Current Issue : July - September Volume : 2013 Issue Number : 3 Articles : 6 Articles
Intel Moore observed an exponential doubling in the number of transistors in every 18 months through the size reduction of\r\ntransistor components since 1965. In viewing of mobile computing with insatiate appetite, we explored the necessary enhancement\r\nby an increasingly maturing nanotechnology and facing the inevitable quantum-mechanical atomic and nuclei limits. Since we\r\ncannot break down the atomic size barrier, the fact implies a fundamental size limit at the atomic/nucleus scale. This means, nomore\r\nsimple 18-month doubling, but other forms of transistor doubling may happen at a different slope. We are particularly interested\r\nin the nano enhancement area. (i) 3 Dimensions: If the progress in shrinking the in-plane dimensions is to slow down, vertical\r\nintegration can help increasing the areal device transistor density. As the devices continue to shrink into the 20 to 30 nm range,\r\nthe consideration of thermal properties and transport in such devices becomes increasingly important. (ii) Quantum computing:\r\nThe other types of transistor material are rapidly developed in laboratories worldwide, for example, Spintronics, Nanostorage,\r\nHP display Nanotechnology, which are modifying this Law. We shall consider the limitation of phonon engineering fundamental\r\ninformation unit ââ?¬Å?Qubyteââ?¬Â in quantumcomputing,Nano/Micro ElectricalMechanical System (NEMS), CarbonNanotubes, singlelayer\r\nGraphenes, single-strip Nano-Ribbons, and so forth....
This paper presents a visual/motor behavior learning approach, based on neural networks. We propose Behavior Chain Model\r\n(BCM) in order to create a way of behavior learning. Our behavior-based system evolution task is a mobile robot detecting a\r\ntarget and driving/acting towards it. First, the mapping relations between the image feature domain of the object and the robot\r\naction domain are derived. Second, a multilayer neural network for offline learning of the mapping relations is used. This learning\r\nstructure through neural network training process represents a connection between the visual perceptions and motor sequence\r\nof actions in order to grip a target. Last, using behavior learning through a noticed action chain, we can predict mobile robot\r\nbehavior for a variety of similar tasks in similar environment. Prediction results suggest that the methodology is adequate and\r\ncould be recognized as an idea for designing different mobile robot behaviour assistance....
We revisit the protein secondary structure prediction problem using linear and backpropagation neural network architectures\r\ncommonly applied in the literature. In this context, neural network mappings are constructed between protein training set\r\nsequences and their assigned structure classes in order to analyze the class membership of test data and associated measures of\r\nsignificance. We present numerical results demonstrating that classifier performance measures can vary significantly depending\r\nupon the classifier architecture and the structure class encoding technique. Furthermore, an analytic formulation is introduced in\r\norder to substantiate the observed numerical data. Finally, we analyze and discuss the ability of the neural network to accurately\r\nmodel fundamental attributes of protein secondary structure....
Energy signature analysis of power appliance is the core of nonintrusive load monitoring (NILM) where the detailed data of the\r\nappliances used in houses are obtained by analyzing changes in the voltage and current. This paper focuses on developing an\r\nautomatic power load event detection and appliance classification based on machine learning. In power load event detection,\r\nthe paper presents a new transient detection algorithm. By turn-on and turn-off transient waveforms analysis, it can accurately\r\ndetect the edge point when a device is switched on or switched off. The proposed load classification technique can identify\r\ndifferent power appliances with improved recognition accuracy and computational speed. The load classification method is\r\ncomposed of two processes including frequency feature analysis and support vector machine. The experimental results indicated\r\nthat the incorporation of the new edge detection and turn-on and turn-off transient signature analysis into NILM revealed more\r\ninformation than traditional NILM methods. The load classification method has achieved more than ninety percent recognition\r\nrate....
A new democracy paradigm is emerging through participatory budgeting exercises, which can be defined as a public space in which the government and the society agree on how to adapt the priorities of the citizenship to the public policy agenda. Although these priorities have been identified and they are likely to be reflected in a ranking of public policy actions, there is still a challenge of solving a portfolio problem of public projects that should implement the agreed agenda. This work proposes two procedures for optimizing the portfolio of public actions with the information stemming from the citizen participatory exercise. The selection of the method depends on the information about preferences collected from the participatory group. When the information is sufficient, the method behaves as an instrument of legitimate democracy. The proposal performs very well in solving two real-size examples....
Rudimentary brain machine interface has existed for the gaming industry. Here, we propose a wireless, real-time, and smartphonebased\r\nelectroencephalogram(EEG) system for homecare applications.Thesystem uses high-density dry electrodes and compressive\r\nsensing strategies to overcome conflicting requirements between spatial electrode density, temporal resolution, and spatiotemporal\r\nthroughput rate. Spatial sparseness is addressed by close proximity between active electrodes and desired source locations and using\r\nan adaptive selection of N active among 10 N passive electrodes to form m-organized random linear combinations of readouts,\r\nm �« N �« 10N. Temporal sparseness is addressed via parallel frame differences in hardware. During the design phase, we took\r\ntethered laboratory EEG dataset and applied fuzzy logic to compute (a) spatiotemporal average of larger magnitude EEG data\r\ncenters in 0.3 second intervals and (b) inside brainwave sources by Independent Component Analysis blind deconvolution without\r\nknowing the impulse response function. Our main contributions are the fidelity of quality wireless EEG data compared to original\r\ntethered data and the speed of compressive image recovery.We have compared our recovery of ill-posed inverse data against results\r\nusing Block Sparse Code. Future work includes development of strategies to filter unwanted artifact from high-density EEGs (i.e.,\r\nfacial muscle-related events and wireless environmental electromagnetic interferences)....
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