Miracle-dz/UniFAR-FTUs huggingface dataset:
Fifth Generation Antennas: A Comprehensive Review of Design and Performance Enhancement Techniques

The intensive research in the fifth generation (5G) technology is a clear indication of technological revolution to meet the ever-increasing demand and needs for high speed communication as well as Internet of Thing (IoT) based applications. The timely upgradation in 5G technology standards is released by third generation partnership project (3GPP) which enables the researchers to refine the research objectives and contribute towards the development. The 5G technology will be supported by not only smartphones but also different IoT devices to provide different services like smart building, smart city, and many more which will require a 5G antenna with low latency, low path loss, and stable radiation pattern. This paper provides a comprehensive study of different antenna designs considering various 5G antenna design aspects like compactness, efficiency, isolation, etc. This review paper elaborates the state-of-the-art research on the different types of antennas with their performance enhancement techniques for 5G technology in recent years. Also, this paper precisely covers 5G specifications and categorization of antennas followed by a comparative analysis of different antenna designs. Till now, many 5G antenna designs have been proposed by the different researchers, but an exhaustive review of different types of 5G antenna with their performance enhancement method is not yet done. So, in this paper, we have attempted to explore the different types of 5G antenna designs, their performance enhancement techniques, comparison, and future breakthroughs in a holistic way.
Real-Time UAV Path Planning for Autonomous Urban Scene Reconstruction

Unmanned aerial vehicles (UAVs) are frequently used for large-scale scene mapping and reconstruction. However, in most cases, drones are operated manually, which should be more effective and intelligent. In this article, we present a method of real-time UAV path planning for autonomous urban scene reconstruction. Considering the obstacles and time costs, we utilize the top view to generate the initial path. Then we estimate the building heights and take close-up pictures that reveal building details through a SLAM framework. To predict the coverage of the scene, we propose a novel method which combines information on reconstructed point clouds and possible coverage areas. The experimental results reveal that the reconstruction quality of our method is good enough. Our method is also more time-saving than the state-of-the-arts.
Deep Merging: Vehicle Merging Controller Based on Deep Reinforcement Learning with Embedding Network

Vehicles at highway merging sections must make lane changes to join the highway. This lane change can generate congestion. To reduce congestion, vehicles should merge so as not to affect traffic flow as much as possible. In our study, we propose a vehicle controller called Deep Merging that uses deep reinforcement learning to improve the merging efficiency of vehicles while considering the impact on traffic flow. The system uses the images of a merging section as input to output the target vehicle speed. Moreover, an embedding network for estimating the controlled vehicle speed is introduced to the deep reinforcement learning network architecture to improve the learning efficiency. In order to show the effectiveness of the proposed method, the merging behavior and traffic conditions in several situations are verified by experiments using a traffic simulator. Through these experiments, it is confirmed that the proposed method enables controlled vehicles to effectively merge without adversely affecting to the traffic flow.
A Flight Envelope Determination and Protection System for Fixed-Wing UAVs

In this work we present a novel, approximate, efficient algorithm for determining the Trim Flight Envelope of a fixed-wing UAV, based on a generic, nonlinear numerical model. The resulting Flight Envelope is expressed as a convex intersection of half-spaces. Subsequently, a Model Predictive Controller (MPC) is designed which takes into account the Flight Envelope constraints, to avoid Loss-of-Control. The overall system is shown to operate in real-time in a simulation environment.
Pixel-Wise Motion Segmentation for SLAM in Dynamic Environments

Visual simultaneous localization and mapping (SLAM) is a key prerequisite for many mobile robotic systems. A common assumption for SLAM methods is a static environment. The interference of dynamic objects can lead to impairment of the camera pose tracking and permanent distortions of the map. This limits the use of many visual SLAM systems in real world scenarios, where dynamic environments are typical. We present a novel method for pixel-wise segmentation of dynamic image sequences based on a scene flow model estimation. We detect and eliminate outlying pixels sparsely by evaluating each pixel motion separately and maintain the most possible area of static scene background for SLAM. The evaluation with the public TUM dataset demonstrates that our proposed method outperforms other comparable state-of-the-art approaches for dynamic removal for SLAM systems.
Microscope-Guided Autonomous Clear Corneal Incision

Clear Corneal Incision, a challenging step in cataract surgery, and important to the overall quality of the surgery. New surgeons usually spend one full year trying to perfect their incision, but even after such rigorous training deficient incisions can still occur. This paper proposes an autonomous robotic system for this self-sealing incision. A conventional ophthalmic microscope system with a monocular camera is utilized to capture the surgical scene, ascertain the robot’s position, and estimate depth information. Kinematics with a remote centre of motion (RCM) is designed for a multi-axes robot to perform the incision route. The experimental results on ex-vivo porcine eyes show the autonomous Clear Corneal Incision has a stricter three-plane structure than a surgeon-made incision, which is closer to the ideal incision.
Non-Central Negative Binomial Regression Model for Count Data

