Advanced Computer Systems Research Articles

Analysis of etched drain based Cylindrical agate‐all‐around tunnel field effect transistor based static random access memory cell design

AbstractThis paper aims to propose a novel method for designing an static random access memory (SRAM) cell using an etched drain based Cyl GAA TFET with a hetero‐substrate material and an elevated density strip. The aim is to reduce power dissipation and improve stability, as demonstrated through analysis utilizing static noise margin (SNM) as well as N‐curve methods. With respect to the 16 nm MOSFET based SRAM cell, the proposed device‐based SRAM cell shows significant improvements with a 68.305% reduction in leakage power, a 15.58% increase in static voltage noise margin (SVNM), an 8.623% increase in static current noise margin (SINM), an 8.152% increase in write trip voltage (WTV), a 12.86% increase in write trip current (WTI), a 27.62% increase in static power noise margin (SPNM), and a 19.95% increase in write trip power (WTP). The design is implemented and analyzed using Cadence Virtuoso software, and a novel approach of look up tables and Verilog A is utilized for the device to circuit application. These results indicate promising advancements in the design of SRAM cells, which could have significant implications for the development of advanced computer systems.

Use of image analysis in the evaluation of radicular nodules in chickpeas

The use of advanced computational systems facilitates the application of a wide range of statistical techniques, available in open-source format, to perform analyses of substantial complexity in biological systems, particularly in the field of plant biology. The aim of this study is to demonstrate the application of image analysis in the evaluation of root nodules in Cicer arietinum plants. The research was conducted in the field, where roots were collected, subjected to rigorous cleaning protocols and subsequently photographed under precisely controlled studio conditions. This photographic process was performed using equipment configured with meticulous parameter settings. Subsequently, image analyses were performed using the statistical software package, R. Parameters of interest were quantified, including metrics such as root area, nodule area, the proportional representation of nodules in relation to the total root mass and the absolute count of nodules present. A critical comparison between the proposed analytical methodology and conventional approaches evidenced a substantial improvement in efficiency, thus highlighting the robustness and validity of this analytical framework for the outlined research objectives. Therefore, the developed methodology has significant potential to facilitate accurate and comprehensive analyses of nodules and root systems, thus making a notable contribution to fostering a deeper understanding and exploration of plant-microorganism interactions. Furthermore, this methodological advancement has the potential to contribute to reducing operational costs and optimizing the time spent on such meticulous evaluative undertakings.

Advanced image analysis for medical diagnostics: a system for segmentation and classification using level set methods and AI algorithms

This work aims to implement and utilize an advanced computer system for image analysis and processing through artificial intelligence. The system will evaluate images from multiple sources. As a result, a comprehensive e-Medicus system will be developed to capture and analyze X-ray data and classify cancer cells. This innovative tool, with its unique features tailored for medical facilities, will assist them in capturing and analyzing X-ray images and CT scan results. The e-Medicus system offers several benefits for medical facilities, including efficient automation of photo analysis, tracking changes in a patient's condition over time, and facilitating the identification of medical changes and data classification. A multimedia presentation of the change process will use the contour set function, allowing for topological changes in solution properties. The system integrates novel procedures and algorithms from theoretical computer science and numerical mathematics, leveraging neural networks, genetic algorithms, semantic networks, image ontology, rough set theory, contour set methods, and hybrid algorithms. These developed algorithms enhance methods and concepts in image segmentation, gathering, transmitting, storing, extracting, and effectively presenting information.

Multi-factors-regulated memristor based on Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 for artificial neural network

The memristor, characterized by its resistive switching (RS) behavior, has garnered significant interest within the scientific community, particularly because of its vast potential applications in the fields of artificial intelligence (AI) and information storage. This is attributed to its unique properties, which align well with the requirements of advanced computational and memory systems. Ferroelectric memristors are currently a thriving area of research, and this study uses Sm-doped Pb(Mg1/3Nb2/3)O3–PbTiO3 (Sm-PMN-PT) and polyvinylidene difluoride (PVDF) as the functional layer. A multi-factor responsive memristor based on a Ag/Sm-PMN-PT:PVDF/ITO sandwich structure is fabricated, for which the RS behavior of the memristor can be adjusted by multi-factors such as voltage scanning rate, bias voltage amplitude, temperature and environmental humidity. Specifically, this device is sensitive to changes in environmental humidity and exhibits the properties of an artificial neural synapse. These advantageous characteristics endow this device with great potential for use in environmental sensors and artificial neural network (ANN) systems.

