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With the development of MEMS and CMOS technologies, the implementation of a huge number of wireless distributed micro-sensors that can be simply and rapidly deployed to form highly redundant, self-configuring, and ad hoc sensor networks. Sensor nodes are generally battery- devices, the critical facets to face concern are how to minimize energy consumption of nodes, so that the lifetime of sensing nodes can be maximized. The first step to achieve this goal is to know completely the sources of energy consumption in wireless sensor networks. In this paper, sources of energy consumption at various communication layers have been studied and investigated. Moreover, the energy consumption for the components of a typical sensor node and the impact of communication protocols stack on the energy consumption are discussed. In the sequel, the sources of energy consumption in each communication layer individually are studied. Then, a survey has been provided for existing energy models and the classification of these models into physical layer, MAC layer and cross-layer energy models

Tissue engineering scaffold is a 3D construction that acts as a template for tissue regeneration. The scaffold should have some basic requirements including biocompatibility, suitable mechanical properties, appropriate surface chemistry, high porosity and interconnectivity. Although several conventional techniques such as solvent casting and gas forming are utilized in scaffold fabrication, these processes show poor interconnectivity and uncontrollable porosity of the produced scaffolds. However, Rapid Prototyping (RP) techniques which are a group of advanced manufacturing processes can produce custom made objects directly from computer data such as Computer Aided Design (CAD), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data. Using RP fabrication techniques, constructions with controllable and complex internal architecture with appropriate mechanical properties can be achieved.The present chapter intends to provide an overview of the current state of the art in the area of tissue engineering scaffolds fabrication, using advanced RP processes. The present work highlights also the existing limitations in addition to future prospects in scaffold fabrication via RP techniques.

This paper proposes a new biometric identifier for humans based on eye blinking waveform extracted from brain waves. Brain waves were recorded using Neurosky Mindwave headset from 25 volunteers. Two approaches are adopted for the pre-processing stage; the first approach uses empirical mode decomposition to isolate electro-oculogram signal from brain waves, then, extracts eye blinking signal. The second approach extracts eye blinking signal directly from brain waves. Features are extracted based on time delineation of the eye blinking waveform and classified using linear discriminant analysis. The best correct identification and equal error rates achieved are 98.51% and 2.5% for identification and verification modes respectively. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

This paper proposes a new biometric identifier for humans based on eye blinking waveform extracted from brain waves. Brain waves were recorded using Neurosky Mindwave headset from 25 volunteers. Two approaches are adopted for the pre-processing stage; the first approach uses empirical mode decomposition to isolate electro-oculogram signal from brain waves, then, extracts eye blinking signal. The second approach extracts eye blinking signal directly from brain waves. Features are extracted based on time delineation of the eye blinking waveform and classified using linear discriminant analysis. The best correct identification and equal error rates achieved are 98.51% and 2.5% for identification and verification modes respectively. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for human recognition task.

In this paper, a new technique for human identification task based on heart sound signals has been proposed. It utilizes a feature level fusion technique based on canonical correlation analysis. For this purpose a robust pre-processing scheme based on the wavelet analysis of the heart sounds is introduced. Then, three feature vectors are extracted depending on the cepstral coefficients of different frequency scale representation of the heart sound namely; the mel, bark, and linear scales. Among the investigated feature extraction methods, experimental results show that the mel-scale is the best with 94.4% correct identification rate. Using a hybrid technique combining MFCC and DWT, a new feature vector is extracted improving the system's performance up to 95.12%. Finally, canonical correlation analysis is applied for feature fusion. This improves the performance of the proposed system up to 99.5%. The experimental results show significant improvements in the performance of the proposed system over methods adopting single feature extraction.