Miracle-dz/UniFAR-FTUs huggingface dataset:
DVB-T2 Receiver: Impact of FFT Window Positioning on Radio Coverage

In modern digital terrestrial broadcasting systems, Single Frequency Network (SFN) is a prominent technique used for improved spectrum efficiency, network gain, and increased flexibility. In an SFN, the placement and positioning of FFT window relative to the multiple received signals strongly affect the receiver's behavior of handling Inter-Symbol Interference (ISI). Thus, the treatment of ISI in receiver further impacts on the radio coverage predictions and network planning. In this paper, the radio coverage due to a typical SFN network is evaluated and analyzed for different FFT window synchronization methods. This is repeated for different transmission and reception modes used in the next generation of broadcasting standards and systems specifically in the DVB-T2 system.
Product/ion-Aware Modeling Approaches that Support Tracing Design Decisions

In collaborative Production Systems Engineering (PSE), decisions of detail engineers depend on results of previous design decisions regarding Product, Process, and Resource (PPR), coming from other basic or detail engineering disciplines. However, the engineering results exchanged between disciplines often represent the production system view only. PPR design decisions are important to facilitate the product/ion-aware exploration of the system design space, but are hard to express and trace in modeling approaches with only limited support for PPR. In this paper, we report on a survey of modeling approaches, which combine process and product modeling. We investigate the PPR modeling capabilities for product/ion-aware systems design and for tracing design decisions in the engineering process. Major finding is that most of the investigated modeling approaches fulfill general requirements well, but address requirements regarding tracing PPR design decisions at most partially. As an exception, the Formal Process Description (FPD) provides good capabilities for PPR modeling and provides the foundations for tracing design decisions between basic to detail engineering.
Partial Discharge Characteristics of Nanofilled Mineral Oil

The oil-paper system forms the predominant insulating material in many of the high voltage transformers. The insulation system of a high voltage transformer should be reliable, efficient and economical. The present focus of insulation material development is on the inclusion of nanoparticles in the transformer liquid dielectric, resulting in nanofluids with improved dielectric properties. The present work involves the development of nanofluids, its stability assessment and analysis of partial discharge characteristics. Insulating alumina nanoparticles of 0.1wt% and 0.03wt% are added to mineral oil, with and without a surfactant Oleic acid. The effect of Oleic acid modified nanoparticles on the stability and dielectric properties of nanofluids are also discussed. Nanofluid with 0.03wt% nanoparticle and 0.1wt% Oleic acid show favourable stability, improved partial discharge and a higher breakdown strength.
Efficient Approximate Solution Path Algorithm for Order Weight L_1-Norm with Accuracy Guarantee

Variable selection is a challenging problem in high-dimensional linear regression problems with a large number of predictors. Thus, sparsity-inducing and clustering-inducing regularization methods are widely used to identify highly correlated covariates. Ordered Weight L_1 (OWL) family of regularizers for linear regression perform well to identify precise clusters of correlated covariates and interpret the effect of each variable. Solution path algorithms are helpful to select hyperparameters to tune the OWL model. Due to over-complex representation of the penalty, so far the OWL model has no solution path algorithms for hyperparameter selection. To address this challenge, in this paper, we propose an efficient approximate solution path algorithm (OWLAGPath) to solve the OWL model with accuracy guarantee. For a given accuracy bound ε, OWLAGPath can find the corresponding solutions for the OWL model with numerous hyperparameters while keeping the sparsity and precise features grouping properties. Theoretically, we prove that all the solutions produced by OWLAGPath can strictly satisfy the given accuracy bound ε. The experimental results on three benchmark datasets not only confirm the effectiveness and efficiency of our OWLAGPath algorithm, but also show the advantages of OWLAGPath for model selection than the existing algorithms.
A 4.6V, 6-bit, 64GS/s Transmitter in 22nm FDSOI CMOS

