Miracle-dz/UniFAR-FTUs huggingface dataset:
Circularly Polarized Log Periodic Dipole Antennas

This Modified Self-complementary LPDA [1] has a second array of dipoles so arranged that each dipole of the second array has a quarter-wavelength phase difference from that of the corresponding dipole of the standard LPDA array for the given radiation field. The cross-element LPDA does not need a broadband 90-degree hybrid junction to produce circular polarization.
Dex-Net AR: Distributed Deep Grasp Planning Using a Commodity Cellphone and Augmented Reality App

Consumer demand for augmented reality (AR) in mobile phone applications, such as the Apple ARKit. Such applications have potential to expand access to robot grasp planning systems such as Dex-Net. AR apps use structure from motion methods to compute a point cloud from a sequence of RGB images taken by the camera as it is moved around an object. However, the resulting point clouds are often noisy due to estimation errors. We present a distributed pipeline, Dex-Net AR, that allows point clouds to be uploaded to a server in our lab, cleaned, and evaluated by Dex-Net grasp planner to generate a grasp axis that is returned and displayed as an overlay on the object. We implement Dex-Net AR using the iPhone and ARKit and compare results with those generated with high-performance depth sensors. The success rates with AR on harder adversarial objects are higher than traditional depth images. The server URL is https://sites.google. com/berkeley.edu/dex-net-ar/home
Recognition of Bulgarian Embroidery with Recurrent Neural Network

This article explores the possibilities for the recognition of Bulgarian embroidery by intelligent multi-agent system. The task of recognizing the embroidery is divided into three distinct parts – recognition of an image as an embroidery, determination of the embroidery as a Bulgarian and its classification according to the area of workmanship. The proposed method uses Hopfield neural network to solve the first task. Results from Java-based algorithm are presented.
Machine Learning based Medical Information Analysis, Estimations and Approximations over Present Health Research Domain

The concept of Artificial Intelligence is come into existence recently. This concept is using in all the domains such as medical, industry, software, media out of this the concept of Machine Learning is widely started using in medical domain based on the prediction and analysis got succeed and it is continuing using in Medical domain these days due to many advantages things such as flexibility, can take quick decisions, portability, reliability, user friendly etc. In this paper we are focusing on the mechanism of making the things such as Machine Learning and Artificial Intelligence with health care as a single platform or a single unit for the completion of the task. Some of the technologies which are linked with Machine Learning and Artificial Intelligence to some cloud services namely Apache Spark, IBM Blue mix, Microsoft Azure, No SQL server. This paper gives the detailed information regarding the mechanisms in the health care domain. The devices help in making the better decisions based on the past collected information by the help of the intelligent process mechanism analyzing as well as Big data techniques.
On a Mixed–Boundary Value Problem Related to the Electrostatics of Plasma Jet Reactors

A semi–analytical model is presented for the determination of the electric field in Cold Atmospheric–Pressure Plasma jet systems (CAPP jets) in applications associated with plasma science and contemporary engineering. We consider a simplified system configuration of a single charged electrode, surrounding a dielectric tube of minor contribution to the field. Introducing the cylindrical geometry, we separate our domain in three distinct areas, as dictated from the external conditions. Based on the electrostatic limit of Maxwell’s equations, we use cylindrical harmonic potential field expansions that produce the corresponding electric fields in each subdomain. Due to the imposed mixed–type boundary value problem, additional linear terms are incorporated, leading to three possible solutions of the physical problem. The efficiency of our method is demonstrated by comparing the final formulae with a numerical solution, followed by the necessary discussion.
Passive Dynamic Balancing and Walking in Actuated Environments

The control of passive dynamic systems remains a challenging problem in the field of robotics, and insights from their study can inform everything from dynamic behaviors on actuated robots to robotic assistive devices. In this work, we explore the use of flat actuated environments for realizing passive dynamic balancing and locomotion. Specifically, we utilize a novel omnidirectional actuated floor to dynamically stabilize two robotic systems. We begin with an inverted pendulum to demonstrate the ability to control a passive system through an active environment. We then consider a passive bipedal robot wherein dynamically stable periodic walking gaits are generated through an optimization that leverages the actuated floor. The end result is the ability to demonstrate passive dynamic walking experimentally through the use of actuated environments.
Impact of Incorporating Disturbance Prediction on the Performance of Energy Management Systems in Micro-Grid

