Miracle-dz/UniFAR-FTUs huggingface dataset:
Double-Channel Guided Generative Adversarial Network for Image Colorization

Image colorization has a widespread application in video and image restoration in the past few years. Recently, automatic colorization methods based on deep learning have shown impressive performance. However, these methods map grayscale image input into multi-channel output directly. In the process, it usually loses detailed information during feature extraction, resulting in abnormal colors in local areas of the colorization image. To overcome abnormal colors and improve colorization quality, we propose a novel Double-Channel Guided Generative Adversarial Network (DCGGAN). It includes two modules: a reference component matching module and a double-channel guided colorization module. The reference component matching module is introduced to select suitable reference color components as auxiliary information of the input. The double-channel guided colorization module is designed to learn the mapping relationship from the grayscale to each color channel with the assistance of reference color components. Experimental results show that the proposed DCGGAN outperforms existing methods on different quality metrics and achieves state-of-the-art performance.
A Review Paper on DES, AES, RSA Encryption Standards

Cryptography is wide domain with many subs. The objective of this review paper is to give light review for readers and students of the three common used algorithm two in symmetric cryptography DES, AES and one in asymmetric cryptography RSA. Allowing the reader to have simple understanding of the background history of the algorithm in review and the key functional cipher operation of the algorithm. Accordingly summary of strength and weakness of each algorithm under the review will be highlighted. And how the security goals is such confidentiality and integrity are achieve using the mentioned algorithms. The paper will sequentially list and review the said algorithms and clarify the relational between them. Such as the relation between the symmetric and asymmetric algorithm the one with secret key and the ones with key pairs. The paper may insight some of the research window for upcoming scholar research in the field of cryptography.
A Flexible Thermoelectric Generator Worn on the Leg to Harvest Body Heat Energy and to Recognize Motor Activities: A Preliminary Study

Wearable devices are commonly used to monitor human movement since motor activity is a fundamental element in all phases of a person’s life. Patients with motor disorders need to be monitored for a prolonged period and the battery life can be a limit for such a goal. Here the technique of harvesting energy from body heat to supply energy to wearable devices is investigated. A commercial flexible thermoelectric generator, equipped with an accelerometer, is placed on the lower leg above the ankle. The accelerometer serves to detect diverse motor activities carried out by ten students of VSB-Technical University of Ostrava involved in the execution of two tasks. To summarize, the motor activities analyzed in the proposed work are: “Sit”, “Walk”, “Rest”, “Go biking”, “Rest after biking”, and “Go down and up the stairs”. The maximum measured value of power density was $20.3~\mu \text{W}$ cm−2 for the “Walk” activity, corresponding to a gradient of temperature between the hot and cold side of the thermocouples constituting the flexible thermoelectric generator of 1.5 °C, while the minimum measured value of power density was $8.3~\mu \text{W}$ cm−2 for the “Sit” activity, corresponding to a gradient of temperature of 1.1 °C. Moreover, a mathematical model was developed for the recognition of motor activities carried out during the execution of the experiments. As a preliminary result, it is possible to state that semi-stationary parts of the signal generated by the thermoelectric generator can be traced back to the performance of an activity.
CHARGE RECOMBINATION IN DYE-SENSITIZED NANOCRYSTALLINE TIO2 SOLAR CELLS

Charge recombination between dye-sensitized nanocrystalline TiO2 electrodes and the I3-/I- couple in nonaqueous solution is described. The sensitizer was [RuL2(NCS)2] (L = 2,2‘-bipyridyl-4,4‘-dicarboxylic acid). An apparent inequality between the dark current and the recombination current is ascribed to a voltage shift caused by a potential drop at the SnO2/TiO2 interface, ohmic losses in the SnO2 and TiO2, and an overpotential for the redox reaction at the Pt counter electrode. Treating the dye-coated TiO2 electrodes with pyridine derivatives (4-tert-butylpyridine, 2-vinylpyridine, or poly(2-vinylpyridine)) improves significantly both the open-circuit photovoltage Voc (from 0.57 to 0.73 V) and the cell conversion efficiency (from 5.8 to 7.5%) at a radiant power of 100 mW/cm2 (AM 1.5) with respect to the untreated electrode. An analytical expression relating Voc to the interfacial recombination kinetics is derived, and its limitations are discussed. Analysis of Voc vs radiant power data with this expressi.
Net 113-Gbps PAM-4 Transmission Using Membrane DML-on-Si with 0.34 pJ/bit at 50 °C

