Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Modulation Classification Using Deep Learning Based on Sparse Autoencoders With Nonnegativity Constraints

We demonstrate a novel method for the automatic modulation classification based on a deep learning autoencoder network, trained by a nonnegativity constraint algorithm. The learning algorithm aims to constrain the negative weights, learns features that amount to a part-based representation of data, and disentangles a more meaningful hidden structure. The performance of this algorithm is tested on the fourth-order cumulants of the modulated signals. The results indicate that the autoencoder with nonnegativity constraint (ANC) improves the sparsity and minimizes the reconstruction error in comparison with the conventional sparse autoencoder. The classification accuracy of an ANC based deep network shows improved accuracy under limited signal length and fading channel.
Mentoring Teachers in Technology Education : Analyzing the Need

Mentoring programs have been shown to have an influence on the overall success of retaining teachers. Studies have shown that not only are teachers who participate in mentoring programs more likely to stay in teaching positions, but also the overall economic value of retaining teachers goes beyond the cost savings related to attrition. Beginning technology education teachers typically participate in the same traditional mentoring programs all teachers follow. These programs tend to overlook the unique nature of a technology education teacher’s job. Because a technology education teacher’s job generally requires additional and sometimes more stressful duties, such as lab components, this study sought to address the areas that traditional mentoring programs overlooked. Specific attention was paid to technology education teachers’ need for assistance regarding technical experts and managing a laboratory environment. This study applies the situational mentoring framework (SMF) model to address the issues related to mentoring programs for technology education teachers. Purpose of this Paper Although mentoring programs have been effective in retaining beginning teachers in general, a review of literature regarding mentoring programs for technology education teachers reveals limited to no research on the topic. More specifically, there is currently no research addressing the overall effectiveness of mentoring programs or the development of a mentoring program (model) for technology education teachers. The purpose of this article is to examine the current status of mentoring pr ograms within technology education by focusing on (a) the overall benefits and effectiveness of mentoring programs, (b) the unique aspects of technology education that are overlooked within traditional mentoring programs, and (c) the methods for developing and implementing effective mentoring programs within technology education. In order to address the unique aspects of technology education, the situational mentoring framework (SMF) will be applied for the systematic development of a model mentoring program for technology education teachers. Teacher Shortage Few would argue that the field of education is facing a significant teacher shortage. Numbers do not lie: there were more than 60,000 reported teaching vacancies in the United States during the 2003-2004 school year (Mihans, 2008). Even though many fields of education have experienced teacher shortages, several areas of study are particularly troubling. Technology education and its allied fields have been experiencing a shortage of qualified teachers for approximately 20 years. This problem is exacerbated because, as demands for a technologically literate society increase, so has the demand for technologyrelated subjects at the elementary, secondary, and post-secondary levels. Meade and Dugger (2004), Ndahi and Ritz (2003), Newberry (2001), Ritz (1999), and Weston (1997) indicated that technology education has experienced and will continue to experience a significant teacher shortage unless educators act to reverse this problem.
Implementation of reversible gates in FPGA structure

The paper presents reversible circuits modelling methods in the FPGA structures. Three different methods of reconfigurable reversible gate descriptions are proposed - a direct method of output calculation and two methods of composing the gate from Fredkin and Toffoli gates. The paper shows both 4-bit and 8-bit gates. Application of developed test bench verified the correctness of our designs. The presented descriptions can be used for modelling of complex elements used e.g. in cipher machines based on quantum algorithms.
Moving object detection in video satellite image based on deep learning

Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.
Saliency detection algorithm based on LSC-RC

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.
3D ranging and virtual view generation using omniview cameras

In this paper we describe elements of an ubiquitous vision system - a system that enables viewers to observe a remote dynamic scene from any viewing perspective at any time instant. This integrated framework has the potential to achieve real-time generation of many 3D virtual views concurrently with high-dynamic events in the scene, by not utilizing 3D model. The paper presents a brief overview of the system including its requirements, components and preliminary results of acquiring 3D range estimation and virtual view generation.
High-stiffness-driven micromechanical resonator oscillator with enhanced phase noise performance