The design and implementation of appropriate advanced control strategies is a key factor for the effective integration of micro-grids into the electrical network. In view of this, the study proposes an Adaptive Model-Based Horizon Control technique in the bid to addressing issues related to the Energy Management System in micro-grid operations. The main objective of the energy management system is to balance energy generation and demand through energy storage, so as to optimize the operation of the micro-grid with high penetrations of renewable energy sources. This paper further investigates the impacts of considering the prediction of disturbances on the performance of the Energy Management System based on the adaptive model predictive control algorithm in order to improve the operating costs of the micro-grid with hybrid-energy storage systems. The adaptive model predictive control algorithm solves the energy optimization problem in a renewable energy-based micro-grid with various types of energy storage systems that exchange energy with the host grid. More so, this optimization problem is resolved at each sampling period in order to determine the minimum running costs while satisfying demand and taking into account technical and physical constraints. The simulation results under different conditions have demonstrated how the use of an adaptive model predictive control based energy management system can enhance micro-grid operation, provided there is effective forecasting, and consequently minimized the running operating costs of micro-grid. More so, it is evident in the cost function, $J$ , obtained from the three scenarios conducted, that the perfect knowledge of the disturbance prediction is essential for effective micro-grid operations.
RJCC: Reinforcement-Learning-Based Joint Communicational-and-Computational Resource Allocation Mechanism for Smart City IoT

With the fast development of smart cities and 5G, the amount of mobile data is growing exponentially. The centralized cloud computing mode is hard to support the continuous exchanging and processing of information generated by millions of the Internet-of-Things (IoT) devices. Therefore, mobile-edge computing (MEC) and software-defined networking (SDN) are introduced to form a cloud-edge-terminal collaboration network (CETCN) architecture to jointly utilize the communicational and computational resources. Although the CETCN brings many benefits, there still exist some challenges, such as the unclear operation mode, low utilization of edge resources, as well as the limited energy of terminals. To address these problems, a reinforcement learning-based joint communicational-and-computational resource allocation mechanism (RJCC) is proposed to optimize overall processing delay under energy limits. In RJCC, a $Q$ -learning-based online offloading algorithm and a Lagrange-based migration algorithm are designed to jointly optimize computation offloading across multisegments and on edge platform, respectively. The simulation results show that the proposed RJCC outperforms the delay-optimal, energy-optimal, and edge-to-terminal offloading algorithm by 42%–74% in long-term average energy consumption while maintaining relatively low delay.
Alerts Correlation and Causal Analysis for APT Based Cyber Attack Detection

The advent of Advanced Persistent Threat (APT) as a new concept in cyber warfare has raised many concerns in recent years. APT based cyber-attacks are usually stealthy, stepwise, slow, long-term, planned, and based on a set of varied zero-day vulnerabilities. As a result, these attacks behave as diverse and dynamic as possible, and hence the generated alerts for these attacks are normally below the common detection thresholds of the conventional attacks. Therefore, the present approaches are not mostly able to effectively detect or analyze the behavior of this class of attacks. In this article, an approach for real-time detection of APT based cyber-attacks based on causal analysis and correlating the generated alerts by security and non-security sensors is introduced. The proposed method computes the infection score of hosts by modeling, discovery, and analysis of causal relationships among APT steps. For this purpose, a dynamic programming algorithm is introduced which works on alerts of each host separately and conducts a long-term analysis on the attack process to combat the outlasting feature of the APT attacks yet coping with a high volume of alert information. The proposed method is implemented and extensively evaluated using a semi real-world dataset and simulation. The experimental results show that the proposed approach can effectively rank hosts based on their infection likelihood with acceptable accuracy.
Inferring the Geometric Nullspace of Robot Skills from Human Demonstrations

In this paper we present a framework to learn skills from human demonstrations in the form of geometric nullspaces, which can be executed using a robot. We collect data of human demonstrations, fit geometric nullspaces to them, and also infer their corresponding geometric constraint models. These geometric constraints provide a powerful mathematical model as well as an intuitive representation of the skill in terms of the involved objects. To execute the skill using a robot, we combine this geometric skill description with the robot’s kinematics and other environmental constraints, from which poses can be sampled for the robot’s execution. The result of our framework is a system that takes the human demonstrations as input, learns the underlying skill model, and executes the learnt skill with different robots in different dynamic environments. We evaluate our approach on a simulated industrial robot, and execute the final task on the iCub humanoid robot.
Stitch-Aware Routing Considering Smart Boundary for Multiple E-Beam Lithography

As one of competitive next generation lithography (NGL), multiple electron beam lithography (MEBL) has been proposed to improve the low throughput issue. For this maskless lithography, each field is split into stripes that are slightly overlapped with each other, and each stripe of layout patterns is written by a single electron beam (e-beam). If a pattern lies on an overlapped region (stitching region) of two neighboring stripes, it can be written by either of the two e-beams, which is known as smart boundary. To avoid layout features written by more than one e-beam and thus suffering from the overlay error between different beams, we propose a full-chip router utilizing smart boundary to minimize stitch-sensitive patterns. Experimental results show that the proposed algorithm can produce stitch-friendly routing solutions and thus greatly improve MEBL manufacturability and yield.
Video Smoke Detection For Surveillance Cameras Based On Deep Learning In Indoor Environment

