Miracle-dz/UniFAR-FTUs huggingface dataset:
Receivers placement for UAV localization in a surveillance area

Ensuring security of sensitive areas from unmanned aerial vehicles threats has become a major issue. In this context, multistatic radars present significant advantages over monostatic radars due to exploitation of spatial diversity and improved target detectability. In that perspective, sensors placement has an important impact in target detection or localization performance. In this paper, we propose an empirical method to place receivers in a multistatic radar network in order to cover an area of interest representing a sensitive site. The performance is measured with localization error estimation for different numbers of receivers and different target positions.
Mean Teacher for Weakly Supervised Polyphonic Sound Event Detection: An Empirical Study

Sound event detection refers to the task of categorizing the types of events occurring in an audio recording, in addition to pinpointing the start and end times of each occurrence. This task has recently grown in popularity as a result of its aptitude to enhance a myriad of applications. Building sound event detection systems heavily relies on the representational power of deep neural network architectures. Deep network architectures require a large amount of strongly annotated audio data, where the exact temporal locations of each sound event are indicated. However, manually annotating audio recordings with the type of events present and the corresponding time boundaries is both costly and laborious. To mend this, learning from weak labels has been adopted in an attempt to bypass the labeling barrier. In this paper, we examine the effect of incorporating weakly-labeled data into the training process of sound event detection systems. Moreover, we analyze the behavior of the Mean Teacher framework under various deep learning configurations. Our experimental results reveal that training a well calibrated Mean Teacher structure; on weakly-labeled data, can improve the predictive performance of sound event detection systems.
E-Band Transmitter with 3 W Complex Modulated Signal Output Power Performance

This paper reports on an E-band transmitter frontend designed to operate on a CubeSat platform to explore high data rate downlink from a low earth orbit. The LO generation, the up-conversion and the RF pre-amplification realized by MMICs based on 50 nm InGaAs metamorphic high electron mobility transistor technology. The final amplification stage comprises two solid-state power amplifier chips, parallelized with waveguide techniques into a single split-block waveguide module, utilizing an AlGaN/GaN-HEMT technology with 100nm gate length. The Tx chain is characterized, both, with single IF tones and wideband complex modulated signals. The transmit frontend achieves a record maximum output power of 3 W for a QPSK modulation with 7.4 Gbit/s data rate, demodulated with a broadband receiver. A maximum QPSK-modulated symbol rate of 6 Gbd could be captured by the signal analyzer software with an EVM of 34% and signal power of 33dBm. In addition higher order modulation formats 16- and 32-QAM are demonstrated.
Doppler Centroid Estimation for Ocean Surface Current Retrieval from Sentinel-1 SAR Data

Synthetic aperture radar (SAR) is the most efficient tool to provide high-resolution data for Earth Observation (EO). Doppler centroid (DC) estimation is indispensable for high precision SAR data analysis such as extracting the ocean surface current, which is important for scientific pursuits. Correlation doppler estimation (CDE), and energy balancing (EB) based DC methods are implemented in this paper. A 2-D sliding window is deployed to estimate DC on small blocks of data while covering the whole scene so that all parts of the scene are potentially represented. We analyzed Sentinel-1 single look complex (SLC) data from the coastline of a non-homogeneous scene. The CDE method utilizes the azimuth shift in the time domain which is associated with the DC, and this $f_{Dc}$ history is used to extract ocean surface current. We find the results of DC estimates are confined to primitive baseband (±PRF/2). Moreover, the corresponding retrieved ocean surface current component is reasonable, particularly values vary within the limit of error bounds. Finally, the parameters of ocean surface current are compared with ocean wave models reported in the literature. Efficacy and simulation of implemented methods are good fit for Sentinel-1 SAR data.
Affordable Custom made Physiological Measurement Instrumentation Based on Raspberry PI

Physiological measurement is in the center of several implications in healthcare, accurate recording and processing those signals allow extraction of several health characteristics information that scientific community needs. Biopotential, bioimpedance and optical measurement are non-invasive approaches to measure and evaluate human body conditions and activity. This paper presents the feasibility of acquiring non-invasively physiological signals using an affordable custom-made measurement device based on Raspberry PI. The acquired signals waveform presents a satisfactory result and are acceptable, encouraging and converge to literature result. Thus, it shows that the device can be used for relatively several biomedical applications for supporting developers in research and engineering education.
Methods for Attenuating and Terminating Waves in Ridge Gap Waveguide at W-Band: Carbon-Loaded Foam, Carbonyl Iron Paint, and Nickel Plating