This paper introduces a three parameter regression count model although involving confluent hypergeometric function (CHF). It has some benefits over classical Poisson (P) and negative binomial (NB) model as an up to mark model for data with excess zeros and displaying over-dispersion. The regression coefficient estimators have been estimated by the method of Maximum Likelihood (ML). Deviance (D) statistic has been used to assess the goodness of fit. Ultimately, an application to the count of number of articles published in the year 2013-15 by Covenant University, Ota, Ogun state Nigeria has been considered to explicate the potentiality of the proposed model.
Calculation of OFF-Current of Tunnel FETs based on Subthreshold Swing: A New Approach

The field-effect-transistors (FETs) working on band-to-band tunneling mechanism provide better switching characteristics than field-effect-transistors working on thermionic emission. In this regard, tunnel FETs (TFETs) are found more promising device among FETs working on band-to-band tunneling mechanism. However, TFETs have ambipolar behavior which is not present in the FETs working on thermionic mechanism and due to this OFF-current of TFETs depends on metal-gate workfunction. Thus, to make OFF-current independent of metal gate workfunction the new approach is proposed to calculate the OFF-current which is also independent of ambipolar behavior of TFETs. The proposed approach is based on the subthreshold-swing (SS) of the device and it is validated by the transfer characteristics of experimental TFETs taken in this work to calculate the subthreshold-swing. The calculated OFF-current is found in same order to the earlier reported data in the reports. The comparison results suggest that the approach is valid and encouraging to replace the existing approaches.
3D Orientation Estimation and Vanishing Point Extraction from Single Panoramas Using Convolutional Neural Network

3D orientation estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. This paper presents a new CNN architecture to estimate the 3D orientation of an omnidirectional camera with respect to the world coordinate system from a single spherical panorama. To train the proposed architecture, we leverage a dataset of panoramas named VOP60K from Google Street View with labeled 3D orientation, including 50 thousand panoramas for training and 10 thousand panoramas for testing. Previous approaches usually estimate 3D orientation under pinhole cameras. However, for a panorama, due to its larger field of view, previous approaches cannot be suitable. In this paper, we propose an edge extractor layer to utilize the low-level and geometric information of panorama, an attention module to fuse different features generated by previous layers. A regression loss for two column vectors of the rotation matrix and classification loss for the position of vanishing points are added to optimize our network simultaneously. The proposed algorithm is validated on our benchmark, and experimental results clearly demonstrate that it outperforms previous methods.
Gender and Age based Census System for Metropolitan Cities

This paper presents a smart computer-vision based system which helps ensure disciplined entry of people in places by ascertaining the allowable age and gender of the person, as well as storing the data for census use. The system involves deep learning based facial image classifiers, which predict the gender and age from the image of face captured by the camera in real-time. The actuating response is generated according to the prediction of the computer vision algorithm, which further keeps the record of encountered faces with the respective counts of persons with corresponding facial traits. The log of the entire surveillance process can be monitored online through the integrated IOT capability, thereby can be used as primary data source. The successful experimental runs demonstrate the system’s usability in the real world applications.
Automating GUI Testing with Image-Based Deep Reinforcement Learning

Users interact with modern applications and devices through graphical user interfaces (GUIs). To ensure intuitive and easy usability, the GUIs need to be tested, where developers aim at finding possible bugs and inconsistent functionality. Manual GUI testing requires time and effort, and thus, its efficiency can be improved with automation. Conventional automation tools for GUI testing reduce the burden of manual testing but also introduce challenges in the maintenance of test cases. In order to overcome these issues, we propose a deep-reinforcement-learning-based (DRL) solution for automated and adaptive GUI testing. Specifically, we propose and evaluate the performance of an image-based DRL solution. We adapt the asynchronous advantage actor-critic (A3C) algorithm to GUI testing inspired by how a human uses a GUI. We feed screenshots of the GUI as the input and let the algorithm decide how to interact with GUI components. We observe that our solution can achieve up to six times higher exploration efficiency compared to selected baseline algorithms. Moreover, our solution is more efficient than inexperienced human users and almost as efficient as an experienced human user in our experimental GUI testing scenario. For these reasons, image-based DRL exploration can be considered as a viable GUI testing method.
Improved Selective Frequency Active Damping for Voltage Source Inverter With Output $LC$ Filter

