There is a lot of pressures from organizations to continually redesign the business processes and the technology infrastructure to support the organization, but to also help organizations manage competing stakeholder needs, changing objectives and behaviour, and so on. A key part of this is making sure that all their activities in the different components of the business are interconnected and that they are moving in the same direction. Enterprise Architecture (EA) is an effective way of dealing with the challenge of designing business activities and supporting IT. This paper will summarize the organization of EA and look more closely at some major components of EA: governance architecture, business architecture, information architecture and technical architecture and consider the overlap and constructive interaction involved. Enterprise Architecture can be best understood through business process redesign. So, it is useful to briefly review the leading frameworks for business process redesign, including COBIT from ISACA, LEAN from the Toyota Group, TOGAF from the Object-Managemen t Group, and the Capgemini Integrated Architecture Framework (IAF). All these frameworks allow us to more successful transformation, to return to the central idea of this paper.

This paper presents a methodology to allocate the savings from expected reduction in grid investment costs in a voltage-constrained distribution grid due to Energy Communities’ (ECs) flexibility. Incentives to provide flexibility are distributed both spatially to the contributing ECs in the grid, and temporally in the form of hourly incentives for providing flexibility. The savings from investment cost reduction are distributed using Shapley values, based on each EC’s marginal contributions to voltage improvement. Given the computational intensity of calculating Shapley values, especially with numerous ECs, we further introduce a stratified sampling method to improve the computational efficiency without compromising the fairness of incentive allocation to the ECs. Finally, using Bayesian regression, an approximation method is proposed to map the EC’s location in the grid to its incentive allocation. Bayesian regression offers a probabilistic approach to estimate the Shapley value, accommodating uncertainties inherent in the calculation by using probability distributions for the parameters of regression. The performance of the proposed methodology and approximation methods are demonstrated through extensive case studies on a representative Norwegian medium voltage distribution grid.

In this paper, we present a novel approach for managing the file system in Linux using a voice assistant. Our system allows users to perform file system operations such as creating directories, renaming files, and deleting files by issuing voice commands. We develop a voice assistant using Python libraries and integrate it with the file system in Linux. The voice assistant is capable of understanding natural language and executing commands based on the user's voice inputs. We conduct experiments to evaluate the performance of the system and demonstrate that our approach is effective and efficient in managing the file system using voice commands. Our system can enhance the accessibility and usability of the file system in Linux for individuals with disabilities or those who prefer a hands-free approach to file management.

Existing methods for simulating missile dynamics and training often rely on three degrees of freedom (3DOF) motions, limiting their ability to represent the complex maneuvers and targeting strategies of modern missiles. This paper proposes integrating virtual reality (VR) technology with six degrees of freedom (6DOF) dynamics to enhance the accuracy and efficiency of missile simulation and training. By incorporating 6DOF dynamics, simulations can capture the full range of missile motion, providing a comprehensive understanding of their capabilities and limitations. Through VR, users can immerse themselves in realistic missile scenarios, interact with dynamic behaviors in real-time, and refine their targeting and interception skills. The integration of VR with 6DOF dynamics also facilitates realtime feedback and evaluation, enhancing training efficiency and proficiency in missile operations. This approach aims to revolutionize missile training by bridging the gap between traditional simulation methods and real-world dynamics.

Human Activity Recognition (HAR) using channel state information (CSI) is crucial for advancing healthcare applications, enabling non-invasive monitoring for improved patient care. However, traditional HAR techniques mainly rely on centralised data processing, which necessitates the sharing of raw data. This approach raises privacy concerns, leading to inefficient bandwidth usage and incurs significant communication overhead and latency, thereby limiting real-time performance and scalability. This paper introduces FedFusionQuant (FFQ), a novel federated learning (FL) framework that integrates advanced signal processing, feature fusion, and model compression during the training process. The proposed framework utilises the federated distance (FedDist) algorithm to adapt parameter adjustments based on neuron dissimilarity measures, effectively mitigating overfitting. Additionally, the incorporation of quantisation-aware training (QAT) allows the model to maintain high accuracy while substantially reducing model size. Extensive empirical evaluations demonstrate the efficacy of FFQ, showing a significant improvement in classification accuracy and energy efficiency through feature fusion and QAT. Furthermore, model compression with QAT achieves a 47% reduction in communication overhead while maintaining accuracy comparable to state-of-the-art methods.