A 6-bit large-swing, digital transmitter was implemented in a production 22nm FDSOI CMOS technology. It features a 35mA/4.8V series-stacked complementary differential DAC output stage with MOSFET gate finger segmentation and which operates in class-D switching mode. The 1.4 pJ/b efficiency output stage has 10 complementary inputs, 7 for the 3 thermometer-coded MSBs, and 3 for the binary-weighted LSBs, which are driven by 0.8V CMOS inverter chains switching at up to 66 Gb/s. Measurements of 4.6Vpp differential PAM-4 eye diagrams at 52 GBaud are reported using the on-chip PRBS7 generator. With external data signals from a 64Gb/s BERT and only 3/4 of the 7 thermometer-coded output-stage MSB sections switching, the transmitter achieves 56GBaud 5-PAM, 60GBaud 4-PAM, 62GBaud 3-PAM, and 66Gb/s NRZ operation. The transmitter consumes 450 mW, which includes the clock amplifier, PRBS7 generator, serializer, ten 66Gb/s input data paths matched to 50 Ω, and the large-swing 6-bit DAC output stage.
Screening of Non-overlapping Apnea and Non-apnea from Single Lead ECG-apnea Recordings using Time-Frequency Approach

This study focused on extracting to finding differences between apnea events and non-apnea events using time-frequency approach. This approach is of particular relevance to obtain the efficiency and accuracy of the support system for the classification model. Heart rate variability(HRV) was calculated using the statistic and frequency approach based on the time-frequency domain. The analysis of HRV, about the occurrence of the short recording, was performed selecting two segments: a class of apnea events and a class of non-apnea events. The experiment findings of the statistical analysis of our feature extraction showed time-domain feature estimation with Heart rate means (BPM) slightly higher for non-apnea events about mean ± standard deviation (72(±4)). The frequency-domain features, at VLF, LF and HF power of apnea events, are monitored over time with non-apnea events. The overall experiment indicates a significantly different feature value in the heart rate during examining apnea events and non-apnea events.
A Preliminary Study of Finger Area and Keystroke Dynamics Using Numeric Keypad With Random Numbers on Android Phones

The paper investigates the effectiveness of finger areas and keystrokes dynamics data as the authentication factors using random numeric keypad of touchscreen smartphones. The users’ finger areas and keystrokes dynamics data are collected via android application. Preliminary experiment was conducted, involving 30 subjects typing a predefined 10-digit numbers for ten times continuously. The features used in the analysis are gender of the subjects, dwell times, flight times and finger areas. Random forest classifier is used and the weighted results were true positive rate of 80.7%, false positive rate of 0.7%, precision of 81.3% and recall of 80.7%.
Prospect of Stein's Unbiased Risk Estimate as Objective Function for Parameter Optimization in Image Denoising Algorithms – A Case Study on Gaussian Smoothing Kernel

Stein's Unbiased Risk Estimate (SURE) is considered as an indirect method for predicting Mean Squared Error (MSE) in the absence of ground-truth, as its computation requires only noisy observation and denoised image. SURE is usually used as an objective function for optimizing the operational parameters of denoising algorithms, adequate for real-time images. Hence, a close analysis of the performance of SURE on standard test images is worthy of investigation. Pearson's Correlation (r) of SURE with Mean Absolute Error (MAE) between denoised images and ground-truth is analyzed in this paper, on Shepp-Logan Phantom and simulated Magnetic Resonance (MR) images, at different noise levels. Denoised images which differ in terms of MAE against ground-truth are produced by varying the standard deviation of a Gaussian smoothing kernel $(\mathbf{0.01\leq\sigma\leq 0.04})$ of fixed dimension, $\mathbf{9\times 9}$. Values of correlation between SURE and MAE on Shepp-Logan and simulated MR images are $\mathbf{r}=-\mathbf{0.99\pm 0.02}$ and $\mathbf{r}= \mathbf{0.48\pm 0.36}$, respectively. Concordance of SURE with MAE is observed to be poor on simulated MR images, especially at higher noise levels. SURE is suitable for optimizing the parameters of denoising kernels only when the underlying function used to compute the kernel is fully differentiable by the noisy observation.
Degradation of Radiation-Hardened Vertical Double-Diffused Metal-Oxide-Semiconductor Field-Effect Transistor During Gamma Ray Irradiation Performed After Heavy Ion Striking