Using Image J System to Process Ostracoda Images

Image J system was used to process the different images of Ostracoda. As this system is considered one of the modern and advanced computer systems used in image processing. Image processing is one of the main branches of computer science and has developed in the current era as a result of the amazing development in computer and communication technologies. Micropaleontology is the systematic study of the morphology and classification of the microscopic fossils and their ecological, and stratigraphic importance. A microscope is the greatest tool for studying a microfossil's distinctive features because it is often tiny. Images of the Ostracoda were taken from the city of Duhok in Iraqi Kurdistan, from the Kolosh Formation, in which these species were studied and diagnosed. Seven species of ostracoda were obtained: Gujaratella sp. ,Acanthocythereis hystrix , Alocopocythere gujaratensis , Alocopocythere sp.1, Alocopocythere fossularis Keijella sp.1 and Sulcostocythere sp.1 The size of the original image was found and many manipulations and calculations were made, including finding the edges of the image, finding the length, height, area, perimeter and mean of the ostracoda image, and drawing the histogram of the original image. These calculations help in processing and analyzing images. This helps to shorten the time and effort of researchers in the field of geology.

Deep-Learning-Based Action and Trajectory Analysis for Museum Security Videos

Recent advancements in deep learning and video analysis, combined with the efficiency of contemporary computational resources, have catalyzed the development of advanced real-time computational systems, significantly impacting various fields. This paper introduces a cutting-edge video analysis framework that was specifically designed to bolster security in museum environments. We elaborate on the proposed framework, which was evaluated and integrated into a real-time video analysis pipeline. Our research primarily focused on two innovative approaches: action recognition for identifying potential threats at the individual level and trajectory extraction for monitoring museum visitor movements, serving the dual purposes of security and visitor flow analysis. These approaches leverage a synergistic blend of deep learning models, particularly CNNs, and traditional computer vision techniques. Our experimental findings affirmed the high efficacy of our action recognition model in accurately distinguishing between normal and suspicious behaviors within video feeds. Moreover, our trajectory extraction method demonstrated commendable precision in tracking and analyzing visitor movements. The integration of deep learning techniques not only enhances the capability for automatic detection of malevolent actions but also establishes the trajectory extraction process as a robust and adaptable tool for various analytical endeavors beyond mere security applications.

A tyrannical societal something: Drawing lessons for twenty-first-century ‘privacy-protecting’ technology from long nineteenth-century literature

This paper demonstrates a novel, literature-based approach to the consideration and regulation of advanced computational and data systems, in particular artificial intelligence (AI). The paper contributes uniquely to the discourse of AI through its proposal that although we may think of AI as presenting a distinctly contemporary challenge, in fact, its roots can only be explored by starting with the discourses, narratives and social imaginaries of the long nineteenth century. To illustrate this, the paper looks at one of the most widely recognised cultural threats associated with big data and AI systems, namely the threat to privacy. It explores how the concept of privacy might be viewed, and ultimately managed, differently through the application of an historical and literary frame of reference, deploying a mixed-methods close and distant reading of novels in English and German. For this, we focus on two specific literary exempla, each of which illustrates the function of privacy in society. This close reading is complemented with exploration of experimental data generated by using a modern privacy dictionary to query a large corpus of nineteenth-century novels and a modern test corpus comprised of policy discussions targeting privacy issues related to the rise of big data and AI.