Stand-alone applications employ voltage source inverter with output <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula> filter to provide a high-quality ac supply at load terminals. These typically use filter inductor current as feedback to actively damp the <inline-formula><tex-math notation="LaTeX">$LC$</tex-math></inline-formula> resonance. But it also magnifies the output impedance of the inverter and, thus, deteriorates the output voltage quality under nonlinear load conditions. Existing literature also reports a selective frequency active damping (SFAD) scheme, using the filter inductor current feedback, to overcome the drawback of the conventional active damping scheme. It effectively localizes the effect of the damping loop around resonance frequency only, hence the output impedance for rest of the frequency region remains unaffected. This article comprehensively examines the performance of the existing SFAD algorithm. It is analytically shown that the damping performance of digitally realized SFAD is adversely affected by digital delays. Then, an improved design is proposed for SFAD, which results in extra phase-lead around the resonance frequency to compensate for the digital delays. Finally, the proposed improvement is experimentally demonstrated to provide a better transient and steady-state performance.
Super-Pixel Sampler: a Data-driven Approach for Depth Sampling and Reconstruction

Depth acquisition, based on active illumination, is essential for autonomous and robotic navigation. LiDARs (Light Detection And Ranging) with mechanical, fixed, sampling templates are commonly used in today’s autonomous vehicles. An emerging technology, based on solid-state depth sensors, with no mechanical parts, allows fast and adaptive scans.In this paper, we propose an adaptive, image-driven, fast, sampling and reconstruction strategy. First, we formulate a piece-wise planar depth model and estimate its validity for indoor and outdoor scenes. Our model and experiments predict that, in the optimal case, adaptive sampling strategies with about 20-60 piece-wise planar structures can approximate well a depth map. This translates to requiring a single depth sample for every 1200 RGB samples (less than 0.1%), providing strong motivation to investigate an adaptive framework. Second, we introduce SPS (Super-Pixel Sampler), a simple, generic, sampling and reconstruction algorithm, based on super-pixels. Our sampling improves grid and random sampling, consistently, for a wide variety of reconstruction methods. Third, we propose an extremely simple and fast reconstruction for our sampler. It achieves state-of-the-art results, compared to complex image- guided depth completion algorithms, reducing the required sampling rate by a factor of 3-4. A single-pixel prototype sampler built in our lab illustrates the concept.
Multi-Robot Control Using Coverage Over Time-Varying Non-Convex Domains

This paper addresses the problem of a domain becoming non-convex while using coverage control of a multirobot system over time-varying domains. When the domain moves around in the workspace, its motion and the presence of obstacles might cause it to deform into some non-convex shape, and the robot team should act in a coordinating manner to maintain coverage. The proposed solution is based on a framework for constructing a diffeomorphism to transform a non-convex coverage problem into a convex one. A control law is developed to capture the effects of time variations (e.g., from a time-varying density, time-varying convex hull of the domain and time-varying diffeomorphism) in the system. Analytic expressions of each term in the control law are found for uniform density case. A simulation and robotic implementation are used to validate the proposed algorithm.
DNN-Based Human Face Classification Using 61 GHz FMCW Radar Sensor

In this paper, we propose a method for classifying human faces using a small-sized millimeter wave radar sensor. The radar sensor transmits a frequency-modulated continuous wave signal operating in the 61 GHz band and it receives reflected signals using spatially separated receiving antenna elements. Because the shape and composition of the human face varies from person to person, the reflection characteristics of the radar signal are also distinguished from each other. Therefore, training a deep neural network (DNN) using signals received from multiple antenna elements enables classification of different human faces. With our trained DNN model, eight human faces can be classified with an accuracy of 92%. We also compare the performance of the proposed method with conventional machine learning techniques (e.g., support vector machine, tree-based methods) and confirm that our method has higher classification accuracy.
The Arm Planning with Dynamic Movement Primitive for Humanoid Service Robot

In order to realize the autonomous motor learning skills of humanoid service robot, we propose a systematic framework for trajectory planning and learning of robotic arms in this paper. The system is presented as a series of differential equations with good attractor properties called dynamic movement primitive (DMP). The core of DMP is to extract the motion characteristics from the human body demonstration, and then realize the reproduction and generalization of human actions by adjusting the nonlinear terms online. In this paper, a dynamic motion capture equipment is used to acquire the human's demonstration. The nonlinear terms of DMP is learned through local weighted regression (LWR) algorithm. Finally, the effectiveness of this method is verified by simulating the one-dimensional and two-dimensional trajectories in MATLAB and applying it to the existing household humanoid service robot in the laboratory.
AI Powered THz Testing Technology for Ensuring Hardware Cybersecurity

