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Fast and accurate transient analysis (TA) requires a meticulously explored nonlinear transient excursion space (NTES). Hence, designing a polyhedral feature selection scheme (FSS) to fix the TA bottleneck raised by feeding non-discriminative transient records into predictive models is a significant task to avoid discordant TA indices results. Hence, we offer a fourfold bi-filter and permuted bi-quad-wrapper (F2FP2QW) FSS to find optimal transient moments (OTMs) from multi-trajectory transient records (MTTRs). The F2FP2QW model exerts varied fourfold repetition of the linked four-stage, including the bi-filter (relevancy rate (RR) and conditional-based RR (CRR) calculations) and bi-wrapper (incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr)). The RR and CRR mounted on mutual information (MI) and conditional MI (CMI) in the filter stages. In the case of wrapper methods, hyperplane-based classifiers (HCs), namely support vector machine (SVM) and twin SVM (TWSVM), play the main role in IWSS and IWSSr tree formation. Due to kernel-based learning necessity in NTES, embedding the radial basis function (RBF), dynamic time warping (DTW), and polynomial (POL) kernels into dual HCs is on the agenda. Generally, wrapper stages include ${ }_{TWSVM}^{SVM} \textrm {IWSS}_{RBF/\,POL}^{RBF/\,DTW\,} $ and ${ }_{TWSVM}^{SVM} \textrm {IWSSr}_{RBF/\,POL}^{RBF/\,DTW\,}$ in a 24-way permutation. By applying the multifaced filter-wrapper scenario of F2FP2QW on 28-trajectory transient data, the OTMs are obtained. Finally, the efficacy of OTMs survived by the F2FP2QW model in TA is evaluated via a cross-validation scenario. The results show that F2FP2QW has a prediction accuracy of 98.87 % and a processing time of 102.587 milliseconds for transient stability prediction.

An H-matrix accelerated direct solver employing the high-order Chebyshev-based Boundary Integral Equation (CBIE) method has been formulated, tested, and profiled for performance on high contrast dielectric materials and electrically large perfect electric conductor objects. The matrix fill performance of the CBIE proves to be fast for small to moderately sized problems compared to its counterparts, e.g., the locally corrected Nyström (LCN) method, due to the way it handles the singularities by means of a global change of variable method. However, in the case of electrically large scattering problems, the matrix fill and factorization still dominate the solution time when using a direct solution approach. To address this issue, an H-Matrix framework is employed, effectively resolving the challenge and establishing the CBIE as a competitive high-order method for solving scattering problems with poorly conditioned matrix equations. The efficacy of this approach is demonstrated through extensive numerical results, showcasing its robustness to problems that are electrically large, near physical resonances, or that have large dielectric permittivities. The capability of the proposed solver for handling arbitrary geometries is also demonstrated by considering various scattering examples from complex CAD models.

The manufacturing industry is facing increasing challenges to improve sustainability performance by using Industry 4.0 technologies like big data analytics, the internet of things, and digital twins, considering their potentials. However, effectively integrating sustainability goals and Industry 4.0 technologies within manufacturing systems can be challenging due to real-time attribute data of digital twins of machinery obtained by IoT tools and the necessity to integrate them into production planning levels. This challenge can be addressed by developing a framework for optimizing the flow of operations in a manufacturing system while incorporating sustainability assessment and Industry 4.0 technologies effectively. This paper investigates the integration of sustainability assessment considering Industry 4.0 technologies and the use of Markov Decision Process capabilities. The framework utilizes Markov Decision Process to model the decision-making process of the manufacturing system and its decision-makers. It includes sustainability goals as constraints or objectives in the Markov Decision Process model. The use of Industry 4.0 technologies is integrated into the framework to gather data and optimize the decision-making process based on that data. The capabilities of the proposed framework are demonstrated through case studies of a single agent on a shop floor. The findings from the case study indicate that the proposed framework can effectively support decision-making at the top-tier level of the enterprise by integrating sustainability assessment and the Industry 4.0 paradigm.

This paper presents the development of an ultra-wideband bistatic radar operating over the frequency range of 0.7–2.1 GHz based on the Radio-Frequency System-on-Chip (RF-SoC) platform and its application to snow measurements. The system utilizes Global Positioning System Disciplined Oscillators (GPS-DO) to synchronize the transmitter and receiver mounted on two small unmanned aerial systems (sUAS). We developed an arbitrary waveform generator to synthesize and transmit chirp signals. A data acquisition system was designed to capture signals with up to 2.2 GHz bandwidth and record the radar data. The transmit and receive antennas were developed using a four-element Vivaldi antenna array over the operating frequency range. Bistatic measurements were performed to determine the Brewster angle to estimate snow dielectric properties. The separation, incidence, and reflection angles were adjusted to collect radar data over an angular region extending from 27 to 68 degrees over snow at the Study of Precipitation, the Lower Atmosphere and Surface for Hydrometeorology (SPLASH) field site near Gothic, Colorado.

Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, state monitoring, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes in ubiquitous IoT systems present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensing, computing, storage, and communication applications in ubiquitous IoT systems. In this paper, we propose a novel VHetNet-enabled asynchronous federated learning (AFL) framework by adopting the compound-action actor-critic (CA2C) algorithm for UAV selection, which enables decentralized UAVs to collaboratively train a global anomaly detection model based on their local sensory data from IoT devices. In the VHetNet-enabled AFL framework, the UAV selection process aims to prevent UAVs with low local model quality and large energy consumption from affecting the learning efficiency and model accuracy. Due to the wide coverage as well as strong storage and computation capabilities, a HAPS operates as a central aerial server for aggregating local models of UAVs asynchronously and making decisions intelligently. Moreover, we propose a CA2C-based joint device association, UAV selection, and UAV placement algorithm to further enhance the overall federated execution efficiency and detection model accuracy under UAV energy constraints. Extensive experimental evaluation on real-world datasets demonstrates that the proposed algorithm can achieve high detection accuracy with short federated execution time and low energy consumption.

In the context of the electric vehicle (EV) industry, multi-level inverters (MLIs) have garnered increasing attention due to their potential to harness the advantages of higher DC link voltages. Among the various MLIs, the three-level T-type neutral-point-clamped (T-NPC) topology stands out as a superior alternative to the conventional two-level six-switch counterpart. This paper presents an adaptive Space Vector Modulation (SVM) technique for the typical three-level T-NPC inverter, with the aim of enhancing inverter performance across a wide speed spectrum in EVs. Through a comprehensive process of design, simulation, and experimental analysis, the findings reveal improvements in efficiency when compared to both the two-level six-switch inverter and the T-NPC inverter employing conventional three-level SVM. These results underscore the advantages and effectiveness of the introduced control scheme, which increases efficiency without incurring additional costs of any additional circuit components or control effort. The paper provides a complete set of in-agreement simulation and experimental results, to provide the proof of concept.

Parkinson’s disease is a degenerative neurological disorder that impairs motor functions and is accompanied by a wide range of non-motor symptoms, such as sleep problems. Parkinsonism is assessed during clinical evaluations and via self-administered diaries and, based on these, the required medication therapies are provided to lessen symptoms. Tri-axial accelerometers and gyroscopes have the potential utility to objectively assess the patient’s condition and aid clinicians in their decision-making. People with Parkinson’s often have significant abnormalities in blood pressure due to comorbid age-related cardiovascular disease and orthostatic hypotension, which result in blurred vision, dizziness, and falls. Frequent blood pressure monitoring may aid in the evaluation of such events and differentiate Parkinson’s disease symptoms from those originated by hypotension. In the present paper, a novel technology for the remote monitoring of Parkinsonian symptoms is presented: the WESAA system. It consists of two devices, worn on the wrist and ankle; its main function is to record accelerations and angular velocities from these body parts, together with photoplethysmograph and electrocardiogram data. This information can be elaborated offline to measure common Parkinson’s disease motor symptoms (e.g., tremor, bradykinesia, and dyskinesia), as well as gait speed, sleep-wake cycles, and cuff-less blood pressure measurements. The overall system requirements, market overview, industrial design and ergonomics, system development, user experience, early results of the gathered inertial raw data, and validation of the photoplethysmograph and electrocardiogram signal waveforms are all thoroughly discussed. The developed technology satisfies all system requirements, and the sensors adopted provided outcomes comparable with gold standard techniques.

High-power microwave applications are growing for both military and civil purposes, yet they can induce brain-related risks and raise important public health concerns. High-power sub-millisecond radio frequency energy pulses have been demonstrated to be able to induce neurological and neuropathological changes in the brain while being compliant with current regulatory guidelines’ limits, highlighting the necessity of revising them.

This paper proposes a generalized control for multiphase multilevel neutral-point-clamped (MPML-NPC) converter in a large spacecraft electric propulsion (EP) system. The spacecraft EP scheme considered in this paper has been proposed by NASA for long-term crewed missions and includes a heat source that acts as a prime mover to a fixed speed permanent magnet generator (PMG) whose output is rectified via a MPML-NPC converter before connecting to a high-power electric thruster. The proposed generalized control is based on space vector modulation (SVM) developed for MPML-NPC converters with any number of phases and levels. The paper will discuss mathematical modelling of multiphase systems and derive the equations that govern the proposed control. The generalized control generates optimized switching commands based on a space vector diagram (SVD). The analytical SVM models are verified through simulations for 3-phase, 6-phase, and 9-phase MPML-NPC converters. Further, the simulations are validated through test results from a scale-down laboratory prototype MPML-NPC converter.