Leveraging Self-Attention Mechanism for Deep Learning in Hand-Gesture Recognition System

This research addresses the complex challenge of recognizing hand gestures irrespective of the user's body posture, a crucial issue in medical treatment for people with speech impairments and human-machine interfaces where precise gesture interpretation is vital. The aim is to engineer an advanced hand gesture recognition system, effective across various body positions and camera viewpoints. A novel flexible camera arrangement was employed, integrating a CNN-Transformer hybrid model, leveraging the strengths of Convolutional Neural Networks and the self-attention mechanism of Transformers. Developed using Python and the PyTorch deep learning framework, the system focuses on sophisticated image processing techniques. A thorough literature review on gesture recognition systems and multi-view analysis was conducted to inform the development. The system demonstrated exceptional accuracy in recognizing hand gestures in diverse body postures and from multiple camera perspectives, significantly outperforming existing methods. It marked a significant advancement in decoding complex gestures, a key aspect for medical applications and intricate human-machine is primarily interactions. This beneficial for people with speech impairments, rehabilitation, and in human-machine interfaces, poised to revolutionize patient care and enhance interaction with advanced machinery and computer systems.

Enhancement of Real-Time Object Detection and Tracking in Collaborative Environment using AI and Mixed Reality

The area of mixed reality has had rapid growth in recent years, with a notable rise in funding. This may be attributed to the rising recognition of the potential advantages associated with the integration of virtual information into the physical environment. The majority of contemporary mixed reality apps that rely on markers use algorithms for local feature identification and tracking. This study aims to enhance the accuracy of object recognition in complicated environment and enable real-time classification operations via the introduction of a unique detection approach known as the lightweight and efficient YOLOv4 model. In the present setting, Computational vision emerges as a very valuable and engaging manifestation of artificial intelligence (AI) that finds widespread application in many aspects of daily existence. The field of computer vision is dedicated to the development of advanced artificial intelligence and computer systems that aim to replace complex elements of the human environment. In recent times, deep neural networks have emerged as a crucial component in several sectors owing to their well-established capacity to process visual input. This study presents a methodology for classifying and identifying objects using the YOLOv4 object detection algorithm. Convolutional neural networks (CNNs) have shown exceptional efficacy in the tasks of object tracking and feature extraction from pictures. Therefore, the enhanced network architecture optimizes both the precision of identification and the speed at which it operates. This research will contribute to developing mixed-reality simulations system for object detection and tracking in collaborative environment that are accessible to everyone, including users in the architectural filed. The model was evaluated in comparison to other object detection approaches. Based on the empirical results, it was observed that the YOLOv4 model exhibited a mean average precision (mAP) of 0.988, surpassing the performance of both YOLOv3 and other object identification models.

Harnessing CRNN Technology for Image-Based Sequence Recognition

We are studying the challenge of finding text in everyday images using advanced computer techniques, specifically deep learning. Our system can accurately spot and read text in images. It uses Convolutional Recurrent Neural Networks (CRNNs), a type of deep learning model, to locate and understand the text. Instead of relying on manual rules, our model learns from the data to identify text patterns. Our projects unique feature is that our model is trained on single- word images, making it versatile in recognizing different text styles and backgrounds. We trained our model using a large dataset, including the “MJ Synthetic Word Dataset”, which consists of 50,000 out of which we have taken 35000 astraining images, 15000 as validation images. The results have been promising, indicating potential real-world applications such as searching for words in large sets of images. Key Words: Advanced computer techniques, Deep learning, System, Spot and read text, Convolutional Neural Networks (CRNNs), Deep learning model

Machine Learning-Based Sensor Data Fusion for Animal Monitoring: Scoping Review.

