Face Recognition Through Different Facial Expressions

Face recognition has become an accessible issue for experts as well as ordinary people as it is a focal non-interfering biometric modality. In this paper, we introduced a new approach to perform face recognition under varying facial expressions. The proposed approach consists of two main steps: facial expression recognition and face recognition. They are two complementary steps to improve face recognition across facial expression variation. In the first step, we selected the most expressive regions responsible for facial expression appearance using the Mutual Information technique. Such a process helps not only improve the facial expression classification accuracy but also reduce the features vector size. In the second step, we used the Principal Component Analysis (PCA) to build EigenFaces for each facial expression class. Then, a face recognition is performed by projecting the face onto the corresponding facial expression Eigenfaces. The PCA technique significantly reduces the dimensionality of the original space since the face recognition is carried out in the reduced Eigenfaces space. An experimental study was conducted to evaluate the performance of the proposed approach in terms of face recognition accuracy and spatial-temporal complexity.

     

Rakeness with block sparse Bayesian learning for efficient ZigBee‐based EEG telemonitoring

Future healthcare systems are shifted toward long‐term patient monitoring using embedded ultra‐low power devices. In this paper, the strengths of both rakeness‐based compressive sensing (CS) and block sparse Bayesian learning (BSBL) are exploited for efficient electroencephalogram (EEG) transmission/reception over wireless body area networks. A binary sensing matrix based on the rakeness concept is used to find the most energetic signal directions. A balance is achieved between collecting energy and enforcing restricted isometry property to capture the underlying signal structure. Correct presentation of the EEG oscillatory activity, EEG wave shape, and main signal characteristics is provided using the discrete cosine transform based BSBL, which models the intra‐block correlation. The IEEE 802.15.4 wireless communication technology (ZigBee) is employed, since it targets low data rate communications in an energy efficient manner. To alleviate noise and channel multipath effects, a recursive least square based equalizer is used, with an adaptation algorithm that continually updates the filter weights using successive input samples. For the same compression ratio (CR), results indicate that the proposed system permits a higher reconstruction quality compared with the standard CS algorithm. For higher CRs, lower dimensional projections are allowed, meanwhile guaranteeing a correct reconstruction. Thus, low computational high quality data compression/reconstruction are achieved with minimal energy expenditure at the sensors nodes.     

An energy-aware forecasting-based framework using a novel moving coordinate scheme for sensor real-time communications

Wireless Networks - In order to save on the energy expended by a sensor node in its communications with the sink, forecasting-based frameworks have recently been proposed. Those frameworks...     

Order selection statistical test for nonstationary AR models

A new statistical test for selecting the order of a nonstationary AR modelyk is presented based on the predictive least-squares principle. This test is of the same order as the accumulated cost function _n = _k=1 ⁿ ( _k ^* – _k )²;i.e., ^* wherey _k ^* is the predictive least-square estimate. It is constructed to show how many times the integrated AR processy _k is differenced in order to obtain a stationary AR process given that the exact order of the process is unknown.     

Using Stochastic Differential Equation for Verification of Noise in Analog/RF Circuits

Today’s analog/RF design and verification face significant challenges due to circuit complexity, process variations and short market windows. In particular, the influence of technology parameters on circuits, and the issues related to noise modeling and verification still remain a priority for many applications. Noise could be due to unwanted interaction between the circuit elements or it could be inherited from the circuit elements. In addition, manufacturing disparity influence the characteristic behavior of the manufactured circuits. In this paper, we propose a methodology for modeling and verification of analog/RF designs in the presence of noise and process variations. Our approach is based on modeling the designs using stochastic differential equations (SDE) that will allow us to incorporate the statistical nature of noise. We also integrate the device variation due to 0.18μ m fabrication process in an SDE based simulation framework for monitoring properties of interest in order to quickly detect errors. Our approach is illustrated on nonlinear Tunnel-Diode and a Colpitts oscillator circuits.     

Formamide driven synthesis of well-aligned ZnO nanorod arrays on glass substrate

A cost effective, low-temperature approach has been developed for the large-area deposition of ZnO nanorod/nanotube arrays on a ZnO coated glass substrate by the natural oxidation of zinc metal in formamide/water mixtures. The two-step seed deposition and wet-chemical approach exhibited well-controlled growth of highly oriented and densely packed ZnO nanorod/nanotube arrays with large-area homogeneity and uniform morphologies. In order to investigate the quality and alignment of ZnO nanorod arrays grown on the ZnO seed layer coated substrate, three different methods of ZnO coating have been deposited by ultrahigh vacuum evaporation system, DC sputtering and RF sputtering, respectively. Our results showed that the ZnO seed layer grown by RF sputtering resulted in high quality ZnO nanorod arrays.     

A Self-powered Wearable Wireless Sensor System Powered by a Hybrid Energy Harvester for Healthcare Applications

Wireless Personal Communications - In this paper, a wearable medical sensor system is designed for long-term healthcare applications. This system is used for monitoring temperature, heartbeat,...     

Collaborative and Cognitive Network Platforms: Vision and Research Challenges

In this paper, we present a visionary concept referred to as Collaborative and Cognitive Network Platforms (CCNPs) as a future-proof solution for creating a dependable, self-organizing and self-managing communication substrate for effective ICT solutions to societal problems. CCNP creates a cooperative communication platform to support critical services across a range of business sectors. CCNP is based on the personal network (PN) technology which is an inherently cooperative environment prototyped in the Dutch Freeband PNP2008 and the European Union IST MAGNET projects. In CCNP, the cognitive control plane strives to exploit the resources to better satisfy the requirements of networked applications. CCNP facilitates collaboration inherently. Through cognition in the cognitive control plane, CCNP becomes a self-managed substrate. The self-managed substrate, in this paper, is defined as cognitive and collaborative middleware on which future applications run without user intervention. Endemic sensor networks may be incorporated into the CCNP concept to feed its cognitive control plane. In this paper, we present the CCNP concept and discuss the research challenges related to collaboration and cognition.     

Ice‐Assisted Synthesis of Black Phosphorus Nanosheets as a Metal‐Free Photocatalyst: 2D/2D Heterostructure for Broadband H2 Evolution

A 2D/2D heterojunction of black phosphorous (BP)/graphitic carbon nitride (g‐C₃N₄) is designed and synthesized for photocatalytic H₂ evolution. The ice‐assisted exfoliation method developed herein for preparing BP nanosheets from bulk BP, leads to high yield of few‐layer BP nanosheets (≈6 layers on average) with large lateral size at reduced duration and power for liquid exfoliation. The combination of BP with g‐C₃N₄ protects BP from oxidation and contributes to enhanced activity both under λ > 420 nm and λ > 475 nm light irradiation and to long‐term stability. The H₂ production rate of BP/g‐C₃N₄ (384.17 µmol g^?1 h^?1) is comparable to, and even surpasses that of the previously reported, precious metal‐loaded photocatalyst under λ > 420 nm light. The efficient charge transfer between BP and g‐C₃N₄ (likely due to formed N? P bonds) and broadened photon absorption (supported both experimentally and theoretically) contribute to the excellent photocatalytic performance. The possible mechanisms of H₂ evolution under various forms of light irradiation is unveiled. This work presents a novel, facile method to prepare 2D nanomaterials and provides a successful paradigm for the design of metal‐free photocatalysts with improved charge‐carrier dynamics for renewable energy conversion.