Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Visual Inspection for Liquid Pouches Using Deep Learning

Visual inspection for liquid pouches using deep learning is under discussion. Pictures of the produced liquid pouches were taken using soft X-ray transmission to generate deep learning datasets. Our deep learning system was designed with an autoencoder and a neural network for two-class classification. Our autoencoder was trained with defect-free datasets and performed for an image pre-processing to extract abnormal defect features. Our neural network for two-class classification was trained with pre-processed defect-free and defective datasets. Our proposed method detected defective products using our test datasets at running time.
Experimenting With Digital Signatures Over a DNP3 Protocol in a Multitenant Cloud-Based SCADA Architecture

The ideas presented in this paper are summarized as follows. The first idea entails improving the security of supervisory control and data acquisition (SCADA) architectures by means of asymmetric cryptography and digital signatures and measuring the performance overhead. This allows achieving some obvious subsequent goals such as data-origin authentication, and the traceability and implicit non-repudiation of commands given to intelligent field and direct control equipment. The possibility to include digital signatures with a minimum impact on a standard and a reliable data communication protocol, such as Distributed Network Protocol version 3 (DNP3), also known to have a mature, industrially validated, open-source implementation, has been tested and the results are presented. A second idea concerns designing and developing a multitenant cloud-based architecture for a SCADA environment. This hypothesis focuses on certain SCADA operators that manage multiple industrial control systems (ICS) and intend to consolidate process data in a centralized manner.
Smart Glasses Application System for Visually Impaired People Based on Deep Learning

The recent development of deep learning has promoted object detection to make rapid progress. In recent years, smart wearable technology is rapidly becoming part of everyday life, including watches, glasses and many other wearable items have been enriched with technology. In this study, we propose a smart glasses application system for visually impaired people based on deep learning. The system can use voice response to visually impaired people about the objects in front of them by uploading the photos to our backend object detection system through the camera function of smart glasses, and then download the text descriptions of the result and then use the text-to-speech function. According to the experimental results, the proposed system takes 3.788 seconds from uploading photos to making voice results, and the success rate of object detection is 96.3%. We hope that this application system can assistant visually impaired people to understand the surrounding environment and interact more closely with the people around them to improve the quality of life of them.
Coupler Integrated Microstrip Patch Linear Phased Array for Self-Calibration

In this letter, a coupler integrated microstrip patch linear phased array is proposed as a new self-calibration system. The proposed coupler placed along the edge of the microstrip patch antenna is weakly coupled to the antenna array. This allows self-calibration without affecting the radiation performance of the antenna array. A 1 × 4 coupler integrated patch linear phased array and a beamformer operating at 5.8 GHz were implemented to verify the new self-calibration system. The measurement result using the self-calibration is very close to the result with a vector network analyzer (VNA). The coupler integrated patch linear phased array proposed as a new self-calibration system has 0.22 dB root-mean-square error (RMSE) magnitude and 1.2$^{\circ }$ RMSE phase. The calibration method using a proposed antenna array is expected to on-site calibration.
Robust Path Integral Control on Stochastic Differential Games

This paper develops a robust path integral (RPI) model predictive control using the Monte Carlo (MC) sampling to address the optimal control (OC) problem for the stochastic differential game (SDG). The two-player zero-sum differential game has been extensively investigated, mostly as its outcome indicates the $H_{\infty}$ optimality. The proposed path integral (PI) control framework provides an analytically sound method for building an algorithm of optimal control for this game based on stochastic trajectory sampling. This is achieved by using Feynman-Kac (F-K) lemma which transforms the value function of stochastic optimal control (SOC) problem into an expectation over all probable trajectories. This transformation makes it possible to solve SOC problems through MC sampling of stochastic processes. Finally, the RPI model predictive control using MC sampling is efficiently implemented for an inverted pendulum system. The RPI control has achieved good performance for changes in inverted pendulum weight and friction when the complete nonlinear swing-up is concerned while such environmental adjustments are not dealt with in a regular PI control.
Design of Inter and Intra Plane Communication Protocol for Software-Defined Wireless Sensor Network

Software Defined Networking (SDN) brings about innovation, simplicity in network management and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependency on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A Wireless Sensor Network (WSN) is a great platform for Low-Rate Wireless Personal Area Networks (LR-WPAN) with little resources and short communication ranges. However, as the scale of WSN expands it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. In this research paper, a protocol is designed in order to communicate inter and intra plane of data and control besides Openflow and ForCes protocol. Moreover, performance of the proposed protocol is compared with the existing protocols in terms of residual energy, delay and lifetime of the sensor nodes.
Visual Servoing System for Local Robot Control in a Flexible Assembly Line

