Miracle-dz/UniFAR-FTUs huggingface dataset:
A 600 kV High-voltage, 2 ns-risetime Pulse Generator Based on a 1 MV Peaking Capacitor

This paper presents a high-voltage fast risetime pulse generator. Marx generators are one of the common approaches to meet this need. However, the serious disadvantages of Marx generators include the delay time due to the series of inductances. To alleviate this effect, peaking capacitors have been used to produce fast risetime pulses. This paper describes results from the design, development, and testing of a 600 kV, 2 ns-risetime, and 25-ns-duration pulse generator using a peaking capacitor. The peaking capacitor, with a capacitance of 160 pF, was designed to be highly stressed at a maximum voltage of 1 MV when insulated with 0.5 MPa SF6. The effects of the peaking capacitor were studied by modeling the generator circuit. The influence of various parameters was investigated. There is good agreement between the theoretical and experimental results.
High-Quality Single Image 3D Facial Shape Reconstruction via Robust Albedo Estimation

In this paper, we present a method for high-quality single image 3D facial shape reconstruction with robust albedo estimation by using a prior face model. Motivated by the state-of-the-art methods for single image-based face reconstruction using the Shape from Shading (SfS), we develop a 3D facial recovery framework with innovative components. Specifically, rather than using the mean albedo, we use an optimized albedo of the 3D Morphable Model (3DMM) as a prior albedo model. In addition, we develop a robust weighting method based on the chromaticity of input and back-projected images. Combining these approaches produces a more robust albedo estimation which directly enhances the result of the SfS process. By using the tetrahedron-based deformation technique, a detailed facial shape can be accurately recovered from a single image. We demonstrate that our 3D facial recovery framework can reconstruct fine geometric details for a single image and outperforms the state-of-the-art in single image face reconstruction.
Deep Convolutional Neural Network for Voice Liveness Detection

In this work, we present the system to detect the liveness by identifying the pop noise in the voice signal in order to avoid the security breach of ASV systems. Pop noise is created due to spontaneous breathing while uttering a certain phonemes, and it has low-frequency characteristics. Given the low-frequency characteristics of the pop noise, we have used the short-time Fourier transform (STFT) with low-frequency contents (0–40 Hz) as a feature set along with a convolutional neural network as a classifier. The experiments are performed using the recently released POp noise COrpus (POCO) dataset. We have considered the approach given in the original POCO dataset paper as a baseline and compared the results with the proposed architecture. The performance of the proposed architecture is measured using 10-fold cross-validation and the customized disjoint partition of the dataset. It is observed that the proposed architecture shows an improvement in accuracy for voice liveness detection in both cases. In particular, the proposed architecture obtained 19.86% and 18.22% absolute improvement in accuracy for 10- fold cross-validation and customized data partition, respectively, as compared to the baseline.
Self-Supervised Learning for Online Speaker Diarization

Speaker diarization deals with the issue of “who spoke when” which is tackled through splitting an utterance into homogeneous segments with individual speakers. Traditional methods were implemented in an offline supervised strategy which constrained the usefulness of a practical system. Real-time processing and self-supervised learning are required. This paper deals with speaker diarization by relaxing the needs of reading the whole utterance and collecting the speaker label. The online pipeline components including feature extraction, voice activity detection, speech segmentation and speaker clustering is implemented. Importantly, an efficient end-to-end speech feature extraction is implemented by an unsupervised or self-supervised method, and then combined with online clustering to carry out online speaker diarization. This feature extractor is implemented by merging a bidirectional long short-term memory and a time-delayed neural network to capture the global and local features, respectively. The contrastive learning is introduced to improve initial speaker clusters. The augmentation invariance is imposed to assure model robustness. The online clustering based on autoregressive and fast-match clustering is investigated. The experiments on speaker diarization over NIST Speaker Recognition Evaluation show the merits of the proposed methods.
Computational Complexity Reduced Belief Propagation Algorithm for Polar Code Decoders

The belief propagation algorithm is desirable for a polar code based decoder, because of the potentially low latency and the ability of integration in digital signal processing units or other multi-core processor systems to parallelize the computations. Although belief propagation polar code decoder algorithms have the ability for a highly parallelized imple-mentation, the algorithms require many iterations to achieve a comparable frame error rate and bit error rate with respect to a successive cancellation polar code algorithm. The iterative nature of the belief propagation algorithms also result in a higher computational complexity, i.e. $O(IN(2\log_{2} N-1))$ compared to the computational complexity $O(N\log_{2}N)$ of the successive cancellation decoder algorithm. In this paper we propose several simplifications for a simplified belief propagation algorithm for polar code decoders, where the arithmetic complexity of the nodes is reduced. The proposed belief propagation algorithm shows preliminary results of a net reduction of the arithmetic complexity of ≈ 13%. This reduction is a result of the reduced number of arithmetic operations, i.e., additions, compares, and multiplications, without a lost in error-correcting performance.
Evaluation Models for Luminous Environment Satisfaction in Green Office Buildings Integrating Environmental and Spatial Attributes Based on Massive Data Samples

