The advent of smart grid in the power infrastructure of the nations has provisioned a proactive and highly efficient power management at the generation end and user end as well. The smart meters being a fundamental part of the grid system are responsible for the intermittent sharing of usage data that further assists the utility for decision making. However, the inclusion of digital technology accompanies some major challenges like data security, data loss and communication issues etc. Smart meters are lightweight devices hence susceptible to errors frequently and therefore can cause loss of data. If the unavailability of usage data is significantly high, this can be problematic for the utility in context with further power generation and distribution decisions. This article presents a comprehensive discussion of the fault-tolerant aggregation models for the smart grid environment. A thorough investigation of the most recent research on handling of unavailable data is provided. Finally, the paper identifies the major challenges and future research possibilities for an efficiently fault-tolerant aggregation in the smart grid environment.

This paper introduces a preliminary concept aimed at achieving Artificial General Intelligence (AGI) by leveraging a novel approach rooted in two key aspects. Firstly, we present the General Intelligent Network (GIN) paradigm, which integrates information entropy principles with a generative network, reminiscent of Generative Adversarial Networks (GANs). Within the GIN network, original multimodal information is encoded as low information entropy hidden state representations (HPPs). These HPPs serve as efficient carriers of contextual information, enabling reverse parsing by contextually relevant generative networks to reconstruct observable information. Secondly, we propose a Generalized Machine Learning Operating System (GML System) to facilitate the seamless integration of the GIN paradigm into the AGI framework. The GML system comprises three fundamental components: an Observable Processor (AOP) responsible for real-time processing of observable information, an HPP Storage System for the efficient retention of low entropy hidden state representations, and a Multimodal Implicit Sensing/Execution Network designed to handle diverse sensory inputs and execute corresponding actions. By combining the GIN paradigm and GML system, our approach aims to create a holistic AGI system capable of encoding, processing, and reconstructing information in a manner akin to human-like intelligence. The synergy of information entropy principles and generative networks, along with the orchestrated functioning of the GML system, presents a promising avenue towards achieving advanced cognitive capabilities in artificial systems. This preliminary concept lays the groundwork for further exploration and refinement in the pursuit of true brain-like intelligence in machines.

In this work, we tackle the estimation of elevation and azimuth direction of arrival (DOA) of uncorrelated wideband (WB) and statistically non-stationary (NS) sources located in the far-field of a sensor array in both free-field and multipath propagation environments. For the case of free-field propagation, we derive the closed-form expression of the Cramer-Rao Bound (CRB) that is applicable to both the overdetermined and underdetermined cases of DOA estimation. We also establish the condition for the existence of the CRB along with a discussion on the dependence of CRB on the signal-to-noise ratio and the upper bound on the total number of WB and NS sources that can be resolved theoretically. Further, we develop a coarray-based DOA estimation algorithm that uses the enhanced degrees of freedom (DOF) of the difference coarray of a non-uniform planar array to localize more uncorrelated WB and NS sources than the number of physical sensors. In the multipath scenario, the correlation between the direct signal and the reflected signal is tackled by proposing a new focusing method based on the decomposition of the array steering function using 2D Fourier basis functions. The proposed focusing approach can be applied to any realworld arbitrary planar array and does not require the initial estimate of the DOAs of the WB and NS sources. After focusing, frequency smoothing is performed to decorrelate the direct and reflected signals. After decorrelation, we propose two coarraybased techniques to identify the time-frequency bins that are dominated by the direct signals which are then used to estimate the DOA of the sources emitting the direct signals. Numerical examples verify the effectiveness of the proposed methods.