This paper proposes a traveling-wave endfire antenna with temporal differentiation function, which is able to directly demodulate a frequency modulated signal at the antenna end. To tailor the frequency response as a differentiator, aperiodic phase loading technique is proposed to design such a multifunctional endfire antenna. Space mapping technique is also adopted in the final tuning process. To verify the proposed design technique, an illustrative example of differentiating endfire antenna is designed and fabricated. The results show that it works within 15.1-15.7 GHz with differentiation capability, and achieves an endfire gain of 17.3 dBi at center frequency. To verify its differentiation function, a frequency modulated signal is radiated by the other standard horn antenna to the designed differentiating antenna. The directly demodulated signal agree well with the ideal transmitted one, which finally validate the proposed technique.

This paper presents a novel single layer compact harmonic transponder tag operating at 2.9 GHz in the forward link (harmonic exciter to harmonic tag) and 5.8 GHz in the return link (harmonic tag to harmonic reader). The proposed tag consists of an asymmetric folded meander-line dipole antenna that is directly matched to the low-voltage Schottky diode at the fundamental and the second harmonic frequencies. The tag is printed on an ultrathin flexible polymide substrate and achieves a low weight of 22 mg and has a largest dimension of less than 15% of the wavelength at the fundamental frequency. The tag performance has been experimentally evaluated and compared with that of traditional wire-based dipole tags. Presented results indicate that the proposed tag provides significantly higher conversion efficiency at low incident power levels and enables a twofold increase of the maximum detectable range.

The widespread application of Unmanned Aerial Vehicles (UAVs) is often constrained by their dependency on GPS, particularly in environments lacking reliable signals due to obstructions, indoor operations, and susceptibility to interference. This limitation underscores the critical need for GPS-denied navigation solutions, essential not only for indoor settings but also for complex environments like dense forests, tunnels, warehouses, and factories where UAVs play vital roles in surveillance and search and rescue operations. Achieving accurate and robust state estimation and control is pivotal for enabling autonomous navigation in these challenging contexts. Vision-based techniques such as Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) have proven indispensable for precisely estimating pose and stabilizing UAVs during flight. This paper introduces a robust stereo vision-based Visual Inertial Odometry (VIO) algorithm tailored for precise waypoint navigation of Micro Aerial Vehicles (MAVs) in complex GPS-denied indoor environments. By leveraging VIO, our approach accurately estimates the MAV's current state and trajectory along cyclic paths, ensuring stable flight and precise waypoint attainment. Experimental results in indoor environments show that our MAV achieves autonomous navigation with an accuracy of 10-20 cm over a total distance of 30 meters.

Indian Sign Language (ISL) is a crucial attribute to carrying out smooth and effective communication between the deaf and the hard-of-hearing community and those that can hear clearly. In this paper, we propose a new approach for word-level Indian Sign Language recognition using the MediaPipe framework, which is aided by the Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) network. Our approach uses robust hand and pose tracking features of MediaPipe to infer precise and comprehensive feature representations of sign gestures. These features are then fed into a hybrid LSTM-GRU network designed to capture Temporal dependencies, hence improving the accuracy of recognition. In this paper, we test the proposed system using a varied dataset, INCLUDE [1], [10], for ISL signs, to show the system's capacity to deal with variation across signing styles and speeds. Experimental results indicate our approach outperforms current methods by large margins in recognition accuracy and processing system speed for the technique to work practically, ensuring real-time processing to ensure fast feedback to the user. Besides, the integration of MediaPipe real-time tracking with state-of-the-art sequential modelling capabilities of LSTM-GRU networks is also very promising for the real-time application of ISL recognition systems. The test accuracy achieved through this research is 89.50%, which is 2.1% improved over existing models [5]. The findings corroborate the potential of using the state-of-the-art tracking frameworks with deep learning techniques for the development of efficient and accurate ISL recognition systems [6]. This work increases academic understanding about the recognition of ISL and has practical implications for the development of assistive technologies that bridge communication gaps in various social contexts.