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by the structure and function of the human brain, designed to process and learn from large amounts of data, making them particularly well-suited for tasks such as image and speech recognition. However, applications of DNNs are experiencing emerging growth due to the deployment of specialized accelerators such as the Google’s Tensor Processing Units (TPUs). In large-scale deployments, the energy efficiency of such accelerators may become a critical concern. In the voltage overscaling (VOS) technique, the operating voltage of the system is scaled down beyond the nominal operating voltage, which increases the energy efficiency and lifetime of digital circuits. The VOS technique is usually performed without changing the frequency resulting in timing errors. However, some applications such as multimedia processing, including DNNs, have intrinsic resilience against errors and noise. In this paper, we exploit the inherent resilience of DNNs to propose a quality-aware voltage overscaling framework for TPUs, named X-TPU, which offers higher energy efficiency and lifetime compared to conventional TPUs. The X-TPU framework is composed of two main parts, a modified TPU architecture that supports a runtime voltage overscaling, and a statistical error modeling-based algorithm to determine the voltage of neurons such that the output quality is retained above a given user-defined quality threshold. We synthesized a single-neuron architecture using a 15-nm FinFET technology under various operating voltage levels. Then, we extracted different statistical error models for a neuron corresponding to those voltage levels. Using these models and the proposed algorithm, we determined the appropriate voltage of each neuron (the voltage level of each column of the X-TPU). Results show that running a DNN on X-TPU can achieve 32% energy saving for only 0.6% accuracy loss.

This paper proposes a generalized space vector modulation (SVM) strategy for 3-level neutral-point-clamped (NPC) converters. The proposed strategy, here referred to as multiphase 3-level SVM (MP3L-SVM), is developed as a generic modulation technique applicable for 3-level NPC converters with any number of phases, while for showcasing in this paper the proposed MP3L-SVM is applied to a wind turbine with a direct drive multiphase permanent magnet generator (PMG) whose output is rectified using a 3-level NPC converter before connection to a high-voltage DC (HVDC) grid. A key challenge in 3-level NPC converters is maintaining a balanced voltage across the HVDC-link capacitors, particularly at low speeds. To address this issue, the paper integrates the proposed MP3L-SVM strategy with a voltage balancing algorithm (VBA), which mitigates capacitor voltage imbalances by choosing the three nearest switching states that result in minimal energy deviations across the capacitors. The paper discusses the mathematical modeling of a multiphase PMG interfaced to a 3-level NPC and derives generalized models that govern the proposed strategy based on a visual space vector diagram (SVD). Analytical models are validated through simulations across various 3-level NPC configurations, including 3-phase, 6-phase, and 9-phase NPC converters. Further, the analytical models and simulation results are validated by test results from a scaled-down laboratory prototype 3-level NPC converter that has been prototyped in-house.

Reliability management is one of the primary concerns in manycore systems design. Different aging mechanisms such as Negative-Bias Temperature Instability (NBTI), Electromigration (EM), and thermal cycling (TC) can reduce the reliability of these systems. However, state-of-the-art works mainly focused on NBTI and EM, whereas a few works have considered the thermal cycling effect. The thermal cycling effect can significantly aggravate the system’s lifetime. Moreover, the thermal effects of cores on each other due to their adjacency may also influence the system’s Mean Time to Failure (MTTF). This paper introduces a new technique to manage the reliability of manycore systems. The technique considers thermal cycling, adjacency of cores, and process variation-induced diversity of operating frequencies. It uses two levels of task mapping to improve system lifetime. At the first level, cores with close temperatures are packed into the same bin, and then, an arrived task is assigned to a bin with a similar temperature. Afterward in the second level, the task is assigned to a core inside the selected bin in the first level, based on performance requirements and the core frequency. Compared to the conventional Thermal cycling aware techniques, the proposed method is performed at a higher level (bins level) to reduce the thermal variations of cores inside a bin, and improves the system MTTFTC, making it a promising solution for manycore systems. The efficacy of our proposed technique is evaluated on 16, 32, 64, and 256 core systems using SPLASH2 and PARSEC benchmark suite applications. The results show up to 20% MTTFTC increment compared to the conventional thermal cycling-aware task mapping techniques.

The Remote Sensing (RS) field continuously grapples with the challenge of transforming satellite data into actionable information. This ongoing issue results in an ever-growing accumulation of unlabeled data, complicating interpretation efforts. The situation becomes even more challenging when satellite data must be used immediately to identify the effects of a natural hazard. Self-supervised learning (SSL) offers a promising approach for learning image representations without labeled data. Once trained, an SSL model can address various tasks with significantly reduced requirements for labeled data. Despite advancements in SSL models, particularly those using contrastive learning methods like MoCo, SimCLR, and SwAV, their potential remains largely unexplored in the context of instance segmentation and semantic segmentation of satellite imagery. This study integrates SwAV within an auto-encoder framework to detect landslides using deca-metric resolution multi-spectral images from the globally-distributed large-scale landslide4sense (L4S) 2022 benchmark dataset, employing only 1% and 10% of the labeled data. Our proposed SSL auto-encoder model features two modules: SwAV, which assigns features to prototype vectors to generate encoder codes, and ResNets, serving as the decoder for the downstream task. With just 1% of labeled data, our SSL model performs comparably to ten state-of-the-art deep learning segmentation models that utilize 100% of the labeled data in a fully supervised manner. With 10% of labeled data, our SSL model outperforms all ten fully supervised counterparts trained with 100% of the labeled data.