The purpose of this study is to investigate and comprehend the performance analysis of a compact planar multiband multiple-input-multiple-output (MIMO) antenna, accomplished as a part of the ECE 533: Advanced Antenna Design course at Washington State University, Vancouver during the Fall 2022 semester. This study has introduced two symmetrical radiating elements joined by a neutralizing line to nullify the reactive coupling that makes up the MIMO antenna's basic structure. The basic MIMO antenna occupies an overall three dimensions of 60x80x0.8mm3 volume on a FR4 substrate with relative permittivity er=4.40 and loss tangent of tanδ=0.02. Coplanar waveguide (CPW) transmission lines of 50Ω have been used to feed the MIMO antenna. Furthermore, the base plane of the basic MIMO antenna has four slits and two compact rectangles of 2x12mm2 cut into it to compensate the mutual coupling. The slit width, neutralizing line, substrate material, and its thickness have all been tuned in the proposed techniques to analyze different antenna parameters. The operating frequency band has been set to 500MHz-3500MHz for all the four cases. By employing simulation results obtained from the ANSYS HFSS environment, the performance of the fundamental MIMO antenna is evaluated and assessed against the optimized models. The optimized versions of the conventional MIMO antenna design have been thoroughly discussed in separate case studies. From our software simulation analysis, we find that optimized geometric shapes of the compact planar MIMO antenna show significant improvement in the isolation parameter of |S21|, from <= 14.9 dB to <= 18.44 dB, <= 20 dB and up to <=27.68 dB for ease of understanding, no servicing frequencies have been predetermined.

A document by Areeg Zarkoosh. Click on the document to view its contents.

We present comprehensive numerical tools to investigate and analyze the surface wave attenuation on a metasurface waveguide that consists of periodic patches deposited on a dielectric substrate. The surface wave attenuation is evaluated in both waveguide and openspace environments at high frequencies around the 28-GHz band. We, first, employ two approaches including a driven modal approach and an integrated eigenmode approach for investigating the attenuation behaviors in a metasurface waveguide operating in transverse electromagnetic (TEM) mode. These results show different attenuation values depending on the waveguide height due to an unavoidable coupling issue despite the same metasurface design. Thus, we propose an alternative method for more accurately evaluating the frequencydependent attenuation characteristics through field distribution in open-space with a customized horn antenna. We establish a relationship between the surface wave attenuation and the propagation distance by analyzing the analytical electric field distribution at different points on the propagation path in the open space. Thus, this study provides an improved method for assessing surface wave attenuation characteristics, which contributes to designing surface wave control for wireless communications, wireless power transfer, signal processing, and electromagnetic compatibility.

This study addresses the escalating problem of financial fraud, with a particular focus on credit card fraud, a phenomenon that has skyrocketed due to the increasing prevalence of online transactions. The research aims to strengthen anti-money laundering (AML) systems, thereby improving the detection and prevention of fraudulent transactions. For this study, a Dense Neural Network (DNN) has been developed to predict fraudulent transactions with high accuracy. The model is based on deep learning, and given the highly unbalanced nature of the dataset, balancing techniques were employed to mitigate the bias towards the minority class and improve performance. The DNN model demonstrated robust performance, generalizability, and reliability, achieving over 99% accuracy across training, validation, and test sets. This indicates the model's potential as a powerful tool in the ongoing fight against financial fraud. The results of this study could have significant implications for the financial sector, corporations, and governments, contributing to safer and more secure financial transactions.

Food freshness classification is a growing concern in the food industry, mainly to protect consumer health and prevent illness and poisoning from consuming spoiled food. Intending to take a significant step towards improving food safety and quality control measures in the industry, this study presents two models based on deep learning for the classification of fruit and vegetable freshness: a robust model and an efficient model. Models' performance evaluation shows remarkable results; in terms of accuracy, the robust model and the efficient model achieve 97.6% and 94.0% respectively, while in terms of AUC score, both models achieve more than 99%, with the difference in inference time between each model over 844 images being 14 seconds.

The transition to a sustainable and low-carbon energy landscape has created extensive interest in green hydrogen as a sustainable and environmentally friendly energy carrier. This paper presents a comprehensive review of green hydrogen applications and optimization strategies employed in these applications within the power sector. We categorize the hydrogen system into two groups: grid-dependent green hydrogen systems (GDGHS) and grid-independent green hydrogen systems (GIGHS), recognizing their distinct operational characteristics and challenges. The paper also assesses a range of optimization methods and models employed to maximize the efficiency and economic viability of green hydrogen production, storage, and utilization within GDGHS and GIGHS. Furthermore, we discuss the key challenges and opportunities in deploying green hydrogen solutions within the power sector. We highlight emerging trends and potential future developments. This review aims to provide a holistic understanding of green hydrogen's transformative potential in the power sector. It also sheds light on cutting-edge optimization methodologies that can unlock its full potential in a sustainable energy future.

