This study evaluates the sustainability of domestic natural gas transportation and usage in Nigeria, focusing on diversification into Compressed Natural Gas (CNG) and Liquefied Petroleum Gas (LPG) for the transportation and domestic sectors. The research is motivated by Nigeria Liquefied Natural Gas (NLNG) company’s 18% profit decline due to East Africa’s offshore discoveries and US shale gas emergence..Using computer software applications like Aspen HYSYS v7.1, Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), and Auto-CAD, the study:- Assesses gas diversification capacity- Determines pipeline pressure distribution- Designs a 24’ ’ carbon steel pipeline- Simulates process separation of heavy hydrocarbons (C3 and C4) using HYSYS and P&ID ColumnThe research finds that 45% local utilization of natural gas, either industrially or domestically, can enhance Nigeria’s GDP growth. The study recommends diversification into CNG and LPG, and investment in infrastructure and technology to enhance sustainability in Nigeria’s natural gas sector.

Bio-inspired whisker sensors are engineered for a range of applications, including fluid-flow sensing, environmental exploration, and texture analysis. While numerous existing sensor designs exist, current reviews primarily focus on use cases and transduction methods. However, a noticeable gap remains in developing durable whisker sensors that maintain consistent hysteresis, are easy to fabricate, and can be configured for different purposes. Our study compares four in-house designed whisker-inspired sensors to identify an efficient fabrication method, considering sensing schemes, compliance, and whisker elements. Comparative analysis of each configuration provides new insights into optimal whisker-based sensor design. Additionally, we evaluate the performance of these sensors through standard roughness measurements. The central goal is to fabricate a whisker sensor that is long-lasting and easy to build and reconfigure for various applications. Based on our observations, we recommend suitable elements for whisker sensor fabrication.

The high penetration of converter-based renewables (CBRs) is challenging the stability and security of modern power systems, e.g., power balance, due to the fast fluctuation of renewables. Storage energies (SEs) are good choices for addressing the power balance issues, due to their flexibility of bi-directional power regulation. However, the bi-directional power regulation of SEs increases the complexity and difficulty for analyzing the CBRinduced small-signal stability issues in a multi-CBR system, which is one kind of key stability issues in modern power systems, especially in weak grids. It has not been theoretically revealed how the placement of SEs influences the CBR-induced small-signal stability issues, when the SEs absorb active power from grids. This paper aims to fill this gap. Based on modal decomposition theory, this paper explicitly demonstrates that the placement of SEs (when absorbing active power from grids) is equivalent to increasing the grid strength and thus enhancing the CBR-induced small-signal stability. Moreover, this paper investigates the optimal placement of SEs for effectively improving the small-signal stability by using a grid-strength-evaluation indicator, named as generalized shortcircuit ratio (gSCR). Finally, the analysis in this paper is validated based on a modified 39-bus test system.

Cyberattacks leveraging encryption-based extortion techniques have grown both in frequency and complexity, creating an urgent need for more advanced detection mechanisms capable of identifying malicious activity before significant harm occurs. The Dynamic Encryption Pattern Analysis (DEPA) system is introduced as a novel solution to address this challenge, focusing on the detection of ransomware through real-time monitoring of encryption behaviors. Unlike traditional methods that rely on static signatures or general anomaly detection, DEPA analyzes file modification patterns and entropy changes, allowing it to identify ransomware activity at an early stage. The system's adaptive detection algorithm further enhances its effectiveness, adjusting to evolving ransomware techniques through continuous feedback loops. Extensive experiments were conducted using a diverse dataset of ransomware families, demonstrating DEPA's high detection accuracy, low false positive rates, and efficient resource management across multiple operating systems and environments. In addition to its technical advantages, DEPA's modular architecture supports seamless integration into various cybersecurity infrastructures, making it suitable for large-scale deployments in both enterprise and cloud-based systems. This work highlights the potential of DEPA as a robust, scalable, and efficient solution for mitigating the rising threat of encryptionbased cyberattacks, offering a promising approach to enhancing automated ransomware detection capabilities.

This study presents a compact, on-chip Analog Probe Module (APM) to augment the IEEE 1149.4 (or P1687.2) standard, for efficiently probing multiple internal nodes. The complete approach to APM implementation from conceptualization to practical application is discussed in detail. The APM aims to utilize minimum area for a maximum number of probe channels, achieving an optimal size of 4:15. At the transistor level, the design minimizes the impact of glitches in asynchronous operations through a symmetrical layout and a unique arrangement of all probe channels. However, glitches in asynchronous circuits can still exist; hence, a state transition matrix (STM) concept is devised. STMs help visualize hazardous transitions, allowing the identification of a common hazard-free transition sequence suitable for hardware implementation. The verified APM design is integrated with several analog/RF circuits fabricated in a commercially available 0.18µm RF-CMOS process as part of a radar-on-chip system. An important APM application of enabling the prediction of an IC’s corner disposition by measuring DC-node voltages during post-silicon IC testing, is demonstrated for six fabricated ICs.

