Miracle-dz/UniFAR-FTUs huggingface dataset:
Filters With Linear Frequency-Dependent Couplings: Matrix Synthesis and Applications

Filters are one of the most fundamental and indispensable components in wireless systems for various communications (5G/6G), radar sensing, and electromagnetic (EM) imaging applications. The development of a quality filter usually involves a substantial amount of design, modeling, and optimization procedures commensurate with its electrical performance and physical realization based on selected high-frequency building blocks, such as resonators and coupling sections over a specific frequency range of interest.
Finite Element Analysis of Shock & Vibration of a Printed Circuit Board Assembly Using the Beam Elements to Model the Solder Joints

This paper presents a finite element analysis (FEA) of shock & vibration (S&V) with the solder balls modeled by the beam elements instead of the traditional solid elements. The modeling degree of freedom is reduced by 2 orders in magnitude thus the computing time can be reduced from days to hours while the modeling accuracy is acceptable. To avoid spurious oscillations of nodal responses at the interface of different types of elements with degree-of-freedom mismatch, the appropriate tied contact is used between beam or shell elements and solid elements. It is found the beam elements can present the local bending moment and shear forces over each solder ball well but cannot present the axial force over the solder ball. The planar assumption of the beam theory is consistent with the deformation of majority area of the cross section of a solder but cannot reflect the normal stress concentration near the solder ball rim. Therefore, there is certain error on stress calculation from the beam theory. However, the relative relationship of the solder ball stresses calculated from the beam theory are consistent with that obtained from using solid elements on the solder balls. This strategy provides a quick alternative to perform FEA on a complicated board with multiple BGA to assess the risks of solder joint reliability during S&V.
Multi-level Adaptive Execution Tracing for Efficient Performance Analysis

Troubleshooting system performance issues is a challenging task that requires a deep understanding of various factors that may impact system performance. This process involves analyzing trace logs from the kernel and user space using tools such as ftrace, strace, DTrace, or LTTng. However, pre-set tracing instrumentation can lead to missing important data where not enough components of the system include observability coverage. Also, having too much coverage may result in unnecessary noise in the data, making it extremely difficult to debug. This paper proposes an adaptive instrumentation technique for execution tracing, which dynamically makes decisions not only for which components to trace but also when to trace, thus reducing the risk of missing important data related to the performance problem and increasing the accuracy of debugging by reducing unwanted noises. Our case study results show that the proposed method is capable of handling tracing instrumentation dynamically for both kernel and application levels while maintaining a low overhead.
Loss-rescaling VQA: Revisiting Language Prior Problem from a Class-imbalance View

Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem, which refers to making predictions based on the co-occurrence pattern between textual questions and answers instead of reasoning visual contents. To tackle it, most existing methods focus on enhancing visual feature learning to reduce this superficial textual shortcut influence on VQA model decisions. However, limited effort has been devoted to providing an explicit interpretation for its inherent cause. It thus lacks a good guidance for the research community to move forward in a purposeful way, resulting in model construction perplexity in overcoming this non-trivial problem. In this paper, we propose to interpret the language prior problem in VQA from a class-imbalance view. Concretely, we design a novel interpretation scheme whereby the loss of mis-predicted frequent and sparse answers of the same question type is distinctly exhibited during the late training phase. It explicitly reveals why the VQA model tends to produce a frequent yet obviously wrong answer, to a given question whose right answer is sparse in the training set. Based upon this observation, we further develop a novel loss re-scaling approach to assign different weights to each answer based on the training data statistics for computing the final loss. We apply our approach into three baselines and the experimental results on two VQA-CP benchmark datasets evidently demonstrate its effectiveness. In addition, we also justify the validity of the class imbalance interpretation scheme on other computer vision tasks, such as face recognition and image classification.
Motor Load Models for Fault-Induced Delayed Voltage Recovery Studies in Bulk Transmission Systems

As power systems have become more heavily loaded, infrastructure investments have declined, and high-power factor generation has increased, exposures to fault-induced delayed voltage recovery problems and sustained low voltages have increased. The issues are exacerbated by an ever-increasing penetration of induction motor loads. These motor loads are a major driver of these types of voltage control issues and properly representing them in bulk power system studies is important. All existing system simulation packages offer dynamic motor models ranging from complete time-domain options to so-called “reduced order” models where some of the motor electromagnetic transient response characteristics are neglected. In any case, the dynamic model for a single motor is typically from $\mathbf{3^{rd}}$ to $\boldsymbol{5^{\text{th}}}$ order and may involve small time constants. Performing system simulations where tens of thousands of such motors are included is very time consuming and often results in a reduced number of contingency events being evaluated which can of course lead to an operational degradation of system reliability. The purpose of this paper is to present an analysis and evaluation of two motor models that could be used for these types of studies where one is the most detailed practical option and the other is the most simple. The objective is to compare each model's performance in simulation scenarios associated with bulk transmission system voltage dynamics.
HOMOGENIZE COUPLED STOKES–CAHN–HILLIARD SYSTEM TO DARCY'S LAW FOR TWO-PHASE FLUID FLOW IN POROUS MEDIUM BY VOLUME AVERAGING