The exponential growth of vulnerable Internet-of-Things (IoT) devices, like networked cameras, sensors, or actuators, has raised concerns about the cybersecurity risks they pose to individuals, organizations, governments, and the Internet at large. Ensuring the integrity and security of these devices and their networks is crucial and requires immediate attention. However, implementing robust security measures on devices can be costly, and imposing stringent requirements may pose challenges for many manufacturers. A network-level approach appears promising and more practical, particularly with the adoption of the Manufacturer Usage Description (MUD) standard ratified by the Internet Engineering Task Force (IETF), which significantly aids in reducing network attack surfaces. This paper aims to enhance the adoption of this lightweight standard by proposing a systematic approach to verify the conformity of IoT devices with MUD specifications. Our specific contributions are threefold. (1) We map the standard MUD specifications, as described in RFC8520, into 26 specific tests that focus on three critical components: IoT devices, cloud servers, and content of MUD files; (2) We categorize these tests into six progressive levels, allowing for gradual adoption of and staged conformance to the MUD standard; and, (3) We build a testbed, configure a Raspberry Pi device to represent a reference device under test (DUT), develop an automated software tool that performs 26 specific tests on the DUT, and evaluate the conformity of more than 70 MUD files from two public repositories.

In this study, we predict inventory for an IoTenabled vending machine warehouse servicing approximately 1,500 vending machines with the goal of timely replenishing, achieving cost effectiveness, reducing stock waste, optimising the available resources and ensuring fulfilment of consumer demand. The study deploys four different ML algorithms, namely, Extreme gradient boosting, Autoregressive integrated moving average with/without exogenous variables (ARIMA/ARIMAX), Facebook Prophet (Fb Prophet), and Support Vector Regression (SVR). The study unfolds in two phases. First, we utilise conventional historical sales data variables to make the prediction whereas in the second phase, we systematically introduced external variables including weekday, sales deviation flag, and holiday flags into our ML algorithms. The results indicate a significant performance boost using external variables with extreme gradient boosting achieving the lowest (Mean Absolute Error) MAE of 22, followed by ARIMAX, FB Prophet, and SVR with MAE values of 27, 37, and 38, respectively.

Beal conjecture can be proved extending the understanding of Goldbach's discussion.

Automatic modulation classification (AMC) has a wide range of applications in both civilian and military fields, such as industrial internet of things (IIoT) security, communication spectrum management, and military electronic countermeasures. However, label mislabeling often occurs in practical scenarios, significantly impacting the performance and robustness of deep neural networks (DNNs). In this article, we propose a meta-learning-guided label noise distillation method to enhance the robustness of AMC models against label noise or errors. Specifically, we propose a teacher-student heterogeneous network (TSHN) to discriminate and distill label noise. Following the notion that labels represent information, a teacher network, utilizing trusted few-shot labeled samples, reevaluates and corrects labels for a considerable number of untrusted labeled samples through meta-learning. By dividing and conquering untrusted labeled samples according to their confidence levels, the student network learns more effectively. Additionally, we propose a multiview signal (MVS) method to further enhance the performance of hard-to-classify categories with few-shot trusted labeled samples. Extensive experiments on the RadioML2016 and HisarMod2019.1 datasets demonstrate that our methods significantly improve accuracy and robustness in signal AMC across diverse label noise scenarios, including symmetric, asymmetric, and mixed label noise. For example, compared to the baseline CNN with cross entropy loss, our proposed TSHN achieves a remarkable 1.26% to 36.84% accuracy improvement under symmetric label noise and 0.12% to 38.59% accuracy improvement under mixed label noise. Moreover, TSHN exhibits greater robustness to varying noise rates compared to existing methods.

