A Middleware for the Internet of Things

The Internet of Things (IoT) connects everyday objects including a vast array of sensors, actuators, and smart devices, referred to as things to the Internet, in an intelligent and pervasive fashion. This connectivity gives rise to the possibility of using the tracking capabilities of things to impinge on the location privacy of users. Most of the existing management and location privacy protection solutions do not consider the low-cost and low-power requirements of things, or, they do not account for the heterogeneity, scalability, or autonomy of communications supported in the IoT. Moreover, these traditional solutions do not consider the case where a user wishes to control the granularity of the disclosed information based on the context of their use (e.g. based on the time or the current location of the user). To fill this gap, a middleware, referred to as the Internet of Things Management Platform (IoT-MP) is proposed in this paper.Comment: 20 pages, International Journal of Computer Networks & Communications (IJCNC) Vol.8, No.2, March 201

The Application of Deep Learning for Classification of Alzheimer's Disease Stages by Magnetic Resonance Imaging Data

Detecting Alzheimer’s disease (AD) in its early stages is essential for effective management, and screening for Mild Cognitive Impairment (MCI) is common practice. Among many deep learning techniques applied to assess brain structural changes, Magnetic Resonance Imaging (MRI) and Convolutional Neural Networks (CNN) have grabbed research attention because of their excellent efficiency in automated feature learning of a variety of multilayer perceptron. In this study, various CNNs are trained to predict AD on three different views of MRI images, including Sagittal, Transverse, and Coronal views. This research use T1-Weighted MRI data of 3 years composed of 2182 NIFTI files. Each NIFTI file presents a single patient's Sagittal, Transverse, and Coronal views. T1-Weighted MRI images from the ADNI database are first preprocessed to achieve better representation. After MRI preprocessing, large slice numbers require a substantial computational cost during CNN training. To reduce the slice numbers for each view, this research proposes an intelligent probabilistic approach to select slice numbers such that the total computational cost per MRI is minimized. With hyperparameter tuning, batch normalization, and intelligent slice selection and cropping, an accuracy of 90.05% achieve with the Transverse, 82.4% with Sagittal, and 78.5% with Coronal view, respectively. Moreover, the views are stacked together and an accuracy of 92.21% is achived for the combined views. In addition, results are compared with other studies to show the performance of the proposed approach for AD detection

The Internet of Things: New Interoperability, Management and Security Challenges

The Internet of Things (IoT) brings connectivity to about every objects found in the physical space. It extends connectivity to everyday objects. From connected fridges, cars and cities, the IoT creates opportunities in numerous domains. However, this increase in connectivity creates many prominent challenges. This paper provides a survey of some of the major issues challenging the widespread adoption of the IoT. Particularly, it focuses on the interoperability, management, security and privacy issues in the IoT. It is concluded that there is a need to develop a multifaceted technology approach to IoT security, management, and privacy.Comment: 18 pages, International Journal of Network Security & Its Applications (IJNSA) Vol.8, No.2, March 201

