Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying Parasitic Losses From Metal Scattering Structures in Solar Cells: How Uncertainty in Optical Constants Affects Simulation Results

The optical constants of many metals commonly used in solar cells, e.g. as contacts or for light trapping structures, are not documented consistently in the literature, with different sources giving different values. In the case of metallic structures designed to improve absorption in a solar cell junction, the use of data from different sources can give strongly varying results for the effectiveness of nanophotonic light-trapping structures. The trade-off between diffraction into more oblique orders in the junction, enhancing absorption in the photovoltaic material, and the number of photons absorbed parasitically in the metal means small differences in the optical constants can lead to different very conclusions about the EQE and $J_{SC}$. This work documents the different optical constants for silver, aluminium, gold and titanium from several sources, the effect this has on plasmon quality factors, and quantifies the effect on modelling outcomes by considering the optimization of a test structure using a grid of metal nanodisks on the front surface of a thinned-down GaAs cell. Finally, we consider the effect for a structure previously predicted to give a very high $J_{SC}$ for a solar cell with an ultra-thin GaAs layer.
Automated Generation of Modular PLC Control Software from P&ID Diagrams in Process Industry

Piping and instrumentation diagrams (P&IDs) are commonly used in the process industry’s detailed engineering to describe process flows and to serve as a basis for the systems design of processing plants. These diagrams (documents) serve as a basis for upcoming engineering activities, especially the development of control software. Changes in P&IDs often entail adaption of the respective control software. Although these adaptions often realized manually, the development of control software is characterized by painstaking effort and a tendency to prolong projects. This paper therefore proposes an approach that segments P&IDs according the ISA-88 standard. An implemented prototype subsequently generates modular control software based on the PLCopen standardized exchange format. Within an implemented prototype, the approach was evaluated on a sample P&ID derived from a representative lab-size demonstrator, which highlighted the ability to reduce time and costs during the development of control software.
Joint User Clustering, Resource Allocation and Power Control for NOMA-based Mobile Edge Computing

Mobile edge computing (MEC) has been considered as a promising technology to reduce the energy consumption and task execution latency of smart mobile devices (SMDs). To improve the number of user access and spectrum efficiency, we exploit non-orthogonal multiple access (NOMA) into the MEC system. NOMA will cause mutual interference between users, which will increase user’s transmission latency. Therefore, we formulate an optimization problem which minimizes the average latency of the NOMA-based MEC system via jointly optimizing user clustering, resource allocation and transmission power. To solve the optimization problem, we first propose heuristic algorithms to solve user clustering and resource allocation. Then, we propose an iterative power optimization algorithm based on particle swarm optimization (PSO). Simulation results show that the proposed optimization strategy can effectively reduce the average latency of the system.
Skip-Connected Deep Convolutional Autoencoder for Restoration of Document Images

The denoising and deblurring of images are the two essential restoration tasks in the document image processing task. As the preprocessing stages of the processing pipeline, the quality of denoising and deblurring heavily influences the result of subsequent tasks, such as character detection and recognition. In this paper, we propose a novel neural method for restoring document images. We named our network Skip-Connected Deep Convolutional Autoencoder (SCDCA), which is composed of multiple layers of convolution followed by a batch normalization layer and the leaky rectified linear unit (Leaky ReLU) activation function. Inspired by the idea of residual learning, we use two types of skip connections in the network. One is identity mapping between convolution layers and the other is used to connect the input and output. Through these connections, the network learns the residual between the noisy and clean images instead of learning an ordinary transformation function. We empirically evaluate our algorithm on an open and challenging document images dataset. We also assess our restoring results using the optical character recognition (OCR) test. Experimental results have demonstrated the effectiveness and efficiency of our proposed algorithm by comparing with several state-of-the-art methods.
Alternative Approach to Quantitative Estimation of the SWOT Analysis Solutions

The methodology of SWOT analysis involves the identification of internal strong and weak properties of the system under study, as well as external opportunities and threats for its functioning. Due to the apparent ease of mastering the SWOT analysis methodology, a large number of articles on the application of SWOT analysis to various practical situations has appeared. However, the conclusions and recommendations contained in such articles were given (as a rule) without proper justification of the plausibility (repeatability, nonrandomness) of the results obtained. An alternative approach to the SWOT analysis to ensure the plausibility of its results is proposed. This approach involves the use of a group of experts to assess the SWOT factors and the Delphi procedure with the property of (partial) convergence of expert estimates for rounds of the procedure. The sufficient conditions for the existence some possible states and their resistance to disturbances in the explored system are formulated in the form of first-order logic formulas. The possibilities of practical applications of the proposed approach are discussed.
Coded Frameless ALOHA