A two-port micromechanical beam resonator driven by its high stiffness locations has been used to enable a series-resonant resonator oscillator, for the first time, with enhanced power handling and phase noise performance as compared with the same resonator design but using low-stiffness driving configuration. The key to attaining better power handling capability relies on driving electrode arrangement where critical handling power becomes much larger by driving the resonator at its high-stiffness locations than low-stiffness areas since power handling of a resonator is proportional to its effective stiffness. With 16.9X improvement on power handling capability for a 9.7-MHz beam resonator via the proposed high-stiffness driving concept, a MEMS-based oscillator referenced to it greatly benefit from power handling enhancement, therefore leading to 26.5 dB reduction in far-from-carrier phase noise as compared to its low-stiffness driving counterpart.
Combined optimal quantization and lossless coding of digital holograms of three-dimensional objects

Digital holography is an inherently three-dimensional (3D) technique for the capture of real-world objects. Many existing 3D imaging and processing techniques are based on the explicit combination of several 2D perspectives (or light stripes, etc.) through digital image processing. The advantage of recording a hologram is that multiple 2D perspectives can be optically combined in parallel, and in a constant number of steps independent of the hologram size. Although holography and its capabilities have been known for many decades, it is only very recently that digital holography has been practically investigated due to the recent development of megapixel digital sensors with sufficient spatial resolution and dynamic range. The applications of digital holography could include 3D television, virtual reality, and medical imaging. If these applications are realized, compression standards will have to be defined. We outline the techniques that have been proposed to date for the compression of digital hologram data and show that they are comparable to the performance of what in communication theory is known as optimal signal quantization. We adapt the optimal signal quantization technique to complex-valued 2D signals. The technique relies on knowledge of the histograms of real and imaginary values in the digital holograms. Our digital holograms of 3D objects are captured using phase-shift interferometry. We complete the compression procedure by applying lossless techniques to the quantized holographic pixels.
The generalized scattering matrix separation technique combined with the MM/FE method for the efficient modal analysis of a comprehensive class of 3D passive waveguide circuits

A novel and versatile technique for the efficient modal analysis of passive waveguide structures, called the generalized scattering matrix separation (GSMS) technique, is introduced. Its combination with the well-proven hybrid mode-matching/finite element (MM/FE) method achieves the fast and rigorous modal analysis of a comprehensive class of three-dimensional waveguide components which include homogeneous segments of arbitrarily shaped cross-section and one or more side coupled rectangular waveguide ports. This combined method takes the higher order mode coupling between all discontinuities or ports into account and requires only one frequency independent application of the two-dimensional finite element (FE) method. The method is verified by excellent agreement with measurements. <<ETX>>
Adjunctive lithium treatment in the prevention of suicidal behavior in patients with depression and comorbid personality disorders

Abstract Objective. Patients with both major depression and personality disorders have a high risk of suicidal behavior. Lithium is meant to have anti-suicidal properties in patients with affective disorders. The anti-suicidal effect of lithium in patients with affective disorders and comorbid personality disorders has not been investigated yet. Methods. A post-hoc analysis of a subsample of patients with depression and comorbid personality disorder (PD) and a recent suicide attempt (n = 19) from the prospective, placebo-controlled lithium intervention study (N = 167), was conducted. Results. Three patients in the lithium group (n = 8) and two patients in the placebo group (n = 11) presented a suicide attempt throughout the course of the study. No differences related to suicidal behavior could be detected between the placebo group and the group with lithium intervention. Conclusions. On the basis of the small sample size, among patients with comorbid PD, lithium does not seem to have an effect on suicidal behavior in contrast to patients with affective disorders without comorbid PD.
DNA methylation profiling: a new tool for evaluating hematologic malignancies.

The addition of a methyl group (CH3) to specific cytosines in mammalian DNA controls early development by regulating gene activity (reviewed in 1–12). DNA methylation is defined as an “epigenetic” modification that results in a heritable change in gene function without altering the primary DNA sequence itself. The requirement for a properly functioning DNA methylation system in mammals is best appreciated when observing the severe consequences that result when this system fails. Mutations in the enzymes controlling methyl group addition, the DNA methyltransferases (DNMTs), cause deficient or nonphysiologic DNA methylation patterns that interrupt normal development. For example, targeted deletion of DNMT1 in mice is lethal to developing embryos, while naturally occurring DNMT3B mutations may cause the ICF (immunodeficiency, centromere instability, and facial anomalies) syndrome in humans (13, 14). Also, deletion of MeCP2, a protein that binds to methylated DNA, results in a Rett syndrome-like neurodevelopmental disorder in mice (15, 16). These findings highlight the importance of DNA methylation in epigenetic control of early mammalian development. The role of DNA methylation following embryogenesis is not resolved. However, DNA methylation patterns have been determined for numerous genes in multiple types of human malignancy. One rationale for this effort has been that nonphysiologic methylation patterns could promote transformation of normal cells to malignant cells through disruptions in physiologic gene regulatory processes. For example, hypermethylation could lead to aberrant tumor suppressor gene silencing, while hypomethylation could lead to inap-
Optical-integrated NH3 sensor design based on WO3 thin films: influence of gas adsorption and chromic effects

