Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L?a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback-Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.     

A patient-mounted robotic platform for CT-scan guided procedures

In this paper, we present a novel robotic assistant dedicated to medical interventions under computed tomography scan guidance. This compact and lightweight patient-mounted robot is designed so as to fulfill the requirements of most interventional radiology procedures. It is built from an original 5 DOF parallel structure with a semispherical workspace, particularly well suited to CT-scan interventional procedures. The specifications, the design, and the choice of compatible technological solutions are detailed. A preclinical evaluation is presented, with the registration of the robot in the CT-scan.     

Registration and real-time visualization of transcranial magnetic stimulation with 3-D MR images

This paper describes a method for registering and visualizing in real-time the results of transcranial magnetic stimulations (TMS) in physical space on the corresponding anatomical locations in MR images of the brain. The method proceeds in three main steps. First, the patient scalp is digitized in physical space with a magnetic-field digitizer, following a specific digitization pattern. Second, a registration process minimizes the mean square distance between those points and a segmented scalp surface extracted from the magnetic resonance image. Following this registration, the physician can follow the change in coil position in real-time through the visualization interface and adjust the coil position to the desired anatomical location. Third, amplitude of motor evoked potentials can be projected onto the segmented brain in order to create functional brain maps. The registration has subpixel accuracy in a study with simulated data, while we obtain a point to surface root-mean-square error of 1.17+/-0.38 mm in a 24 subject study.     

A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

A large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the most from nanodevices because SNN architectures are intrinsically tolerant to defective devices and performance variability. Here, spike‐timing‐dependent plasticity (STDP), a basic and primordial learning function in the brain, is demonstrated with a new class of synapstor (synapse‐transistor), called nanoparticle organic memory field‐effect transistor (NOMFET). This learning function is obtained with a simple hybrid material made of the self‐assembly of gold nanoparticles and organic semiconductor thin films. Beyond mimicking biological synapses, it is also demonstrated how the shape of the applied spikes can tailor the STDP learning function. Moreover, the experiments and modeling show that this synapstor is a memristive device. Finally, these synapstors are successfully coupled with a CMOS platform emulating the pre‐ and postsynaptic neurons, and a behavioral macromodel is developed on usual device simulator.     

计算机辅助设计大大简化了功率因素变换器的设计

由于能源调节的持续增长，电源变得越来越复杂。人们意识到需要一个更简单的方式进行设计时，出现了计算机辅助设计工具。这篇文章就论述了此工具如何很快产生一个连续导通模式升压功率因素校正设计的优势。全部设计已经得到证实，并且会提供一些相关的公式。     

Real-Time Adaptive Multiple Transforms for the Next Generation Software Video Decoders

Journal of Signal Processing Systems - The next generation of video codecs incorporate multiple transforms as a part of their transform core. However, some of the new transform types introduced in...     

Robust stability of hybrid Roesser models against parametric uncertainty: a general approach

This paper aims at proposing a general framework for the establishement of LMI conditions to analyse the robust stability of a singular hybrid Roesser model subject to parametric uncertainties. The uncertain parameters are involved through implicit Linear Fractional Representations (LFR). Special focus is put on the influence of the number of uncertain parameters and the dimensionality of the model. More precisely it is shown that each dimension can nearly be regarded as an uncertain parameter and the other way around. Therefore, their influence on the conservatism of the obtained condition is very similar.