We propose a random access scheme named coded frameless ALOHA to completely eliminate the inherent error floor of the original frameless ALOHA, and show the optimized performance of the system. Coded frameless ALOHA uses erasure correcting codes, and transmitted packets are retrieved by an iterative process between successive interference cancellation and a message-passing decoder for erasure correcting codes. The packet loss rate of coded frameless ALOHA is theoretically analyzed and used to optimize parameters so that the achievable throughput is maximized. Numerical examples show that the proposed scheme exhibits better performance than conventional frameless ALOHA schemes.
Progress Rate Control for Computer Applications

Self-adaptive software applications often include some form of progress rate control. Various frameworks were proposed to measure progress and provision resources to govern it, hence- in control terms- for sensors and actuators. The same is not true for control laws, however. In this paper we address this part of the overall problem, proposing a standard control structure that can be easily configured and tuned to match a variety of progress control needs. We completely analyse the simplest case, namely a single application under fixed rate control, and spend a few words on extensions to multiple application and event-based realisation. Simulation examples are reported to support the proposal.
Analysis of the Influence of Social and Political Factors Towards the Success of the Government IT Investment

Information Technology (IT) plays a strategic role for an Organization to benefit from improving business performance and productivity, creating efficiency and service innovation, increasing market share, and the organization's ability to improve their business processes. These benefits encourages an organization to invest in IT. However, many IT investments do not run as expected, and may even fail. Failure is a risk that must be considered by an organization, especially by government agencies, when investing in IT. The problems examined in this study are the social and political variables for an IT investment. This research aims to design a government IT investment model that is expected to provide guidance for the government agencies in order to find out the magnitude of the social and political variables on IT investment.
Continuous Refocusing for Integral Microscopy with Fourier Plane Recording

Integral or light field imaging is an attractive approach in microscopy, as it allows to capture 3D samples in just one shot and explore them later through changing the focus on particular depth planes of interest. However, it requires a compromise between spatial and angular resolution on the 2D sensor recording the microscopic images. A particular setting called Fourier Integral Microscope (FIMic) allows maximizing the spatial resolution for the cost of reducing the angular one. In this work, we propose a technique, which aims at reconstructing the continuous light field from sparse FIMic measurements, thus providing the functionality of continuous refocus on any arbitrary depth plane. Our main tool is the densely-sampled light field reconstruction in shearlet domain specifically tailored for the case of FIMic. The experiments demonstrate that the implemented technique yields better results compared to refocusing sparsely-sampled data.
Secure Data Sharing of Personal Health Records in Cloud Using Fine-Grained and Enhanced Attribute-Based Encryption

Personal Health Records (PHR) is essential health related management system to preserve the health data for someone. These facts are stored on untrusted servers which make secure Information sharing System. The accessing process of cloud servers are normally access by 3rd-User, Generally the PHR policies done through on these cloud, the Major Encryption Strategies is used can be carried out - encrypted Public Health Record's are stored on storage Cloud. To recommend these troublesome commitments, the Enhanced Attribute-Based Encryption (ABE) strategies are used. Fine-grained information collects the right of entry to manipulate is guaranteed on un-trusted servers. In these methodologies, the data owners are in charge of encrypting the data before upload or downloading them on the cloud and also re-encryption done when there is a change in client credentials. To perform the fine-grained enhanced ABE is able to access the information data securely for PHR from these access and share each patient's PHR file by securely. The results show the efficient and effectiveness of our methodology for data access and sharing in cloud computing. Our methodical approach will reasonable for the resources constrained gadgets. Our Approach will diminish the computational cost of customers is finished by outsourcing process.
Weakly Supervised Vehicle Detection in Satellite Images via Multiple Instance Ranking

Given the difficulty in labeling sufficient amount of instances across different resolutions and imaging environment of satellite images, weakly supervised vehicle detection is with great importance for satellite images analysis and processing. To prevent such cumbersome and meticulous manual annotation, naturally we have introduced the weakly supervised detection that has recently explosively prevalent in ordinary viewing angle images. Our program merely stands in need of region-level group annotation, i.e., whether this district convers vehicle(s) without plainly pointing out the coordinates of vehicles. There are two major problems are often encountered for Weakly Supervised Object Detection. One is that it is often chooses only a most expressive instance contains multiple target objects which often have a bigger probability when selecting a target block. For this problem, the number of vehicles can be estimated based on the object counting, a combinatorial selection algorithm can be used to select patch which contains at most one vehicle instance. Another problem is that precise object positioning becomes more difficult due to the lack of instance-level supervision. This problem can be optimized by a progressive learning strategy. Experiments was carried on wide-ranging remote sensing dataset and achieved better results compared to the state-of-the-art weakly supervised vehicle detection schemes.
A Polyimide-Based 3-D Ultrathin Bioelectrode With Elastic Sites for Neural Recording