Several methods for electromagnetic waves matched termination and attenuation in a ridge gap waveguide (RGW) are experimentally investigated at W-band. At these frequencies, the implementation of matched loads and attenuators is especially complicated due to small sizes of RGW design features that limits application of traditional waveguide absorbing structures (e.g., absorbing sheets and finlines, ferrite insets, carbonyl iron walls, etc.). The following three techniques are considered: (i) filling an RGW gap with a carbon-loaded foam; (ii) covering a ridge (and pins) with a carbonyl iron paint; (iii) selective nickel plating of an RGW line segment. It was found that the first method exhibits a great broadband absorbing performance and can be easily implemented in a lab environment, whereas the second method can realize a more accurate and predictable attenuating performance. Finally, nickel plating allows for designing resonant RGW terminations and is more interesting from the industrial perspective.
Wideband High-Gain Transmitarray Antenna for Point-to-Point Communications at 300 GHz

The design and characterization of a 60×60-element high-gain, wideband transmitarray antenna at 300 GHz are presented. A numerical model is used to accurately design the 8 phase-shifting anisotropic unit-cells of the array (3-bit phase quantization). The planar lens comprises only three metal layers and no metallized vias. It is fabricated using a low-cost, standard printed circuit board technology. A 20-dBi horn is used as a feed. The experimental characterization demonstrates a peak gain of 37.0 dBi with a maximum aperture efficiency of 48.6% and a 1-dB gain bandwidth of 19.0%. These features, combined with the cost-efficient fabrication process, make the prototype a very attractive candidate for point-to-point communications in the 300-GHz band.
Compressive Sensing for Direction-of-Arrival Estimation Using an Electronically Steered Multiple-Input Multiple-Output Array

Multiple-input multiple-output (MIMO) signal processing on large radar sensor arrays can be accomplished by using orthogonality in the time domain with time division multiplexing (TDM), where only one transmit antenna element is active per time slot. In this contribution, the MIMO-TDM system is compared to transmitter-side electronic beamsteering in azimuth direction - for the case that a MIMO array is used and direction of arrival estimation is done via compressive sensing techniques. It can be shown that the angular resolution can be enhanced compared to conventional MIMO-TDM processing thanks to additional degrees of freedom provided by transmitter-side beamforming (BF), using the same number of physical antenna elements.
Augmenting HLS with Zero-Overhead Application-Specific Address Mapping for Optane DCPMM

FPGAs have been introduced to datacenters as a mainstream computing device to accelerate a wide range of data-intensive applications when paired with heterogeneous memory. Leveraging High-Level Synthesis (HLS), application engineers not only can accelerate their applications but also the development time of designing, debugging and validating accelerators. However, existing HLS flows do not have effective support for emerging memory devices such as Intel’s Optane DC Persistent Memory Modules (Optane DCPMM) – a storage-class memory in a DIMM form factor. In fact, we observe that some HLS kernels can at best utilize only one-tenth of the total memory bandwidth of Optane DCPMM.To remedy the poor performance of HLS with Optane DCPMM, we augment the existing HLS external memory interface with zero-overhead, application-specific address mapping capabilities. The proposed scheme utilizes both fine-grained information from variable access patterns and coarse-grained variable-interleaving information to select an optimal hybrid address mapping for high memory bandwidth utilization, compared to a default fixed address mapping in existing HLS. Furthermore, our scheme is compatible with existing tool flows such as the Intel FPGA SDK for OpenCL and Vitis Application Flow to maintain a low adoption barrier. We observe that by using our proposed address mapping scheme and interface, we achieve 10× speedup on a diverse set of benchmarks including merge join, matrix multiplication and convolution without any additional hardware cost.
Real-Time Smart Attendance Monitoring System With Thermal Scanning

In conventional attendance system, taking roll call in class time or monitoring the presence of student/employee in an educational institute or an industry has been a time-consuming process. It is also very important to see that all educational institutes are concerned about the consistency of student's performance. The main cause of this is decrease is due to inadequate attendance. So, an advanced student attendance checking system is required which supports the faculty to keep records of attendance. In this project we have introduced an intelligent face recognition-based attendance system. We have proposed to implement a “Smart Attendance through Face Recognition” which uses viola-jones algorithm for training and detection of faces. Because viola jones is highly strong, and its use has proved to be particularly noteworthy in real-time detection. The present implementation includes time-saving facial identification and eradicates the possibilities of proxy attendance due to facial authorization. In this pandemic season to alert the staff about the infected covid patients we are using thermal scanning to detect the temperature of a person. So that we would be the reason to decrease the spread of covid and save innocent lives. This system can now be used in an area in which participation plays an important role.
LocSeq: Automated Localization for Compiler Optimization Sequence Bugs of LLVM

