Rotten-fruit-sorting robotic arm: (Design of low complexity cnn for embedded system)

Industrial Automation has revolutionized the processing industry due to its high accuracy, the time it saves, and its ability to work without tiring. Being the most fundamental part of automation machines, robotic arms are being used as a fundamental component in many types of domestic as well as commercial automation units. In this paper, we proposed a low-complexity convolutional neural network (CNN) model and successfully deployed it on a locally generated robotic arm with the help of a Raspberry Pi 4 module. The designed robotic arm can detect, locate, and classify (based on fresh or rotten) between three species of Mangos (Ataulfo, Alphonso, and Keitt), on a conveyor belt. We generated a dataset of about 6000 images and trained a three-convolutional-layer-based CNN. Training and testing of the network were carried out with MatLab, and the weighted network was deployed to an embedded environment (Raspberry Pi 4 module) for real-time classification. We reported a classification accuracy of 98.08% in the detection of fresh mangos and 95.75% in the detection of rotten mangos. For the designed robotic art, the achieved angle accuracy was 93.94% with a minor error of only 2°. The proposed model can be deployed in many food- or object-sorting industries as an edge computing application of deep learning

Effect of Sustainable HRM Practices on Job Performance: Mediating Role of Employee Retention

The primary objective of this research was to investigate the mediating role of employee retention in the relationship between employee empowerment, high-performance work systems, and job performance in the health sector. A cross-sectional study design was used, and data was collected from the health sector operating in South Punjab, Pakistan. Statistical random sampling method was employed, to a sample of 550 employees the questionnaires was randomly distributed for data collection through a self-administered plan. SPSS software was used and Partial Least Squares Structural Equation Modelling (PLS-SEM) technique was employed for testing of research hypotheses. Current study based its investigation into theory of performance. The findings showed that the relationship between employee empowerment and job performance was insignificant, but the relationship between high-performance work systems and job performance was significant. Additionally, employee retention has a significant mediating effect on the relationship between High Performance Work System, and job performance. The results provide insight for the government, practitioners, and policymakers to better understand the impact of various sustainable human resource management practices on job performance.

A Novel Dual Ultrawideband CPW-Fed Printed Antenna for Internet of Things (IoT) Applications

This paper presents a dual-band coplanar waveguide (CPW) fed printed antenna with rectangular shape design blocks having ultrawideband characteristics, proposed and implemented on an FR4 substrate. The size of the proposed antenna is just 25 mm × 35 mm. A novel rounded corners technique is used to enhance not only the impedance bandwidth but also the gain of the antenna. The proposed antenna design covers two ultrawide bands which include 1.1–2.7 GHz and 3.15–3.65 GHz, thus covering 2.4 GHz Bluetooth/Wi-Fi band and most of the bands of 3G, 4G, and a future expected 5G band, that is, 3.4–3.6 GHz. Being a very low-profile antenna makes it very suitable for the future 5G Internet of Things (IoT) portable applications. A step-by-step design process is carried out to obtain an optimized design for good impedance matching in the two bands. The current densities and the reflection coefficients at different stages of the design process are plotted and discussed to get a good insight into the final proposed antenna design. This antenna exhibits stable radiation patterns on both planes, having low cross polarization and low back lobes with a maximum gain of 8.9 dB. The measurements are found to be in good accordance with the simulated results

Genome-Wide Diversity of MADS-Box Genes in Bread Wheat is Associated with its Rapid Global Adaptability

MADS-box gene family members play multifarious roles in regulating the growth and development of crop plants and hold enormous promise for bolstering grain yield potential under changing global environments. Bread wheat (Triticum aestivum L.) is a key stable food crop around the globe. Until now, the available information concerning MADS-box genes in the wheat genome has been insufficient. Here, a comprehensive genome-wide analysis identified 300 high confidence MADS-box genes from the publicly available reference genome of wheat. Comparative phylogenetic analyses with Arabidopsis and rice MADS-box genes classified the wheat genes into 16 distinct subfamilies. Gene duplications were mainly identified in subfamilies containing unbalanced homeologs, pointing towards a potential mechanism for gene family expansion. Moreover, a more rapid evolution was inferred for M-type genes, as compared with MIKC-type genes, indicating their significance in understanding the evolutionary history of the wheat genome. We speculate that subfamily-specific distal telomeric duplications in unbalanced homeologs facilitate the rapid adaptation of wheat to changing environments. Furthermore, our in-silico expression data strongly proposed MADS-box genes as active guardians of plants against pathogen insurgency and harsh environmental conditions. In conclusion, we provide an entire complement of MADS-box genes identified in the wheat genome that could accelerate functional genomics efforts and possibly facilitate bridging gaps between genotype-to-phenotype relationships through fine-tuning of agronomically important traits

