Key metrics was analysed microlevel in NIRF ranking framwork. Steps to achieve the goal is clearly discussed.

This research introduces a pioneering approach in quantum computing that harnesses the fundamental principles of the Mobius strip to store and preserve the state of qubits. Leveraging the capabilities of quantum gates and entanglement, a custom quantum circuit is designed and implemented using the Qiskit framework. Through rigorous experimentation, the circuit successfully emulates the behavior of a Mobius strip, ensuring the enduring preservation of stored information. The devised methodology encompasses the creation of a quantum circuit comprising an input qubit and an output qubit. The initial state of the input qubit is determined by user input, setting the foundation for subsequent operations. By skillfully applying specialized gates and entanglement operations, the circuit faithfully represents the unique characteristics of the Mobius strip. Crucially, the output qubit’s state is measured, unveiling the resultant information encoded within. The experimental findings unequivocally establish the circuit’s unrivaled ability to steadfastly preserve information, irrespective of the number of iterations. Each execution yields the desired output, substantiating the efficacy of the bespoke gate arrangement in storing and upholding qubit states. This seminal research not only underscores the immense potential of integrating non-trivial mathematical concepts, exemplified by the Mobius strip, into quantum computing but also opens up unprecedented vistas for information storage and manipulation. The preservation of qubit states within the circuit paves the way for cutting-edge advancements in diverse quantum algorithms and applications. By seamlessly embodying the principles of scientific inquiry, this study serves as a trailblazing foundation for future explorations in the burgeoning field of quantum computing.

Digital forensics is a vital field in the IT industry that deals with the identification, collection, preservation, and analysis of electronic data. This paper explores the fundamental aspects of digital forensics in the IT industry, including its importance, key challenges, and various techniques used in the investigation process. Furthermore, the paper provides insights into the various tools and technologies that have been developed to aid in digital forensics investigations. Finally, the paper concludes by highlighting the significance of digital forensics in the IT industry and the need for professionals to stay up-to-date with the latest trends and practices in the field. GENERAL TERMS: Digital forensics, Incident response, Evidence collection and preservation, Data analysis, Cyber security, Investigation, Cybercrime

This paper investigates the feasibility of a Graph Neural Network (GNN)-based Deep Reinforcement Learning (DRL) for tackling complex optimization problems in modern communication systems deployed to tactical networks. Our methodology consists of three interacting agents: an environment builder agent responsible for generating complex network graph environments, a DRL agent situated within the control plane that possesses a global view of the current network state and makes decisions based on information gathered from various layers of the multi-layer tactical system, and an adversary designed to perturb the DRL agent, thereby evaluating its performance and robustness against data perturbations. Our numerical results indicate that enabling GNN in conjunction with adversarial training is crucial for the agent to learn the underlying network topology and parameters, ultimately enhancing the robustness of modern tactical communication systems operating in hostile environments.

It is crucial for credit card issuers to be aware of unauthorized credit card sales so that clients aren’t billed for things they didn’t buy. Mechanical learning cannot be skipped in dealing such issues due to its relevance and the use of data science. The goal of this study is to demonstrate how modelling data sets are utilized in machine learning to detect credit card fraud. Credit Modelling historical credit card transactions, data from who look to be such fraud are key components of the Finding Card Fraud Problem. This model is then applied to determine if the activity is genuine or not. While reducing the types of fraudulent fraud, our aim is to identify 100% of false employment. A common sample separation to check for credit card scams. We are concentrating on assessing and ranking data sets in this procedure, as well as providing a variety of perplexing algorithm postings, Local Outlier Factor and Isolation Forest method in PCA changed statistics about how credit cards are processed.

In the evolving landscape of fog-cloud hybrid computing, efficient task scheduling plays a vital role in meeting real-time requirements while optimizing resource utilization and energy consumption. This work proposes balanced minimum response time (MRT) and balanced minimum energy consumption (MEC) heuristics to address the challenges of task allocation on hybrid computing platforms in a balanced manner. These heuristics are employed to assess the efficacy of complementing cloud or fog networks, supported by dynamic voltage and frequency scaling (DVFS), leveraging deadline laxities without compromising user satisfaction. Three hybrid computing sub-scenarios are introduced: cloud-oriented, fog-oriented, and balanced-hybrid, based on the relative compute capacity of fog and cloud networks. The results reveal that a cloud network complementing a fog network is more beneficial for achieving lower makespan, while for lower energy consumption, it is preferable for fog network to complement the cloud network.  The research emphasizes the advantages of balanced heuristics under different computing scenarios to optimize makespan, energy consumption, and workload allocation, leading to overall computing cost reduction using realistic workloads. Further, judiciously applying DVFS to low-capacity fog nodes is more fruitful than powerful cloud nodes due to extravagant idle energy consumption in the generated scheduling holes.

