Deep-MEG: spatiotemporal CNN features and multiband ensemble classification for predicting the early signs of Alzheimer’s disease with magnetoencephalography

Neural Computing and Applications - In this paper, we present the novel Deep-MEG approach in which image-based representations of magnetoencephalography (MEG) data are combined with ensemble...     

Does maintainability relate to the energy consumption of software? A case study

Software Quality Journal - Energy consumption of software has been becoming increasingly significant, since it can vary according to how the software has been developed. In recent years, developers...     

CFAR processing with switching exponential smoothers for nonhomogeneous environments

Conventional constant false alarm rate (CFAR) methods use a fixed number of cells to estimate the background variance. For homogeneous environments, it is desirable to increase the number of cells, at the cost of increased computation and memory requirements, in order to improve the estimation performance. For nonhomogeneous environments, it is desirable to use less number of cells in order to reduce the number of false alarms around the clutter edges. In this work, we present a solution with two exponential smoothers (first order IIR filters) having different time-constants to leverage the conflicting requirements of homogeneous and nonhomogeneous environments. The system is designed to use the filter having the large time-constant in homogeneous environments and to promptly switch to the filter having the small time constant once a clutter edge is encountered. The main advantages of proposed Switching IIR CFAR method are computational simplicity, small memory requirement (in comparison to windowing based methods) and its good performance in homogeneous environments (due to the large time-constant smoother) and rapid adaptation to clutter edges (due to the small time-constant smoother).     

Extracting trust information from security system of a service

The issue of trust is a research problem in emerging open environments, such as ubiquitous networks. Such environments are highly dynamic and they contain diverse number of services and autonomous entities. Entities in open environments have different security needs from services. Trust computations related to the security systems of services necessitate information that meets needs of each entity. Obtaining such information is a challenging issue for entities. In this paper, we propose a model for extracting trust information from the security system of a service based on the needs of an entity. We formally represent security policies and security systems to extract trust information according to needs of an entity. The formal representation ensures an entity to extract trust information about a security property of a service and trust information about whole security system of the service. The proposed model is applied to Dental Clinic Patient Service as a case study with two scenarios. The scenarios are analyzed experimentally with simulations. The experimental evaluation shows that the proposed model provides trust information related to the security system of a service based on the needs of an entity and it is applicable in emerging open environments.     

Face recognition with lattice independent component analysis and extreme learning machines

We focus on two aspects of the face recognition, feature extraction and classification. We propose a two component system, introducing Lattice Independent Component Analysis (LICA) for feature extraction and Extreme Learning Machines (ELM) for classification. In previous works we have proposed LICA for a variety of image processing tasks. The first step of LICA is to identify strong lattice independent components from the data. In the second step, the set of strong lattice independent vector are used for linear unmixing of the data, obtaining a vector of abundance coefficients. The resulting abundance values are used as features for classification, specifically for face recognition. Extreme Learning Machines are accurate and fast-learning innovative classification methods based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-to-output weights. The LICA-ELM system has been tested against state-of-the-art feature extraction methods and classifiers, outperforming them when performing cross-validation on four large unbalanced face databases.     

Optimal configuration of Energy-Efficient Ethernet

The IEEE 802.3az standard provides a new low power mode that Ethernet network interfaces can use to save energy when there is no traffic to transmit. Simultaneously with the final standard approval, several algorithms were proposed to govern the physical interface state transition between the normal active mode and the new low power mode. In fact, the standard leaves this sleeping algorithm unspecified to spur competition among different vendors and achieve the greatest energy savings. In this paper, we try to bring some light to the most well known sleeping algorithms, providing mathematical models for the expected energy savings and the average packet delay inflicted on outgoing traffic. We will then use the models to derive optimum configuration parameters for them under given efficiency constraints.     

A multi-objective approach for the motion planning of redundant manipulators

Kinematic redundancy occurs when a manipulator possesses more degrees of freedom than those required to execute a given task. Several kinematic techniques for redundant manipulators control the gripper through the pseudo-inverse of the Jacobian, but lead to a kind of chaotic inner motion with unpredictable arm configurations. Such algorithms are not easy to adapt to optimization schemes and, moreover, often there are multiple optimization objectives that can conflict between them. Unlike single optimization, where one attempts to find the best solution, in multi-objective optimization there is no single solution that is optimum with respect to all indices. Therefore, trajectory planning of redundant robots remains an important area of research and more efficient optimization algorithms are needed. This paper presents a new technique to solve the inverse kinematics of redundant manipulators, using a multi-objective genetic algorithm. This scheme combines the closed-loop pseudo-inverse method with a multi-objective genetic algorithm to control the joint positions. Simulations for manipulators with three or four rotational joints, considering the optimization of two objectives in a workspace without and with obstacles are developed. The results reveal that it is possible to choose several solutions from the Pareto optimal front according to the importance of each individual objective.     

An XPS and reduction study of PrCoO3

The perovskite-type oxide PrCoO₃ has been studied by means of X-ray photoelectron spectroscopy (XPS), reduction in H₂ and X-ray diffraction. Two types of oxygen were detected: lattice oxygen (binding energy = 528.4 eV) and adsorbed oxygen (binding energy = 530.9 eV). The increase in relative intensity of the peak corresponding to the latter species after reduction of PrCo0₃ to 3e^– per molecule is assigned to the formation of hydroxyl groups. Temperature-programmed reduction (TPR) results showed two reduction steps: to 1 e^– per molecule (Co^3.1 Co²⁺) at 475 to 635 K, and to 3e^– per molecule (Co²¹ Co⁰) at 725 to 815 K. Reduction in the first and second steps occurs according to the contracting sphere model and the nucleation mechanism, respectively. Reduction of Co³⁺ to Co²⁺ causes minimal structural changes in the perovskite. Reduction to 3e^– per molecule yielded Pr₂O₃ and metallic cobalt. After this reduction and reoxidation at 973 K, the perovskite structure was regained. By XPS and TPR it was shown that PrCo0₃ is more easily reducible than LaCo0₃. It is concluded that the cation in the A position of the structure plays a significant role in the bulk and surface properties of LnCo0₃ (Ln, lanthanide elements) oxides.     

A study of behavioural groupings of polyphenolic species in "fino" sherry wine. Evolution during vinification and mechanical treatment of the grape

The different groups and/or classes among phenolic acids and aldehydes identified during the elaboration process of "fino" sherry wine have been studied. The study was carried out using different processes during the elaboration of the wine and we have attempted to establish whether the different treatments of the grape affect these groupings. The study was undertaken by means of cluster and multifactorial analysis on the data obtained by the quantitative determination of phenolic acids and aldehydes during vinification and in experiments carried out on the grape under different mechanical treatments. The analysis of phenolic acids and aldehydes was done with HPLC, using a C-18 mu-Bondapak and a stepwise composite gradient. The samples were previously extracted with ethylic ether.     

Current oscillations in austenitic stainless steel induced by the presence of chloride ions

The conditions for current oscillations in austenitic stainless steel (AISI Type 303) in 1M H₂SO₄ containing Cl^? ions are given. The periodic oscillations are produced in a close potential range determined at the active-passive transition region. It requires a non-homogeneous distribution of inclusions and carbides at the metal surface and a concentration range of Cl^? ion where active and passive areas coexist on the metal surface. The constraints at the surface determining these two regions are related to the Cl^? ion competitive adsorption and the local accumulation of corrosion products. Electrochemical data and SEM observations are correlated.