Design and implementation of a quadruped amphibious robot using duck feet

Roaming complexity in terrains and unexpected environments pose significant difficulties in robotic exploration of an area. In a broader sense, robots have to face two common tasks during exploration, namely, walking on the drylands and swimming through the water. This research aims to design and develop an amphibious robot, which incorporates a webbed duck feet design to walk on different terrains, swim in the water, and tackle obstructions on its way. The designed robot is compact, easy to use, and also has the abilities to work autonomously. Such a mechanism is implemented by designing a novel robotic webbed foot consisting of two hinged plates. Because of the design, the webbed feet are able to open and close with the help of water pressure. Klann linkages have been used to convert rotational motion to walking and swimming for the animal’s gait. Because of its amphibian nature, the designed robot can be used for exploring tight caves, closed spaces, and moving on uneven challenging terrains such as sand, mud, or water. It is envisaged that the proposed design will be appreciated in the industry to design amphibious robots in the near future

Wind Power Integration with Smart Grid and Storage System: Prospects and Limitations

Wind power generation is playing a pivotal role in adopting renewable energy sources in many countries. Over the past decades, we have seen steady growth in wind power generation throughout the world. This article aims to summarize the operation, conversion and integration of the wind power with conventional grid and local microgrids so that it can be a one-stop reference for early career researchers. The study is carried out primarily based on the horizontal axis wind turbine and the vertical axis wind turbine. Afterward, the types and methods of storing this electric power generated are discussed elaborately. On top of that, this paper summarizes the ways of connecting the wind farms with conventional grid and microgrid to portray a clear picture of existing technologies. Section-wise, the prospects and limitations are discussed and opportunities for future technologies are highlighted. It is envisaged that, this paper will help researchers and engineering professionals to grasp the fundamental concepts related to wind power generation concisely and effectively

Microwave brain imaging system to detect brain tumor using metamaterial loaded stacked antenna array

In this paper, proposes a microwave brain imaging system to detect brain tumors using a metamaterial (MTM) loaded three-dimensional (3D) stacked wideband antenna array. The antenna is comprised of metamaterial-loaded with three substrate layers, including two air gaps. One 1 x 4 MTM array element is used in the top layer and middle layer, and one 3 x 2 MTM array element is used in the bottom layer. The MTM array elements in layers are utilized to enhance the performance concerning antenna's efficiency, bandwidth, realized gain, radiation directionality in free space and near the head model. The antenna is fabricated on cost-effective Rogers RT5880 and RO4350B substrate, and the optimized dimension of the antenna is 50 x 40 x 8.66 mm3. The measured results show that the antenna has a fractional bandwidth of 79.20% (1.37-3.16 GHz), 93% radiation efficiency, 98% high fidelity factor, 6.67 dBi gain, and adequate field penetration in the head tissue with a maximum of 0.0018 W/kg specific absorption rate. In addition, a 3D realistic tissue-mimicking head phantom is fabricated and measured to verify the performance of the antenna. Later, a nine-antenna array-based microwave brain imaging (MBI) system is implemented and investigated by using phantom model. After that, the scattering parameters are collected, analyzed, and then processed by the Iteratively Corrected delay-multiply-and-sum algorithm to detect and reconstruct the brain tumor images. The imaging results demonstrated that the implemented MBI system can successfully detect the target benign and malignant tumors with their locations inside the brain. 2022, The Author(s).This research work is supported by the Universiti Kebangsaan Malaysia (UKM) research Grant DIP-2020-009.Scopu

A Review and Analysis of the Effects of Colors of Light On the Performance of Solar Photovoltaic Panels

Solar energy is quite simple as the energy can be obtained from the sun directly. Solar energy is categorized as one of the best renewable energy since it does not emit carbon dioxide and because of unlimited supports from the sun. In this paper, three main sections of solar technologies like photovoltaic solar panel, concentrating solar power, heating and cooling system which is available present days have been investigated.  In the second part of this research, an experiment has been carried out to evaluate the effects of colors of light on the performance of solar photovoltaic panels. Different colors of light having different wavelength, resulting in different frequency and hence different energy. In general, the solar spectrum influences the performance of the solar panels. The results show that the solar panels are influenced more by the red color of light. This report will start off by detailing the three main solar technologies, followed by the testing on the colors of light with the solar panels

A Review and Analysis of the Effects of Colors of Light On the Performance of Solar Photovoltaic Panels

Peer reviewedPublisher PD

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.COVID19 Emergency Response Grant #QUERG-CENG-2020-1 from Qatar University, Doha, Qatar provided the support for the work and the claims made herein are solely the responsibility of the authors

Genomic landscape of prominent XDR Acinetobacter clonal complexes from Dhaka, Bangladesh

