Miracle-dz/UniFAR-FTUs huggingface dataset:
Demystifying the Capability of Sublook Correlation Techniques for Vessel Detection in SAR Imagery

This paper examines the attainable performance of various Doppler sublook or subband cross correlation techniques for vessel detection in synthetic aperture radar images. Research from the past two decades claims that these techniques were capable of improving the detection of small ships in challenging maritime environments. Despite many published experimental examples, a thorough analytical investigation corroborating this claim is noticeably absent. This paper is based on a rigorous theoretical analysis founded on the statistical properties of a textured sea surface model in thermal noise with simultaneous consideration of a constant false alarm rate. Emphasis has been placed on the correct accounting for detrimental physical effects caused by the Doppler spectrum being split into nonoverlapping parts, which have not been sufficiently considered in the literature to date. The theoretical results are confirmed via simulations and are substantiated with real RADARSAT-2 data. The analysis in this paper has neither found theoretical nor empirical evidence that sublook correlation techniques outperform the classical detector based on the image magnitude.
Application of the New Generic Quench Detection System for LHC's 11 T Dipole Magnet

The high luminosity upgrade of large hadron collider (LHC) introduces a large number of new superconducting elements of various technologies. In order to ensure protection of these elements, a modular, versatile quench detection system had been developed. Designed to be flexible enough to detect quenches in superconducting bus-bars as well as in magnets, one generic base system is applied to various detection tasks. The fully customized system is based on a field programmable gate array that is connected to 16 galvanically insulated front-end channels. Due to the presence of radiation in some of the dedicated installation areas, the system is designed to be radiation tolerant up to 1 Gy/y. The first application of this system is the 11 T dipole magnet in Nb3Sn technology, which is foreseen to be installed during LHC's long shutdown 2. To gain experience with magnets made with this superconductor, which had not been used in LHC so far, the development of the detection system follows the tests of the magnet prototypes. Especially, the mitigation of flux jumps to avoid false triggers is a challenge for the system. A description of the hardware and firmware is given. Measurements from the recent magnet prototype and mitigation techniques against electrical oscillations are presented.
RoadNet: Learning to Comprehensively Analyze Road Networks in Complex Urban Scenes From High-Resolution Remotely Sensed Images

It is a classical task to automatically extract road networks from very high-resolution (VHR) images in remote sensing. This paper presents a novel method for extracting road networks from VHR remotely sensed images in complex urban scenes. Inspired by image segmentation, edge detection, and object skeleton extraction, we develop a multitask convolutional neural network (CNN), called RoadNet, to simultaneously predict road surfaces, edges, and centerlines, which is the first work in such field. The RoadNet solves seven important issues in this vision problem: 1) automatically learning multiscale and multilevel features [gained by the deeply supervised nets (DSN) providing integrated direct supervision] to cope with the roads in various scenes and scales; 2) holistically training the mentioned tasks in a cascaded end-to-end CNN model; 3) correlating the predictions of road surfaces, edges, and centerlines in a network model to improve the multitask prediction; 4) designing elaborate architecture and loss function, by which the well-trained model produces approximately single-pixel width road edges/centerlines without nonmaximum suppression postprocessing; 5) cropping and bilinear blending to deal with the large VHR images with finite-computing resources; 6) introducing rough and simple user interaction to obtain desired predictions in the challenging regions; and 7) establishing a benchmark data set which consists of a series of VHR remote sensing images with pixelwise annotation. Different from the previous works, we pay more attention to the challenging situations, in which there are lots of shadows and occlusions along the road regions. Experimental results on two benchmark data sets show the superiority of our proposed approaches.
Underwater Image Enhancement Using Guided Joint Bilateral Filter

The human incapability in diving in the deep ocean for a long time has increased the challenges of underwater analysis. However, the challenges contain problems like aggressive light absorption and disrupting scattering effect in the deep ocean enabling lighting problem. These days AUV (Autonomnous Underwater Vehicle) are used to acquire images in deep ocean which contains additional artificial lighting still unable to provide good and clear images. In this paper, we have focused on the underwater image enhancements techniques mainly required for sea floor exploration and navigation, underwater environment monitoring, finding possibilities for applied purposes for civil engineering and assessment of coral reefs [1]. A dehazing method from single image based on filtering. So mainly, we concentrate on the underwater image enhancement through haze removal algorithm by guided joint bilateral filter technique. Our experimental results show that the proposed algorithm has significantly improved quality of underwater images visually as well as quantifiably by enhancing the contrast of the image and reducing noise as well as artifacts in the image.
Image Segmentation Using Meta-heuristic Algorithms