Compiler bugs may be triggered when programs are optimized with optimization sequences. However, diagnosing compiler optimization sequence bugs is difficult due to limited debugging information. Although some techniques (e.g., DiWi and RecBi) have been proposed to automatically localize compiler bugs, no systematic work has been conducted to automatically localize compiler optimization sequence bugs. In this article, we propose LocSeq, a novel technique to automatically localize compiler optimization sequence bugs of LLVM. The core insight of LocSeq is based on the fact that the behaviors of optimizations may be influenced by each other, and thus, the innocent files may be excluded by constructing bug-free optimization sequences. First, given a buggy optimization sequence that triggers a compiler bug, in LocSeq, we transform the problem of the localization for a compiler optimization sequence bug to the problem of the construction for bug-free optimization sequences, which are helpful to localize buggy compiler files. Then, a constrained genetic algorithm is presented in LocSeq to generate a set of bug-free optimization sequences that share similar compiler execution traces with the buggy optimization sequence. Finally, LocSeq leverages a spectrum-based bug localization technique to localize the compiler optimization sequence bug by comparing the execution traces between bug-free optimization sequences and the buggy optimization sequence. To evaluate the effectiveness of LocSeq, we build a benchmark, including 60 optimization sequence bugs of LLVM, and compare LocSeq with the state-of-the-art techniques DiWi and RecBi. The experimental results show that LocSeq significantly outperforms DiWi and RecBi by up to 366.66%/72.27% and 250.00%/56.00% for localizing optimization sequence bugs within Top-1/5 files, respectively.
A Comparison of Convolutional Neural Network Models for Driver Fatigue Detection

Road traffic crashes are among the significant risks facing millions of people around the world every day. Driver fatigue is a salient factor in road accidents. However, overcoming this factor has become possible with the use of artificial intelligence. In fact, with the development of technology, industrial companies in the automotive sector are working on intelligent cars capable of identifying the risks and avoiding them. In this work, we propose a method that identifies driver fatigue. First, we established a comparison between 10 models of Convolutional Neural Networks (CNNs), to classify the state of both mouth and eyes. After selecting the best of them, we compute the percentage of eye closure (PERCLOS) and yawning frequency of mouth (FOM) to conclude the driver state. The proposed method obtained an accuracy of 87.5%.
Condition Monitoring Of Operating Pipelines With Operational Modal Analysis Application

Abstract In the petroleum, natural gas and petrochemical industries, great attention is being paid to safety, reliability and maintainability of equipment. There are a number of technologies to monitor, control, and maintain gas, oil, water, and sewer pipelines. The paper focuses on operational modal analysis (OMA) application for condition monitoring of operating pipelines. Special focus is on the topicality of OMA for definition of the dynamic features of the pipeline (frequencies and mode shapes) in operation. The research was conducted using two operating laboratory models imitated a part of the operating pipeline. The results of finite-element modeling, identification of pipe natural modes and its modification under the influence of virtual failure are discussed. The work considers the results of experimental research of dynamic behavior of the operating pipe models using one of OMA techniques and comparing dynamic properties with the modeled data. The study results demonstrate sensitivity of modal shape parameters to modification of operating pipeline technical state. Two strategies of pipeline repair – with continuously condition-based monitoring with proposed technology and without such monitoring, was discussed. Markov chain reliability models for each strategy were analyzed and reliability improvement factor for proposed technology of monitoring in compare with traditional one was evaluated. It is resumed about ability of operating pipeline condition monitoring by measuring dynamic deformations of the operating pipe and OMA techniques application for dynamic properties extraction.
Information gap decision theory for voltage stability constrained OPF considering the uncertainty of multiple wind farms

This study deals with the voltage stability constrained optimal power flow (VSC-OPF) problem considering the wind power generation uncertainty. The main feature of the proposed model is to handle the uncertainty of multiple wind farms (WFs) in a way that for a given worsening of total cost, maximum tolerable uncertainty of wind power generation is achieved for all WFs. This maximum uncertainty is determined in a way that a required loading margin (LM), is preserved. It is worth noting that LM is the most important measure of voltage stability which reflects the distance from the current operating point to the voltage collapse point. For this aim, information gap decision theory (IGDT) is utilised to handle the uncertainty of wind power generation. The proposed model is implemented on IEEE 39-bus standard test system. In order to evaluate the effectiveness of the proposed VSC-OPF model for uncertainty handling of multiple WFs, the results obtained by IGDT technique are compared with Monte Carlo simulations and scenario-based approach. The simulation results imply that the uncertainty radius and the desired LM are inversely related, such that for a given tolerable increase of cost, the radius of uncertainty decreases by increasing the desired LM.
Human Productivity Enhancement: Training and Human Factors in Systems Design

From the Publisher: This book offers psychologists and those who are involved with systems design an overview of changing technology and guidelines for using new techniques in the design and development of systems in the workplace. The first part of the book concerns productivity and cognitive assessment. The papers provide state of the art appraisals of robotic perception, artificial intelligence, knowledge representation, decision making and the control of automation systems. The second part, on systems design research, discusses a number of key applied human factors problems, including supervisory control systems, human workloads, methods of systems design, job aiding, and human factors considerations.
Sustainable Solid Waste Management System: Barangay Bayog, Los Baños, Laguna