A technique of local volume averaging is applied to obtain general equations which depict mass and momentum transport of incompressible two-phase flow in porous media. Starting from coupled Stokes-Cahn-Hilliard equations for incompressible twophase fluid flow, the averaging is performed without oversimplifying either the porous medium or the fluid mechanical relations. The resulting equations are Darcy’s law for two-phase flow with medium parameters which could be evaluated by experiment.
Assembly Challenges and Approaches for 2.5D Chiplet Based System

In recent years, the semiconductor industry has seen a slow down in Moore's law and witnessed longer time spans between process technology nodes. In addition, the integration of transistors on a monolithic chip is becoming more challenging due to available options for device miniaturization [1], [2]. The other issues in chip design and manufacturing are its cost and performance. The application of chip level heterogeneous integration or called the Chiplet technology is a promising solution for this node size limitation. Chiplets are small IC dies with specialized functionality, designed to be combined to make up a larger integrated circuit, following the semiconductor industry's trend of heterogeneous integration [3]. In this paper, 3 different HDFOWLP MCP is designed integrated with chip-to chip interconnections on the RDL-first FOWLP platform. One of the MCP package is using the Intel functional FinFet 22nm chips.
Microgrids Provision of Frequency Response to Low-Inertia Grids

This paper analyzes the ability of microgrids to provide ancillary services, and in particular frequency response, to low inertia grids. Frequency response in power grids is highly dependent on the total available inertia. As the penetration of renewable energy resources is increased, the inertia of the grid reduces making the task of frequency control more difficult. Microgrids equipped with inverter-based resources (IBRs) can provide frequency response to assist low-inertia grids in stabilizing the frequency and hence, facilitating the penetration of more renewables. We compare two types of inverters in terms of their ability to support the frequency: 1) grid-following (GFL) inverters, and 2) grid-forming (GFM) inverters. The support of frequency is measured under multiple microgrid sizes and multiple levels of grid inertia. Three frequency deviation metrics are used in the comparison which are: 1) maximum frequency over/undershoot, 2) maximum rate of change of frequency, and 3) frequency settling time. The analysis is conducted through time-domain simulation in MATLAB/Simulink.
Key Technologies and Development Directions of Brain-Computer Interface Technology for Manned Space Mission

The application of brain-computer interface (BCI) technology in manned space mission can improve the safety of astronauts and the reliability of space operation. The BCI technology for manned space mission is introduced firstly, and then the application of BCI technology in manned space mission such as the control of astronauts' living environment, the rapid response of astronauts' emergency system, the flexible control of space manipulator, the remote operation and emergency maintenance of space robots is given. Then key technologies of BCI in manned space engineering such as Non-invasive EEG signal acquisition and recognition of astronauts, efficient transmission of brain-computer signals in space mission, high- precision control of BCI, Rapid feedback of BCI in spacecraft engineering is discussed. And the Impact of space environment on BCI hardware and software for space engineering is analyzed. At last, future development directions of BCI technology for manned space Mission is proposed.
Singe Image Dehazing using Deep Neural Networks

In dusty and hazzy circumstances, pictures caputured tend to be unclear. To tackle this challenge researchers have founded different image-dehazing approaches. Lately, high quality pictures which could be used to extract maximal data from autonomous systems are in great demand. In order to minimize the haze from the picture, this research study uses various deep learning (also known as DL) architectures to identify the information recovered and extract key details from the image. The research paper focuses on utilizing deep learning techniques to remove haze from clear images.The first phase of the suggested architecture involves applying multiple pre-processing methods, including estimating the airlight, implementing contextual regularization and boundary constraints, as well as contrast enhancement techniques (CS, CLAHE, HE) and Image Fusion. The subsequent goal of this study is to pinpoint the optimal deep learning model that can produce clear images based on the resulting dehazed images. The quality of the dehazed images is evaluated by comparing them with clear images using metrics such as PSNR and SSIM. Experimental findings shows that Alex Net upon the implementation of pre-processing beats other state of the art techniques with regard to PSNR value and holds its own w.r. t SSIM values.
La structure fine des paradigmes de flexion