The quality of luminous environment is an important aspect of the operational performance of green buildings. A reliable evaluation for occupants’ satisfaction of luminous environment based on not only illuminance but with other more factors is indispensable. Compared to existing research, this study developed an evaluation model for luminous environment satisfaction based on the illuminance, the occupants’ distance from the window and weather conditions by a large- scale field measurement and questionnaire survey. To improve typicality, more than 3000 data samples were obtained from 14 green office buildings located in two different daylight climate zones in China. Analysis showed that the satisfaction rate of the luminous environment decreased obviously with the increase of the distance from window and the satisfaction rates tended to be higher on sunny days, especially for the daylight climate zone IV. Based on the above analysis, the luminous environment satisfaction was modelled via binary logistic regression method. Thus, the evaluation models for luminous environment satisfaction in green office buildings located in two daylight climate zones in China were proposed, which can offer a simple way to assess visual comfort and thus support the optimization of indoor environment and software development for green office building management.
Água salina e biofertilizante bovino na produção de frutos e alocação de biomassa em noni (Morinda citrifolia L.)

Organic compounds have shown to exercise a mitigating action on salts in plants and they promote growth and development in salt stress environments. In this sense, an experiment was carried out in order to evaluate the effects of saline water irrigation and bovine biofertilizer on yield and biomass allocation by the various organs of noni plants. Treatments were arranged in a randomized block design with four replications and two plants per plot using the 5 x 2 factorial design, which correspond to the salinity levels of irrigation water of 0.5, 1.5, 3.0, 4.5 and 6.0 dS m-1 in substrates with and without bovine biofertilizer, applied to the soil once, in the volume corresponding to 10% of the volume of the substrate one day before the implementation of the experiment. The variables evaluated were soil salinity, expressed as electrical conductivity of the soil saturation extract, fruit number, fruit weight and fruit yield per plant, and biomass allocation by roots, stems, leaves and fruit. The increasing salinity of the water for irrigation increased the saline character of the soil from non-saline to saline soil and inhibited the production and accumulation of dry biomass in noni plants in general but with more intensity in the plants of the treatments without bovine biofertilizer applied to the soil in liquid form. The biofertilizer stimulates the growth and production of noni plants grown under irrigation with high salinity water.
Intra-System Interference Avoidance for Packet-Level Index Modulation in Internet of Things

The packet-level index modulation (PLIM) has been proposed as the efficient method to increase a few transferring bits in addition to data transmitted by packet. The time and frequency indexes are utilized to transmit additional bits. Moreover, in Internet-of-Things (IoT), Low Power Wide Area (LPWA) has been expected to be used. In IoT, the data transmission with PLIM leads to periodic packet loss when each node periodically transmits packets with the same indexes of PLIM. In this study, the method is proposed to avoid a periodic interference caused by PLIM for LPWA in IoT. The proposed method transmits packets with indexes shifted according to a node's ID and a transmission packet ID. Finally, this paper shows the simulation result to confirm the efficiency of the proposed method.
A Comprehensive Study of Face Recognition Using Deep Learning

With the advent of deep learning, the performance of face recognition has been dramatically improved. On the other hand, there are few reports that discuss why the performance has been improved. In this paper, through comprehensive ex-periments, we analyze which regions are important in CNN-based face recognition. We employ the major four CNNs, AlexNet, ResNet, and EfficientNet, to be able to perform face recognition and three CNN visualization methods, Grad-CAM, Grad-CAM++, and Score-CAM, to visualize the regions in the face image that are emphasized by each face recognition method.
Temperature Control of a Shell and Tube Heat Exchanger using PID Algorithms

Integral windup problem and its accommodation in the field of process control, especially for the PID controllershas been under discussion for nearly about five decades(Since 1960) with limited structures and modifications in it. In this paper two control methodologies are analyzed mainly focusing on the control effort reduction under various uncertainties of a process. Also as a case study the Internal Model Control (IMC) and Ms based Two Degrees of Freedom (2DOF) PID controllers were designed and implemented for a shell and tube heat exchanger using MATLAB® in the lab environment
Remote Sensing Systems for Ocean: A Review (Part 2: Active Systems)