A common theme in all the above areas is designing a dynamical system to accomplish desired objectives, possibly in some predefined optimal way. Since control theory advances the idea of suitably modifying the behavior of a dynamical system, this paper explores the role of control theory in designing efficient algorithms (or dynamical systems) related to problems surrounding the optimization framework, including constrained optimization, optimization-based control, and parameter estimation. This amalgamation of control theory with the above-mentioned areas has been made possible by the recently introduced paradigm of Passivity and Immersion (P&I) based control. The generality and working of P&I, as compared to the existing approaches in control theory, are best introduced through the example presented below.

The rapid advancement of neural networks in various applications, from healthcare diagnostics to financial modeling, has significantly improved the accuracy and efficiency of decision-making processes. However, these models often operate as black boxes, providing little to no insight into how they arrive at specific predictions. This lack of interpretability presents a major barrier to their adoption in critical domains where trust, accountability, and transparency are paramount. This study aims to address this issue by developing a novel framework that integrates multiple Explainable AI (XAI) techniques to enhance the interpretability of neural networks. The proposed framework combines feature importance analysis, layer-wise relevance propagation (LRP), and visual explanation methods such as Gradient-weighted Class Activation Mapping (Grad-CAM). These techniques collectively offer a comprehensive view of the decision-making processes of neural networks, making them more transparent and understandable to stakeholders. Our experimental results demonstrate that the integrated XAI framework not only improves interpretability but also maintains high levels of accuracy, thereby bridging the gap between performance and transparency. This research provides a foundational basis for the deployment of interpretable neural networks in critical applications, ensuring that AI-driven decisions are both reliable and comprehensible.

The inverted-F antenna with a parasitic branch (IFA&PB) is a commonly used antenna design in contemporary metal-bezel smartphones. In this structure, the primary branch of the IFA is typically directly excited through the feed, while the parasitic branch is activated via electromagnetic coupling. However, the traditional single feed excitation method for the IFA&PB often results in the radiation efficiency local minimum, significantly impacting the antenna's overall performance. To address this issue, this paper proposes a novel parallel feed approach with two different feeding phases for the IFA&PB in smartphones. This innovative method aims to eliminate the radiation efficiency local minimum observed in conventional IFA&PB structures. To validate this method, a high efficiency MHB antenna, consisting of the IFA&PB and a parallel feed with two different feeding phases, is achieved without the radiation efficiency local minimum in the MHB frequency band (1.71-2.69 GHz). Furthermore, this design is extended by integrating a traditional LB antenna, covering the LB frequency band (0.704-0.96 GHz), resulting in an octa-band mobile antenna with high efficiency. A prototype of the proposed design demonstrated measured-6 dB impedance bandwidths of 270 MHz (0.69-0.96 GHz) and 1750 MHz (1.35-3.10 GHz), effectively covering the LB and MHB frequency bands. The prototype's measured average efficiencies were-7.7 dB (17%) and-4.3 dB (37%), respectively. The proposed parallel feed with two different feeding phases can eliminate the radiation efficiency local minimum of the IFA&PB structure, presenting a promising solution for modern metal-bezel mobile antennas.

A document by Takuma Yoshimura . Click on the document to view its contents.

Efficient and informative representation of system topologies is critical in AI-driven power system analysis (PSA). Despite a major breakthrough, recent approaches employing Graph Neural Networks (GNNs) face significant challenges in large-scale PSA, including high computational demands for sufficient labeled data and poor generalization to unseen topologies. To tackle these issues, we propose a self-supervised strategy for pre-training GNNs that enhances their expressiveness at both the individual node feature level and the whole graph structure. Integrating physics-informed techniques, our strategy allows GNNs to internalize fundamental principles applicable to multiple downstream tasks. We demonstrate that our method enables efficient training of GNNs on extensive topology datasets without supervision, effectively addressing the noted challenges. By pre-training GNNs with 145 million parameters on 20 million unlabeled topologies and subsequently fine-tuning them, we observe a significant performance improvement, averaging over 13%, compared to existing state-of-the-art (SOTA) methods across four challenging tasks.