A membrane-III-V-on-Si laser with a ∼47.5-GHz bandwidth is demonstrated by achieving stable photon-photon resonance. Direct modulation of 60-GBaud PAM-4 under KP4-FEC (net 113.42 Gbps) over 2-km SSMF with 0.17 pJ/bit at 25 °C and 0.34 pJ/bit at 50 °C have been achieved.
Research on Full-Space Spectrum-Sharing Strategy for Massive MIMO Cognitive Radio Systems

Large-scale antenna technology is a key technology for the fifth-generation mobile communication (5G) system to improve capacity and spectrum utilization; meanwhile, cognitive radio (CR) can vastly improve the utilization of unexploited spectrum resources. This paper employs three CR base stations to realize spectrum sharing in full space, where three CR base stations are placed at neighboring walls of a cell and distinguish CRs in the cell by their angular information. To reduce pilot contamination and overhead emerged during uplink and downlink training, this paper performs two dimensional discrete Fourier Transform to obtain the direction of arrivals and the angular spread of primary radios (PRs) and CRs. At last, this paper proposes a greedy CR scheduling algorithm for three CBS system. To testify three CBS system’s efficiency and effectiveness, performs similar greedy CR scheduling algorithms on two CBS systems. The three CBS systems demonstrate remarkable superiorities by recognizing and scheduling more CRs in less scheduling time, especially when the number of CRs exceeds 100.
ON-OFF Privacy Against Correlation Over Time

We consider the problem of ON-OFF privacy in which a user is interested in the latest message generated by one of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> sources available at a server. The user has the choice to turn privacy ON or OFF depending on whether he wants to hide his interest at the time or not. The challenge of allowing the privacy to be toggled between ON and OFF is that the user’s online behavior is correlated over time. Therefore, the user cannot simply ignore the privacy requirement when privacy is OFF. We represent the user’s correlated requests by an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-state Markov chain. Our goal is to design ON-OFF privacy schemes with optimal download rate that ensure privacy for past and future requests. We devise a polynomial-time algorithm to construct an ON-OFF privacy scheme. Moreover, we present an upper bound on the achievable rate. We show that the proposed scheme is optimal and the upper bound is tight for some special families of Markov chains. We also give an implicit characterization of the optimal achievable rate as a linear programming (LP).
Requirements Prioritization Techniques in the last decade: A Systematic Literature Review

Requirements Prioritization is an activity, part of Requirements Engineering, whose purpose is to determine the relevance that each requirement has within a software system to be developed. In order to conduct this activity, several requirements prioritization techniques have been proposed. However, companies and software developers do not know which techniques can give the best results possible or when to apply them. Choosing the wrong technique might lead the prioritization process to an inappropriate approach. This systematic literature review aims to know the state of the art of the prioritization techniques which have been used in software projects developed during the last decade, as well as knowing the benefits provided by their implementation. Our results show that AHP, Cumulative Voting, Cost-Value Approach, and Numerical Assignment were the most documented techniques. According to the studies reviewed, these techniques have specific advantages, for example, accuracy and effort reduction benefits. They also have disadvantages such as scalability issues and time consumption problems. However, the use of Requirements Prioritization Techniques, in general terms, generate cost and effort benefits as well as a considerable reduction in the time needed to conduct the Prioritization Process
Separation and Preconcentration of Cesium from Acidic Media by Extraction Chromatography