The increasing importance of social media stems from its pervasive influence on customer behavior, particularly with the rising prominence of influencers. Understanding how influencers impact user loyalty and behavior has become a focal point for researchers. This study delves into the capabilities of social media analytics and the influential roles of key individuals in shaping customer behaviors. Employing a system dynamics approach and leveraging Python for sentiment analysis, this study investigates the interplay between influencers and customer behaviors. A system dynamics model is developed to elucidate these capabilities, integrating them through omnichannel touchpoints across product lifecycle management (PLM) cycles to drive sustainability in customer behavior models. The core concept of Social Media Analytics serves as the foundation for integrating influencer roles into touchpoints where customers engage with social media, particularly within omnichannel strategies. The developed causal model captures the interactions among agents and the influencing parameters, exploring various scenarios to analyze the influencer-driven paradigm shift. Case study models focusing on the Cell phone and E-Auto industries provide insights into the capabilities of influencer-driven trends in shifting consumer behavior paradigms toward sustainability perspectives.

Integrating the feature selection process into machine learning-based transient analysis (MLTA) is essential for ensuring precise and timely transient stability prediction (TSP). Therefore, it’s important to prioritize the development of a hybrid feature selection scheme (FSS) to find optimal transient features (OTFs) from high-dimensional transient data within MLTA projects. This study introduces the double filter-encircled wrappers FSS (DFEWFSS) to survive OTFs from transient 28-trajectory data. Each fold of DFEWFSS consists of interconnected encircled wrappers by filters. The filter phase is exerted by relevancy score (RS) and conditional RS (CRS) which stem from mutual information (MI) and conditional MI (CMI). The wrapper phase is conducted by kernelized hyperplane-based classifiers, which are mounted on incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Following the completion of OTFs, the evaluation of OTFs in TSP is carried out through a cross-validation scenario. The results show that DFEWFSS-based OTFs have a prediction accuracy of 99 % and a processing time of 102.155 milliseconds for TSP.

Co-designing energy systems across multiple energy carriers is increasingly attracting attention of researchers and policy makers, since it is a prominent means of increasing the overall efficiency of the energy sector. Special attention is attributed to the so-called energy hubs, i.e., clusters of energy communities featuring electricity, gas, heat, hydrogen, and also water generation and consumption facilities. Managing an energy hub entails dealing with multiple sources of uncertainty, such as renewable generation, energy demands, wholesale market prices, etc. Such uncertainties call for sophisticated decision-making techniques, with mathematical optimization being the predominant family of decision-making methods proposed in the literature of recent years. In this paper, we summarize, review, and categorize research studies that have applied mathematical optimization approaches towards making operational and planning decisions for energy hubs. Relevant methods include robust optimization, information gap decision theory, stochastic programming, and chance-constrained optimization. The results of the review indicate the increasing adoption of robust and, more recently, hybrid methods to deal with the multi-dimensional uncertainties of energy hubs.

Switches (SWs) and renewable distributed generations (RDG) are subject to failure as well as other components of a system that are usually presumed fully reliable in the literature. It can lead to an overestimation of their contribution to reliability enhancement disregarding their dysfunction. This study considers the failure rate of SWs and RDGs to assess their impact on the reliability cost of the system and their effect on the optimal placement using a Markov-based approach. The optimal placement of SWs and RDGs in radial distribution systems is carried out simultaneously aim at minimizing the overall cost including investment, maintenance of SWs, and RDGs alongside the interruption cost. The performance of the model is verified through different scenarios and tested on RBTS bus 2. The results show that high values of the failure rates lead to fewer allocations to prevent plummeting the reliability of the system and increasing the capacity of the RDG results in fewer SWs allocation.

The way that computing systems digest geographic space is fundamentally different from people’s understanding of space. In human discourse, a geographic space is referred to by a place name, and the reasoning about a place are based on its characteristics. This is in contrast with computing systems where geographical spaces are handled by the definition of coordinate systems. Hence, when recommending places, a recommendation method that leverages textual content, as a medium of communication among people, can be better understood. In this paper, we use elements of Natural Language Processing (NLP), such as Positive Point-wise Mutual Information (PPMI), Term Frequency - Inverse Document Frequency (TF-IDF), and Multi-Dimensional Scaling (MDS), to infer a conceptual space of the items of a place-based recommender system. By applying a Support Vector Machine (SVM) classifier on the resulting conceptual space, some meaningful directions are extracted. Shannon entropy is used as a measure to identify the directions that imply a valid geographic region. We apply the method on a dataset of advertisement descriptions of rental properties and a dataset of Persian Wikipedia articles. The results showed the proposed method is able to measure the similarity of items in the inferred conceptual space with 88% of accuracy. A comparison with BERT algorithm demonstrates the superiority of the proposed method over the baseline models.

This article studies remote sensing image retrieval using kernel-based support vector data description (SVDD). We exploit deep SVDD, which is a well-known method for one-class classification to recover the most relevant samples from the archive. To this end, a deep neural network (DNN) is jointly trained to map the data into a hypersphere of minimum volume in the latent space. It is expected that similar samples to the query are compressed inside of the hypersphere. The closest embedding to the center of the hypersphere is related to the most relevant sample to query. We enhance deep SVDD by injecting the statistical information of data to the DNN by means of additional terms in the cost function. The first enhancement method takes advantage of covariance regularization of batches of the training set to penalize unnecessary redundancy and minimize the correlation between the different dimensions of the embedding. The second method involves unlocking the hypersphere's predefined center while preventing network divergence during training. Therefore, two parameters are designed to control the importance of the drifting of the center and the importance of a fixed predefined center (convergence), respectively. This has been implemented by considering the average of batches of embedding in each iteration as the updated center. This pushes irrelevant samples away from query samples, making data clustering easier for the DNN. The performance of the proposed methods is evaluated on benchmark datasets.