As discussed in the previous part of this review article, remote sensing (RS) creates unprecedented opportunities by providing a variety of systems with different characteristics to study and monitor oceans. Part 1 of this review article was dedicated to reviewing passive RS systems and their main applications in the ocean. Here, in part 2, seven active RS systems, including scatterometers, altimeters, gravimeters, synthetic aperture radar, light detection and ranging, sound navigation and ranging, high-frequency radars are comprehensively reviewed. For consistency, this part is structured similarly to part 1. The aforementioned systems, along with their characteristics and primary applications, are introduced in separate sections. This review article provides useful information to all students and researchers who are interested in the oceanographic applications of active RS systems.
3D Numerical Simulation and Electrical Performance Analysis of p-channel Cylindrical TFTs for New Man-machine Interface Applications

Thin film transistors using cylindrical geometry are advantageous in scaling down the size of the devices without short-channel effects. This research work designs and analysis the electrical performance of p-channel TFTs with cylindrical technology using the 3D numerical simulator. The SnO material has been chosen for the channel layer because of its better performance than the other p-type oxide semiconductors. The maximum field-effect mobility, On-Off current ratio from the electrical characteristics has been calculated as 0.17 cm2/V-s and $0.85 \times 10^{2}$, respectively. The obtained electrical performance is low compared with countertype TFTs. The defect parameters played a significant role in the degradation of the device’s electrical performance.
Defending against malicious nodes in closed manets through packet authentication and a hybrid trust management system

Wireless links and lack of central administration make MANETs far more susceptible to attacks than conventional networks. MANETs must provide various levels of security guarantees to different applications for their successful deployment and usage. Their security requirements depend greatly on their architecture. In this dissertation we are focusing on closed MANETs where only designated nodes are supposed to access the network (e.g., in a military or corporate setting). We define "outsider" nodes as those nodes that are not authorized to access the network, and " insider" nodes as those that are allowed to access the network. The objective of this research is to develop mechanisms that protect a closed MANET against malicious behavior from outsider nodes as well as insider nodes through packet authentication and a Hybrid Trust Management System, respectively. To defend against outsider nodes, we present a new Hop-by-hop, Efficient Authentication Protocol, called HEAP, which is suitable for unicast or multicast applications. HEAP is independent of the routing protocol used and it is based on a modified HMAC algorithm that uses two keys and is very efficient. We compare the performance of HEAP against other algorithms and provide proofs of its security. To combat against insider attacks, we propose a new hybrid trust management system that is based on Reputation Systems (RS) and Role Based Trust Management (RBTM). We develop a novel Machine Learning based RS, called EMLTrust and delineate its advantages. We compare its performance against other RSs and demonstrate the improvements in performance. We also highlight the challenges associated with using RBTMs in MANETs and offer some solutions. Finally, we propose a hybrid TM system that combines EMLTrust with RBTM, and evaluate it to illustrate its efficacy in thwarting insider attacks.
Multi-View Variational Autoencoder for Robust Classification against Irrelevant Data

Multi-view variational autoencoders (MVAEs) can extract latent variables with high discriminative power for classification by considering correlations among multi-view data, i.e., multiple kinds of data. However, if we input irrelevant data, i.e., multiple kinds of data that capture the different object, to MVAEs, discriminative power is reduced. To solve this problem, we propose an MVAE including a novel objective function. Our proposed MVAE reconstructs multi-view data without the negative effect of irrelevant data. Specifically, we derive an objective function that focuses on latent variables of relevant data, i.e., multiple kinds of data that capture the same object. Experimental results show that the proposed method improved the discriminative power of latent variables even if irrelevant data are input.
Super-Resolution Imaging Using a Focus Pixel Sensor

Modern camera sensors are equipped with a focus pixel, a special type of pixel that can collect separate light rays from the left (L) and right (R) directions. The phase difference between the corresponding L/R pixels is utilized to facilitate quick auto-focusing. In this study, we expand the usability of the special pixels to super-resolve an image. We design a neural net to best fuse multiple low-resolution focus pixel images with a normal image based on repetitive channel and spatial attention layer structures. Empirical results show that focus pixel images contribute to the creation of fine details by providing additional information to super-resolve an image, especially for textured areas and that the proposed neural net-based method enhances the state-of-the-art super-resolution methods that do not use focus pixels in quantitative and qualitative measures.
Performance assessment model for academic libraries: the Covenant University Library example

The study explores performance assessment models in academic libraries and show-cases the practical experiences at the Covenant University Library. The paper which is based on an observational study of the researchers’ daily work experiences and review of literature identified constraints to performance assessment in academic libraries and has attempted to give solutions. The paper concludes that academic libraries should overcome constraints and imbibe the culture of performance assessment that involves a continuous and periodic process of fine-tuning critical management and functional processes, either reactively or proactively through deliberately designed parameters (indicators). It extrapolated from the unique experiences of the Centre for Learning Resources, Covenant University to construct a model (Lib-PERFQUAL) for libraries around the world. This is a model that comprises all indicators necessary to maintain continuous relevance and achieve utmost efficiency.
Spatial multi-criteria decision analysis for safe school site selection