The development of technology, such as the Internet of Things and artificial intelligence, has significantly advanced many fields of study. Animal research is no exception, as these technologies have enabled data collection through various sensing devices. Advanced computer systems equipped with artificial intelligence capabilities can process these data, allowing researchers to identify significant behaviors related to the detection of illnesses, discerning the emotional state of the animals, and even recognizing individual animal identities. This review includes articles in the English language published between 2011 and 2022. A total of 263 articles were retrieved, and after applying inclusion criteria, only 23 were deemed eligible for analysis. Sensor fusion algorithms were categorized into three levels: Raw or low (26%), Feature or medium (39%), and Decision or high (34%). Most articles focused on posture and activity detection, and the target species were primarily cows (32%) and horses (12%) in the three levels of fusion. The accelerometer was present at all levels. The findings indicate that the study of sensor fusion applied to animals is still in its early stages and has yet to be fully explored. There is an opportunity to research the use of sensor fusion for combining movement data with biometric sensors to develop animal welfare applications. Overall, the integration of sensor fusion and machine learning algorithms can provide a more in-depth understanding of animal behavior and contribute to better animal welfare, production efficiency, and conservation efforts.

Artificial intelligence support for 5G/6G-enabled Internet of Vehicles networks: An overview

Improving transportation efficiency and on-road safety using Intelligent Transportation Systems (ITSs) has become crucial as road congestion and vehicle complexity increase coupled with ongoing rapid development and deployment of electric vehicles across the globe. Recent advances in computer systems and wireless communications have ushered in more possibilities for smart solutions to road traffic safety, congestion reduction, convenience, and overall efficiency. The evolution and deployment of 5G have opened up new technologies and features that can provide the much needed high-mobility wireless networks for the emerging Internet of Vehicles (IoV). The application of AI consisting of Deep Learning (DL), Machine Learning (ML) and Swarm Intelligence (SI) techniques have emerged in both conventional and vehicular wireless networks with strong promises towards enhancing traditional data-centric methods. Particularly, in the application domains of IoV, big data is frequently generated from various sources within the vehicular communication environment. The collected big data is usually processed and used for both safety and infotainment services including routing, broadening drivers' awareness, traffic mobility prediction for hazardous situation avoidance to improve overall safety and passenger comfort, and general quality of road experience. Applying data-driven methods enables AI to address high mobility and dynamic vehicular communications and network issues facing traditional solutions and approaches like network optimization techniques and conventional control loop design. This study provides a concise review of DL, ML and SI techniques and applications that are currently being explored by different research efforts within the application area of vehicular networks. The paper further discusses the strengths and weaknesses of the proposed AI-based solutions for the IoV networks.

Injection-locked highly Yb3+-doped uncoupled-61-core phosphate fiber laser.

Uncoupled multicore fibers are promising platforms for advanced optical communications, optical computing, and novel laser systems. In this paper, an injection-locked highly ytterbium (Yb3+)-doped uncoupled-61-core phosphate fiber laser at 1030 nm is reported. The 61-core fiber with a core-to-core pitch of 20 μm was fabricated with the stack-and-draw technique. Each core doped with 6-wt.% Yb3+ ions has a diameter of 3 μm and numerical aperture of 0.2. Linearly polarized single-frequency output of 9.1 W was obtained from the injection-locked cavity with a 10-cm-long gain fiber at a pump power of 23.6 W. The injection locking of all 61 cores was confirmed by inspecting the longitudinal modes of the individual lasers with a scanning Fabry-Perot interferometer. The performance of the injection-locked 61-core fiber laser was characterized and compared to that of the free-running operation in terms of optical spectrum, near- and far-field intensity profiles, and relative intensity noise.

Cloud Computing Challenges in Security Applications – A Review

Cloud computing platforms provide an advanced computer system that allows organizations and individuals to operate a wide range of tasks such as using online storage capacity, implementing business applications, developing customised computer software, and creating a "realistic" communication network. Because of numerous security and privacy concerns, cloud technology has struggled to gain popularity among many large and established organizations. This research discusses the security challenges of cloud computing networks, beginning with a description of cloud computing and its various types. Furthermore, it provides a brief explanation of the most common cloud computing service challenges.

Cloud Computing Challenges in Security Applications – A Review

Cloud computing platforms provide an advanced computer system that allows organizations and individuals to operate a wide range of tasks such as using online storage capacity, implementing business applications, developing customised computer software, and creating a "realistic" communication network. Because of numerous security and privacy concerns, cloud technology has struggled to gain popularity among many large and established organizations. This research discusses the security challenges of cloud computing networks, beginning with a description of cloud computing and its various types. Furthermore, it provides a brief explanation of the most common cloud computing service challenges.