The paper presents a solution for visual servoing problem that is implemented on a robotic arm from an industrial flexible assembly line. This line respects the Industry 4.0 paradigm, thus having a modular structure consisting of five stations. Each station includes a Programmable Logic Controller equipment for local control and communication modules for connection with other stations. The paper is focused on the design and implementation of the visual servoing system which ensures the line flexibility. The experimental results validate the system functionality and indicate significant performances.
Effective anti-noise morphological feature point detector for UAVs

Unmanned Systems, especially Unmanned Air Vehicle (UAV) development grows rapidly and presents a predominant research field. Unmanned systems have several military and civilian applications. UAVs flight paths in the national airspace should be free from obstacles that might threaten mission success. Computer vision introduces different facilitates for autonomous obstacle avoidance to ensure UAVs flight safety. In this paper, we present a proposed feature point detector to sense static obstacles of the surrounding environment structures for low flight altitude and during UAVs take-off phase. The proposed detector could be classified as a rotation-invariant detector oriented by an integrated method between a mathematical morphology method and the well-known image second-order derivative of the normal vector component based on a homogeneity examination test. For performance assessment, the proposed detector results are compared with several well-known feature point detectors based on some quantitative evaluation indexes. From experimental results, the proposed detector is capable of detecting most of static feature points of cluttered environment structures and resisting image distortion with low computational power.
Study of UV Skin Image Generation from an RGB Color Image with Deep Learning for Beauty Industries

Skin visualization for beauty industries using deep learning is discussed. UV skin images were taken by a medical dermoscopy digital camera, and we created datasets for training. Neural networks called U-net and convolutional autoencoder were constructed and trained with our datasets. Once our neural network was trained, skin images that approximated the UV image could be generated without the medical camera. The performance of our U-net and convolutional autoencoder is discussed.
Design of a Microstrip Slotted Array Antenna for Bandwidth Enhancement

Microstrip antenna research still in progress to mitigate the gain and bandwidth limitations. Day-to-day the demand for data communication is increasing where bandwidth improvement is a major fact. In this piece of work the approach with slotted array antenna is designed for improving the bandwidth. The rectangular patch is etched in stepped T-structure with the rectangular slot which is responsible to maintain the bandwidth and gain. Further the defective ground structure is utilised for the proposed design. It is found by simulation and shown in result section in terms of return loss of -22dB and -34dB at 5.75GHz and 12 GHz with a bandwidth of 0.6GHz and 3GHz
A Comprehensive Study on Facial Expression Recognition Techniques using Convolutional Neural Network

This Research article studies the Facial Expression Recognition (FER) research area and the evolvement of the techniques employed for FER using the Convolution Neural Network (CNN). Expressions of a human being are a significant aspect of predicting emotions, intentions, social relationships, and health conditions as well. Automated facial Expression Recognition systems have enormous applications like data-driven animation, interactive games, social robotics, neuromarketing, medical treatment, and many other human-interaction systems. In the past few decades, several researchers have come up with many methods to improve accuracy, reduce computational resources, and reduce overfitting problems. To bring significant comparisons between the techniques proposed by researchers, the research work of the last four years is taken into consideration. Based on the survey and analysis, the techniques have been summarized in the table with the issues which require attention while using CNNs for Facial Expression Recognition. This survey study focuses on the challenges faced while using CNNs for Facial Expression Recognition and the solutions to those issues. We observed that Li et al. achieved 98.2% accuracy using CK+ dataset.
Design and Automatic Fabrication of Novel Bio-Inspired Soft Smart Robotic Hands

Soft material robots are developing rapidly benefited from their inherent flexibility, adaptability and safety compared to rigid-bodied robots. However, most soft robots are unable to offer high force/strength due to the low rigidity of soft materials that they are composed of. Absence of position feedback is another problem for soft robots. In this research, we aim to address these two challenges in a novel designed soft smart robotic hand. The design of this soft hand is also delicately considered to make it 3D printable, which shortens the design cycle and reduces the fabrication time. This hand consists of five fingers and a palm, all of which can be actuated independently. The finger is designed with two chambers: one air-tight active chamber which can be actuated by compressed air, one passive chamber filled with loosely arranged particles. During bending actuation, particles inside the passive chamber are squeezed by pressurized air, which causes passive jamming. As a result, the stiffness of the finger is strengthened during bending, which endows the hand with larger force output and load-holding capability. Furthermore, position feedback modules made of conductive elastomers are integrated and co-printed with the finger during fabrication. In this research, a hand prototype is manufactured and several experiments regarding to its characteristics and performance are conducted for evaluation. From experimental results, the soft hand achieves maximum holding weight of 1.452kg with particles and 0.8425kg without particles at same actuation pressure of 450kPa.
A Comparative Study of Machine Learning Algorithms in Demand Power Prediction