In this paper, inter-class variance of the two classes of image pixels formed on either side of the threshold value is maximized to perform image segmentation. This process has been automated by the use of evolutionary meta-heuristic algorithms such as Bat Algorithm (BA), Cuckoo Search Algorithm (CSA) and Interior Search Algorithm (ISA). The performance of these algorithms was compared on the basis of various parameters such as accuracy of convergence to optimal threshold values under noisy conditions and the speed of convergence.
Classification of Science, Technology and Medicine (STM) Domains with PSO and NBC

Science, Technology, and Medicine (STM) is a field of research that has a characteristic in each document. These characteristics are different from most documents that are used as a corpus in mining text research. Documents derived from Newswire are more dominant in previous research. However, in this study will try to classify documents from STM field. Complex technical terms, symbols, position information, and the number of citations would be a challenge itself. Previous studies have used the Naive Bayes Classifier (NBC) classification method. There are also those who apply Particle Swarm Optimization to assist its classification. From the Newswire field generated a fairly high accuracy Therefore, it would be applied to the optimization method with PSO and combine it with NBC method. This study produced accuracy value in classification model without using PSO equal to 82,73%. While in the classification model using PSO, the accuracy value is 87.27%. This shows that the use of PSO optimization is very influential on the classification
Demystifying the Crowd Intelligence in Last Mile Parcel Delivery for Smart Cities

Recent years have witnessed an explosive growth of online shopping, which has posted unprecedented pressure on the logistics industry, especially the last mile parcel delivery. Existing solutions mostly rely on dedicated couriers, which suffer from high cost and low elasticity when dealing with a massive amount of local addresses. Advances in the Internet of Things, however, have enabled vehicle information to be readily accessible anytime anywhere, forming an Internet of Vehicles (IoV), which further enables intelligent vehicle scheduling and management. New opportunities therefore arise toward efficient and elastic last mile delivery for smart cities. In this article, we seek novel solutions to improve the last mile parcel delivery with crowd intelligence. We first review the existing and emerging solutions for last mile parcel delivery. We then discuss the advances of the ride-sharing- based delivery mechanism, identifying the unique opportunities and challenges therein. We further present Car4Pac, an IoV-enabled intelligent ride-sharing-based delivery system for smart cities, and demonstrate its superiority with real trace-driven evaluations.
A Tri-Band Circularly Polarized G- Shaped Patch Antenna for Wireless Communication Application

A compact tri-band circularly polarized (CP) micro strip patch antenna has been designed here where the antenna designed consists of a single G-shaped patch with CPW (Co-planar Waveguide) -fed ground. This configuration has been considered for bandwidth enhancement. Two semicircular-shaped notch has been etched from the lower periphery of CPW ground to increase the ARBW. In order to further increase the axial ratio bandwidth (ARBW) by a significant amount, a spring shaped notch at a particular position on the upper periphery of the CPW ground has been etched. By using this said structure the achievable impedance bandwidth (IBW) has become 20.525 GHz ( i.e. 9.71 GHz – 30.24 GHz, 102.62 %), center frequency(fc) 20 GHz, and ARBW has become 250 MHz (fc 12.85 GHz), 574 MHz (fc 23.9 GHz), and 115 MHz (fc 29.35 GHz). The maximum simulated peak gain come was 4.09dBi at 25.85 GHz. The proposed antenna suitable for ‘Ku’, ‘K’ and ‘Ka’ band wireless communication application.
VANET: Its Applications, Security Requirements, Types of Attacks and its Corrective Measures

VANET is a self-organizing architecture which comprises of mobile nodes(vehicle) and these nodes communicate with each other forming Intelligent Transportation System(ITS), such that Vanet helps in contributing in improved road safety and efficiency, traffic monitoring and safety. In this review paper, we have covered various aspects related to Vanet such as its architecture, characteristics, applications, standard and security attacks in vanet as well as its security requirements.
Two Dimensional Analytical Modeling based Threshold Voltage Characteristics of Proposed Linearly Graded Work Function Engineered Gate all around SB MOSFET

This work presents 2D electrostatic potential and threshold voltage modeling of linearly graded work function engineered gate (WFEG) all around (GAA) schottky barrier (SB) MOSFET. Considering suitable boundary conditions across source/channel and drain/channel schottky junctions, Poisson’s equation is solved to derive the potential model of the proposed structure. Threshold voltage expression is then formulated by applying the minimum surface potential concept accordingly. Performance assessment of the present model is demonstrated and compared with conventional SB GAA MOSFET equivalent to establish its superiority in terms of surface potential, threshold voltage degradation and drain induced barrier lowering (DIBL). Comparison with data simulated by ATLAS device simulator is also performed showing accurate agreement between analytical and simulation results.
A study on low-cost sensor fusion for automatic railway coupler inspection