An early fire detection in indoor environment is essential for people’s safety. During the past few years, many approaches using image processing and computer vision techniques were proposed. However, it is still a challenging task for application of video smoke detection in indoor environment, because the limitations of data for training and lack of efficient algorithms. The purpose of this paper is to present a new smoke detection method by using surveillance cameras. The proposed method is composed of two stages. In the first stage, motion regions between consecutive frames are located by using optical flow. In the second stage, a deep convolutional neural network is used to detect smoke in motion regions. Besides, to overcome the problem of lacking data, simulated smoke images are used to enrich the dataset. The proposed method is tested on our data set and real video sequences. Experiments show that the new method is successfully applied to various indoor smoke videos and significant for improving the accuracy of fire smoke detection. Source code and the dataset have been made available online.
A Study on Ionization Damage Effects of Anode-Short MOS-Controlled Thyristor

The mymargin metal–oxide–semiconductor mymargin (MOS)-controlled thyristor (MCT) has been characterized by MOS gating, high current rise rate, and high blocking capabilities. The anode-short MCT (AS-MCT) is distinguished from the conventional MCT by an anode-short structure, which forms an extracting path for the electron current at the gate ground and develops a normally-OFF characteristic. As a composite structure made of MOS and bipolar junction transistors, the AS-MCT is susceptible to ionization damage. The total ionization dose (TID) effects on the XND1 AS-MCT (breakdown voltage 1800 V grade) with a dose up to 9160 Gy(SiO2) are reported. The experimental results of transfer, forward conductive, and forward blocking characteristics are presented. A novel phenomenon, denoted as “self-trigger”, is identified for the AS-MCT following $\gamma $ -ray exposures, which can account for the significant increase in anode current in the AS-MCT. This article proposes the mechanism behind the characteristic degradation from the TID damage in the AS-MCT, from a device physics perspective.
Data Services Sales Design With Mixed Bundling Strategy: A Multidimensional Adverse Selection Approach

In the era of the Internet of Things (IoT), an immense amount of data is generated from numerous sensors and devices. Data as a service (DaaS) represents a new market whose time has come, and DaaS-based businesses are emerging quickly. Businesses across sectors begin seeing their data not only as fundamentally valuable but economically viable to distribute. Due to the exponential growth of the DaaS market, the current pricing models gradually become less suitable for the selling of data sets. A more sophisticated pricing strategy is needed to unlock the value of that data for the data vendor’s (DV’s) revenue growth and their customers’ benefits such as online service providers (SPs). In this article, we aim to maximize the DV’s profits by designing a mixed sales mechanism, which allows the DV to sell data sets separately or bundled. Particularly, we apply a multidimensional adverse selection model from contract theory to model the data set trading between DVs and SPs. The DV’s surplus maximization problem is solved in the single-product case first, then extended to the multiproduct case. Furthermore, the analysis of the solution of the pricing strategy in single-product and multiproduct cases is provided. Finally, the simulation results show that the proposed pricing model can improve the DV’s profits efficiently.
A Data-driven Methodology to Characterize the Dynamic Pattern of Human Motion Based on Plantar Pressure Measurements

Foot plantar pressure measurements provide a window into the analysis of gait and posture and offer valuable insights into movement quality that are used in the health, sports and commodities (e.g. shoes) sectors. In this paper, we propose a novel data-driven methodology to profile subjects performing a running exercise and to identify groups with unique gait characteristics. The methodology quantifies trial-base similarities using dynamic time warping and, thereby, group different trials based on their precise spatial and temporal dynamics. The characterization of each group revealed the existence of strikingly different pressure profiles. This methodology opens the possibility to develop accurate benchmarking and prediction algorithms on homogeneous groups of running profiles and enables the followup in time of the evolution of pressure patterns during movement execution.
Energy-based Safety in Series Elastic Actuation

This work presents the concept of energy-based safety for series-elastic actuation. Generic actuation passivity and safety is treated, defining several energy storage and power flow properties related to passivity. Safe behaviour is not guaranteed by passivity, but can be guaranteed by energy and power limits that adapt the nominal behaviour of an impedance controller. A discussion on power flows in series-elastic actuation is presented and an appropriate controller is developed. Experimental results validate the effectiveness of the energy-based safety in elastic actuation.
Application of Bio geography based Fractional order PID controller in DC motor drive speed control