We report on a novel non-destructive testing method for integrated circuits (ICs) by measuring their response to scanning terahertz and sub-terahertz radiation at the pins. Analyzing the response using artificial intelligence (AI), counterfeit, altered, or damaged ICs can be identified as in-package and in-situ.
JND-Guided Perceptually Color Image Watermarking in Spatial Domain

Watermarking is an effective solution for copyright protection and forensics tracking via hiding information into the image signal. Recently, spatial-embedding watermarking methods from different transform domain have been proposed rapidly and effectively protect the copyright of the color image. Compared to the individual frequency domain method, it has both advantages of spatial domain and frequency domain. Here, we proposed a novel spatial-embedding watermarking method based on an attended just noticeable difference (JND) model with color complexity, to achieve a good tradeoff between robustness and perceptual quality. In particular, at the spatial embedding level, the direct current (DC) coefficients are selected for embedding and the perceptual JND model is used to guide the amount of pixel modification to improve visual quality. Different from the previous JND model, we proposed an attended JND model that considers color complexity, which is more consistent of the human visual perception model. Compared with the other JND models, the proposed JND model is more suitable for the watermarking framework. Experimental results show that the proposed watermarking scheme has better performance than other existing watermarking schemes. This greatly benefits the practical implementations of the spatial-embedding watermarking methods.
Study of a Single-Photon Avalanche Diode Models

In this paper, we present a study of circuit models for single-photon avalanche diodes (SPADs). We used Orcad PSpice software to model the device and to represent the detector's behavior in Geiger mode. This model properly characterizes the self-sustaining avalanche and the self-quenching process. Through this model, we were able to correctly present the avalanche current signal, the voltage drop and the recovery time in the SPAD's cathode. The results obtained with such a model show a good corresponding with experimental measurements acquired with passive quenching circuit (PQC). Such a study allows us to present better results for the avalanche process and enable us to detect weak avalanches.
Temporal information integration for video semantic segmentation

We present a temporal Bayesian filter for semantic segmentation of a video sequence. Each pixel is a random variable following a discrete probabilistic distribution function representing possible semantic classes. Bayesian filtering consists in two main steps: 1) a prediction model and 2) an observation model (likelihood). We propose to use a datadriven prediction function derived from a dense optical flow between images t and t + 1 achieved by a deep neural network [1]. Moreover, the observation function uses a semantic segmentation network. The resulting approach is evaluated on the public dataset Cityscapes. We show that using the temporal filtering increases the accuracy of the semantic segmentation.
Dual Band, Dual Slant $\pm 45^{\circ}$ Polarized $2 \times 2$ MIMO (8T 8R) Antenna Array with Low Mutual Coupling for A-LTE(4G) Band 41/42/43(5G) BTS Application

This article offers a dual band, dual slant $\pm 45^{\circ}$ polarized, $2 \times 2$ MIMO (8T 8R) antenna array with low mutual coupling for LTE Band 41/42/43 BTS application. Two slotted flared bow-tie dipole antenna elements are placed in the slant $\pm 45^{\circ}$ orientation and are fed with two coaxial cables and achieve dual polarization. The design consists of a two slotted bow-tie arms of the antenna with metal shorting posts which enhance port-to-port isolation. The unidirectional radiation pattern is obtained by placing a metal ground plane at a distance of $\lambda_{0}/4$. The radiating elements are designed to operate at the center frequencies of 2.65GHz and 3.5GHz. Simulated results of an anticipated dual-polarized slotted bowtie antenna with Band 41(2.496-2.690 GHz), Band 42 (3.4 to 3.6 GHz) and Band 43 (3.6 to 3.8 GHz) sub-6GHz band with VSWR < 1.5. Isolation between the two ports is -35dB and mutual coupling is around -33dB for both the polarization and gain of 8.75dBi is observed. The overall height of the antenna is $0.25 \lambda_{0}$ and the design is simple and easy to fabricate.
A 10-MHz Current-mode Constant On-time Boost Converter with a Translinear Loop-based Current Sensor

DC-DC converters with a high switching frequency are very attractive when fast-response, and small-area are required. In this paper, a translinear loop (TLL)-based digitally controlled current sensor is proposed for boost converters with the switching frequency of 10 MHz. Compared to previous current sensors, the proposed circuit helps to minimize the sensing response time by removing a slow feedback loop. It also uses a digital control to get an accurate sensing waveform. In addition, a ripple-based constant on-time (COT) control helps to further reduce the load-transient response. The proposed boost converter has been fabricated in a 180-nm CMOS process with a total chip area of 1.28 mm $\times 1.18$ mm. The maximum conversion efficiency of 93.3% has been achieved even at the 10-MHz switching frequency, and 3.5-$\mu$s transient response time is measured at 400 mA of a load current step.