The combined impact of single event effect (SEE) and total ion dose (TID) on Si-based radiation-hardened Vertical Double-diffused Metal-Oxide-Semiconductor (VDMOS) Field-Effect Transistor (VDMO-SFET) is investigated in this letter. As previous research of VDMOS on TID effect and SEE carried out separately, in fact, the combined impact could degrade the device performance more seriously, because of the greater numbers of oxide-trapped charges and interface states generated, which is more pronounced during multiple gamma irradiation and post-irradiation annealing. This phenomenon indicates that SEE is not only a transient effect, influencing whether the devices can be driven to failure, but also a potential threat to the long-term reliability of MOSFETs used in aerospace applications.
Entity Resolution and Blocking: A Review

Entity Resolution (ER) is a prerequisite to several Web applications including enhancing semantic searches and information extraction from the Web, strengthening the Web of Data by interlinking entity descriptions from autonomous sources, and supporting reasoning using related ontologies. While designing an ER system, it is assumed that each entity profile consists of an exclusively identified set of attribute-value pairs, each entity profile matches to a solitary real-world object, and two similar profiles are identified, while they co-occur in at least one block. ER is an inherently quadratic problem (i.e., O (n2)), given that every entity must draw a comparison with others. Moreover, existing ER techniques relinquishes to scale for large entity collections, Web data. The most well-known solution for addressing large-scale ER in the literature is blocking, which is an approximate solution where similar entities are grouped into blocks and comparisons are limited to within blocks. The process of entity resolution and the types of entity resolution in relational and Web data are discussed in this paper. Further, the paper reviews the literature on the approaches introduced by former researchers on the entity resolution system. The data integration, block building, and block processing phases, and the challenges involved for designing an efficient ER system are discussed. This paper concludes with the measures required to evaluate entity resolution approaches.
Implementation of NTP Protocol in an Isolated Corporate Network

Nowadays time synchronization presents a crucial part of infrastructure in any distributed system. A request for precise time synchronization of teleprotection equipment in the transmission network of the Serbian transmission system operator (JSC EMS), was generated due to the need for more precise analysis of the devices status and events that conditioned sending of teleprotection commands, as well as their time positions. The solution had to be developed in accordance with the current state of the transmission network, as well as the required reliability and accuracy for precise analysis of teleprotection events. The greatest challenge was implementation of standard time synchronization protocol on a loaded embedded processor system. The paper presents the overview of the realization including measurement methods and results.
EyeMath: Increasing Accessibility of Mathematics to Visually Impaired Readers

Mathematics education for visually impaired students is challenging because their learning materials are generally limited to braille books, and audiobooks. In order to increase the chance of learning mathematical content for people with visual impairment, this paper presents the design and development of a cloud-based mobile application called EyeMath, using serverless microservices in Amazon AWS. Users can provide images of page snippets for the application to process and read their content to the users. EyeMath segments an input image into smaller pieces and separates pieces that have only plain text from pieces with mathematical symbols. The mathematical-related pieces are further processed into an Abstract Syntax Tree (AST) and then parsed into Thai sentences. For plain text pieces, EyeMath relies on Tesseract OCR to convert them into text. Finally, results for all pieces are combined together systematically for the device’s screen reader program to read aloud. The performance evaluation of the application shows high correctness in reading math content within test images and our usability testing confirms the potential usefulness of the application to visually impaired readers.
Analysis of NB-IoT Deployment, Obstacles and Opportunities for Indian Telecommunication Operators