This paper presents the mathematical model of chopper fed DC motor drive containing both speed and current control loops. The model is simulated with Integer Order PID controller and Fractional Order PID (FOPID) controller. The time domain characteristics of motor like peak overshoot, rise time and settling time are found to be improved in presence of FOPID controller. Optimization techniques namely Particle Swarm Optimization (PSO) and Bio Geography Based Optimization (BBO) are implemented on the proposed model for both the controllers and it has been observed that the speed control is more efficient when optimized with BBO compared to PSO with improvement in time domain specifications of DC motor. Simulation is carried out in MATLAB/SIMULINK environment.
Design and Validation of a Wearable Hand Exoskeleton System

This paper presents a novel wearable hand exoskeleton (WHE) system with light weight and adjustable character. The mechanical structure of the WHE is based on articulated linkages. The multi-mode signal acquisition module consists of two wearable sensors for detecting the electromyography (EMG) signals and the fingertip force signals. EMG based control is applied to the WHE system. The performance of the WHE system was tested through the free motion task and the grasping object task. Results showed that the WHE could assist hand function in the daily grasping tasks. In the future work, training experiments will be performed on patients.
A Novel Compact Dual-Mode Substrate Integrated Waveguide Cavity based Bandpass Filter for WLAN Applications

In this paper, a dual-mode bandpass filter based on substrate integrated waveguide (SIW) is presented. Initially, a SIW cavity is designed at higher frequency range. The stepped impedance resonator (SIR) slots are embedded as a slot on the top of the cavity structure. Four SIR slots are etched on the top surface of the cavity which helps to eliminate the higher resonating modes of the cavity. The pass band is centered at 4.7 GHz to pass the WLAN band. The resonating slots generated dual mode characteristics with excellent selectivity. The stop band characteristic of the proposed filter is also satisfactory for mitigation of higher order modes. The proposed structure is EM-simulated to verify the theoretical analysis.
Area Optimization of CMOS Full Adder Design Using 3T XOR

GDI (Gate Diffusion Input) is a new technique of low power digital circuit design is proposed. This technique allows minimization of area and power consumption of digital circuits. In this design XOR gate is designed using 3 transistors and CMOS full adder is designed based on two 3T XOR and one 2T Mux. Using 8 transistors the full adder is designed in this paper and voltage scaling also done by reducing supply voltage. In this proposed full adder, the power consumption 4. 604µW is achieved and the total area is 144µm2.
Ibex: A reconfigurable ground vehicle with adaptive terrain navigation capability

This paper presents a unique unmanned ground vehicle with a dynamic wheelbase and an adaptive thrust based friction optimization scheme that aids in the traversal of steep slopes and slippery surfaces. The vehicle is capable of adapting itself to the surface topography using an impedance-based stabilization module to minimize the mechanical oscillatory transients induced during its motion. A detailed analysis of its modules has been elucidated in this paper based on the vehicle parameters. The proposed methodologies have been integrated and tested on a customized prototype. Experimental validation and simulation for the proposed modules at various terrain conditions have been carried out to authenticate its performance.
Portable Omni-Directional Micro Deformation Monitoring Radar System

Ground-based synthetic aperture radar (GB-SAR) has become an efficient and accurate micro-variation monitoring technology, which has played an important role in the micro- deformation monitoring of open mines, dams, geological disasters and some other fields. However, GB-SAR is limited by linear orbits and cannot be used for Omnidirectional micro deformation monitoring. [1] A portable omnidirectional micro deformation monitoring radar system has been successfully developed which is capable observing large scenes featuring with large angle of view. In addition, total weight of the radar system less than 20 kg and it can be operated by single person in areas where large equipment cannot be deployed. The portable omni-directional micro deformation monitoring radar improve the inadequacies of ground-based linear orbit synthetic aperture radar.
A Flexible, Low-Power Platform for UAV-Based Data Collection From Remote Sensors

This article presents the design and characterisation of a new low-power hardware platform to integrate unmanned aerial vehicle and wireless sensor technologies. In combination, these technologies can overcome data collection and maintenance problems of in situ monitoring in remote and extreme environments. Precision localisation in support of maximum efficiency mid-range inductive power transfer when recharging devices and increased throughput between drone and device are needed for data intensive monitoring applications, and to balance proximity time for devices powered by supercapacitors that recharge in seconds. The platform described in this article incorporates ultra-wideband technology to achieve high-performance ranging and high data throughput. It enables the development of a new localisation system that is experimentally shown to improve accuracy by around two orders of magnitude to 10 cm with respect to GNSS and achieves almost 6 Mbps throughput in both lab and field conditions. These results are supported by extensive modelling and analysis. The platform is designed for application flexibility, and therefore includes a wide range of sensors and expansion possibilities, with source code for two applications made immediately available as part of a open source project to support research and development in this new area.