A document by Oloruntimilehin Onalaja. Click on the document to view its contents.

This research investigates the application of machine learning for diagnosing COVID-19 from chest X-rays. We analyze various popular architectures, including efficient neural networks (EfficientNet), multiscale vision transformers (MViT), efficient vision transformers (EfficientViT), and vision transformers (ViT), on a dataset categorized into COVID, lung opacity, normal, and viral pneumonia. While multiscale models demonstrate a tendency to overfit, our proposed fine-tuning ViT model achieves significant accuracy, reaching 95.79% in four-class classification, 99.57% in a clinically relevant three-class grouping, and similarly high performance in binary classification. Validation through quantitative metrics and visualization solidifies the model's effectiveness. Comparative analysis showcases the superiority of our approach. Overall, these findings showcase the potential of ViT for accurate COVID-19 diagnosis, contributing to the advancement of medical image analysis.

The convergence of Web3, Metaverse, and Digital Twins technologies is bringing a transformative revolution to Cyber-Physical-Social systems. Web3, which is driven by blockchain and decentralization, allows users to have control over their data and digital assets. Meanwhile, the Metaverse is creating a virtual space where people can interact, work, and live together. Digital twins offer a real-time digital representation of physical objects or spaces. When these three concepts intersect, they create a dynamic and interconnected digital ecosystem where the physical and virtual worlds blend seamlessly. This paper focuses on discussing the convergence of Metaverse, Web3 technologies, and Digital Twin. We will focus on the architecture of a Web3-enabled Metaverse, which leverages the decentralized nature of Web3 to offer a distributed Metaverse. The digital twin technology will realize a Cyber-Physical-Social data binding that enables the seamless mapping of physical and social activities into cyberspace. Finally, we will discuss the challenges of a Web3-enabled Metaverse, such as security, privacy, interoperability, and ethical concerns.

We experimentally show novel phenomena: (1) in the same double slit or cross double slit experiment, within macroscopic distances from diaphragm, the patterns are non-interference and gradually evolve to the interference patterns near the screen;

The GPT language model, developed by OpenAI, has gained widespread attention for its impressive ability to engage in conversational interactions with human users, thanks to its extensive training on vast amounts of written data. However, it is crucial to acknowledge that GPTs and their derivatives can exhibit unethical behaviors. Despite this, there has been limited research conducted on the ethical implications of Large Language Models (LLMs) in higher education. To address these concerns, this paper aims to comprehensively examine the harmful behaviors displayed by GPT and its spin-offs. These behaviors encompass social and monolingual biases, issues of reliability and trustworthiness, data privacy and security concerns, toxicity in generated content, challenges in human-computer interaction, and environmental impact.by conducting this study, valuable insights will be provided to educators, administrators, and students, enhancing their awareness of the ethical risks inherent in GPT and its various versions. This will enable them to proactively address and mitigate potential ethical concerns arising from the use of LLMs in educational settings.

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel worldwide to plan and test robot motions, even collaboratively. Instead, they will have a personalized access to robots and their environments from anywhere, thus spending more time with family and friends. Students enrolled in robotics courses will be taught under authentic industrial conditions in real-time. This paper describes objectives of Metarobotics in society, industry, and in-between. It identifies and surveys technologies likely to enable their completion and provides an architecture to put forward the interplay of key components of Metarobotics. Potentials for self-determination, self-efficacy, and work-life-flexibility in robotics-related applications in Society 5.0, Industry 4.0, and Industry 5.0 are outlined.

Modular multilevel and cascaded converters are pivotal in various applications, including offshore wind, power transmission, large drives, or reconfigurable battery systems. Enabling parallel connectivity in these converters enhances their scalability and flexibility and introduces features such as switched-capacitor functionalities, hardware-safe sensorless voltage balancing, and reduced source impedance. While bringing the advantages of being more compact and more highly integrated electronics inherent to switched-capacitor circuits, parallelization can introduce challenges in balancing current surges under substantial voltage differences and/or very low inter-module impedance. Previous research has primarily examined the energy loss during this process, assuming that parallelization intervals significantly longer than the time required for energy exchange. However, in recent applications, particularly with fast-switching semiconductors, the oscillatory balancing dynamics between paralleling modules and the switching can interact. This study extends the characterization of paralleling dynamics and introduces a generalized analytical model. This model covers both scenarios where the switching and equilibration are independent or interacting. Additionally, we introduce a compact module interconnection with engineered inductance. The differential-mode inductance is tuned independently of the common mode to control the balancing current separately while contributing a negligible source impedance to the overall load. We validated our analytical model and tested the current surge control on an experimental prototype based on a cascaded double-H bridge circuit. The practical implementation of the module interconnection with engineered differential-mode inductance reduced the current surge by 86.5 % and the voltage overshoot by 42 %, consequently enhancing system efficiency by 2.2 %.

