• Energy Research
• 2017-2021close

This chapter presents an overview of the state-of-the-art research on computational effort reduction of the intra-frame prediction, and inter-frame and inter-view predictions, which are the two main topics that this book is focused.

This chapter summarizes the conclusions and the contributions described along with this book. It highlights the importance of reducing the encoding effort of the Three-Dimensional High Efficiency Video Coding (3D-HEVC) depth maps and presents a brief description of the main contributions described throughout the chapters. Besides, this chapter shows and discusses open research possibilities.

Abstract Energy efficient routing is the most important design criterion for WSN (Wireless Sensor Network) as the nodes are either powered by batteries, or by other external power sources. In a Sensor network, the nodes communicate with each other in a multi-hop fashion. The availability of each node is equally important to the proper functioning of the network. The power failure of nodes greatly affects the overall performance of the network. The Quality of Service (QoS) has been measured using the parameters of data quality, energy utilization and position of the sensor nodes. In this paper Energy efficient routing algorithm called Location and Energy Aware Trusted distance source routing (LEATDSR), Energy-aware Spatial Trusted Routing algorithm (ESTR) and Intelligent Weighted Fuzzy Cluster based Secure Routing Algorithm (IWFCSRA) were analyzed in term of energy efficiency performance. This paper implements LEATDSR, ESTR and IWFCSRA by using NS2 simulator. Energy Consumption and Security Analysis are used to evaluate LEATDSR, ESTR and IWFCSRA routing protocols performance analysis in WSN.

Removing different types of artifact from the electroencephalography (EEG) recordings is a critical step in performing EEG signal analysis and diagnosis. Most of the existing algorithms aim for removing single type of artifact, leading to a complex system if an EEG recording contains different types of artifact. With the advancement in wearable technologies, it is necessary to develop an energy efficient algorithm to deal with different types of artifact for single-channel wearable EEG devices. In this paper, an automatic EEG artifact removal algorithm is proposed that effectively reduces 3 types of artifact, i.e., ocular blink artifact (OA), transmission-line/harmonic wave artifact (TA/HA), and muscle artifact (MA), from a single-channel EEG recording. The effectiveness of the proposed algorithm is verified on both simulated noisy EEG signals and real EEG from CHB-MIT dataset. The experimental results show that the proposed algorithm effectively suppresses OA, MA, TA/HA from a single EEG channel record as well as physical movement artifact.