-frequency based features (namely MAV, RMS, VAR, SD, ZC, SSC and WL) have been chosen to train the neural network.

Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

This paper aims to design and implement English digits speech recognition system using Matlab (GUI). This work was based on the Hidden Markov Model (HMM), which provides a highly reliable way for recognizing speech. The system is able to recognize the speech waveform by translating the speech waveform into a set of feature vectors using Mel Frequency Cepstral Coefficients (MFCC) technique This paper focuses on all English digits from (Zero through Nine), which is based on isolated words structure. Two modules were developed, namely the isolated words speech recognition and the continuous speech recognition. Both modules were tested in both clean and noisy environments and showed a successful recognition rates. In clean environment and isolated words speech recognition module, the multi-speaker mode achieved 99.5% whereas the speaker-independent mode achieved 79.5%. In clean environment and continuous speech recognition module, the multi-speaker mode achieved 72.5% whereas the speaker-independent mode achieved 56.25%. However in noisy environment and isolated words speech recognition module, the multi-speaker mode achieved 88% whereas the speaker-independent mode achieved 67%. In noisy environment and continuous speech recognition module, the multi-speaker mode achieved 82.5% whereas the speaker-independent mode achieved 76.67%. These recognition rates are relatively successful if compared to similar systems.

/pdf/english-digits-speech-recognition-system-based-on-hidden-9ftcekvm6t.pdf

English digits speech recognition system based on Hidden Markov Models

Video encryption algorithms have becomes an important field of research nowadays. As an increasing rate of applying video is getting high, the security of video data becomes more important. A digital media can be transmitted easily in real time anywhere at any time due to the advanced development of communications, Internet and multimedia technology. Information availability has increased dramatically with the advent of mobile devices. However, with this availability comes a problem of maintaining the security of information that is displayed in public. Many approaches have been used or proposed to provide security for information disseminated over the networks. These include encryption, authentication, and digital signatures. For video, the method has been adopted to protect unwanted interception and viewing of any video while in transmission over the networks. In this thesis, a development of an enhanced selective video encryption scheme for H.264/AVC based on Advanced Encryption Standard (AES) was reported. A proposed scheme been used instead of encrypting the entire video stream bit by bit, only the I-Frames bitstreams were encrypted. This scheme took into consideration the good features of former selective encryption algorithms with regard to computational complexity, and data compression performance. The proposed system was tested in the simulated environment using different video sequences. The experimental results show that the proposed method provides adequate security to video streams. It has no effect on compression ratio and does not reduce the original video compression efficiency. Moreover, the proposed scheme provides a good trade-off between encryption robustness, flexibility, and real-time processing. It is an appropriate ii technique for secure H.264 bitstreams that require transmission or storage in un-trusted intermediate devices.

/pdf/enhancing-selective-encryption-for-h-264-avc-using-advanced-1fvfw7p6qi.pdf

Othman Omran Khalifa

Papers

EMG signal classification for human computer interaction: a review

Real time lane detection for autonomous vehicles

Electromygraphy (EMG) signal based hand gesture recognition using artificial neural network (ANN)

English digits speech recognition system based on Hidden Markov Models

Enhancing selective encryption for H.264/AVC using advanced encryption standard