Schools which are located in a strategic and safe area play an important role in improving students’ performance and excellence. To ensure both success and long -term sustainability of the school planning,the finding of suitable sites for school is important and challenging. This study delves into a site selection process to establish a systematic public school. It was carried out by geographic information system (GIS)and multi-criteria evaluation model. A set of criteria was used to design a number of potential sites using a spatial analysis model. Mukim Batu which is located in Federal Territory of Kuala Lumpur (WPKL) has been selected as the study area. The final safety model outputs were compared with the field verification data and found to be reliable.
Analysis of Bolted Flange Joints for Lids of Packages for Radioactive Materials

The design of bolted flange joints for the German double barrier closure system of spent fuel and high-level waste transport and storage casks is described. Additionally, the basic load assumptions are presented for service conditions and for hypothetical accident scenarios. The modelling details and the results of numerical analyses are discussed with respect to those of experiments. The treatment of the numerical results for the bolts is presented considering safety factors derived by nominal stress concepts. The lids of the double barrier system are designed for a specified helium leak tightness. In addition to interior pressure, the accelerations during usual service and in particular during accidental drop tests are considered. The sliding and the gap formation at the gasket surfaces is analysed with respect to drop test loads. The consequences for the containment analysis are discussed on basis of the tightness characteristics. Concerning the bolts, the transfer of local stresses provided by numerical analysis to nominal stress values is presented. This transfer enables the use of safety factors based on nominal stress concepts for the local stresses given by numerical analysis.Copyright © 2009 by ASME
A new engineering approach to cyclone design for cotton gins

The prediction of emission concentrations from cyclone collectors is integral to the permitting of agricultural facilities including cotton gins. One method for predicting emission concentrations utilizes fractional efficiency curves (FEC's). A new engineering procedure to develop the fractional efficiency curves was developed, and the new and more accurate fractional efficiency curves were generated by using this procedure. The FEC's were used to compare performance of the four cyclone designs currently being used by cotton gins to abate PM10. The performance characteristics of the 1D2D, 1D3D, 2D2D and Barrel cyclones were evaluated using particulate matter from three types of cotton gin trash. The results suggest that the 1D3D cyclone is the best design for fine dust or trash without lint fiber, and the 1D2D design is better than the 1D3D and 2D2D cyclones when the tests were conducted with high lint content trash. The test results were used to define the performance characteristics of different cyclone designs. With these characteristics, cotton gins can utilize different cyclone designs for different processing streams to minimize PM concentrations emitted. The effect of smoothness of cyclone inner surface on its emission concentration was tested. The results were used to quantify this effect.
Integral operators on the halfspace in generaliz Lebesgue spaces Lp ( · ) , part

In this paper we generalize a version of the classical Calderón–Zygmund theorem on pr value integrals in generalized Lebesgue spaces Lp(·) proved in [J. Reine Angew. Math. 563 (200 197–220], to kernels, which do not satisfy standard estimates on Rd+1. This result will be used in part II of this paper to prove the classical theorem on halfspace estimates of Agmon, Dougl Nirenberg [Comm. Pure Appl. Math. 12 (1959) 623–727] for generalized Lebesgue spaces Lp(·).  2004 Elsevier Inc. All rights reserved.
Evaluating Oleaginous Fungi for Sustainable Biodiesel Production: Screening, Identification and Optimization of Lipid Production

strains from two petroleum-polluted soil samples and screened their ability to accumulate lipids. Aspergillus terreus, A. niger, and A. flavus were found to be the most potent lipid producers. All isolates were identified at the species level by morphological (macroscopic and microscopic) examination and molecularly confirmed by performing 18S rRNA sequencing. Furthermore, we optimized culture conditions to improve lipid accumulation. Aspergillus terreus, the most potent lipid producer, showed the highest lipid production (38.33%) in the presence of 50g/L sucrose and 0.5 g/L ammonium nitrate (initial pH, 6) after seven days of incubation under static conditions. Gas chromatography was performed for fatty acid analysis after lipid transesterification. Fatty acid methyl/ethyl ester profile indicated that saturated fatty acids were more common than polyunsaturated fatty acids. The total concentration of fatty acids was 107.98, 38.29, and 37.48mg/100g of lipids accumulated by A. terreus, A. niger, and A. flavus, respectively. Gas chromatographic analysis of A. terreus lipids indicated that oleic acid (C18:1, 18.51%) was the most abundant, followed by stearic acid (C18:0, 15.91%) and myristic acid (C14:0, 14.64%). The fatty acid profile of A. terreus confirmed it to be a promising new commercial biodiesel feedstock.