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
Anomaly Detection in Intrusion Detection System using Amazon SageMaker

Applying artificial intelligence and machine learning to analyzing network traffic has the potential to be transformative in protecting organizations from cyber threats. Intrusion detection systems (IDS) are historically rule-based; however, they could be improved. Applying machine learning in the form of Anomaly Detection could be the next step in preventing cyber threats from causing malicious activity on the network. Two algorithms that are implemented in anomaly detection through the use of Amazon SageMaker are Random Cut Forest (RCF) and XGBoost. The data for this project are the training and testing data set provided by the UNSW-15 data set. The models are created using the Jupiter Notebook on the Amazon SageMaker Studio Lab platform. The models were tested using the metrics of accuracy, precision, recall, and F1 score. The best-performing model was the XGBoost model, with an accuracy of 61.83%. The recall for this model was 96.49%, and the f1 score was 73.24%.
Bayesian bootstrap inference for the receiver operating characteristic surface

Accurate diagnosis of disease is of great importance in clinical practice and medical research. The receiver operating characteristic (ROC) surface is a popular tool for evaluating the discriminatory ability of continuous diagnostic test outcomes when there exist three‐ordered disease classes (e.g., no disease, mild disease, and advanced disease). We propose the Bayesian bootstrap, a fully nonparametric method, for conducting inference about the ROC surface and its functionals, such as the volume under the ROC surface (VUS). The proposed method is based on a simple, yet interesting, representation of the ROC surface in terms of placement variables and has the appealing feature of producing point and interval estimates for the ROC surface and its corresponding VUS in a single integrated framework. Results from a simulation study demonstrate the ability of our method to successfully recover the true ROC surface and to produce valid inferences in a variety of complex scenarios. An application to data from the Trail Making Test to assess cognitive impairment in Parkinson's disease patients is provided.
Asymmetric Learned Image Compression With Multi-Scale Residual Block, Importance Scaling, and Post-Quantization Filtering

Recently, deep learning-based image compression has made significant progresses, and has achieved better rate-distortion (R-D) performance than the latest traditional method, H.266/VVC, in both MS-SSIM metric and the more challenging PSNR metric. However, a major problem is that the complexities of many leading learned schemes are too high. In this paper, we propose an efficient and effective image coding framework, which achieves similar R-D performance with lower complexity than the state of the art. First, we develop an improved multi-scale residual block (MSRB) that can expand the receptive field and capture global information more efficiently, which further reduces the spatial correlation of the latent representations. Second, an importance scaling network is introduced to directly scale the latents to achieve content-adaptive bit allocation without sending side information, which is more flexible than previous importance map methods. Third, we apply a post-quantization filter (PQF) to reduce the quantization error, motivated by the Sample Adaptive Offset (SAO) filter in video coding. Moreover, our experiments show that the performance of the system is less sensitive to the complexity of the decoder. Therefore, we design an asymmetric paradigm, in which the encoder employs three stages of MSRBs to improve the learning capacity, whereas the decoder only uses one stage of MSRB, which reduces the decoder complexity and still yields satisfactory performance. Experimental results show that compared to the state-of-the-art method, the encoding and decoding time of the proposed method are about 17 times faster, and the R-D performance is only reduced by about 1% on both Kodak and Tecnick-40 datasets, which is still better than H.266/VVC(4:4:4) and other leading learning-based methods. Our source code is publicly available at https://github.com/fengyurenpingsheng.
Thinking About Crime

As crime rates inexorably rose during the tumultuous years of the 1970s, disputes over how to handle the violence sweeping the nation quickly escalated. James Q. Wilson redefined the public debate by offering a brilliant and provocative new argument--that criminal activity is largely rational and shaped by the rewards and penalties it offers--and forever changed the way Americans think about crime. Now with a new foreword by the prominent scholar and best-selling author Charles Murray, this revised edition of Thinking About Crime introduces a new generation of readers to the theories and ideas that have been so influential in shaping the American justice system.
Identification of a nonlinear spring and damper characteristics of a motorcycle suspension using test ride data