Crutch walking, especially when using a swing-through gait pattern, is associated with high, repetitive joint forces, hyperextension/ulnar deviation of the wrist, and excessive palmar pressure that compresses the median nerve. To reduce these adverse effects, we designed a pneumatic sleeve orthosis that utilized a soft pneumatic actuator and secured to the crutch cuff for long-term Lofstrand crutch users. Eleven non-disabled young adult participants performed both swing-through and reciprocal crutch gait patterns with and without the custom orthosis for comparison. Wrist kinematics, crutch forces, and palmar pressures were analyzed. Significantly different wrist kinematics, crutch kinetics, and palmar pressure distribution were observed in swing-through gait trials with orthosis use (p

Compact and low-power CMOS-compatible hardware can be used for on-chip optical neural networks (ONNs), enabling affordable and portable image classification solutions for applications like autonomous vehicles, healthcare, and optical communication. In this work, we propose a novel one-dimensional Optical Convolutional Neural Network (OCNN) architecture that significantly reduces the number of learnable parameters required for an ONN. Our OCNN achieves an impressive accuracy of over 96% as a pattern classifier, utilizing only 90 learnable parameters, leading to a simpler structure compared to existing on-chip ONNs. Additionally, our OCNN demonstrates scalability and robustness, with an accuracy exceeding 89% in handwritten digit classification. The OCNN’s convolutional layer employs a lenslet 4f system for convolving desired kernels on input images, while an on-chip lens facilitates the desired Fourier Transform effortlessly. The subsequent layer consists of a single metaline layer, implementing a fully connected layer. By parallelizing pre-trained OCNNs, an on-chip deep convolutional neural network (CNN) can be realized, where each OCNN functions as a separate kernel within a conventional CNN.

Cell-free massive multiple-input multiple-output (CF-mMIMO) technology seeks to enable wireless connectivity with high-rates, flexibility and scalability, by distributing the antennas of the system among multiple access points (APs), allowing the spatial degrees-of-freedom (DoF) of the system to be fully exploited. The distributed nature of CF-mMIMO systems also raises, however, a security challenge, because messages intended to a given user may be opportunistically eavesdropped by other users of the systems, leaking potentially private information. As a step towards addressing this issue, we consider the problem of beamforming (BF) design that maximize the downlink (DL) secrecy rate of the CF-mMIMO system, considering that existing approaches have shortcomings including rate performance degradation, limitations to point-to-point (P2P) and/or multiple-input single-output (MISO) scenarios, and/or poor scalability, all of which contradict the aforementioned goals of CF-mMIMO technology. With that in mind, our focus is on scalable solutions to the problem, in a manner that does not sacrifice rate or scalability. To that end, we propose an improved and accelerated power-optimizing fractional programming (FP)-based sumrate maximization (SRM)-BF, and provide an accompanying semidefinite programm (SDP)-based secrecymaximization beamforming (SecBF) which can be used to optimize the previous’ SRM-BF towards higher aggregate secrecy rates.Acomplexity analysis and comparisons via simulation demonstrate that the proposed FP-based SRM-BF outperforms the state-of-the-art (SotA), and that the SDP-based SecBF convergences quickly from the latter BF state yielding superior secrecy without significant losses in communication rate.

Supplier Managed Inventory (SMI) can be considered an enabler for supply chain coordination in which the supplier takes over the customer’s inventory to optimize the supply chain. However, the successful implementation of SMI is centered on the high level of trust, accurate data transfer, and efficient interaction between parties. This requires the sharing of information through supply chain stakeholders which face resistance and challenges due to the fear that this information will be revealed to its competitors and transparency of data. This paper has investigated the application of Blockchain technology and its potential for successful SMI implementation. The paper has proposed a Blockchain framework for the coordination of suppliers and customers. The framework includes a mathematical model for multiple supplier-customer order fulfillment which is embedded in the blockchain framework. The paper has demonstrated case studies to evaluate the performance of the proposed model with literature discussing the details of its blockchain framework.

This paper presents the design and development of a digital two-channel chirp synthesizer using a field-programmable gate array (FPGA) device. To achieve an integrated solution, the design was implemented on radio-frequency system-on-chip (RF-SoC) technology that includes digital-to-analog converters (DACs) and other radio-frequency components on-chip. To overcome the timing errors in high-speed design with DACs operating at 6.144 GHz, a memory-stitching concept was used. A prototype was developed to validate this concept by generating a baseband chirp with a bandwidth of 1.7 GHz and a sweep time of 36 $\mu \text{s}$ . The synthetic chirp was upconverted to 3.572-5.272 GHz for use as the transmit signal for an ultra-wideband radar to characterize the chirp using a 1 km long optical delay line. The transmit signal was analyzed in terms of phase and amplitude errors and corrected for these errors. The root-mean-square (RMS) frequency deviation of the predistorted chirp from linearity over the 1.7 GHz bandwidth is 9.64 kHz, realizing a chirp linearity of 0.00057%. The measurement data show comparable performance of our chirp synthesizer against a commercially available arbitrary-waveform-generator (AWG) operating at a sampling rate of 60 GHz. The reported chirp synthesizer can be used in frequency-modulated continuous-wave (FM-CW) and stretch radars. Such radars are widely used for a variety of remote sensing measurements.