The usage of electrical energy is evident. Therefore the generation of electrical power is not so easy. Consumption of electrical energy can be classified into two categories viz. residential and commercial. In the case of the residential sector, heavy fluctuation may occur according to the energy usage pattern concerning time. This unusual variation in power patterns may give stress to generating units. Therefore, if we can predict the demand in early stages we could take remedies like increasing/decreasing power generation capacity, inheriting data power and reducing its usage, giving less stress and also will not transfer stress to other appliances. Thus the prediction of electricity consumption is required. During peak and off-peak hours mainly our aim is for approximately predicting the required demand with the help of demand response to match the available energy resources. As a result of this load at the power station would be balanced. From the demand prediction, we try to swap the resources from peak hours (which consume high energy) to off-peak hour (which consume minimal energy).
Behaviour of African and European Subspecies of Apis Mellifera Toward the Small Hive Beetle, Aethina Tumida

SHB. We previously demonstrated that adult SHB will feed on European honey bee eggs in a laboratory setting, even in the presence of excess honey and pollen (Elzen et al., 1999). Five 0.473-litre jars were established with five adult beetles, a known number of Cape honey bee eggs in uncapped comb, and excess honey and pollen. Five control jars consisted of a known number of eggs in comb and excess honey and pollen (no beetles added). All 10 jars were held 24 h at 32°C and the number of eggs remaining were counted. Results showed overall, within 24 h the SHB ate 94% of the Cape honey bee eggs.
Comparison of Power Factor Correction Controllers for CRM Boost Converters

This paper presents modified critical conduction mode (CRM) boost converter for low and high-power applications. The main aim is to improve the power factor (PF) reduce the total harmonic distortion (THD) of the CRM boost converter. The converter consists of EMI filter, bridge rectifier and a boost stage. The power factor correction (PFC) in ac-dc power conversions can improve PF and reduce THD. The CRM mode for this converter makes switches turn-on at zero current crossing and no reverse recovery losses for diodes. In this configuration the PI controller improves the PF and lowers THD value. The boost converter with variable on time (VOT) controller makes the circuit bulky and losses are more. In the proposed converter the PF and efficiency can be improved. Also, the controller reduces the size of the circuit and lower the THD. Hence CRM boost converter PFC controller based on PI controller is used to improve the above-mentioned parameters. Using MATLAB Simulink software, the existing and the modified controller is verified, and results proves the effectiveness of the controller for the CRM boost converter.
Enhanced Charge carrier Dynamics and Defects Passivation in Hybrid Perovskite Solar Cell Using a Novel Organic Halide Salt

ENHANCED CHARGE CARRIER DYNAMICS AND DEFECTS PASSIVATION IN HYBRID PEROVSKITE SOLAR CELL USING A NOVEL ORGANIC HALIDE SALT MD ASHIQUR RAHMAN LASKAR 2020 In the last few years, active research works on hybrid perovskite solar cells (HPSCs) have been going on throughout the world as a part of clean energy revolution. HPSCs have already proved their potentiality as future solar technology through their promising photovoltaic performances and ability for low temperature based solution processing. Nevertheless, still there are remaining challenges of better charge carrier dynamics and defects minimization in HPSCs. Overcoming these challenges are very important to achieve high power conversion efficiency (PCE) practically. Aiming this, a novel organic halide salt called Phenylhydrazinium Iodide (PHAI) is introduced as additive with CH3NH3PbI3 perovskite precursor in this study. Incorporation of a very small amount of PHAI causes significant enhancement of charge carrier dynamics in HPSCs resulting ~15% increase of device’s short circuit current density. In addition, PHAI treatment helps to reduce perovskite grain boundary defects more than 0.5 times than the pristine CH3NH3PbI3 HPSCs. This reduction in defects contributes to minimize the non-radiative recombination of photogenerated charge carriers. Consequently, overall 20% improvement in PCE is achieved through PHAI additive treatment providing ~18% efficient CH3NH3PbI3 inverted hybrid perovskite solar cells. Besides, PHAI treated HPSCs exhibit superior ambient stability than the pristine devices in unencapsulated condition.
Stress Detection with Machine Learning and Deep Learning using Multimodal Physiological Data

