An Introduction to MultiAgent Systems.

The fifth edition of "Numerical Methods for Engineers" continues its tradition of excellence. Instructors love this text because it is a comprehensive text that is easy to teach from. Students love it because it is written for them--with great pedagogy and clear explanations and examples throughout. The text features a broad array of applications, including all engineering disciplines. The revision retains the successful pedagogy of the prior editions. Chapra and Canale's unique approach opens each part of the text with sections called Motivation, Mathematical Background, and Orientation, preparing the student for what is to come in a motivating and engaging manner. Each part closes with an Epilogue containing sections called Trade-Offs, Important Relationships and Formulas, and Advanced Methods and Additional References. Much more than a summary, the Epilogue deepens understanding of what has been learned and provides a peek into more advanced methods. Approximately 80% of the end-of-chapter problems are revised or new to this edition. The expanded breadth of engineering disciplines covered is especially evident in the problems, which now cover such areas as biotechnology and biomedical engineering. Users will find use of software packages, specifically MATLAB and Excel with VBA. This includes material on developing MATLAB m-files and VBA macros.

Numerical methods for engineers

Numerical methods for Engineers and Scientists

Currently, there is an urgent need for efficient tools to assess the diagnosis of COVID-19 patients. In this paper, we present feasible solutions for detecting and labeling infected tissues on CT lung images of such patients. Two structurally-different deep learning techniques, SegNet and U-NET, are investigated for semantically segmenting infected tissue regions in CT lung images. We propose to use two known deep learning networks, SegNet and U-NET, for image tissue classification. SegNet is characterized as a scene segmentation network and U-NET as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly. The results show the superior ability of SegNet in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET shows better results as a multi-class segmentor (with 0.91 mean accuracy). Semantically segmenting CT scan images of COVID-19 patients is a crucial goal because it would not only assist in disease diagnosis, also help in quantifying the severity of the illness, and hence, prioritize the population treatment accordingly. We propose computer-based techniques that prove to be reliable as detectors for infected tissue in lung CT scans. The availability of such a method in today’s pandemic would help automate, prioritize, fasten, and broaden the treatment of COVID-19 patients globally.

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COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet.

Hand gestures have been the key component of communication since the beginning of an era. The hand gestures are the foundation of sign language, which is a visual form of communication. In this paper, a deep learning based convolutional neural network (CNN) model is specifically designed for the recognition of gesture-based sign language. This model has a compact representation that achieves better classification accuracy with a fewer number of model parameters over the other existing architectures of CNN. In order to evaluate the efficacy of this model, VGG-11 and VGG-16 have also been trained and tested in this work. To evaluate the performance, 2 datasets have been considered. First, in this work, a large collection of Indian sign language (ISL) gestures consisting of 2150 images is collected using RGB camera, and second, a publicly available American sign language (ASL) dataset is used. The highest accuracy of 99.96% and 100% is obtained by the proposed model for ISL and ASL datasets respectively. The performance of the proposed system, VGG-11, and VGG-16 are experimentally evaluated and compared with the existing state-of-art approaches. In addition to accuracy, other efficiency indices have been also used to ascertain the robustness of the proposed work. The findings indicate that the proposed model outperforms the existing techniques as it has the potential to classify maximum gestures with a minimal rate of error. The model is also tested with the augmented data and is found as invariant to rotation and scaling transformation.

Vision-based hand gesture recognition using deep learning for the interpretation of sign language

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients' death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder-decoder is named SegNet, and consists of a hierarchical correspondence of encode-decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.

https://www.mdpi.com/1424-8220/20/5/1516/pdf

Liver Tumor Segmentation in CT Scans Using Modified SegNet.

Sign language is the most natural and effective way for communications among deaf and normal people. American Sign Language (ASL) alphabet recognition (i.e. fingerspelling) using marker-less vision sensor is a challenging task due to the difficulties in hand segmentation and appearance variations among signers. Existing color-based sign language recognition systems suffer from many challenges such as complex background, hand segmentation, large inter-class and intra-class variations. In this paper, we propose a new user independent recognition system for American sign language alphabet using depth images captured from the low-cost Microsoft Kinect depth sensor. Exploiting depth information instead of color images overcomes many problems due to their robustness against illumination and background variations. Hand region can be segmented by applying a simple preprocessing algorithm over depth image. Feature learning using convolutional neural network architectures is applied instead of the classical hand-crafted feature extraction methods. Local features extracted from the segmented hand are effectively learned using a simple unsupervised Principal Component Analysis Network (PCANet) deep learning architecture. Two strategies of learning the PCANet model are proposed, namely to train a single PCANet model from samples of all users and to train a separate PCANet model for each user, respectively. The extracted features are then recognized using linear Support Vector Machine (SVM) classifier. The performance of the proposed method is evaluated using public dataset of real depth images captured from various users. Experimental results show that the performance of the proposed method outperforms state-of-the-art recognition accuracy using leave-one-out evaluation strategy.