Optimizing energy consumption in buildings is a significant challenge in today’s society. A major part of energy consumption is in heating, ventilation and air conditioning (HVAC) systems. In this paper, the aim is to reduce the energy consumption of air handling units (AHU) by applying optimal control. This system used in this study has four AHUs, all of which are assumed to be the same. Due to the uncertainty of the temperature of the heat exchanger’s (H/E) inlet and outlet water, a model of the system was first made using its hypothetical capacity according to the ASHRAE standards. The inlet and outlet water temperatures are calculated using simulated and real data. In order to increase the model’s accuracy and facilitate implementation on a real system, the data obtained is used to train a dynamic recurrent neural network (RNN) for the H/E. Furthermore, to increase the system’s stability and bolster its response to disturbances, which change system parameters over time and reduce the accuracy of neural network models, an online recursive least squares (RLS-based) adaptive constrained generalized predictive controller (AGPC) is used to control its outlet air temperature. The AGPC attempts to minimize the computational load and estimates the transfer function by using continuously updated input-output data from the model; this model has fewer parameters than the RNN model. Finally, the power consumption of the H/E is calculated. The outlet humidity and airflow are controlled using an optimal controller to minimize energy consumption. The results show a reduction in the energy consumption of 54.95% with respect to the previous work and of 69.9% compared to the dataset from the real system.

This paper presents a rolling horizon optimal energy management mechanism using real-time grid monitoring data. The proposed mechanism includes novel approaches in terms of real-time data processing, net-load forecasting, and optimal scheduling of battery energy storage systems. The proposed real-time data processing and net-load forecasting techniques use fast training and computationally efficient methods based on grid monitoring data. The data processing and parameter forecasting methods are based on auto-regressive with exogenous variables (ARX). Two sets of features including similar values in previous hours, days, and weeks as well as calendar effects are used for training the forecast model. Furthermore, the impact of additional non-synchronized weather features adopted from meteorology databases on the forecasts’ accuracy is discussed. Finally, a real-time optimal scheduling is proposed to optimize battery energy storage systems (BESS), maximizing the self-consumption at grid and community levels. The application of real-time grid measurement in the proposed algorithm allows handling the impacts of loads and generators behind the meter without having their detailed information. The developed method is being effectively used in a real low voltage distribution grid in Switzerland.

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. Over the past few years, DL-based models have achieved performance that meets expectations on image interpretation due to the development of convolutional neural networks. The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models, such as the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies, such as hard example mining, self-training, and mix-up data augmentation, are also considered. Moreover, we describe the L4S benchmark dataset in order to facilitate further comparisons and report the results of the accuracy assessment online. The data are accessible on Future Development Leaderboard for future evaluation at https://www.iarai.ac.at/landslide4sense/challenge/, and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article.

This article presents the application of cluster analysis (CA) to data proceeding from a testbed microgrid located at Sapienza University of Rome. The microgrid consists of photovoltaic (PV), battery storage system (BESS), emergency generator set, and different types of load with a real-time energy management system based on supervisory control and data acquisition. The investigation is based on the area-related approach - the CA algorithm considers the input database consisting of data from all measurement points simultaneously. Under the investigation, different distance measures (Euclidean, Chebyshev, or Manhattan), as well as an approach to the optimal number of cluster selections. Based on the investigation, the four different clusters that represent working conditions were obtained using methods to define an optimal number of clusters. Cluster 1 represented time with high PV production; cluster 2 represented time with relatively low PV production and when BESS was charged; cluster 3 represents time with relatively high PV production and when BESS was charged; cluster 4 represents time without PV production. Additionally, after the clustering process, a deep analysis was performed in relation to the working condition of the microgrid.

This paper details a new approach for enhancing the radiation characteristics of a coplanar Vivaldi antenna (CVA) array. First, the inter-element mutual coupling is reduced by introducing slots in the conventional CVA array. Then, a low-profile dielectric patch antenna (DPA) is situated between the CVA flares when it acts as a traveling wave radiator at the higher frequency band. The DPA is excited by the loop current at the radiation part of the CVA, and a set of TE modes with omnidirectional radiation patterns are excited. This combination enhances the directivity in the broadside direction of the CVA and suppresses the grating lobes by as much as 10 dB. The 4 $\times $ 1 linear array covers the frequency range of 2.77-13.6 GHz, with 132% fractional bandwidth. Then, four of the proposed linear arrays are connected to constitute an 8 $\times $ 2 planar array to achieve a gain of 15.2-24.6 dBi. The proposed arrays are fabricated, tested, and mounted on a small unmanned aerial system (sUAS) for radar measurements. The real-life field radar results with the proposed arrays are presented including an echogram of the scanned area.