Background: Acinetobacter calcoaceticus-A. baumannii (ACB) complex pathogens are known for their prevalence in nosocomial infections and extensive antimicrobial resistance (AMR) capabilities. While genomic studies worldwide have elucidated the genetic context of antibiotic resistance in major international clones (ICs) of clinical Acinetobacter spp., not much information is available from Bangladesh. In this study, we analysed the AMR profiles of 63 ACB complex strains collected from Dhaka, Bangladesh. Following this, we generated draft genomes of 15 of these strains to understand the prevalence and genomic environments of AMR, virulence and mobilization associated genes in different Acinetobacter clones. Results: Around 84% (n = 53) of the strains were extensively drug resistant (XDR) with two showing pan-drug resistance. Draft genomes generated for 15 strains confirmed 14 to be A. baumannii while one was A. nosocomialis. Most A. baumannii genomes fell under three clonal complexes (CCs): the globally dominant CC1 and CC2, and CC10; one strain had a novel sequence type (ST). AMR phenotype-genotype agreement was observed and the genomes contained various beta-lactamase genes including blaOXA-23 (n = 12), blaOXA-66 (n = 6), and blaNDM-1 (n = 3). All genomes displayed roughly similar virulomes, however some virulence genes such as the Acinetobactin bauA and the type IV pilus gene pilA displayed high genetic variability. CC2 strains carried highest levels of plasmidic gene content and possessed conjugative elements carrying AMR genes, virulence factors and insertion sequences. Conclusion: This study presents the first comparative genomic analysis of XDR clinical Acinetobacter spp. from Bangladesh. It highlights the prevalence of different classes of beta-lactamases, mobilome-derived heterogeneity in genetic architecture and virulence gene variability in prominent Acinetobacter clonal complexes in the country. The findings of this study would be valuable in understanding the genomic epidemiology of A. baumannii clones and their association with closely related pathogenic species like A. nosocomialis in Bangladesh. 2022, The Author(s).This work was funded by North South University Conference Travel and Research Grants (NSU CTRG) Committee under the grant number: NSU-RP-18-042.Scopu

Modeling and simulation of electromagnetic damper to improve performance of a vehicle during cornering

In order to achieve the desired ride comfort, road handling performance, many researches has been conducted. A new modified skyhook control strategy with adaptive gain that dictates the vehicle’s semi-active suspension system is presented. This thesis also describes the development of a new analytical full vehicle model with nine degrees of freedom, which uses the new modified skyhook strategy incorporating road bank angle to control the full vehicle. This research has indicated the potential of the SKDT suspension system in improving cornering performances of the vehicle and paves the way for future work on vehicle’s integrated system for chassis control

Humanoid Robot as a Teacher's Assistant: Helping Children with Autism to Learn Social and Academic Skills

Autism Spectrum Disorder (ASD) is becoming a growing concern worldwide. Parents are often not aware of the different nature of children with ASD and attempt to treat him/her the same way as other children. However, that causes more and more isolation of such children from the social interactions around them, resulting in more secluded and people-phobic behaviors. Nevertheless, similar to other children, children with ASD also like to play with toys. This observation has led to the use of toys in a way that mere playful activities could become sources of learning and skill-building, somewhat serving or assisting in the role of a human teacher. Robots have been observed to be fascinating for all children and compensating for a human companion to a certain extent. In this paper, a short study has been presented involving a humanoid robot programmed for a number of teaching and therapeutic behaviors, such as exercises, singing, explaining, and playing with children. Tests were performed on a small group of 15 children with ASD (ages 7�11) using these activities at a local school for children with special needs for a number of weeks. The objective of the study was to quantify the improvement in a number of behavior and learning parameters when children performed the activities with NAO robot present with the teacher, as opposed to the same type of activities performed by the teacher alone. The performance improvement was quantified in terms of the NAO robot activity as independent variable, and following dependent behavioral variables observed from the responses of children: (a) number of trials, (b) activity response time, (c) response type, and (d) behavior retention. Quantified findings from these tests are reported in this paper against average performance values (based on teachers and psychologists' evaluation). The results of the study have been found to be very encouraging which demonstrates the capability of robotic toys to improve the learning process for children with ASD. The results of this study also encourage the low-cost development and usage of such robotic toy systems for teaching and therapeutic applications that help such children to become better members of society. - 2019, Springer Nature B.V.We would like to thank the KINDI Research Center and Shafallah (Center for Children with Special Needs) that provided the platform to gain practical experience and to develop innovative solutions to improve the future of our country. We would also like to acknowledge the support of the Step by Step School Qatar, the Shafallah Institute Qatar, and the Al-Awsaj Academy Qatar. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Scopu