Abstract A novel extraction chromatographic resin for the separation and preconcentration of cesium from acidic nitrate media comprising an inert polymeric substrate impregnated with 1,3‐calix[4]bis‐o‐benzo‐crown‐6 (“BC6B”) in a chlorinated diluent is described. Cesium is shown to be both strongly and selectively retained by the resin at low (<1 M) acid concentrations and readily eluted from it using 6 M HNO3. Only potassium ion (at concentrations exceeding ca. 0.01 M) exerts a significant adverse impact on cesium retention. Unexpectedly, cesium uptake by the resin does not exhibit the acid dependency anticipated from liquid‐liquid extraction data. This is also the case for a resin employing a related macrocyclic extractant, calix[4]arene‐bis‐(t‐octylbenzocrown‐6) (“BobCalix”), prepared and partly characterized in an effort to overcome certain limitations of the BC6B‐based material. Despite this, the resin is shown to be well suited to the isolation of radiocesium from acidic solution for subsequent determination or for the removal of cesium intereference in the quantitation of other radionuclides.
Decomposition characteristics of C3F7CN under the presence of H2O and O2 based on molecular dynamics

SF<inf>6</inf> (Sulfur hexafluoride) was widely used in gas insulated electrical equipment. However, it is a strong greenhouse gas with a GWP (global warming potential) of 23900. In recent years, C<inf>3</inf>F<inf>7</inf>CN (heptafluoro-iso-butyronitrile) was proved to be a potential substitute gas with excellent insulation performance and low greenhouse effect. In this paper, the decomposition of C<inf>3</inf>F<inf>7</inf>CN under the presence of H<inf>2</inf>O and O<inf>2</inf> was studied based on the ReaxFF-MD (reactive force field molecular dynamics). It was found that H<inf>2</inf>O would promote the decomposition of C<inf>3</inf>F<inf>7</inf>CN. Compared with the 130 C<inf>3</inf>F<inf>7</inf>CN molecule remained in the pure C<inf>3</inf>F<inf>7</inf>CN system, the number of C<inf>3</inf>F<inf>7</inf>CN in the adding 50 H<inf>2</inf>O system is 100 at 1000ps. HF, COF<inf>2</inf> were produced in the adding H<inf>2</inf>O systems. The number of HF increased with the number of H<inf>2</inf>O added and there were 34 HF formed in the adding 50 H<inf>2</inf>O system in the end. 1 COF<inf>2</inf> was generated in the adding 30, 50 H<inf>2</inf>O systems. O<inf>2</inf> had no significant effect on the decomposition of C<inf>3</inf>F<inf>7</inf>CN, although a small amount of COF<inf>2</inf> were formed. The simultaneous presence of H<inf>2</inf>O and O<inf>2</inf> promoted the decomposition of C<inf>3</inf>F<inf>7</inf>CN, and it was close to the promotion of pure H<inf>2</inf>O. The number of HF and COF<inf>2</inf> in the adding H<inf>2</inf>O/ O<inf>2</inf> systems were close with that in the adding H<inf>2</inf>O systems. Utilizing molecular dynamics to study the decomposition of C<inf>3</inf>F<inf>7</inf>CN under the presence of H<inf>2</inf>O and O<inf>2</inf> on the provides theoretical support for the study of C<inf>3</inf>F<inf>7</inf>CN gas characteristics.
Manufacture of Alkyd Resin from Castor Oil

� Abstract— Oil-modified alkyd resin was prepared from crude castor oil. The experiment started with investigating the optimum conditions for neutralization of crude castor oil and bleaching of neutralized oil. Then the characteristics (iodine value, viscosity, acid value, refractive index and color) of oils were determined. Refined oil has iodine value of 90, kinemetic viscosity of 4 St, free fatty acid value of 1, refractive index of 1.474 and color number of 8. These results showed that the refined oil was qualified to prepare dehydrated castor oil. Dehydration of refined oil was carried out at 210-220˚C under 600-640mmHg with the help of 1% (wt%) NaHSO4 catalyst. The dehydrated castor oils were analyzed for iodine value, viscosity and its set to touch time and drying time were also investigated. Dehydrated castor oil has iodine value of 140, kinemetic viscosity of 1.6 St, set to touch time of 4 hr and drying time of 5 days. And then , oil modified alkyd resin (acid value 6.6) was prepared from dehydrated castor oil by alcoholysis method in excess of glycerol and phthalic anhydride in the presence of 0.3% (wt%) NaOH catalyst. The obtained resin was characterized by Fourier Transform Infrared Spectrophotometer (FTIR) and the properties were determined.
A Study of the Evolution of Video Codec and its Future Research Direction