In this study, an adaptive proportional-integral-derivative (PID) sliding mode control technique combined with the super-twisting algorithm is planned for the stabilization of rotational inverted pendulum in the appearance of exterior perturbation. The state-space model of rotational inverted pendulum in the existence of exterior disturbance is attained. Then, the super-twisting PID sliding mode controller is designed for finite time stability control of the considered underactuated control system. The upper bounds of perturbation are presumed to be unknown; consequently, the adaptive control procedure is taken into account to approximate the uncertain bounds of external disturbances. The stability control of rotational inverted pendulum system is verified by means of the Lyapunov stability theory. In order to validate the accuracy and efficiency of the recommended control technique, some simulation outcomes are prepared and compared with other existing scheme. Finally, the experimental results are implemented to show the success of the designed method.

The prediction of protein-protein interactions (PPIs) is essential to understand the cellular processes from a medical perspective. Among the various machine learning techniques, kernel-based Support Vector Machine (SVM) has been commonly employed to discriminate between interacting and non-interacting protein pairs. The main drawback of employing the kernel-based SVM to datasets with many features, such as the primary sequence-based protein-protein dataset, is the significant increase in computational time of training stage. This increase in computational time is mainly due to the presence of the kernel in solving the quadratic optimisation problem (QOP) involved in nonlinear SVM. In order to fix this issue, we propose a novel and efficient computational algorithm by approximating the kernel-based SVM using a low-rank truncated Mercer series as well as desired. As a result, the QOP for the approximated kernel-based SVM will be very tractable in the sense that there is a significant reduction in computational time of training and validating stages. We illustrate the novelty of the proposed method by predicting the PPIs of “S. Cerevisiae” where the protein features extracted using the multiscale local descriptor (MLD), and then we compare the predictive performance of the proposed low-rank approximation with the existing methods. Finally, the new method results in significant reduction in computational time for predicting PPIs with almost as accuracy as kernel-based SVM.

Cooperative relaying can be implemented in spectrum sharing networks to extend the range of reliable communication. In this paper, we incorporate multiple-antenna technology and buffer-aided relaying to guarantee a highly reliable connectivity for the secondary network with several source nodes. The multi-antenna configuration for the sources and destination nodes suggests that there are several potential source-to- destination channels which can be auspicious for the network in two ways. First: to balance up the buffer states. Second: to expand the link selection opportunity at each time step. Motivated by this rationale, we propose a buffer-aware communication protocol to incorporate the direct transmissions along the relaying links without incurring excess overhead for circulation of channel-state-information (CSI). Considering Nakagami-m fading, we derive closed-form expressions for the end-to-end (ete) outage probability and average packet delay of the secondary network under the proposed protocol. Through Monte-Carlo simulations, we investigate the influential network parameters and evaluate the proposed technique in comparison to two benchmark schemes, one with buffer-based link-prioritization and one without prioritization. Findings demonstrate that the proposed protocol outperforms both benchmarks in terms of outage probability and ete delay, especially as the number of nodes scales up. However, the superior performance comes at the cost of more CSI circulation. Furthermore, it is shown that if the global CSI cannot be collected accurately, then the dependency of the link selection on the global CSI should be relaxed to mitigate the performance loss. The theoretical and Monte-Carlo results coincide in several simulation examples, verifying the presented theoretical analysis.

In this paper, a 6-Degrees-of-Freedom (6-DOF) Unmanned Aerial Vehicle (UAV) system with external disturbance corresponding to sensor failure is considered. The control method is presented in two parts. In the first part, the upper bound of external disturbance is known and a Proportional-Integral-Derivative (PID) Sliding Mode Control (SMC) technique is planned for maintaining the desired position in the finite time. Whereas, the upper bound of the external disturbance is considered unknown in the second part and the adaptive PID-SMC method is offered for stability and position tracking control of UAV systems. Using the Lyapunov stability notion, the offered control method proves that the quadrotor's states can be tracked and stabilized in the finite time. Moreover, for the approximation of unknown bound of the external disturbances which are entered in the quadrotor dynamic model at any moment, adaptive control laws have been applied. Finally, simulation outcomes are provided to display the efficiency of the recommended technique.

A new generation of Oxide Dispersion Strengthened (ODS) alloys called Oxide Precipitation Hardened (OPH) alloys, has recently been developed by the authors. The excellent mechanical properties can be improved by optimizing the chemical composition in combination with heat treatment. However, the behavior of such materials requires the consideration of a large number of variables, nonlinearities, and uncertainties in the analyses, and the modeling of such alloys by analytical methods is not accurate enough. Therefore, artificial intelligence (AI) methods, such as machine learning (ML), can be beneficial to alleviate the problems associated with the complexity of these alloys. In this work, three different hybrid ML techniques have been employed to estimate the ultimate tensile strength (UTS) and elongation in these special alloys. The proposed methods include a feedforward artificial neural network (FF-ANN) trained using particle swarm optimization (PSO) and two adaptive neuro-fuzzy inference system (ANFIS) methods trained using both fuzzy C-means (FCM) clustering and subtractive clustering (SC). Since OPH alloys are mainly produced via mechanical alloying (MA) of a mixture of powder components followed by consolidation and hot rolling, a series of standard tensile tests were performed on the different variants of the OPH alloy. In this way, some critical parameters such as UTS and elongation could be extracted from the experimental results. The main contribution of the present study is to estimate these important parameters based on some material properties including Aluminum (Al), Molybdenum (Mo), Iron (Fe), Chromium (Cr), Tantalum (Ta), Yttrium (Y) and Oxygen (O), MA and the heat treatment conditions. The results show that the proposed strategies are not only able to accurately determine the complex behavior of OPH alloy with an accuracy of about 95%, but they can also help the designer to benefit from these powerful tools to design new versions of such materials without analytical calculations.