ZnO Nano-Flowers Assembled on Carbon Fiber Textile for High-Performance Supercapacitor’s Electrode

Herein, a crystalline nano-flowers structured zinc oxide (ZnO) was directly grown on carbon fiber textile (CFT) substrate via a simple hydrothermal process and fabricated with a binder-free electrode (denoted as ZnO@CFT) for supercapacitor (SC) utilization. The ZnO@CFT electrode revealed a 201 F·g−1 specific capacitance at 1 A·g−1 with admirable stability of >90% maintained after 3000 cycles at 10 A·g−1. These impressive findings are responsible for the exceedingly open channels for well-organized and efficient diffusion of effective electrolytic conduction via ZnO and CFT. Consequently, accurate and consistent structural and morphological manufacturing engineering is well regarded when increasing electrode materials’ effective surface area and intrinsic electrical conduction capability. The crystalline structure of ZnO nano-flowers could pave the way for low-cost supercapacitors

Autonomous Transportation in Emergency Healthcare Services, Challenges and Future Work

In pandemics like Covid-19, the use of autonomy and machine learning technologies are of high importance. Internet of things (IoT) enabled autonomous transportation system (ATS) envisions a fundamental change in the traditional transportation system. It aims to provide intelligent and automated transport of passengers, goods, and services with minimal human interference. While ATS targets broad spectrum of transportation (Cars, trains, planes etc.), the focus of this paper will be limited to the use of vehicles and road infrastructure to support healthcare and related services. In this paper, we offer an IoT based ATS framework for emergency healthcare services using Autonomous Vehicles (AVs) and deep reinforcement learning (DRL). The DRL enables the framework to identify emergency situation smartly and helps AVs to take faster decision in providing emergency health aid and transportation services to patients. Using ATS and DRL for healthcare mobility services will also contribute towards minimizing energy consumption and environmental pollution. This paper also discusses current challenges and future works in using ATS for healthcare services

A Jug-Shaped CPW-Fed Ultra-Wideband Printed Monopole Antenna for Wireless Communications Networks

A type of telecommunication technology called an ultra-wideband (UWB) is used to provide a typical solution for short-range wireless communication due to large bandwidth, low power consumption in transmission and reception. Printed monopole antennas are considered as preferred platform for implementing this technology because of its alluring characteristics like light weight, low cost, ease of fabrication, integration capability with other systems, etc. Therefore, a compact size an ultra-wideband (UWB) printed monopole antenna with improved gain and efficiency is presented in this article. Computer simulation technology microwave studio (CSTMWS) software is used to build and analyze the proposed antenna design technique. This broadband printed monopole antenna contains a jug-shaped radiator fed by a coplanar waveguide (CPW) technique. The designed UWB antenna is fabricated on a low-cost FR-4 substrate with relative permittivity of 4.3, loss tangent of 0.025, and a standard height of 1.6 mm, sized at 25 mm × 22 mm × 1.6 mm suitable for wireless communication system. The designed UWB antenna works with maximum gain (peak gain of 4.1 dB) across the whole UWB spectrum 3–11 GHz. The results are simulated, measured and debated in detail. Different parametric studies based on numerical simulations is involved to arrive to the optimal design through monitoring the effects of adding cuts on the performance of the proposed antennas. Therefore, these parametric studies are optimized to achieve maximum antenna bandwidth with relatively best gain. The proposed patch antenna shape is like a Jug with handle that offers greater bandwidth, good gain, higher efficiency, and compact size