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User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features

Deep Convolutional Neural Networks (DCNN) are currently the predominant technique commonly used to learn visual features from images. However, the complex structure of most recent DCNNs impose two major requirements namely, huge labeled dataset and high computational resources. In this paper, we develop a new efficient deep unsupervised network to learn invariant image representation from unlabeled visual data. The proposed Deep Convolutional Self-organizing Maps (DCSOM) network comprises a cascade of convolutional SOM layers trained sequentially to represent multiple levels of features. The 2D SOM grid is commonly used for either data visualization or feature extraction. However, this work employs high dimensional map size to create a new deep network. The N-Dimensional SOM (ND-SOM) grid is trained to extract abstract visual features using its classical competitive learning algorithm. The topological order of the features learned from ND-SOM helps to absorb local transformation and deformation variations exhibited in the visual data. The input image is divided into an overlapped local patches where each local patch is represented by the N-coordinates of the winner neuron in the ND-SOM grid. Each dimension of the ND-SOM can be considered as a non-linear principal component and hence it can be exploited to represent the input image using N-Feature Index Image (FII) bank. Multiple convolutional SOM layers can be cascaded to create a deep network structure. The output layer of the DCSOM network computes local histograms of each FII bank in the final convolutional SOM layer. A set of experiments using MNIST handwritten digit database and all its variants are conducted to evaluate the robust representation of the proposed DCSOM network. Experimental results reveal that the performance of DCSOM outperforms state-of-the-art methods for noisy digits and achieve a comparable performance with other complex deep learning architecture for other image variations.

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Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition

Radio frequency identification (RFID) is a rapidly developing technology, and RFID sensors have become important components in many common technology applications. The passive ultra-high frequency (UHF) tags used in RFID sensors have a higher data transfer rate and longer read range and usually come in unique small and portable application designs. However, these tags suffer from significant frequency interference when mounted on metallic materials or placed near liquid surfaces. This paper presents the recent advancements made in passive UHF-RFID tag designs proposed to resolve the interference problems. We focus on those designs that are intended to improve antenna read range as well as scalability designs for miniaturized applications.

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Recent Advances in Passive UHF-RFID Tag Antenna Design for Improved Read Range in Product Packaging Applications: A Comprehensive Review

Distributed Denial of Service (DDoS) attack is a major threat impeding service to legitimate requests on any network Although the first DDoS attack was reported in 1996, the complexity and sophistication of these attacks has been ever increasing A 2 TBps attack was reported in mid-August 2020 directed towards critical infrastructure, such as finance, amidst the COVID-19 pandemic It is estimated that these attacks will double, reaching over 15 million, in the next 2 years A number of mitigation schemes have been designed and developed since its inception but the increasing complexity demands advanced solutions based on emerging technologies Blockchain has emerged as a promising and viable technology for DDoS mitigation The inherent and fundamental characteristics of blockchain such as decentralization, internal and external trustless attitude, immutability, integrity, anonymity and verifiability have proven to be strong candidates, in tackling this deadly cyber threat This survey discusses different approaches for DDoS mitigation using blockchain in varied domains to date The paper aims at providing a comprehensive review, highlighting all necessary details, strengths, challenges and limitations of different approaches It is intended to serve as a single platform to understand the mechanics of current approaches to enhance research and development in the DDoS mitigation domain

Sultan Almotairi

Papers

Liver Tumor Segmentation in CT Scans Using Modified SegNet.

User-Independent American Sign Language Alphabet Recognition Based on Depth Image and PCANet Features

Deep Convolutional Self-Organizing Map Network for Robust Handwritten Digit Recognition

Recent Advances in Passive UHF-RFID Tag Antenna Design for Improved Read Range in Product Packaging Applications: A Comprehensive Review

Distributed denial of service (Ddos) mitigation using blockchain—a comprehensive insight