The intrinsic nature of tic disorders, characterized by symptom variability and fluctuation, poses challenges in clinical evaluations. Currently, tic assessments predominantly rely on subjective questionnaires administered periodically during clinical visits, thus lacking continuous quantitative evaluation. This study aims to establish an automatic objective measure of tic expression in natural behavioral settings. A custom-developed smartphone application was used to record selfie-videos of children and adolescents with tic disorders exhibiting facial motor tics. Facial landmarks were utilized to extract tic-related features from video segments. These features were then passed through a tandem of custom deep neural networks to learn spatial and temporal properties for tic classification. The model achieved a mean accuracy of 95% when trained on data across all subjects, and consistently exceeded 90% accuracy in leave-one-session-out and leave-one-subject-out cross validation training schemes. This automatic tic identification measure may provide a valuable tool for clinicians in facilitating diagnosis, patient follow-up, and treatment efficacy evaluation. Combining this measure with standard smartphone technology has the potential to revolutionize large-scale clinical studies, thereby expediting the development and testing of novel interventions.

Brain tumors, characterized by the emergence of abnormal cell growths within or around the brain, stand as a significant medical challenge with the potential for grave consequences. Regardless of their categorization as benign or malignant, the imperative for swift diagnosis and treatment remains paramount. This research explores the integration of pretrained deep learning models, particularly Convolutional Neural Networks (CNNs) including VGG16, InceptionV3, ResNet50, and NasNetMobile in automating the diagnosis process using MRI scans for the ease of patient and Healthcare Providers. This approach leverages transfer learning and Computer-Aided Diagnosis (CAD) to streamline the detection process. Hyperparameter tuning is integrated to optimize pretrained model parameters encompassing factors such as optimizer choices, activation functions, number of neurons in each dense layer and learning rates. By systematically fine tuning the hyperparameters remarkable enhancements in tumor classification accuracy are demonstrated. This research emphasizes the significance of customized hyperparameter optimization for pretrained models, advancing the accuracy and efficiency of brain tumor detection.

We present an automatic procedure to fully design a metasurface antenna, including the feeding structure. This feature is achieved by performing preliminary full-wave simulations of the feeding structure and by properly modelling PEC regions inside a current-based numerical design method. The synthesized metasurface antennas are implemented and simulated with a fullwave commercial solver, showing excellent agreement with the predicted performances.

 This paper applies the Design of Experiments approach for detecting detrimental weak emergent behavior of an Autonomous Surface Vessel operating in a dynamic environment on a Search and Rescue mission. The research utilizes Orthogonal Arrays in combination with regression analysis to systematically test the parameter space of an engineered system function. We explored the parameter space of interest and detected where the system model does not comply with a defined Measure of Effectiveness. The findings from this case study suggest that these methods enable a systematic exploration of the system’s parameter space, allowing for effective detection of detrimental weak emergent behavior. This approach potentially enhances test coverage, expands system operating knowledge, and facilitates mitigation efforts more efficiently.