Productivity Enhancement of Solar Water Desalination Unit Using a Solar Electric Water Heater

The biggest problem faced by the world these days is pure drinkable water, and in a few years pure drinkable water will not be easily available, as it is becoming brackish and saline due to pollution. By using solar energy, a solar still can produce pure water which can be used for drinking, cooking, and also for industrial purposes. In this research, a solar still based on clean technology using solar energy to drive the system is used. It can be operated easily and with an approximately negligible maintenance cost. A pyramid solar water desalination unit with modification of the solar electric water heater (used to increase water temperature) is developed to increase the water yield per day. A theoretical model of the solar still unit with and without an electric water heater is developed and performance is compared. Based on this theoretical design, fabrication is carried out and experiments are performed to predict the overall output. It is observed that the output distilled water has a TDS (total dissolved salts) value much lower than the TDS of groundwater. Additionally, the average output of a solar water desalination unit with an electric water heater is found to be enhanced compared with the unit without an electric water heater

Adaptive Neural Network Q-Learning-Based Full Recurrent Adaptive NeuroFuzzy Nonlinear Control Paradigms for Bidirectional-Interlinking Converter in a Grid-Connected Hybrid AC-DC Microgrid

The stability of a hybrid AC-DC microgrid depends mainly upon the bidirectional interlinking converter (BIC), which is responsible for power transfer, power balance, voltage solidity, frequency and transients sanity. The varying generation from renewable resources, fluctuating loads, and bidirectional power flow from the utility grid, charging station, super-capacitor, and batteries produce various stability issues on hybrid microgrids, like net active-reactive power flow on the AC-bus, frequency oscillations, total harmonic distortion (THD), and voltage variations. Therefore, the control of BIC between AC and DC buses in grid-connected hybrid microgrid power systems is of great importance for the quality/smooth operation of power flow, power sharing and stability of the whole power system. In literature, various control schemes are suggested, like conventional droop control, communication-based control, model predictive control, etc., each addressing different stability issues of hybrid AC-DC microgrids. However, model dependence, single-point-failure (SPF), communication vulnerability, complex computations, and complicated multilayer structures motivated the authors to develop online adaptive neural network (NN) Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms for BIC in a grid-connected hybrid AC-DC microgrid. The proposed strategies successfully ensure the following: (i) frequency stabilization, (ii) THD reduction, (iii) voltage normalization and (iv) negligible net active-reactive power flow on the AC-bus. Three novel adaptive NN Q-learning-based full recurrent adaptive neurofuzzy nonlinear control paradigms are proposed for PQ-control of BIC in a grid-connected hybrid AC-DC microgrid. The control schemes are based on NN Q-learning and full recurrent adaptive neurofuzzy identifiers. Hybrid adaptive full recurrent Legendre wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, Hybrid adaptive full recurrent Mexican hat wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control, and Hybrid adaptive full recurrent Morlet wavelet-based Neural Network Q-learning-based full recurrent adaptive NeuroFuzzy control are modeled and tested for the control of BIC. The controllers differ from each other, based on variants used in the antecedent part (Gaussian membership function and B-Spline membership function), and consequent part (Legendre wavelet, Mexican hat wavelet, and Morlet wavelet) of the full recurrent adaptive neurofuzzy identifiers. The performance of the proposed control schemes was validated for various quality and stability parameters, using a simulation testbench in MATLAB/Simulink. The simulation results were bench-marked against an aPID controller, and each proposed control scheme, for a simulation time of a complete solar day

Design and Development of AI-Based Tourist Facilitator and Information Agent

Due to the rapid increase in the demand for information that supports tourists after, before, and during the trip, many tour systems are available. However, these systems are not able to successfully replace a human facilitator that is expensive to hire. The primary key qualities of a human tourist guide are his/her knowledge, communication skills, and interpretation of destination attractions. Traditional tourist facilitator systems are lacking in these qualities. The main idea of the research is to design an agent to guide tourists, provide them accurate information about visitable places, without having any bound for a specific region and it will have human-like communication skills along with the point of interest knowledge, which depends on its internal knowledge base as well as its online searching techniques