No abstract

No abstract

No abstract

Handoff management is an indispensable component in supporting network mobility. The handoff situation raises while the Mobile Router (MR) or Mobile Node (MN) crosses the different wireless communication access technologies. At the time of inter technology handoff the multiple interface based MR can accomplish multihoming features such as enhanced availability, traffic load balancing with seamless flow distribution. These multihoming topographies greatly responsible reducing network delays during inter technology handoff. This article proposes a multihoming based Mobility management in Proxy NEMO (MM-PNEMO) scheme that considers benefits of using multiple interfaces. To support the proposed scheme design a numerical framework is developed that will be used to assess the performance of the proposed MM-PNEMO scheme. The performance is evaluated in the state-of-art numerical simulation approach focusing the key success metrics of signalling cost and packet delivery cost, that eventually scaling the total handoff cost. The numerical simulation result shows that the proposed MM-PENMO delightedly reduces the average handoff cost to 60% compared to existing NEMO Basic support protocol (NEMO-BSP) and PNEMO.

Problem statement: The social demands for the Quality Of Life (QOL) are increasing with the exponentially expanding silver generation. To improve the QOL of the disabled and elderly people, robotic researchers and biomedical engineers have been trying to combine their techniques into the rehabilitation systems. Various biomedical signals (biosignals) acquired from a specialized tissue, organ, or cell system like the nervous system are the driving force for the entire system. Examples of biosignals include Electro-Encephalogram (EEG), Electrooculogram (EOG), Electroneurogram (ENG) and (EMG). Approach: Among the biosignals, the research on EMG signal processing and controlling is currently expanding in various directions. EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. An EMG signal based graphical controller or interfacing system enables the physically disabled to use word processing programs, other personal computer software and internet. Results: Depending on the application, the acquired and processed signals need to be classified for interpreting into mechanical force or machine/computer command. Conclusion: This study focused on the advances and improvements on different methodologies used for EMG signal classification with their efficiency, flexibility and applications. This review will be beneficial to the EMG signal researchers as a reference and comparison study of EMG classifier. For the development of robust, flexible and efficient applications, this study opened a pathway to the researchers in performing future comparative studies between different EMG classification methods

No abstract

EMG signal based research is ongoing for the development of simple, robust, user friendly, efficient interfacing devices/systems for the disabled. The advancement can be observed in the area of robotic devices, prosthesis limb, exoskeleton, wearable computer, I/O for virtual reality games and physical exercise equipments. Additionally, electromyography (EMG) signals can also be applied in the field of human computer interaction (HCI) system. This paper represents the detection of different predefined hand motions (left, right, up and down) using artificial neural network (ANN). A backpropagation (BP) network with Levenberg-Marquardt training algorithm has been utilized for the classification of EMG signals. The conventional and most effective time and timefrequency based feature set is utilized for the training of neural network. The obtained results show that the designed network is able to recognize hand movements with satisfied classification efficiency in average of 88.4%. Furthermore, when the trained network tested on unknown data set, it successfully identify the movement types.

No abstract