It has been observed in the literature, developments about characterization of several materials for gas sensing application; simultaneously, also it has been observed that only some of them show a procedure of design for the implementation of these materials in a device or an optical system. In consequence, appears differences between the predicted by theory and the experiments. For that reason, the present work proposes a new approach for the design of optical-integrated (OI) sensors. We show the advances in the design of an OI sensor for ammonia detection (NH3), using tungsten trioxide (WO3) as sensitive material. We considered a model that incorporates the concepts of the kinetic of gas adsorption on solids, the effects of the real and imaginary part of the refractive index in the redistribution of the optical field, and the influence of the photo and thermochromism, before and during the sensing mechanism. Although the work has not been concluded, we can affirm that the concepts incorporated in this proposal, must be considered as critical parameters in the design of an OI sensor or an optical fiber (FO) sensor.
The PDF of the lth Largest Eigenvalue of Central Wishart Matrices and its Application to the Performance Analysis of MIMO Systems

The research of closed form expressions for the PDF of the lscrth ordered eigenvalue of a Wishart matrix has received a great attention in the past years owing to its applications in the performance analysis of multiple input multiple output (MIMO) systems in fading environments. Although several closed form expressions for this PDF were obtained in the past years, to the authors' knowledge, no one was very friendly for further analysis. We propose a methodology to obtain the PDF for the lscrth largest eigenvalue whose expression is given as a sum of terms x betae-xdelta . This expression is easily usable to obtain closed form results for the performance of many MIMO systems, such as, for instance, MIMO beamforming, and MIMO with singular value decomposition (SVD). The methodology is valid for both uncorrelated and correlated central Wishart, allowing the investigation of MIMO systems with uncorrelated and correlated Rayleigh fading.
Dissecting and Recording from The C. Elegans Neuromuscular Junction

Neurotransmission is the process by which neurons transfer information via chemical signals to their post-synaptic targets, on a rapid time scale. This complex process requires the coordinated activity of many pre- and post-synaptic proteins to ensure appropriate synaptic connectivity, conduction of electrical signals, targeting and priming of secretory vesicles, calcium sensing, vesicle fusion, localization and function of postsynaptic receptors and finally, recycling mechanisms. As neuroscientists it is our goal to elucidate which proteins function in each of these steps and understand their mechanisms of action. Electrophysiological recordings from synapses provide a quantifiable read out of the underlying electrical events that occur during synaptic transmission. By combining this technique with the powerful array of molecular and genetic tools available to manipulate synaptic proteins in C. elegans, we can analyze the resulting functional changes in synaptic transmission. The C. elegans NMJs formed between motor neurons and body wall muscles control locomotion, therefore, mutants with uncoordinated locomotory phenotypes (known as unc s) often perturb synaptic transmission at these synapses 1. Since unc mutants are maintained on a rich supply of a bacterial food source, they remain viable as long as they retain some pharyngeal pumping ability to ingest food. This, together with the fact that C. elegans exist as hermaphrodites, allows them to pass on mutant progeny without the need for elaborate mating behaviors. These attributes, coupled with our recent ability to record from the worms NMJs 2,3,7 make this an excellent model organism in which to address precisely how unc mutants impact neurotransmission. The dissection method involves immobilizing adult worms using a cyanoacrylic glue in order to make an incision in the worm cuticle exposing the NMJs. Since C. elegans adults are only 1 mm in length the dissection is performed with the use of a dissecting microscope and requires excellent hand-eye coordination. NMJ recordings are made by whole-cell voltage clamping individual body wall muscle cells and neurotransmitter release can be evoked using a variety of stimulation protocols including electrical stimulation, light-activated channel-rhodopsin-mediated depolarization 4 and hyperosmotic saline, all of which will be briefly described.
Electromagnetic metamaterials