IoT constitutes the emerging market of smart devices, with the potential deployment of 3.5 billion devices until the year 2023. The coalescence of a large number of devices is imperative for a smart city, and existing techniques are relatively expensive for this purpose, which might be provided with neither data output and needing longer ranges to aid the requirements of IoT. Albeit cell innovations and Wi-Fi have demonstrated massive achievement in associating billions of clients to the Internet, they aren’t optimal to bridge objects with restricted throughput necessities, restricted processing power, and low power expenditure demands. From the wide assortment of utilization instances of IoT gadgets stem the need for new communication models and standards, ready to work productively with the low power, low execution gadgets on a large user base. We comprehensively review one of the most modern and fastest growing M2M radio technologies, NB-IoT. We pay particular attention to advantages and obstructions in the deployment of NB-IoT by Telecommunication Service Providers in developing countries like India, with a targeted focus towards Smart City applications. NB-IoT and LoRa are Low Power Wireless Area Network technologies enabling deployment of long-range and broad.
Machine learning for assistance systems: pattern-based approach to online step recognition

Computer-aided assistance systems are entering the world of work and production. Such systems utilize augmented– and virtual–reality for operator training and live guidance as well as mobile maintenance and support. This is particularly important in the modern production reality of ever-changing products and ‘lot size one’ customization of production.This paper focuses on the application of machine learning approach to extend the functionality of assistance systems. Machine learning provides tools to analyse large amounts of data and extract meaningful information. The goal here is to recognize the movement of an operator which would enable automatic display of instructions relevant to them.We present the challenges facing machine learning applications in human-centered assistance systems and a framework to assess machine learning approaches feasible for this scenario. The approach is assessed on a historical data set and then deployed in a work station for live testing. The post-hoc, or historical, analysis yields promising results. The ad-hoc, or live, analysis is a complex task and the results are affected by multiple factors, most of which are introduced by the human influence.The contribution of this paper is an approach to adapt state– of–the–art machine learning to operator movement recognition with a special focus on approaches to spatial time series data pre-processing. Presented experiment results validate the approach and show that it performs well in a real-world scenario.
The Image Game: Exploit Kit Detection Based on Recursive Convolutional Neural Networks

Malware has been installed through drive-by downloads via exploit kit attacks. However, the prior signature- or dynamic-based detection approach to the continuously increasing number of suspicious samples is time-consuming. In such circumstances, convolutional neural networks (ConvNets) can help in rapid detection owing to their direct image-feature generation using exploit codes. However, the general ConvNet model entails the vanishing gradient problem, where the features used for a deep learning-based detection method will become less effective as the network is deepened to improve detection accuracy. In this paper, we propose a multiclass ConvNet model to classify exploit kits, where we adopt various image processing techniques and adjust the size and other parameters of images. The proposed ConvNet model recursively updates images and is designed for fully preserving image properties. This model updates the output of feature maps and pooling using an original image. This model was tested using 36,863 real-world datasets, achieving a 98.2% accuracy in exploit kit detection and family classification. Most importantly, the proposed model is 38 times faster than previous machine learning models, and training time is reduced by 77.8% when compared with prior well-known ConvNet models.
Resource Allocation for Epidemic Network under Complications

In this work, we study the optimal resource allocation to control an epidemic outbreak in a network of population. In particular, we utilize a Susceptible-Infected-Complicated-Susceptible (SICS) model in which complication phenomena is considered. Due to the limitation of the resources, the epidemic is modelled with two levels of recovery rate: one associated with people under medical treatment and the other associated with people who can get rid of the disease by themselves. The resource allocation is considered in these two cases as well. The problem is formulated as a Geometric Program with budget constraint and rate constraint. We illustrate the performance of our approach through numerical simulations.
Storage performance analysis of iSCSI and Fibre Channel protocol

In this paper we tested the performance of two different network protocols iSCSI and Fiber Channel. The performance analysis relies on the comparison of the flow velocity between these two protocols, i.e. the speed of the disks where the initiators are iSCSI and FC. Thus, for such comparison analysis, one of the tested disks was plugged in via the iSCSI initiator, while the other was connected via Fiber Channel. The testing procedure relies on the use of the HD Tune Pro tool and includes disk analysis in Random Read, Random Write, Sequential Read, and Sequential Write. The obtained results show significantly better performance of the Fiber Channel compared to the iSCSI.
A Novel AMC Backed Wide Band Wearable Antenna