In recent years, the error-state Kalman filter (ErKF) has been extensively employed across various applications, including but not limited to robotics, aerospace, and localization. However, incorporating constraints into the ErKF framework when state constraint is necessary has remained a challenging task due to its intrinsic properties. This paper explores all possible ways to achieve this goal in the context of the estimate projection method. In particular, the constraint can be enforced before or after the ErKF’s correction step. We approach the problem from a mathematical perspective by deriving analytical solutions and discussing their statistical properties. We prove that the two mentioned methods are statistically identical for a linear system with linear constraints. Conversely, the filter’s behavior remains uncertain in the presence of linearized constraints. However, we provide a special case of the nonlinear constraint, wherein the results of the linear case remain valid. To support our theorem and verify the filter’s performance when the assumptions are invalidated, we present two Monte Carlo simulations under the increasing initialization error and the constraint’s incompleteness. The simulation results clearly confirm our insights and lead to the conclusion that constraining the error-state after the correction may offer superior outcomes compared to its competitor.

Document authentication is a critical part of forensic analysis, which ensures the veracity of a document’s origins. A significant challenge in this field is the detection of ink mismatches, especially in instances of disproportionate ink distribution. This issue is effectively handled with hyperspectral images of documents, whereas traditional imaging techniques struggle to distinguish between visually similar inks. To address this problem, we introduce a new approach that leverages hyperspectral unmixing for ink mismatch detection in unbalanced clusters. The proposed method identifies unique spectral characteristics of different inks and their respective proportions, thus facilitating significant ink distinction. The proposed approach utilizes Elbow estimation and Silhouette coefficient for number of inks estimation and performs color segmentation for all unique ink types by employing k-means clustering and Gaussian mixture models (GMMs), outperforming existing methods in this regard which rely on prior knowledge about the number of inks used in the document. For abundance estimation, a unique method of dimensional reduction of HSI and channel wise analysis is proposed. We evaluate our approach on the iVision Handwritten Hyperspectral Images Data set (iVision HHID), a comprehensive and rich dataset that surpasses the commonlyused UWA Writing Inks Hyperspectral Images (WIHSI) database in size and diversity. Our results, in comparison with state-of-theart methods, demonstrate the effectiveness of our approach in hyperspectral ink mismatch detection. This paper thus promotes the application of hyperspectral imaging for document analysis and encourages further exploration toward automated questioned document examination.

In this study, ferroelectric field-effect-transistors (FeFETs) with nitride (SiON) interface having various gate lengths (LG) and gate widths (WG) were investigated to study the influence of gate dimensions on the memory window (MW) and endurance characteristics of the FeFETs. The experimental results show the MW of FeFET increases with decreasing LG and increasing of WG due to the increased fringe effect of the metal gate electrode from parasitic fringing capacitance. Thus, in the design of FeFET, fringe effect must be considered carefully. However, no considerable impact was observed on endurance characteristics of the FeFETs as the gate dimensions were varied.

ABET accreditation is a rigorous and demanding process that requires substantial institutional effort when evaluating engineering and technology programs. This evaluation process comprises numerous intricate and time-consuming steps, often demanding years of prior experience. This study aims to explore the potential of artificial intelligence tools, particularly ChatGPT, in enhancing the efficiency of ABET accreditation preparation. The authors conducted a series of experiments to assess the applicability of this tool across various stages of the ABET accreditation process. These stages included comprehending ABET procedures, assisting in the compilation and editing of ABET documentation, providing proactive suggestions, conducting diagnostic reviews of the due process response, generating action plans to address accreditation deficiencies, and proposing training plans for specific ABET criteria. The outcomes of these preliminary investigations demonstrated that leveraging artificial intelligence tools can significantly enhance the quality and efficiency of ABET accreditation preparation. However, it is crucial to acknowledge that incorporating AI tools in the accreditation process raises concerns about data security, and these considerations must be duly addressed.

In this paper, an oil-heating tank is presented, developing its respective modeling through the application of the laws of conservation of mass and energy. Then a set of nonlinear differential equations are obtained representative of the process. Subsequently, this system is linearized obtaining its linearized model. With this linear model, the design of two control systems is presented, based on a PI feedback output compensator and based on a linear-quadratic regulator, where the control objectives are the storage temperature and the fuel oil level. Finally, the process is simulated, verifying the feasibility of the operation with the designed control system.