Metamaterials are so-named to recognize and emphasize their purpose, which is to achieve material performance beyond the limitations of conventional composites. The holy grail of conventional composite design is to achieve an optimum combination of the constituent's properties without requiring that they react. Success is measured by how close the composite properties are to a volumetric average of those of its constituents. One of the goals of this paper is to outline the strategy of metamaterial design, its hallmark being the exploitation of low dimensional phenomena to extend composite performance. Another is to discuss the generality of the metamaterial strategy, and to illustrate its successful implementation in diverse application areas. It is also shown that ideal electromagnetic composite responses cannot be achieved by implementing designs based on utilizing the prevalent effective media theories. More disturbing is the observation that the deviation of the composite response from the ideal is greatest when the disparity of the constituent properties is largest. Unfortunately, this is the situation of most interest, where a volumetrically averaged response has the greatest practical value. This response can be achieved, however, by utilizing electromagnetic metamaterials design. The rules for this approach are illustrated by the successful design of a laminated electromagnetic composite that possesses some remarkable, and previously unattainable properties.
Dorsal Lateral Plate Mesoderm Influences Proliferation and Differentiation of Hemopoietic Stem Cells Derived From Ventral Lateral Plate Mesoderm During Early Development of Xenopus laevis Embryos

The developmental pattern of dorsal and ventral lateral plate mesoderm was examined by explanting these regions and culturing them in serum‐free conditions for 10 days. Under these circumstances, ventral lateral plate mesoderm gave rise to hemopoietic cells, and dorsal lateral plate mesoderm gave rise to stromal cells. Comparison of the differentiation pattern observed when dorsal and ventral mesoderm were cultured separately versus the pattern observed when dorsal and ventral mesoderm were cultured together revealed that dorsal mesoderm influenced the differentiation of ventral‐derived hemopoietic cells. Ventral mesoderm cultured alone gave rise to erythrocytes and granulocytes. However, in the presence of dorsal mesoderm, ventral mesoderm gave rise to erythrocytes, monocytes, and granuloyctes. Moreover, dorsal mesoderm functioned to maintain the proliferation and differentiation potential of progenitor cells as determined using a colony‐forming cell assay (CFU‐c).
Numerical simulation of hot imprint process of periodical lamellar microstructure into polycarbonate

The aim of this paper was to create finite element model of the hot imprint process for analysis of the heating, imprinting and demolding steps. Polycarbonate was modeled as elasto-plastic material, because it was analyzed below glassing temperature. In general, the simulation model and numerical results provide a useful understanding of the fundamental formation mechanism during the hot imprint process and serve as a useful guide for specifying the optimal processing conditions for variety of hot imprint applications.
Simultaneous bilateral mismatch response to right‐ but not leftward sound lateralization

Magnetoencephalography (MEG) was used to compare mismatch responses between hemispheres to changes in sound-source direction. Sixteen adults listened passively to two types of complex non-language sounds presented in separate blocks with midline standards and right- and left-lateralized deviants. Mismatch dipole amplitudes were larger contra- than ipsilaterally to the deviants. Both hemispheres processed right deviants simultaneously, whereas to left deviants, the left dipole peaked 20 ms later than the right dipole. A second experiment using the same standards but midline spectral deviants showed no interhemispheric differences. Here mismatch latencies were about 60 ms longer than in the location mismatch experiment. This suggested both fast, contralaterally dominant location mismatch responses and facilitated detection of auditory spatial deviance in the right hemifield.
Blind detection of fabric defects using multiple image features

This paper proposes a blind detection of fabric defects using multiple image features. The aim of proposed method is to detect the new types of fabric defects that have not been learned. For general learning of image features, this paper first learns the characteristics of image features between normal and defective image patches for various types of fabric structures and defects. The image features are frequency coefficients, color histogram, and edge orientation histogram. The mean vectors of 3 image features are calculated, and the vector distance distributions of normal and defective patches are learned using support vector machine. Since the decision boundary is determined from general distributions of image features, the proposed method is not restricted to the detect types and fabric structures which have not used in the learning phase. According to experiments with the real fabric images and defects types that have not been learned, the proposed method detects fabric defects with 96.4% accuracy at 0.4% failure including false positive and false negative errors. This result outperforms the usual CNN (Convolutional Neural Network) approaches.