Electro Deposition of Cuprous Oxide for Thin Films Solar Cell Applications

Des couches semi-conductrices d’oxyde de cuivre de type p et de type n pour des applications photovoltaïques ont été fabriquées par voie électrochimique avec des approches nouvelles. Les couches minces ont été électro-déposées par polarisation cathodique sur une feuille de cuivre et des substrats d'oxyde d'indium-étain (ou oxyde d'indium dopé à l'étain (ITO)). Les conditions optimales de dépôt (composition, pH et température de l’électrolyte, domaine de potentiel à appliquer) des couches sous forme de films minces ont été identifiée, En particulier les conditions qui permettent d’avoir des couches de type n ont été bien identifiée pour la première fois. La configuration d’une pile photo-électrochimique a été utilisée pour caractériser la réponse spectrale des couches. Il a été montré que les couches p délivre un photo-courant dans le domaine cathodique et les couches n dans le domaine de potentiel anodique. Les mesures des résistivités électriques des couches électro chimiquement déposées de Cu2O, de type p et n, ont montré que la résistivité du Cu2O de type p varie de 3.2×105 à 2.0×108 Ω.cm selon les conditions de dépôt telles le pH de la solution, le potentiel de dépôt et la température. L'influence de plusieurs paramètres d'électrodéposition de couches de Cu2O de type, tels que le potentiel appliqué, le pH et la température du bain, sur la composition chimique, le degré de cristallinité, la taille des grains et l'orientation a été systématiquement étudiée en utilisant la diffraction des rayons X et la microscopie électronique à balayage. Selon le potentiel d’électrodéposition, deux morphologies différentes de surface avec des orientations cristallines préférentielles variées ont été obtenues pour des températures de l’électrolyte de dépôt de 30 °C et un pH de 9.Pour la même température, les couches de Cu2O de type p, hautement cristallines, se trouvent sont obtenues à pH de 12, ce qui indique que la cristallinité dépend du pH du bain. Aussi, il a été montré que la morphologie des couches de Cu2O était modifiable en variant le potentiel et la durée de déposition, ainsi que la température de la solution. Les conditions d’électrodéposition de Cu2O de type n ont été identifies de manière systématique la première fois. L’électrolyte de déposition est à base de 0,01 M d’acétate de cuivre et 0,1 M d’acétate de sodium: a un pH compris entre 4 et 6.3, un potentiel compris entre -0,25 V vs Ag/AgCl et une température de 60oC. La température optimum de recuit des couches n est de 120-150oC pour des durées de 30 à 120 minutes. La résistivité des films de type n varie entre 5x103 et pH 4 à 5x104 à pH 6.4. Nous avons montré pour la première fois que le barbotage de l’azote dans la cellule d’électrodéposition des couches de type n améliore manière significative leur réponse spectrale. Un procédé d’électrodéposition en deux étapes à été mis en œuvre pour fabriquer la l’homo jonction p-n de l’oxyde oxyde cuivreux sur le substrat l'oxyde d'indium-étain (ITO) qui a été utilisé comme un oxyde conducteur transparent. La performance photovoltaïque d'une cellule solaire à homo-jonction p-n de Cu2O a été déterminée. Le courant en court-circuit et la tension de circuit ouvert ont été respectivement déterminés à 235 μA/cm2 et 0,35 Volt. Le facteur de remplissage (FF) et le rendement de conversion de la lumière en électricité des cellules ont été respectivement évalués à 0,305 et 0,082%. ---------- p and n type copper oxide semiconductor layers were fabricated by electrochemistry using new approaches for photovoltaic applications. Thin films were electroplated by cathodic polarization on a copper foil or indium tin oxide (ITO) substrates. The optimum deposition conditions (composition, pH and temperature of the electrolyte and applied potential) of the layers as thin films have been identified; in particular the conditions that allow getting the n-type layers have been well identified for the first time. The configuration of a photo - electrochemical cell was used to characterize the spectral response of the layers. It was shown that the p type layers exhibit a photocurrent in the cathode potential region and n layers exhibit photo current in the anode potential region. Measurements of electrical resistivity of electro chemically deposited layers of p and n type Cu2O, showed that the resistivity of p-type Cu2O varies from 3.2 × 105 to 2.0 × 108 Ωcm. These values depend the electrodepositing conditions such as the pH of the solution, the deposition potential and temperature. The influence of several plating parameters of the p-type layers of Cu2O, such as applied potential, pH and temperature of the bath on the chemical composition, degree of crystallinity, grain size and orientation parameters of the sample was systematically studied using X-ray diffraction and scanning electron microscopy. Depending of the electro-deposition potential, two different surface morphologies with various preferential crystal orientations were obtained for the temperatures of the electro-deposition of 30 ° C and pH 9. For the same temperature, the layers of p-type Cu2O of highly crystalline p-type are obtained at pH 12, indicating that the crystallinity depends on the pH of the bath. Also, it has been shown that the morphology of Cu2O layers was changed by varying the potential and the duration of deposition, as well as the temperature of the solution. The conditions for the electro-deposition of Cu2O n-type were identified consistently for the first time. The electro-deposition electrolyte is based 0.01M acetate copper and 0.1 M sodium acetate: it has a pH between 6.3 and 4, a potential of from 0 to -0.25 V vs. Ag / AgCl and a temperature of 60oC. The optimum annealing temperature of the n-type Cu2O layers is between 120-150oC for the annealing time of 30 to 120 minutes. Resistivity of the n-type films varies between 5x103 and 5x104 at pH 4 to pH 6.4. We have shown for the first time that bubbling nitrogen gas in the electroplating cell improves significantly the spectral response of the electro-deposited n-type thin film. A two steps electro-deposition process was implemented to make the p-n homojunction cuprous oxide. Indium tin oxide (ITO) was used as a transparent conductive oxide substrate. A p-Cu2O was electrodeposited on ITO. After heat treatment a thin film layer of n-Cu2O was electrodeposited on top of previous layer. The performance of a p-n homojunction photovoltaic solar cell of Cu2O was determined. The short-circuit current and the open circuit voltage were respectively determined to be as 0.35 volts and 235 μA/cm2. The fill factor (FF) and conversion efficiency of light into electricity were respectively measured to be 0.305 and 0.082%

An intelligent multimodal biometric authentication model for personalised healthcare services

With the advent of modern technologies, the healthcare industry is moving towards a more personalised smart care model. The enablers of such care models are the Internet of Things (IoT) and Artificial Intelligence (AI). These technologies collect and analyse data from persons in care to alert relevant parties if any anomaly is detected in a patient’s regular pattern. However, such reliance on IoT devices to capture continuous data extends the attack surfaces and demands high-security measures. Both patients and devices need to be authenticated to mitigate a large number of attack vectors. The biometric authentication method has been seen as a promising technique in these scenarios. To this end, this paper proposes an AI-based multimodal biometric authentication model for single and group-based users’ device-level authentication that increases protection against the traditional single modal approach. To test the efficacy of the proposed model, a series of AI models are trained and tested using physiological biometric features such as ECG (Electrocardiogram) and PPG (Photoplethysmography) signals from five public datasets available in Physionet and Mendeley data repositories. The multimodal fusion authentication model shows promising results with 99.8% accuracy and an Equal Error Rate (EER) of 0.16

Adaptive categorization of complex system fault patterns

Due to large amount of information and the inherent intricacy, diagnosis in complex systems is a difficult task. This can be somehow simplified by taking a per-step towards categorizing the system conditions and faults. In this paper, the development and implementation of an approach that establishes class membership conditions, using a labelled training set, is described. More specifically, the use of negative recognition for classification and diagnosis of complex system faults are discussed. The adaptive recognition to achieve the classification is based on discovery of pattern features that make them distinct from objects belonging to different classes. Most of the existing approaches to fault diagnosis, particularly for large or complex systems, depend on heuristic rules. The approach proposed in this work does not resort to any heuristic rules, which makes it more suitable for diagnosis of faults in dynamic and complex systems. For evaluation purposes, using the data provided by the protection simulator of a large power system, its fault diagnosis is carried out. The results of those simulations are also reported. They clearly reveal that even for complex systems, the proposed approach, based on making use of the distinctive features of encountered fault patterns, is capable of fault classification with minimal supervision

Fuzzy detection of network intrusions and integrated network management

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