On scientific understanding with artificial intelligence.

An oracle that correctly predicts the outcome of every particle physics experiment, the products of every possible chemical reaction or the function of every protein would revolutionize science and technology. However, scientists would not be entirely satisfied because they would want to comprehend how the oracle made these predictions. This is scientific understanding, one of the main aims of science. With the increase in the available computational power and advances in artificial intelligence, a natural question arises: how can advanced computational systems, and specifically artificial intelligence, contribute to new scientific understanding or gain it autonomously? Trying to answer this question, we adopted a definition of ‘scientific understanding’ from the philosophy of science that enabled us to overview the scattered literature on the topic and, combined with dozens of anecdotes from scientists, map out three dimensions of computer-assisted scientific understanding. For each dimension, we review the existing state of the art and discuss future developments. We hope that this Perspective will inspire and focus research directions in this multidisciplinary emerging field.

Improved Visual SLAM Using Semantic Segmentation and Layout Estimation

The technological advances in computational systems have enabled very complex computer vision and machine learning approaches to perform efficiently and accurately. These new approaches can be considered a new set of tools to reshape the visual SLAM solutions. We present an investigation of the latest neuroscientific research that explains how the human brain can accurately navigate and map unknown environments. The accuracy suggests that human navigation is not affected by traditional visual odometry drifts resulting from tracking visual features. It utilises the geometrical structures of the surrounding objects within the navigated space. The identified objects and space geometrical shapes anchor the estimated space representation and mitigate the overall drift. Inspired by the human brain’s navigation techniques, this paper presents our efforts to incorporate two machine learning techniques into a VSLAM solution: semantic segmentation and layout estimation to imitate human abilities to map new environments. The proposed system benefits from the geometrical relations between the corner points of the cuboid environments to improve the accuracy of trajectory estimation. Moreover, the implemented SLAM solution semantically groups the map points and then tracks each group independently to limit the system drift. The implemented solution yielded higher trajectory accuracy and immunity to large pure rotations.

Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">global-local policy search</i> method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.

An ensemble-based statistical methodology to detect differences in weather and climate model executables

Abstract. Since their first operational application in the 1950s, atmospheric numerical models have become essential tools in weather prediction and climate research. As such, they are subject to continuous changes, thanks to advances in computer systems, numerical methods, more and better observations, and the ever-increasing knowledge about the atmosphere of earth. Many of the changes in today's models relate to seemingly innocuous modifications associated with minor code rearrangements, changes in hardware infrastructure, or software updates. Such changes are meant to preserve the model formulation, yet the verification of such changes is challenged by the chaotic nature of our atmosphere – any small change, even rounding errors, can have a significant impact on individual simulations. Overall, this represents a serious challenge to a consistent model development and maintenance framework. Here we propose a new methodology for quantifying and verifying the impacts of minor changes in the atmospheric model or its underlying hardware/software system by using ensemble simulations in combination with a statistical hypothesis test for instantaneous or hourly values of output variables at the grid-cell level. The methodology can assess the effects of model changes on almost any output variable over time and can be used with different underlying statistical hypothesis tests. We present the first applications of the methodology with the regional weather and climate model COSMO. While providing very robust results, the methodology shows a great sensitivity even to very small changes. Specific changes considered include applying a tiny amount of explicit diffusion, the switch from double to single precision, and a major system update of the underlying supercomputer. Results show that changes are often only detectable during the first hours, suggesting that short-term ensemble simulations (days to months) are best suited for the methodology, even when addressing long-term climate simulations. Furthermore, we show that spatial averaging – as opposed to testing at all grid points – reduces the test's sensitivity for small-scale features such as diffusion. We also show that the choice of the underlying statistical hypothesis test is not essential and that the methodology already works well for coarse resolutions, making it computationally inexpensive and therefore an ideal candidate for automated testing.

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Background: With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences. Aim: The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics. Methods: Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey. Results: It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models. Conclusion: Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.