Synchronization of single-phase inverters is a challenging task due to the difficulty of deriving a rotating voltage frame, in the absence of adequate information from the other two phases. Moreover, modern standards, such as the fault ridethrough (FRT) directives, require inverter-based distributed generators (IBDGs) to respond as fast as possible to grid disturbances; thus, it is necessary to rely on accurate and fast synchronization algorithms. The scope of this paper is to propose a new synchronization technique, which satisfies three important requirements: a) fast dynamic response, b) adequate double frequency rejection, c) low computational complexity. Our synchronization technique is a flexible method relying on variablelength transfer delay, which calculates network frequency by analyzing voltage angle differentials. To enhance this differentiation process and address potential discontinuities, we introduce Heaviside-based functions. Both simulations conducted in MATLAB/Simulink and experimental trials demonstrate that our proposed synchronization method outperforms the most widely adopted existing techniques in terms of dynamic response and computational efficiency. Due to its excellent dynamic performance, the proposed method can offer a stable FRT capability, with fast detection of the voltage dips and seamless resynchronization following fault clearance, all while preventing DC-link overvoltage issues.

In this work, we attempt to find a suitable oxide resistive layer among four popular metal oxides, including HfOX, NiOX, TaOX, and TiOX for GE-based RRAM devices by investigating the device performance using a fully experimentally calibrated numerical simulation model. The results reveal that among four metal oxides, HfOX resistive layer with GE provides lower reset voltage (0.12 V), lower sneak current (81.07 nA) with better power efficiency (45.92%) for 2 8 bit capacity. On the other hand, NiOX-based RRAM shows a higher switching current (14.45 µA), more thermal stability, better uniform and distinguishable multilevel states, and higher readout margin for crossbar array size. Our study offers a comprehensive examination of the performance characteristics of distinct resistive layer materials that can provide an important guide to experimental efforts and trigger more efforts.

This study presents a novel technique for generating terahertz (THz) waves with frequency modulation capabilities ranging from linear sweeping to random hopping. Departing from conventional methods that utilize wavelength-tunable lasers to produce optical waves with variable frequency differences, this approach harnesses an optical frequency comb to generate optical wave frequencies at fixed intervals. Crucially, it employs a uniquely configured optical filter with adjustable dual passbands, allowing for the selection of optical beats with frequency variances that are multiples of the comb's interval. These beats are subsequently converted into THz waves using a THz photomixer, aligning the THz frequency with the adjustable interval between the filter's passbands. This innovation significantly enhances the versatility and scope of THz wave frequency modulation, offering substantial potential for advancements in high-speed communication, precision imaging, and spectroscopy.

A document by Ramitha Kalhara Dissanayake Ralalage. Click on the document to view its contents.

Detecting human emotional states, crucial in research for human-machine interaction and affective computing, often relies on objective and spontaneous physiological signals such as Electroencephalogram (EEG). Despite the wealth of stateof-the-art methods for emotion recognition, lingering challenges in terms of generalization power persist. Addressing this issue, we introduce an approach employing a transformer-based autoencoder for the emotion classification using EEG. An autoencoder is applied to connectivity matrices to distil essential features and reduce data dimensionality. Additionally, a multihead attention transformer directs attention to pertinent features for emotion recognition which subsequently added to a fully connected network for classification. Extensive experiments have been conducted on DEAP and DREAMER datasets. In subjectdependent evaluation on the DEAP dataset, the model achieves an accuracy of 99.68% and 99.47% for valence and arousal, respectively. In the subject-independent scheme, the model demonstrates an accuracy of 94.08% and 94.98% for valence and arousal. In the DREAMER experiment, subject-dependent evaluation results in an accuracy of 99.54% and 99.30% for valence and arousal prediction. For the subject-independent experiment, the model accurately predicts valence and arousal levels with 98.38% and 97.32%, respectively. The results are further elucidated through ablation experiments and the visualization of learned features.

Approximate computing has been extensively employed in arithmetic circuits such as a ripple carry adder (RCA); in an approximate adder, errors are introduced by design. VLSI circuits are also prone to errors caused by external and physical phenomena (such as cosmic rays or a stuck-at); hereafter, these errors are referred to as functional. This paper investigates the combined effects of single functional error (SFE) in an approximate cell and an RCA. The exact and approximate cell designs are considered using a state transition diagram-based analysis to identify relationships between the types of functional error in a cell against the expected behavior for all possible cases. A probabilistic analysis for an exact RCA is proposed; it shows excellent agreement with the simulation results for different metrics such as the error rate (ER). Next, an error analysis is performed on the RCA by considering the number of approximate cells as well as the location of the erroneous cell; the simulation results and modeling analysis of the exact RCA show and prove that the ER and MED (Mean Error Distance) of odd-numbered cases is higher than for evennumbered cases. Moreover, the MED for an approximate RCA in the presence of an SFE is higher than for the exact RCA. Simulation and analytical results show that an approximate RCA in the presence of an SFE can have a serious degradation in accuracy performance if the approximate cell type is not properly selected. In addition, the binary tree-based mathematical analysis and pseudocode are provided to support the exhaustive simulation results for the ER of the RCA using different approximate cells.