Flexible sheet electrode can record electrocorticography signals without penetrating the blood-brain barrier. However, the attachment between the cortex and the electrode influences the quality of the signal directly. Here, we present a 3-D ultrathin bioelectrode with elastic electrode sites to achieve conformal coverage on brain tissue and increase the flexibility of the electrode. Inspired by kirigami, the micro-warping elastic structures on the electrode can increase the conformality significantly when compared with the conventional planar electrodes. It has been proved that the warping structure has an excellent springback curve and maintains at least one week under the maximum stress of 7.5 kPa. Furthermore, the electrode sites were modified with platinum black to improve the electrochemical performance, and electrochemical characterization was performed. The electrode was compared with a plane one in vitro, which demonstrated the prominent attachment performance from the surface with sulci. Finally, the local field potential was recorded in vivo. [2018-0088]
A Crystal-Less Programmable Clock Generator with RC-LC Hybrid Oscillator for GHz Applications in 14 nm FinFET CMOS

This paper presents a crystal-less programmable clock generator. The programmable clock generator takes advantages of both RC and LC oscillators. The frequency reference is generated by the RC oscillator without using expensive external crystals. The sawtooth signal generated from the RC oscillator is sampled by low phase-noise differential clocks which are divided from the LC oscillator. The timing information is then amplified by the sampler which uses hysteresis. An additional block, gain adjuster (GA), reduces lock time and dithering. After the system gets locked, it achieves 0.01 %/V and 25.5 ppm/°C frequency variations for 100 MHz generated clock. The 14 nm FinFET CMOS programmable clock generator draws 28 mA current from a single 1.8 V supply and occupies an active area of 0.12 mm2, It achieves 163 dBc/Hz FoM for 100 MHz test clock.
Architectures and Challenges for the Household Energy Management Systems

The Internet of Things (IoT) household energy management system (HEMS) is presented in this paper. It is the integrated application of technologies that help to improve the quality of life of the residents. Besides the energy efficiency, data extracted from HEMS combined with computer learning algorithms can be used for many other potential application areas, next to controlling household appliances and multimedia equipment, providing assistance to elderly and disabled, and increasing safety and security. This paper aims to address several opened challenges of HEMS with key focus on the metrological issues and communication architectures for data integration. It has been shown that there is a gap between the need of energy data on the appliance level and the metering hardware complexity. Undoubtedly, the realization of one possible communication architectures of HEMS has been accelerated and enabled by the novel researches and development in the Internet of Things paradigm, as the interconnection concept of intelligent devices and management platforms that collectively enable the "smart world" around us.
Coefficient Constraint LIC with ADMM

Local Intensity Compensation (LIC) is an intra-frame motion compensation for video coding, and was a candidate for HEVC. LIC compensates a target block using motion vectors of reference blocks and linear coefficients of the blocks; thus, from a view point of data compression, not only compensation error but also the range of the motion vectors and coefficients should be as small as possible. Our previous work employs Alternating Direction Method of Multipliers (ADMM) to obtain reference blocks and their coefficients of LIC. This paper proposes to limit the range of coefficients, and experimental results tell us that the proposed method shows almost equivalent compensation accuracy to the conventional method.
Scene Text Detection via Deep Semantic Feature Fusion and Attention-based Refinement

Despite tremendous progress in scene text detection in the past few years, efficient text detection in the wild remains challenging, particularly for the texts have large rotations, and the complicated background areas that are easily confused with text. In this paper, we propose an effective approach for scene text detection, which consists of initial text detection using the proposed deep semantic feature fusion of a fully convolutional network (FCN), and text detection refinement by our attention based text vs. non-text classifier learned in a fine-to-coarse fashion. The proposed approach outperforms the state-of-the-art scene text detection algorithms on the public-domain ICDAR2015 dataset, achieving an accuracy of 0.83 in terms of F-measure.
Comprehensive Evaluation System of Voltage Sag Based on Combination of Improved Analytic Hierarchy Process and Entropy Weight Method