This paper presents findings of a case study conducted to introduce industrial robots into the inspection process of the electrical connector panel in automatic railway couplers. Detection and localization of smaller components in a highly unstructured and dynamic environment similar to a railway maintenance workshop is a non-trivial task. Therefore, a visual perception solution equipped with depth and RGB sensing hardware is suggested. The proposed solution is robust to environmental noise and precise positioning of the target with respect to the camera is not required. The accuracy of the overall system and robustness of the proposed solution are validated with the aid of well-established methodologies, data found in literature and on the actual coupler in usual working environments.
Ferroelectric-Insulator-Germanium Double Gate (F-I-GeDG) FET with steep switching characteristics and improved current drivability

This work presents an analytical model for Ferroelectric-Insulator-Germanium Double Gate Field Effect Transistor (F-I-GeDGFET) with a gate stack comprises of a layer of linear dielectric and a layer of ferroelectric material. Using the Poisson’s equation, L-K equations and Gauss’s law, various device characteristics have been derived. It has been shown that the negative capacitance offered by ferroelectric material present in proposed device results in sub-60 mV/dec device operation, significantly increased total capacitance and improved ON-current in channel. By speculating the characteristics of F-I-GeDGFET and conventional p-type devices, it has been shown that the proposed device offers better gate controllability, improved switching characteristics and enhanced ON-current drivability and carrier transport in the channel.
Multifrequency 3-D Inversion of GREATEM Data by BCGS-FFT-BIM

A newly designed grounded electrical-source airborne transient electromagnetics (GREATEM) system was introduced recently. Detailed data preprocessing techniques to acquire the high-precision measured magnetic field are discussed here. Different from the previous work in which the reconstruction of the underground structure is performed in 1-D, we interpret the GREATEM data in 3-D by the volume integral equation (VIE) method in the frequency domain. Therefore, the VIE in the forward electromagnetic scattering model is formulated in the low-frequency regime. It is solved by using the stabilized biconjugate gradient fast Fourier transform (BCGS-FFT) method. In the nonlinear inversion, the Born iterative method (BIM) and the conjugate gradient method are adopted to minimize the cost function. A synthetic model of GREATEM survey is used to validate the proposed 3-D forward and inversion algorithms. Then, the field data from two GREATEM surveys are used to test the effectiveness and accuracy of the proposed inversion algorithm. The reconstructed conductivity structures are consistent with geological drilling results, confirming the potential of our method for solving the 3-D GREATEM inversion problems in geophysical engineering applications. This paper represents the first application of the BCGS-FFT and BIM algorithms to a GREATEM system.
Data Bandwidth Reduction in Deep Neural Network SoCs using History Buffer and Huffman Coding

Convolutional neural networks deliver state of the art accuracy in classification and detection of objects which are of interest for many consumer and automotive applications. However, they require significant memory bandwidth and storage for intermediate computations apart from huge computing resources. There is an important requirement to reduce the bandwidth and memory for the cost and yield reasons. Here, a method and circuit is discussed to reduce the memory bandwidth for intermediate results.
Classification of Lower Back Pain Using K-Nearest Neighbor Algorithm

Back pain is the second most popular disease after the common cold. More than half of the world’s human population is affected by lower back pain in their activities. Lower back pain can be categorized into two categories namely normal and abnormal lower back pain. Lower back pain has symptom similarity then it is difficult to determine whether or not the pain is felt without direct examination. It takes the right diagnosis, then treatment of the disease can be done immediately to prevent the worsening impact. In the previous study, using a classification algorithm such as Support Vector Machine, they developed methods to study the effect of the brain structures that occur in lower back pain patient. This study classified specifically normal or abnormal lower back pain suffered based on 12 Range of Motion (ROM) attributes using the K-Nearest Neighbor algorithm as the proposed method. In this study, we used replace missing value and selected attribute methods to preprocessing the data, and in classifying lower back pain, machine learning algorithms such as KNN, Logistic Regression, Naïve Bayes, Random Forest, Decision Tree, and CART were used with the result of accuracy level compared to previous research increased by 15.96%.
Machine Learning Based Effort Estimation Using Standardization

Accurate estimations of software project effort is one of the most important tasks of software project development. The machine learning models have proven to provide high accuracy due to the learning natures of these techniques. Taking this into consideration, this paper aims at employing machine learning models of Random Forest (RF), Multilayer Perceptron (MLP) and Support Vector Machines (SVM) for purpose of predicting the effort. The Use Case Points (UCP) software size metric is used due to its advantage of providing estimation at initial stages of software development. The standardization preprocessing technique was applied on the dataset before training the models. The RF, MLP and SVM models were examined for the prediction accuracy. The experimental results obtained from RF model were better as compared to MLP and SVM.
Path Reference Generation for Upper-Limb Rehabilitation with Kinematic Model