Surgical disconnection of Seizure Onset Zones (SOZs) at an early age is an effective treatment for Pharmaco-Resistant Epilepsy (PRE). Pre-surgical localization of SOZs with intra-cranial EEG (iEEG) requires safe and effective depth electrode placement. Resting-state functional Magnetic Resonance Imaging (rs-fMRI) combined with signal decoupling using independent component (IC) analysis has shown promising SOZ localization capability that guides iEEG lead placement. However, SOZ ICs identification requires manual expert sorting of 100s of ICs per patient by the surgical team which limits the reproducibility and availability of this pre-surgical screening. Automated approaches for SOZ IC identification using rs-fMRI may use deep learning (DL) that encodes intricacies of brain networks from scarcely available pediatric data but has low precision, or shallow learning (SL) expert rule-based inference approaches that are incapable of encoding the full spectrum of spatial features. This paper proposes DeepXSOZ that exploits the synergy between DL based spatial feature and SL based expert knowledge encoding to overcome performance drawbacks of these strategies applied in isolation. DeepXSOZ is an expert-in-the-loop IC sorting technique that a) can be configured to either significantly reduce expert sorting workload or operate with high sensitivity based on expertise of the surgical team and b) can potentially enable the usage of rs-fMRI as a low cost outpatient pre-surgical screening tool. Comparison with state-of-art on 52 children with PRE shows that DeepXSOZ achieves sensitivity of 89.79%, precision of 93.6% and accuracy of 84.6%, and reduces sorting effort by 6.7-fold. Knowledge level ablation studies show a pathway towards maximizing patient outcomes while optimizing the machine-expert collaboration for various scenarios.

All the datasets we used in this research are public datasets.

Planning for eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis – vehicles extensively used in sub-Saharan Africa’s public transport systems – vary greatly in their results. In this paper, we analyse, compare and correct the only two existing studies that project energy efficiency values of electric minibus taxis in the region. One of the studies projected an efficiency of 0.39kWh/km, while the other projected 0.93kWh/km. We carefully analyse the simulation tools and models and cumulatively applying corrections from literature and scientific analyses. As a result, we eliminate the discrepancy between the two simulation tools for a given data input and obtain a final efficiency estimation that ranges from 0.49–0.52kWh/km, depending on the input data.

Hybrid amplifiers combine discrete transistors with operational amplifiers (OAs) achieving superior characteristics compared with commercially available OAs, while keeping simple structures and a reduced number of parts. A single-ended amplifier topology based on a junction field-effect transistor (JFET) is proposed as a means of achieving very low input noise levels, both in current and voltage. It is direct-coupled and allows to place a high-pass filter at its input to preserve input range, or at its output to optimize noise. The proposed topology results in a low input capacitance and low input-current noise, making it suitable for applications where multiple amplifiers are connected in parallel to achieve ultra-low noise levels. The amplifier’s gain is set by a resistor ratio, being suitable for instrumentation front ends. The circuit analysis and design equations are provided to adapt it for different applications and, as an example, an amplifier with a gain of 40 dB and a bandwidth from DC to 1 MHz was designed, built, and tested.

This paper proposes a method for tracking 3D extended target based on superquadric models.

A document by Yağmur Ceren Alataş . Click on the document to view its contents.

Due to the difficulty of reproducing inertial motion of urban scenarios in moving-base driving simulators, accurate evaluation methods of the motion are required. In this paper, an open-loop driving experiment in an urban scenario is discussed, in which 61 participants evaluated the motion cueing through an overall rating and a continuous rating method. Three MCAs were tested that represent different levels of motion cueing quality. It is investigated under which conditions the continuous rating method provides reliable data in urban scenarios through the estimation of Cronbach’s Alpha and McDonald’s Omega. Results show that the worse the motion cueing is rated, the higher the reliability of that rating data is and the less the continuous rating and overall rating correlate. This suggests that subjective ratings for motion quality are dominated by (moments of) incongruent motion while congruent motion is less important. Through a forward regression approach it is shown that participants’ rating behaviour can be described by a delayed and first-order low-pass filtered response to the lateral specific force mismatch (66.9%), as well a similar response to the longitudinal specific force mismatch (33.1%). By this better understanding of incongruent motion, and the reliability of the evaluations, incongruences in urban driving simulations can be more accurately targeted and reduced.