Crowd counting has been a popular research topic due to its broad applicability, such as safety monitoring, urban planning, and disaster management. The crowd counting task aims to accurately estimate the number of people in a dynamic video sequence or a static image. Timely and accurate estimation of the crowd is crucial for public safety and monitoring. Recent focus in crowd counting is on developing deep learning-based models such as convolutional neural networks (CNNs) and vision transformers (ViTs). In addition, most existing crowd counting methods require point-level annotation of each person in the scene (ground truth) to train the model. This annotation process is laborious and susceptible to errors. Due to this, there has been a shift in focus towards developing weakly-supervised methods that require only the total person count in the image as ground truth. This work proposes a new pipeline for weaklysupervised crowd counting and explores the utility of a robust mean absolute percentage error (MAPE) loss function in crowd counting. Performance evaluations on widely used datasets validate the effectiveness of the proposed method. Its performance is on par with the fully-supervised crowd counting methods and significantly better than the weakly-supervised approaches.

Stability of multi-vendor power electronics-dominated grids requires proper specifications for converters interoperability, e.g. tuning boundaries for the converters' control loops to be respected by all vendors. The confidentiality and variety of implementation of the converters' control system by different vendors and the nonlinear dynamics of power converters make this task arduous. This paper formulates this research question as a robust stability problem, embedding frequency-dependent uncertainties in the converter control systems and analytically quantifying the amount of uncertainty able to warranty the power grid stability. Grid following converters are considered and modeled with a nonlinear grey-box approach, in contrast with conventional small-signal black-box models which suffer from inaccuracy when their operating point deviates from the nominal one. This paper also demonstrates both analytically and with Hardware-In-the-Loop (HIL) experiments that different converters operating points (e.g. active power injections, ac bus voltages) radically affect their stability, empathizing the necessity of nonlinear converters models to realize accurate power grid stability analyses.

This paper deals with a three-dimensional multiple input, multiple output (MIMO) passive coherent location (PCL) system in a scenario with multiple transmitters and receivers. The goal is to accurately estimate the location of a single observed target despite challenges such as undetected measurements and outliers due to non-line-of-sight effects. A volumetric search method is proposed to overcome outlier presence. This method, which divides the search region into cuboids, offers a fresh perspective on identifying and discarding outliers while selecting consistent measurements. The centroid of the selected cuboid provides an alternative accurate location estimate, which is particularly valuable when many outliers are present. We also introduce an iterative approach to outlier removal based on model-mismatch cost functions for benchmarking. The selected consistent measurements are used to feed a nonlinearly constrained least squares (NLCLS) algorithm explicitly designed for MIMO PCL systems. A unified mathematical framework is established for this NLCLS approach, which accounts for the nonlinear relationships among the optimization variables via constraints. Additionally, a simplified and efficient version of the NLCLS algorithm is proposed. This streamlined approach iteratively minimizes the model mismatch by using a single constraint, ensuring accurate estimation with reduced computational complexity. The proposed NLCLS method and its simplified version are compared against unconstrained counterparts, namely spherical interpolation and spherical intersection. The Cramer-Rao lower bound is derived for this application, and numerical experiments highlight the robustness of the cuboid-based volumetric search and the nonlinearly constrained algorithms.

Classification of modern architectures of devices for input of analog sensor signals, as well as IP-modules of the first level of complexity, which can be made on the basis of high-temperature technologies (SiGe, SOI, GaN, SiC, GaAs), is given. It turns out that at present there are more than 250 construction variants of primary converters of analog sensor signals. They include 105 operational amplifier's modifications, 7 differential difference amplifier's modifications, 22 buffer amplifier's modifications, and also more than 100 construction variants of special active elements for signal selection tasks under the general name of "current conveyors". Priorities in realization of basic functional nodes of high-temperature interfaces based on wide-gap semiconductors (GaN, SiC, GaAs) and high-temperature platforms (SiGe, SOI) are determined.

In today's fast-paced world, efficient route experiences are crucial for individuals, businesses, and transportation agencies. Whether routing goods, managing logistics operations, or planning supply chain strategies, the goal is to minimize time, reduce costs, and enhance satisfaction. This project addresses these challenges by leveraging data, intelligent analysis, and innovative technologies to optimize routes. By personalizing services, improving efficiency, and driving affordability, the initiative aims to enrich the route experience for all stakeholders. Key algorithms like Dijkstra's algorithm, K-means, VRP, and TSP will play significant roles. The project also focuses on integrating emerging technologies and predictive analytics to enhance precision and efficiency. Through collaboration with various stakeholders, it aims to create practical, efficient, environmentally friendly, and socially just routes.