The underlying principle of video compression is methodically subject to optimizing the data required for the storage, transmission, and processing of video streaming applications without compromising the video object's perceptual quality. Owing to its potential features, the core idea of video compression has become widely adopted prominent technology among bandwidth-hungry mobile streaming applications, where the prime aim is to reduce the redundant features from the video frame sequence, which in result optimizes the storage and transmission cost to a greater extent. The proposed methodology of this research study explores the traditional frequently adopted video codec and also defines its future research direction and evolutionary scope from both fundamental and theoretical view-pint. As research outcome, it outlines the scope and limitations associated with the traditional approaches of video compression and how it can be improvised to maintain futuristic dynamic streaming application demands where storage and processing complexity of frame sequence can be reduced while preserving acceptable video quality.
Vector-to-Vector Regression via Distributional Loss for Speech Enhancement

In this work, we leverage on a novel distributional loss to improve vector-to-vector regression for feature-based speech enhancement (SE). The distributional loss function is devised based on the Kullback-Leibler divergence between a selected target distribution and a conditional distribution to be learned from the data for each coefficient in the clean speech vector given the noisy input features. A deep model having a softmax layer per coefficient is employed to parametrize the conditional distribution, and deep model parameters are found by minimizing a weighted sum of the cross-entropy between its outputs and respective target distributions. Experiments with convolutional neural networks (CNNs) on publicly available noisy speech dataset obtained from the Voice Bank corpus show consistent improvement over conventional solutions based on the mean squared error (MSE), and the least absolute deviation (LAD). Moreover, our approach compares favourably in terms of both speech quality and intelligibility against the Mixture Density Networks (MDNs), which is also an approach that relies on computing parametric conditional distributions based on Gaussian mixture models (GMMs) and a neural architecture. Comparison against GAN-based solutions are presented as well.
Reduction of Phase-Modulation to Intensity-Modulation Conversion Noise Effect Using Delayed Self-Homodyne Optical Orthogonal Frequency Division Multiplexing System

Self-homodyne optical orthogonal frequency division multiplexing (OFDM) transmission system is a variant of self-coherent systems with a relaxed laser linewidth requirement as opposed to a full coherent system. The system is designed by transmitting a carrier together with the subcarriers, ensuring a strong phase-correlation between them at a receiver compared to the coherent system where only the subcarriers are transmitted. Despite the strong phase-correlation, the laser phase noise (PN) can still degrade the self-homodyne system performance due to the interaction of the PN with a chromatic dispersion (CD) induces a phase-modulation to intensity-modulation (PM-to-IM) conversion noise which cannot be canceled using a balanced-receiver. This imposes a high noise pedestal underneath each subcarrier, with the highest pedestal's peak imposed beneath the subcarriers that are farthest away from the carrier. This in turn produces a phase-decorrelation between the carrier and subcarriers which degrades the Q-factor between the lowest and highest subcarrier frequencies. In this work, the effect of the PM-to-IM conversion noise is reduced for the transmission of up to 174 Gb/s using a simple delay-line assigned to realign the phase-decorrelation of the subcarriers. With this technique, the subcarriers Q-degradation can be improved and the total transmission length can be tripled with Q-improvements of 5.4, 5.3, and 5-dB for 16-quadrature amplitude modulation (QAM) over 1360 km, 32- QAM over 800 km, and 64-QAM over 480 km, respectively, using 10-MHz LW with a 15-GHz total signal bandwidth. For 29 GHz signal bandwidth, the phase-walk-off of the high frequency subcarrier becomes more severe where the introduced delay can increase the transmission length by 2.5 times than without the delay, which increased the signal tolerance to the conversion noise.
Speech Enhancement for Optical Laser Microphone With Deep Neural Network