A simple wide band antenna with a novel artificial magnetic conductor(AMC) for wireless body area network (WBAN) applications is proposed in this paper. The antenna is designed with a semicircular patch on a flexible substrate, which is fed by a 50 Ohm microstrip line. This antenna exhibits an wide band with 10dB return loss bandwidth from 8GHz to 15GHz. To reduce the backward scattering wave towards the human body, a novel AMC surface is proposed consisting of a planar array of semi-circular patch as unit cell which is exactly similar as the top antenna patch. Simulated results show that Specific Absorption Rate(SAR) for the proposed AMC integrated antenna is comparable with other PEC and standard AMC(with square unit cell) integrated antennas. The above comparison depicts that the best result of realized gain at phi=0° is obtained for the proposed antenna which has improved from -2.1 dB to 1.84 dB. Lower SAR value and gain enhancement of the proposed AMC integrated flexible antenna makes it a good candidate for WBAN applications.
Investigation of Power Delay Profiles for 5G Millimeter Wave Railway Communications

Recently, high speed railway (HSR) communication is getting popular with advances of millimeter wave (mmWave) technology of future fifth generation (5G) wireless standard. Researchers are taking initiatives to support dense traffic with higher demand of data rate as well as high mobility. However, several challenges for 5G mmWave channel still exist as HSR faces fading variation and attenuation due to fast switching between different environments of railway networks. A realistic railway communication can introduce several unique scenarios which have different impact on communication channels. The novelty of this work is to investigate several power delay profile (PDP) characteristics, e.g., RMS delay spread, received power, path loss, SNR, and KFactor for three realistic HSR scenarios at different mmWave bands. Simulation results demonstrate the potential of mmWave channels and its suitability in future 5G railway communication.
PSS Design and its Performance Investigation on the Subsystem where the Inter-Area Mode is Most Dominant

This paper presents the study related to the power system stabilizer (PSS) design for northern regional power grid (NRPG) of India. The location of local PSS is suggested by participation factor approach. The power-voltage reference (PVr) characteristic of the suggested generator is used to obtain the compensator block of the local PSS. Internal states are lost while performing model order reduction therefore, the shaft dynamics are not available in the reduced order model for disabling. Therefore compensating block has to be design by utilizing full order system. But, the gain of the PSS can be tuned by utilizing the reduced order model. Next, the model order reduction (MOR) is performed with a target on preserving the characteristics and behavior of dominant inter-area mode in reduced order model (ROM). Thereafter, the tuned gain using ROM is combined with compensator and washout filter to obtain PSS for 44th generator (G44). The performance of designed PSS is investigated under five cases of disturbances.
Heading Judgment for the Waist-Mounted MIMU Using LSTM

In many athlete or firefighter training practices, such as firefighter rescue training or football player dribbling, many movement trajectories will be generated. Recording the exercise data of these trainees can replay the training process and help to improve their learning. In the generation of motion trajectory, the judgment of the motion heading matters and attracts lots of attentions. A large amount of researches have focused on the foot-mounted Miniature Inertial Measurement Unit (MIMU), mainly relying on zero-velocity update (ZUPT) to maintain accuracy. However, ZUPT fails to estimate the gyroscope drift errors, causing the heading angle drifts over time. In this paper, we introduce the waist-mounted MIMU for heading judgment based on deep learning algorithms to alleviate the heading angle drift problem. Although the market is more demanding for the waist-mounted module, there are fewer applications due to its insufficient accuracy. In order to improve the heading judgment of waist-mounted model, we propose to use the stacked LSTM model which we prove is powerful to assist the traditional Pedestrian Dead Reckoning (PDR) in the trajectory drawing, and thus is helpful for the heading judgment. Experimental results show that the proposed model has satisfactory performance in heading judgment.