Over the last decade, digital twin technology has gained increasing interest in various fields, such as industrial engineering and manufacturing, healthcare, transportation, energy, logistics, or city operations. While digital twins of single assets dominated in the beginning, we are seeing more and more digital twins for systems of assets, where digital twins need to collaborate with other digital twins to accomplish their objectives. As in service-oriented systems, digital twins provide services that can be used by other twins. While in service-oriented systems a service is typically identified by type information, this is not sufficient in digital twin systems, where there may be a large number of service instances of the same type that are not interchangeable. In this paper, we present a proximity model that allows digital twins to specify the required services by so-called neighborhoods, whose definition is based on spatial relationships between client and service instances. This concept not only simplifies service discovery in digital twin systems, but also allows for efficient implementations. To support this, we illustrate how service discovery based on the proposed proximity model can be implemented on top of a spatial DBMS.

Machine learning (ML) has become extremely important in communications due to the increasing complexity of networks and due to its promises of improved performance, efficiency, security, cost reduction, and customized user experience. In wireless networks, ML is researched and used in several domains, from access networks to core networks and network services. In network security, the promises of machine learning are vast, from preventive measures to detection to response and remediation. However, machine learning requires a huge amount of resources, mainly due to the fact that ML operates on data, and data volumes are consistently rising. In this review article, we explore the aspect of resource consumption of ML techniques used for network security and provide a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects pertaining to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on the extensive literature review, we summarize key points regarding optimizing resource consumption in ML-based network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area.

This paper derives a set of bulldozer dynamics to test a three-dimensional (3D) blade controller. This work was motivated by a desire to mimic how a human operator would control a bulldozer blade to build an autonomous system control module and by the lack of existing work on 3D bulldozer blade control. To that end, three-dimensional surface-to-track and surface-to-blade dynamics were derived to test the proposed 3D bulldozer blade controller in simulation. The track dynamics included considerations for track slip, while the blade dynamics accounted for non-symmetric mounds in front of the blade. The 3D bulldozer blade control effort was implemented using fuzzy logic to mimic a human operator's nonlinear modes of blade control, thereby following a Human Decision Making Model (HDMM). The inputs to the main portion of the control effort were the yaw, pitch, and roll errors between the blade's current orientation and the desired orientation. Accordingly, the outputs were the blade's yaw, pitch, and roll velocities. The pitch error, in particular, was tied to a separate fuzzy controller that outputted the desired cutting depth depending on the observed soil type, against tying back to the HDMM.

Over the past few years, medical image processing has increased in use of deep learning algorithms, especially in the analysis of magnetic resonance (MR) scans. MRI is a crucial diagnostic tool for Alzheimer's disease (AD), a prevalent type of dementia that ranks seventh among fatal illnesses globally. As there is no known cure for Alzheimer's disease, early detection and intervention are vital to prevent its irreversible progression. This study proposes a comprehensive framework for detecting Alzheimer's disease that employs convolutional neural networks (CNNs) and deep learning approaches. We applied transfer learning to pretrained deep learning models rather than training them from scratch. Three distinct pretrained CNN models (VGG-19, ResNet-50, and Inception V3) with a fine-tuned transfer learning approach were used for five-way classification of AD. We employed the ADNI dataset, which includes MRI scans from 608 patients across five classes: Alzheimer's disease (AD), early mild cognitive impairment (EMCI), mild cognitive impairment (MCI), late mild cognitive impairment (LMCI), and normal control (NC). The models' performance was evaluated based on eight metrics: accuracy, precision, sensitivity/recall, specificity, error rate, false positive rate, F1-score, and kappa. Our findings indicate that the ResNet-50 architecture outperformed other pretrained models, achieving the highest overall accuracy of 98.7% for multiclass AD classification. Additionally, the ResNet-50 model excelled in classifying the EMCI category with an accuracy of 99.25%, indicating its effectiveness in detecting early signs of memory impairment. The proposed framework surpasses the performance of previous studies in terms of overall accuracy, sensitivity, and specificity, setting a new benchmark for five-way AD classification. The outcomes of this study will contribute significantly to early prevention efforts by enabling Alzheimer's disease to be detected before it progresses irreversibly. In conclusion, this research represents a promising approach for improving the early detection and classification of Alzheimer's disease using deep learning methods with MRI data.