Reconfigurable intelligent surfaces (RISs) operate similarly to electromagnetic (EM) mirrors and remarkably go beyond Snell law to generate an applicable EM environment allowing for flexible adaptation and fostering sustainability in terms of economic deployment and energy efficiency. However, the conventional RIS is controlled through high-latency field programmable gate array or micro-controller circuits usually implementing artificial neural networks (ANNs) for tuning the RIS phase array that have also very high energy requirements. Most importantly, conventional RIS are unable to function under realistic scenarios i.e, high-mobility/low-end user equipment (UE). In this paper, we benefit from the advanced computing power of neuromorphic processors and design a new type of RIS named NeuroRIS, to supporting high mobility UEs through real time adaptation to the ever-changing wireless channel conditions. To this end, the neuromorphic processing unit tunes all the RIS metaelements in the orders of ns for particular switching circuits e.g., varactors while exhibiting significantly low energy requirements since it is based on event-driven processing through spiking neural networks for accurate and efficient phase-shift vector design. Numerical results show that the NeuroRIS achieves very close rate performance to a conventional RIS-based on ANNs, while requiring significantly reduced energy consumption with the latter.

When designing a power or CPU constrained device where a four-axis robotic arm is required and access to the Robot Operating System (ROS) is not an option, finding an efficient state space controller for a four-axis arm can be an obstacle. In this paper, I explore a method to optimize the computing power required for a computer algebra system (CAS) to compute linear quadratic regulator (LQR) matrices by precomputing the gain matrix for different states. Example C++ code is provided on Github, along with ideas for further exploration.

Common block ciphers like AES specified by the NIST or KASUMI (A5/3) of GSM are extensively utilized by billions of individuals globally to protect their privacy and maintain confidentiality in daily communications. However, these ciphers lack comprehensive security proofs against the vast majority of known attacks. Currently, security proofs are limited to differential and linear attacks for both AES and KASUMI. For instance, the consensus on the security of AES is not based on formal mathematical proofs but on intensive cryptanalysis over its reduced rounds spanning several decades. In this work, we introduce new security proofs for AES against another attack method: impossible differential (ID) attacks. We classify ID attacks as reciprocal and nonreciprocal ID attacks. We show that sharp and generic lower bounds can be imposed on the data complexities of reciprocal ID attacks on substitution permutation networks. We prove that the minimum data required for a reciprocal ID attack on AES using a conventional ID characteristic is 2 66 chosen plaintexts whereas a nonreciprocal ID attack involves at least 2 88 computational steps. We mount a nonreciprocal ID attack on 6-round AES for 192-bit and 256-bit keys, which requires only 2 18 chosen plaintexts and outperforms the data complexity of any attack. Given its marginal time complexity, this attack does not pose a substantial threat to the security of AES. However, we have made enhancements to the integral attack on 6-round AES, thereby surpassing the longstanding record for the most efficient attack after a period of 23 years.

This paper proposes block-level precoder (BLP) designs for a multi-input single-output (MISO) system that performs joint sensing and communication across multiple cells and users. The Cramer-Rao-Bound for estimating a target's azimuth angle is determined for coordinated beamforming (CBF) and coordinated multi-point (CoMP) scenarios while considering inter-cell communication and sensing links. The formulated optimization problems to minimize the CRB and maximize the minimum-signal-to-interference-plus-noise-ratio (SINR) are nonconvex and are represented in the semidefinite relaxed (SDR) form to solve using an alternate optimization algorithm. The proposed solutions show improved performance compared to the baseline scenario that neglects the signal component from neighboring cells.

Intelligent omni-surfaces (IOSs) have been considered for assisting outdoor-to-indoor millimeter-wave (mmWave) communications. Nevertheless, the existing works have not adequately investigated how the number or the deployment locations of IOSs should be optimized for serving multiple indoor users. In this paper, we study IOS-assisted outdoor-to-indoor mmWave communications where IOSs are installed in an exterior wall of a building to refract mmWave signals from an outdoor base station (BS) to indoor users that locate among indoor blockages. Given a fixed total number of refracting elements, we formulate an optimization problem to maximize the downlink energy efficiency of the outdoor BS while satisfying the dowlink data rate requirements of the indoor users by jointly optimizing the number, locations and phase shifts of IOSs and the beamforming vectors of the BS. To address the varying dimensionality and the non-convexity of the optimization problem, we decompose it into two subproblems that optimize the IOSs' phase shifts together with the BS beamforming vectors and the number and locations of IOSs, respectively, and devise successive convex approximation and Continuous Population-Based Incremental Learning-based algorithms to solve them alternately. Simulation results demonstrate that the proposed algorithms can obtain the optimal number and locations of IOSs, resulting in significantly enhanced energy efficiency of the outdoor BS compared to benchmark schemes.