The neutral form drag coefficient is an important parameter when estimating surface turbulent fluxes over Arctic sea ice. The form drag caused by surface features (𝑪𝒅𝒏,𝒇𝒓) dominates the total drag in the winter, but long-term pan-Arctic records of 𝑪𝒅𝒏,𝒇𝒓 are still lacking for Arctic sea ice. In this study, we first developed an improved surface feature detection algorithm and characterized the surface features (including height and spacing) over Arctic sea ice during the late winter of 2009-2019 using the full-scan laser altimeter data obtained in the Operation IceBridge mission. 𝑪𝒅𝒏,𝒇𝒓 was then estimated using an existing parameterization scheme. This was followed by applying a satellite-derived backscatter coefficient (𝝈𝒐𝒗𝒗 ) to 𝑪𝒅𝒏,𝒇𝒓 regression model to extrapolate, for the first time, 𝑪𝒅𝒏,𝒇𝒓 to the pan-Arctic scale for the entire winter season over two decades (from 1999 to 2021). We found that the surface features have a larger height and smaller spacing over multi-year ice (1.15 ± 0.21 m and 142 ± 49 m) than over first-year ice (0.90 ± 0.16 m and 241 ± 129 m). The monthly mean 𝑪𝒅𝒏,𝒇𝒓 increases through the winter, from 0.2 × 10 −3 in November to 0.4-0.5 × 10 −3 in April. The central Arctic has the largest 𝑪𝒅𝒏,𝒇𝒓 (up to 2 × 10 −3), but experienced a drop of ~50% in the period from 2001/2002 to 2008/2009. The interannual fluctuations in 𝑪𝒅𝒏,𝒇𝒓 are strongly linked to the variability of sea ice thickness and deformation, and the latter has become increasingly important for 𝑪𝒅𝒏,𝒇𝒓 since 2009.

Background: If causal interactions between brain regions, i.e. effective connectivity modelling, could be accurately achieved, early diagnosis of many neurodegenerative diseases would be possible. In many recent comparative studies based on simulations, it has been observed that Bayesian network-based methods are more successful than others due to their non-linear and non-deterministic nature. New method: Although Dynamic Bayesian networks (DBNs) are much more suitable for effective connectivity modelling in the brain because they can inherently model temporal information and cyclic behavior, they have not been tested in comparative studies due to computational complexity problems in structure learning. In this study, reliable modelling by DBNs is achieved, which will accelerate scientific developments in neuroscience. Solutions to the computational problems that are believed to exist for this promising modelling method are proposed. Result: It is shown that discrete DBN structure modelling, which is a data-driven approach, converges to the globally correct network structure when trained with the appropriate data size and the imaginary data size, which are much smaller than the theoretically appropriate amount of data. The quantization method is also very important for convergence. The Hill Climbing (HC) search method is shown to converge to the true structure at a reasonable iteration step size when using the appropriate data and imaginary data sizes. Comparison with existing methods: The method (Improved-dDBN) is applied to commonly used simulation data and it is shown that better and more consistent performance is obtained compared to existing methods in the literature, for realistic scenarios such as varying graph complexity, various input conditions, mixed signal and noise cases and non-stationary connection conditions. Conclusions: Since Improved-dDBN performed the best for all scenarios of the simulated dataset, it is a good candidate for use on real datasets for effective brain connectivity modelling. The sample size of the dataset is very important for convergence and should therefore be checked. Also, the imaginary size should be appropriate for the available sample size. For these computational constraints, the approach proposed in this study can be easily applied.

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.

A document by Martin Stumpf . Click on the document to view its contents.

We propose a technique that assists conversion from a loaded reference layout of an analog circuit to a template-based generator. In order to efficiently convert a complex analog layout into its generator, generators that are already available must be reused to generate as many sub-cells as possible. If there are many sub-cells and many available generators, it is quite timeconsuming to examine which generators can generate which subcells. The proposed convolutional neural network (CNN) model automatically detects sub-cells that can be generated by available generator templates in the library, and suggests using them in the hierarchically correct places of the generator software. Upon the designer's approval, the sub-cell generator template codes are automatically inserted by function call as suggested. In an experiment, the CNN model examined sub-cells of a high-speed wireline receiver that has total of 4,885 sub-cells among which 145 sub-cells are unique. The CNN model classified the 145 unique sub-cells into 52 classes of generation possibilities utilizing available layout generators, including one not-generatable class. The CNN model achieved 99.3% precision in examining the 145 unique sub-cells. In an experiment, the examination time was greatly reduced to 15.3 seconds by the CNN model from 88 minutes of manual examination even when there were only 52 classes of generation possibilities. The time improvement would be much greater if there were many more available generators and thus many more classes of generation possibilities.