Stress is a common part of everyday life that most people have to deal with on various occasions. However, having long-term stress, or a high degree of stress, will hinder our safety and disrupt our normal lives. Detecting mental stress earlier can prevent many health problems associated with stress. When a person gets stressed, there are notable shifts in various bio-signals like thermal, electrical, impedance, acoustic, optical, etc., by using such bio-signals stress levels can be identified. This paper proposes different machine learning and deep learning techniques for stress detection on individuals using multimodal dataset recorded from wearable physiological and motion sensors, which can prevent a person from various stress-related health problems. Data of sensor modalities like three-axis acceleration (ACC), electrocardiogram (ECG), blood volume pulse (BVP), body temperature (TEMP), respiration (RESP), electromyogram (EMG) and electrodermal activity (EDA) are for three physiological conditions - amusement, neutral and stress states, are taken from WESAD dataset. The accuracies for three-class (amusement vs. baseline vs. stress) and binary (stress vs. non-stress) classifications were evaluated and compared by using machine learning techniques like K-Nearest Neighbour, Linear Discriminant Analysis, Random Forest, Decision Tree, AdaBoost and Kernel Support Vector Machine. Besides, simple feed forward deep learning artificial neural network is introduced for these three-class and binary classifications. During the study, by using machine learning techniques, accuracies of up to 81.65% and 93.20% are achieved for three-class and binary classification problems respectively, and by using deep learning, the achieved accuracy is up to 84.32% and 95.21% respectively.
Cancer Prevention and Screening for Older Adults: Part 1. Lung, Colorectal, Bladder, and Kidney Cancer

The incidence of most cancers increases with age. Cancer is the second most common cause of death in older adults after cardiovascular disease. Many common cancers in older adults can be prevented from occurring or can be identified at an early stage and treated effectively. The prevention and identification of cancer in its early stages, in an attempt to reduce discomfort and disability associated with advanced cancer and cancer treatment, is also a priority. Overscreening for cancer in older adults can lead to unnecessary diagnostic testing and unnecessary treatment. Both older adults and their healthcare providers need guidance on the appropriate use of cancer prevention and screening interventions. This first of a two‐part review addresses special considerations regarding cancer prevention for adults aged 65 and older. Screening decisions and the impact of limited life expectancy and an older adult's ability to tolerate cancer treatment are also addressed. Guidance is provided regarding the prevention and early identification of lung, colorectal, bladder, and kidney cancer in older adults. The prevention of breast, prostate, and female urogenital cancers are addressed in Part 2. J Am Geriatr Soc 68:2399–2406, 2020.
Field emission properties of molecular beam epitaxy grown AlGaN nanowires

We investigate the field emission properties of AlGaN nanowires, grown on GaN nanowire templates on Si(111) substrate by means of radio-frequency plasma-assisted molecular beam epitaxy technique. The field emission measurement setup was realized inside the vacuum chamber of a scanning electron microscope equipped with piezo-driven metallic probe tips working as electrodes. Current-voltage characteristics were measured by precisely positioning a tip anode above the emitting AlGaN surface, with cathode-anode separation distance varied in the range 200–1500 nm. Experimental data are discussed according to the Fowler-Nordheim theory to confirm the field emission nature of the measured current and to estimate key figures of merit, such as field enhancement factor and turn-on field. We show that field emission curves become stable after electrical conditioning, that is after the burnout of individual protruding emitters. We demonstrate field enhancement factor as high as 250 for cathode-anode separation distance of 200 nm, and that it decreases for increasing distances. Similarly, minimum turn-on field of 19 V/µm is reported for a separation distance of 800 nm. Finally, we discuss the effect of a Schottky diode in a back-to-back configuration with the field emission device, that limits the exponential growth field emitted current.
Detection of Data Leaks from Android Applications

With the advent of technology the use of mobile phones has been increasing since its existence. Thus, the number of mobile applications that are launched daily to leverage its benefits has experienced massive growth. But the problem which has garnered the attention in recent times is the safety of these applications. It needs to be validated that all applications do not leak private information before deploying them onto the applications markets and for this, a data leak detection tool is required. This paper proposes a novel solution for detection of data leaks using dynamic and static analysis. A dataset of android applications mapped to their data leaks, permissions, activities, library classes and methods are built in this approach. During dynamic analysis, the solution uses runtime permissions and logcat information to detect the data leak type and calculate their probability of occurrence using a k-nearest neighbours similar algorithm. This approach further categorises the leaks on the basis of risk level associated with that application.