The development of distant-talking speech measurement systems is drawing attention since it can be used for security and disaster relief. An optical laser microphone that measures the vibration caused by sound using laser beam is suitable for the system. It can capture only the target sound even in a noisy place. However, the sound acquired by the optical laser microphone depends on the irradiated objects, and most of the objects will cause degradation of sound quality. Due to various deteriorations in the sound acquired by the laser microphone, the results of the conventional speech enhancement methods are insufficient. Therefore, in this paper, we propose a speech enhancement method using a two-stage process of noise suppression and acoustic structure reconstruction for optical laser microphones and conduct an objective experiment to evaluate the effectiveness of the proposed method.
A Lightweight Processing Method for Hand-Drawn Pressure-Sensitive Trajectories Oriented Toward Web-Based 3D Modeling

Digital geometry processing of hand-drawn pressure-sensitive trajectories oriented toward Web-based three-dimensional (3D) modeling becomes attractive problem. Existing practices usually focus on converting raw trajectories into 3D stroke meshes with consistent line width, which the pressure information can not been fully utilized for denoising, simplification and reflecting realistic graphics of 3D stroke mesh. Also, there are little research on the high quality of trajectory with data transmission fluency and light weight in the Web environment. We propose a lightweight processing method for hand-drawn pressure-sensitive trajectories that is oriented toward Web-based 3D modeling. An angle-chord height united trajectory simplification (ACUTS) algorithm is proposed, in which the discrete points are simplified by comprehensively considering the angle deviation and chord height deviation with the sliding window mechanism. In addition, a lightweight reconstruction algorithm based on pressure -sensitivity for discrete points (LRPDP) is also proposed, in which the pressure value can be fully utilized for simplifying the redundant points while ensuring the reconstructed 3D stroke mesh more realistic. Besides, the experimental results show that compared to some other methods, we perform best in terms of data reduction using compression ratio and reduction percentages. We also perform best in terms of high accuracy of 3D stroke mesh using mean stochastic error distance, local maximum error and total length error. Although the insufficient utilization of the pressure-sensitive information limits our research to some extents, the significance of our research is to provide a new strategy to realize Web-based 3D modeling using hand drawing mode.
Mathematical Reconstruction of Patient-Specific Vascular Networks Based on Clinical Images and Global Optimization

Cancer is a major cause of death worldwide and becomes particularly threatening once it begins to metastasize. During metastasis, the blood vessels serve as pathways for cancerous cell transportation and hence are crucial for understanding cancer growth. Existing medical imaging modalities can provide 3-D contrast images of the vascular tissues but with limited quality and detailedness. A much-needed tool for cancer research is thus one that can reconstruct vascular networks from low-quality clinical images. To this end, we developed a computational framework that takes 3-D medical images as input and reconstructs complete, patient-specific vascular network models using a mathematical optimization procedure. Our framework extracts major vessels from the images and uses the organ geometry to select vessel termination points. Then, it generates the remainder network based on physiological optimality principles. Using the framework, we obtained a set of network models with over 3000 terminal segments from a brain MRA scan. We analyzed the Strahler order, vessel radius, and branch length distributions of the models, which match with actual human data. We also performed fluid dynamics simulation inside the reconstructed vessels and showed that the pressure and shear stress distributions agree with existing in vivo measurements. The qualitative and quantitative agreements in vessel morphometry and hemodynamics demonstrate the effectiveness of the framework. Our method bridges the gap between image-based vessel models, accuracy of which is limited by the resolution of clinical images, and hypothetical models.
A convolutional neural network and stacked autoencoders approach for motor imagery based brain-computer interface

In this research, we are investigating Convolutional Neural Networks (CNN) and Stacked Auto Encoders (SAE) to classify EEG Motor Imagery signals. Also, we use Cohen Class Distribution (CCD) to calculate time and frequency features derived from EEG signals to feed to our network. Using this combination of CNN and SAE decrease the data dimensions. the best accuracy percentage according to our method, in an average manner, is 82%. The proposed approach was applied to the dataset IVa from BCI Competition III, a multichannel 2-class motor-imagery dataset obtained from 5 healthy subjects