The study analyzed how Barangay Bayog, Los Banos, Laguna can attain a sustainable solid waste management program. Specifically, it determined how the communities can be classified according to social class and examined how the different social classes can implement a sustainable solid waste management program. The study concludes that the behavior of the different social classes varies in terms of managing their waste. The current solid waste management system of Barangay Bayog undergoes a long process and produces an enormous amount of solid waste. Such current practices are unsustainable. The Barangay does not have the necessary policy program to enhance the sustainability of solid waste management. The study recommends that the barangay captain together with other officials and representatives from the different social classes should convene and discuss several issues with regards to different policy programs that need to be implemented in the Barangay. These include preparation of Barangay strategic solid waste management plan, organization of Barangay solid waste management team and association of classes D and E, putting up a material recovery facility, and development of appropriate technology to reduce waste. Technical and financial assistance can also be secured from the Los Banos Science Community Foundation, Inc. (LBSCFI) members. There should be Information-Education Campaign (IEC) activities and a regular barangay clean-up day, and rehabilitation of rivers to encourage participation. Monitoring and evaluation of the solid waste management activities should be done to provide continuity of the program.
Noisy medieval data, from digitized manuscript to stylometric analysis: Evaluating Paul Meyer's hagiographic hypothesis

Abstract Stylometric analysis of medieval vernacular texts is still a signiﬁcant challenge: the importance of scribal variation, be it spelling or more substantial, as well as the variants and errors introduced in the tradition, complicate the task of the would-be stylometrist, by inducing noise and perhaps even interferences
Design of PID-Swarm Load Frequency Controller of Interconnected Power System

This paper is proposing a Load Frequency (LF) controller design for interconnected power system based on Proportional Integral Differential (PID) controller. The PID gains have been design using Particle Swarm Optimization (PSO) technique. The controller is designed to improve the dynamic response of system frequency and the tie line power flow under a sudden load changes. The LFC model for a two area interconnected power system is implemented in SIMULINK/MATLAB and the simulation results illustrate the effectiveness of the proposed LF controller compared to integral controller.
Modeling and Simulation of Negative Bias Temperature Instability

Revision with unchanged content. Semiconductor process and device simulators are well established tools for the reduction of the development time for semiconductor devices. Nowadays simulation efforts go beyond solving the basic semiconductor device equations. Especially the modeling and simulation of aging processes has tremendously gained in importance. This book gives insight into the topic of semiconductor device simulation and focuses on the modeling of degradation mechanisms. Negative bias temperature instability (NBTI) causes degradation of MOS structures at elevated temperatures and negative gate voltages. An elaborate investigation of literature from the first report to the recent understanding of this degradation mechanism is presented. A comprehensive model is derived, combining research results from different groups and the coupling to the basic semiconductor device equations. The new NBTI model is compared to measurement data and gives excellent results. This book is addressed to researchers in the field of semiconductor process development but also recommended to engineers in IC design to strengthen their understanding for device degradation.
Can Evidence-Based Software Engineering Contribute To Safer Software?

Over the past decade evidence-based software engineering (EBSE) has emerged to offer an adaptation of the methods of evidence based medicine for the needs of software engineering. Whilst potentially complementary to established practices, evidence to date of successful translation of the approach from the aca- demic environment to industrial practice is scarce. With an emphasis on struc- tured arguments, rigorous analysis and bodies of evidence software safety is one engineering sub-discipline apparently well placed to benefit from adopting the approach. This paper assesses whether EBSE has or can, in practice, make a con- tribution to engineering safer software.
PANOM: Automatic Hyper-parameter Tuning for Inverse Problems

Automated hyper-parameter tuning for unsupervised learning, including inverse problems, remains a long-standing open problem due to the lack of validation data. In this work, we design an automatic tuning criterion for inverse problems and formulate it as a bilevel optimization task. We demonstrate the efficiency of our tuning scheme on various inverse problems and different test and out-of-distribution image samples at no expense of performance drops.
Thermal calculations of underground oil pipelines

Operation of oil pipelines in the frozen soil causes heat exchange between the pipeline and the soil and formation of a melt zone which leads to deformation of pipelines. Terms of construction and operation of oil pipelines are greatly related to their temperature conditions. In this regard it is necessary to know the laws of formation of thawing halos around oil pipelines. Thus, elucidation of laws of formation of thawing halos around oil pipelines and determination of optimal conditions for their installation during construction in areas of permafrost in the north of Tyumen region is a very urgent task. The authors developed an algorithm and a computer program for construction of the temperature field of the frozen soil. Some problems have been solved basing on the obtained dependences and graphs of the dependence were constructed. Research and calculations made on the underground oil pipeline construction allowed the authors to give recommendations aimed at increasing the reliability of oil pipelines.