This paper presents a comprehensive evaluation system of voltage sag based on combination of improved analytic hierarchy process and entropy weight method. The two essential characteristic values, duration and sag amplitude are both taken into consideration. Appropriate weight coefficients calculated by the two methods are added to measure effect on voltage sag. The calculation results obtained by this evaluation system well correspond to actual severity of voltage sag.
Environmental Characteristics of the Atmosphere of Residential Agglomerations II. Main, Minor, and Trace Elements in the Gravitation Dust Sediments

It is necessary to observe the dustiness of residential agglomerations with respect to the chemical character of the gravitation dust sediments (precipitates), which secondarily influence also the chemical character of the airborne dust. The chemical character of the general dustiness was studied on the samples from four localities of the residential agglomeration of Kosice on the N-S axis. This axis is recently the most frequent direction of prevailing winds (ca. 94 %) of the Kosice basin. The influence of the geography of Kosice basin on the total dustiness and also the chemical composition of the gravitation dust sediments were thus also observed. The Kosice basin has a funnel-shaped character opening in the south direction. The elements of sediments were analyzed by atomic spectrochemical methods. The results of the monthly-collected samples were evaluated in differential histograms. With the selection and analysis of the chosen typical elements of the gravitation dust sediments, namely the main element Fe, the minor element Ti, and the cluster of trace elements As, Pb, Cu, Yn, Sr, Rb, and Zr, characteristic input data matrixes were acquired. These describe the situation not only in the Kosice basin in Slovakia, but also in four typically different residential agglomerations in the GFR. The evaluation of the results of sampling of the sedimentary particles and the chemical determinations of the given elements enable to evaluate the element variability and the level of the toxicity of the studied residential agglomerations.
Sparse Method for Tip-Timing Signals Analysis with Non Stationary Engine Rotation Frequency

Blades vibrations must be measured in operations to validate blade design. Tip-timing is one of the classical measurement methods but its main drawback is the generation of sub-sampled and non-uniform sampled signals. This paper presents a new sparse method for tip-timing spectral analysis that makes use of engine rotation variations. Assuming that blade vibration signals yield to line spectra, a sparse signal model is introduced as a linear system. The solution to the problem is obtained by ADMM (Alternating Direction Method of Multipliers) with a $p^{1}$ -regularization. Results for simulated and real signals are given to illustrate the efficiency of this method. The main advantages of the proposed method are to provide a fast solution and to take into account the variations of the rotation speed. Results show that this approach reduces frequency aliasings caused by the low sampling frequency of the measured signals.
Optimizing Energies for Pose-Invariant Face Recognition

One of the most difficult challenges in face recognition is the large variation in pose. One approach to handle this problem is to use a 2D-Warping algorithm in a nearest-neighbor classifier. The 2D-Warping algorithm optimizes an energy function that captures the cost of matching pixels between two images while respecting the 2D dependencies defined by local pixel neighborhoods. Optimizing this energy function is an NP-complete problem and is therefore approached with algorithms that aim to approximate the optimal solution. In this paper we compare two algorithms that do this without discarding any 2D dependencies and we study the effect of the quality of the approximate solutions on the classification performance. Additionally, we propose a new algorithm that is capable of finding better solutions and obtaining better energies than the other methods. The experimental evaluation on the CMU-MultiPIE database shows that the proposed algorithm also achieves state-of-the-art recognition accuracies.
Particle Filter for Underwater Passive Bearings-Only Target Tracking with Random Missing Measurements

In underwater passive bearings-only tracking (BOT), it is generally assumed that bearing measurements are valid and available at each time index. However, in practice, some of the measurements could be randomly unavailable or invalid on the account of low signal-to-noise ratio (SNR). This work proposes a particle filter (PF) with modified importance weights to tackle random missing instances in the observed bearings. A measurement model which replaces the missing measurements according to the last received measurement and the knowledge of process dynamics, is formulated. Finally, the proposed PF is implemented on a real-life underwater BOT scenario with random missing measurements. Root mean square error and track-loss count obtained from the simulation show that the proposed PF which accounts for the randomness in missing measurements, performs with improved accuracy compared to the existing PF and other Gaussian filters.
Features of the Neural Network for Determining the Position and Geometric Characteristics of Cavernous Inclusions

This article describes the process of developing a neural network (NN) capable of restoring the structure of the geological and physical model of the medium (GPMM) based on a known picture of the propagation of a wave field. The architecture of the NN itself and its components are indicated, information on the process of its training is provided. Also, the time of operation of the NN on different devices and the results obtained are shown. The work was supported by the Novosibirsk State Technical University (Project C-19, 2018).