During test rides of motorcycles modifications are made to the suspension. In order to quantify those changes, the nonlinear spring and damper characteristics must be determined. This is usually done on a test bench. However, measurements on a test bench are closely related to high costs and high time exposure. Hence, a parameter identification after a test run, formulated as an optimization task, seems to be an auspicious approach. For this purpose a cost function is defined, which is minimized by considering the dynamics of the system. The strength of the contribution is the efficient gradient computation using the adjoint variable approach. In order to approximate the nonlinear spring and damper characteristics cubic splines are used. The values of the spline functions at specified grid points (knots) are adjusted such that the deviation between simulation and measurement is minimal.
Thermal-Aware SoC Macro Placement and Multi-chip Module Design Optimization with Bayesian Optimization

The effect of temperature on the reliability and performance of electrical components and integrated circuits warrants the inclusion of thermal considerations in the early stages of electronic system design. However, the many design parameters involved in the design of the package and the die or macro placement, along with the use of expensive thermal simulations, poses difficulty for conventional optimization algorithms. To overcome that hurdle, this work proposes an efficient Bayesian optimization algorithm which is demonstrated for two early-stage design problems. First, the proposed algorithm is used for the thermal-aware placement of macros with a variable aspect ratio. Second, the proposed Bayesian optimization algorithm is utilized in a two-stage process to optimize the die placement and the package design parameters of a multi-chip module.
A compact high-gain parasitic patch antenna with electronic beam-switching

A high beam steering antenna using HPND PIN Diode is proposed with a capability of steering its beam into three different directions -40 º, 0º and 40 º with respective switching condition. The reconfigurable parasitic antenna consists of a driven element and two reconfigurable parasitic elements, is designed with operating range of 9.5GHz. The parasitic elements act as reflectors or director depending on the switching conditions. Both parasitic elements are connected to ground plane via shorting pins. The reconfiguration is controlled by the two HPND PIN Diode switch that embeds to the parasitic element. An average gain value of 8dBi is achieved at all reconfiguration scenarios. All the simulated design has been carried out using CST software.
Managing pediatric hepatitis C: current and emerging treatment options

: Since 1992, the maternal–fetal route of transmission has become the dominant route for acquisition of hepatitis C (HCV) infection by children. With increasing knowledge of antiviral treatment for HCV infection, the main goal of therapy is to achieve a sustained virological response (SVR) as defined by undetectable serum HCV RNA by polymerase chain reaction assay six months after cessation of therapy. In young children, interferon therapy is more effective than in adults with chronic HCV infection (CHC). Although children clearly have a milder degree of liver pathology, data have indicated that hepatic inflammation from HCV infection can progress to fibrosis or cirrhosis in children. Hepatocellular carcinoma has been reported in adolescents with CHC. In this article, recent improvements in therapy of children with CHC and in the clinical development of new emerging drugs with potential use in children will be reviewed.
Orchestrating Apache NiFi/MiNiFi within a Spatial Data Pipeline

In many smart city projects, a common choice to capture spatial information is the inclusion of LiDAR data, but this decision will often invoke severe growing pains within the existing infrastructure. In this paper, we introduce a data pipeline that orchestrates Apache NiFi (NiFi), Apache MiNiFi (MiNiFi), and several other tools as an automated solution in order to relay and archive LiDAR data captured by deployed edge devices. The LiDAR sensors utilized within this workflow are Velodyne Ultra Pucks sensors that capture at a rate of 10 frames per second and produces 6-7 GB packet capture (PCAP) files per hour. By both compressing the file after capturing it and compressing the file in real-time, we discovered that gzip produced a file of 5 GB and saved about 5 minutes in transmission time to NiFi, as well as saving considerable CPU time when compressing the file in real-time. Alternatively, we chose XZ as the compression algorithm for the ingestion of LiDAR data onto an institution compute cluster due to its high compression ratio. In order to evaluate the capabilities of our system design, the features of this data pipeline were compared against existing third-party services, namely Globus and RSync.
Home Load Disaggregation using Deep Learning and Bayesian Optimization: A Case Study in Arctic Climate in Northern Norway

Load monitoring is an essential task in energy management systems. In this paper, an approach that relies on a long short-term memory (LSTM) model and a discrete wavelet transform (DWT) filter is presented to estimate the energy usage of flexible appliances. In the preprocessing stage, the main features of the aggregated power signal are extracted using DWT. Deep learning methods are very sensitive to hyperparameters, and choosing optimal values can significantly improve the accuracy of the model. To optimize the performance of the LSTM model, a Bayesian optimization algorithm is used to find the optimal set of hyperparameters. The performance of the proposed approach is evaluated using real-world data collected from a residential building in northern Norway. The results show that the proposed methodology can accurately disaggregate the power consumption of different appliances, with higher accuracy compared to existing methods.