Edge case or long-tail detection in computer vision presents a significant challenge due to the inherent data imbalance and scarcity of rare event samples. Traditional convolutional neural networks (CNNs) often struggle to generalize well to such infrequent instances. In this paper, we propose a novel Transformer-based framework that effectively addresses the long-tail distribution problem by leveraging attention mechanisms to focus on subtle and rare patterns in images. Our approach incorporates a specialized loss function and data augmentation techniques to further enhance performance on edge cases. We evaluate our method on several benchmark datasets and demonstrate that it outperforms stateof-the-art models in detecting rare events, highlighting the potential of Transformers in tackling the long-tail problem in computer vision.

An islanded AC microgrid (MG) operates independently from the main grid, and delivers power to a localized area through distributed energy resources (DERs). Effective power management and control strategies, like droop control, are essential for balancing supply and demand, particularly under variable load conditions in an islanded microgrid (IMG). Droop control can be communication-based or non-communication-based. In Non-communication-based droop control methods, droop gains and control gains are obtained by minimizing voltage, real power, and reactive power deviations over a limited set of operating conditions, but in a practical system, numerous operating conditions are possible. On the other hand, communication-based methods track generation and load changes to update droop gains and control gains but face challenges like noise, signal loss, delays, and cyber-attacks. This work uses DG voltage, loads, frequency, and real and reactive power as a range/interval to consider uncertainty. An interval state space model is developed for IMG. The interval state matrix incorporates all the possible operating conditions. An objective function is formulated which is solved by particle swarm optimization (PSO). The proposed method is implemented on a test system and simulation results compared with a multi-objective evolutionary algorithm based on decomposition (MOEA/D) called centripetal force-gravity search algorithm (CF-GSA) method that is based on communication. The simulation results demonstrate that the proposed method performs as good as MOEA/D method, but without the need for communication.

Millimeter wave (mmWave) commutation standards in the unlicensed band, including IEEE 802-led wireless personal area network (WPAN), wireless local area network (WLAN), and 3GPP-led cellular network (CN), have been facing challenges for over ten years to acquire commercial acceptance. Towards widespread mmWave communication systems supporting the 6G connectivity, an innovation concept called mmWave virtual community network (VCN) has been proposed recently, where decentralized WPAN-type device-to-device (D2D) community coexists with centralized WLANs/CNs in the unlicensed mmWave band via multihop links without full use of high-gain beamforming. Based on this concept, this paper further proposes a strategic ecosystem to spur mmWave VCN deployment with the mutual benefit of decentralized and centralized entities. Therein, a decentralized community via mmWave D2D communication helps coverage area expansion of mmWave WLAN/CN while the CN operator incentivizes the distributed communities to expand themselves with a reward mechanism to ensure mutual benefits. To drive the proposed ecosystem, we propose a new radio sidelink mmWave unlicensed (NR SL-mmW-U) as a decentralized D2D communication framework in detail including an initial physical (PHY) layer specification, such as subchannel size and slot format. We holistically evaluate the proposed NR SL-mmW-U for both control and user data transmissions based on the latest stochastic 60 GHz channel model tailored for D2D communication and 60 GHz-specific hardware impairment models. The evaluation demonstrates the feasibility of NR SL-mmW-U fulfilling the key requirement for deriving the proposed ecosystem, where the transmission distance of 1-10 m is achievable without high-gain beamforming antennas for all types of examined data and channel environments.

Exoskeleton robots hold immense promise in rehabilitation, serving as crucial aids for patient mobility and exercise. However, harnessing their capabilities requires overcoming significant control challenges arising from complex nonlinear dynamics and uncertainties in both models and actuators. This paper introduces a novel adaptive backstepping controller designed specifically for exoskeleton robots navigating uncertain dynamics and actuator parameters. Unlike conventional approaches that rely on basis functions, the proposed controller integrates a Modified Function Approximation Technique (MFAT) to approximate dynamic parameters without the need for such functions. The MFAT effectively manages mismatched perturbations, while the backstepping control compensates for uncertainties associated with state variables, enhancing resilience to disturbances, especially in scenarios where the exoskeleton's dynamics model is unknown. Lyapunov stability analysis ensures uniformly ultimately bounded signals within the closed-loop system. A comparative study conducted on the industrial robot IRB 120, validates the effectiveness of the proposed controller and highlights its superior performance. Real-time implementation on a 7 Degrees of Freedom (DOFs) wearable robot named ETS-MARSE confirms the efficiency of the control algorithm. The results from simulations and experiments underscore the efficacy of the proposed approach. The insights gained from this research pave the way to unlocking the full potential of exoskeleton robots in rehabilitation settings, promising improved patient outcomes and advancing human-technology interaction to new heights.