An upper-limb kinematic model is studied for generating a path reference for rehabilitation. An eight-degrees-of-freedom (8-DOF) model of the upper-limb is presented and a path reference is generated based on the model. The path reference is generated with regards to the subject parameters, such as the length from the chest to shoulder, the length of upper arm, and the length of lower arm that can be implemented for individual subjects. Different with trajectory generation, the time is not considered when a subject is asked to follow the desired movement. In other words, the subject needs to follow an ideal movement with no constraints to the time when a configuration of upper-limb should be achieved. The implementation of the path reference generation and the comparison between the path reference and the actual measurement of a healthy subject is conducted performing shoulder forward flexion motion. The result shows the potential use of the proposed method for upper limb rehabilitation.
Joint Petrophysical and Structural Inversion of Electromagnetic and Seismic Data Based on Volume Integral Equation Method

A joint petrophysical and structural inversion method for electromagnetic (EM) and seismic data based on the volume integral equation (VIE) is proposed in this paper. In the forward EM problem, only the contrast of conductivity is solved by the electric field integral equation method. However, in the forward seismic problem, both the contrasts of velocity and mass density are solved by the combined field VIE method. Both forward solvers are accelerated by the fast Fourier transform. In the inversion problem, by using the petrophysical equations about the porosity and saturation and applying the chain rule, we fuse the EM and seismic data and construct the joint petrophysical inversion equations, which can be solved by the variational Born iteration method. Then, in order to further enhance the reconstructed results of the joint petrophysical inversion, we enforce the structural similarity constraint between porosity and water saturation and add the cross-gradient function to the joint petrophysical inversion cost function. Two typical geophysical models based on the remote sensing measurement are used to validate the proposed methods. One is the cross-well model, and the other is the marine surface exploration model. The advantage of the joint inversion compared with the separate inversion is evaluated based on the resolution and the data misfits of the reconstructed profiles as well as the antinoise ability.
Risk-Sensitive Linear Control for Systems With Stochastic Parameters

A novel risk-sensitive (RS) control law is proposed for linear systems with stochastic system parameters. RS control laws are efficient means of handling risk in various failures in control caused by stochastic disturbances. However, stochastic parameters invoke problems, resulting controllers may become nonlinear in the state variable and incompatible with linear systems, cannot be obtained in an exact sense, and are defined only on a bounded state region. To solve these problems, this paper presents a risk-sensitive linear (RSL) control method. The important idea is that a standard RS-type cost function is converted to an expectation of a weighted cost function such that the resulting optimal controller is linear in the state. The weight is designed such that the weighted cost function preserves the characteristics of the original RS control. The designed weight allows one to derive the proposed RSL controller whose exact solution is obtained as a linear feedback law for all states. Furthermore, the RSL control law over an infinite horizon guarantees stochastic stability of the feedback system with the control law. The effectiveness of the proposed RSL control law is demonstrated by a numerical simulation.
A Forward-Body-Bias CMOS LNA With Ultra-Low Device Junction Leakage Using Intrinsic Self-Balanced Pseudo Resistor

A fully differential LNA with forward-body-bias (FBB) technique using an intrinsic self-balanced MOSFET is presented in this brief for low-impact device junction leakage and process, voltage, and temperature (PVT) variations. A proposed natural back-to-back connected diode directly from the source-bulk and bulk-drain junctions of a MOSFET performs as a stable and balanced pseudo resistor for minimizing leakage current at the transconductance stage of a FBB LNA. In addition, the FBB voltage is injected into the bulk of the MOSFET to allow a full-swing operation, in particular simultaneously at a gate-driven and body-driven input stage. In comparison to the conventional topologies of pseudo resistor, the simulated and measured results show that the proposed FBB technique applied at the LNA with the dual cross coupling configuration can indeed extend the overall dynamic range of the pseudo resistor and relief PVT variations under the same extreme condition, with a larger forward bias voltage close to the breakdown leakage current transition corner of the P-N junction.
Multi-Level Approximate Accelerator Synthesis Under Voltage Island Constraints

Approximate computing forms a promising paradigm shift for energy efficient design by aggressively decreasing power consumption of inherently error-tolerant applications. However, approximate computing architectures exacerbate the design complexity due to the diversity of inexact techniques and their impact on final circuit implementations. In this brief, we introduce approximate accelerator synthesis, which enables the design of power-optimized inexact circuits under error bound constraints, by leveraging the incorporation of diverse multi-level approximate techniques. We show the high efficiency of adopting multi-level approximation techniques and present a systematic approach for integrating multi-level approximation in hardware accelerator synthesis under error bound and voltage island constraints.