In this study, we consider the downlink beamforming problem in millimeter wave (mmWave) systems subjected to both path blockages and imperfect channel state information (CSI), and propose a new robust hybrid sum-outage minimizing design as a solution. We first formulate the problem as an empirical risk minimization (ERM) stochastic learning problem, whose solution can be obtained by the alternate iteration of a baseband digital and a radio frequency (RF) analog Riemann manifold-constrained beamforming updates through a mini-batch stochastic gradient descent (MSGD) approach, with gradient minimizing update rules given in closed-form, and learning rates optimized based on the lower-bounds of the corresponding Lipschitz constants. Unlike existing solutions to the path blockage-robust mmWave beamforming problem, wherein out-of-band side information is required or perfect CSI is assumed, our method relies only on the estimates and statistical knowledge of the channel’s angles of departure (AoD) and complex gains, which are simultaneously captured in a Bernoulli-Gaussian model and used to generate the training data for the MSGD-based optimizer. Further, unlike preceding fully-digital or fully-connected hybrid contributions, the proposed scheme assumes a virtually-configured partially-connected setup; therefore, it is compatible with coordinated multipoint (CoMP) architectures, which are known to be crucial in terms of exploiting the full potential of mmWave systems. Simulation results confirm the effectiveness of our MSGD-based robust hybrid CoMP mmWave beamformer in mitigating the effects of path blockage and CSI error, demonstrating its superiority to state-of-the-art (SotA) alternatives.

In this brief, we propose a new neuro-fuzzy reinforcement learning-based control (NFRLC) structure that allows autonomous underwater vehicles (AUVs) to follow a desired trajectory in large-scale complex environments precisely. The accurate tracking control problem is solved by a unique online NFRLC method designed based on actor-critic (AC) structure. Integrating the NFRLC framework including an adaptive multilayer neural network (MNN) and interval type-2 fuzzy neural network (IT2FNN) with a high-gain observer (HGO), a robust smart observer-based system is set up to estimate the velocities of the AUVs, unknown dynamic parameters containing unmodeled dynamics, nonlinearities, uncertainties and external disturbances. By employing a saturation function in the design procedure and transforming the input limitations into input saturation nonlinearities, the risk of the actuator saturation is effectively reduced together with nonlinear input saturation compensation by the NFRLC strategy. A predefined funnel-shaped performance function is designed to attain certain prescribed output performance. Finally, stability study reveals that the entire closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and can provide prescribed convergence rate for the tracking errors so that the tracking errors approach to the origin evolving inside the funnel-shaped performance bound at the prescribed time.

Full-duplex amplify-and-forward multiple-input multiple-output relaying has been the focus of several recent studies, due to the potential for achieving a higher spectral efficiency and lower latency, together with inherent processing simplicity. However, when the impact of hardware distortions are considered, such relays suffer from a distortion-amplification loop, due to the inter-dependent nature of the relay transmit signal covariance and the residual self-interference covariance. The aforementioned behavior leads to a significant performance degradation for a system with a low or medium hardware accuracy. In this work, we analyze the relay transfer function as well as the mean squared-error performance of a full-duplex amplify-and-forward multiple-input multiple-output relaying communication, under the impact of collective sources of additive and multiplicative transmit and receive impairments. Building on the performed analysis, an optimization problem is devised to minimize the communication Mean Squared-Error and solved by employing the recently proposed penalty dual-decomposition framework. The proposed solution converges to a stationary point of the original problem via a sequence of convex quadratic programs, thereby enjoying an acceptable arithmetic complexity as the problem dimensions grow large. Numerical simulations verify the significance of the proposed distortion-aware design and analysis, compared to the common simplified approaches, as the hardware accuracy degrades.

This article presents an adaptive finite time second-order sliding mode tracking control scheme for robotic manipulators. A new control law is suggested to force the trajectories of the robot manipulator to move from all initial conditions to proportional-integral-derivative switching surface in the finite time and stay on it. Moreover, the adaptation law rejects the requirement of knowledge about upper bound of the external perturbations. It is difficult to exactly determine the upper bound of the perturbations in the practical systems, such as robot manipulators. Unlike the existing methods, the new adaptive finite time second-order sliding mode tracker for $n$ -link robot manipulators enables accurate tracking control, robust performance and parameter tuning. The suggested approach presents the design of a robust controller such that the tracking errors of robot manipulator can reach the equilibrium in the finite time. Through the combination of the finite time tracker and disturbance observer, the position tracking purpose of manipulator joints is accurately performed not only in the nominal environment, but also in the existence of different types of perturbations. The robustness performance and effectiveness of the offered technique are studied in simulation and experimental results.

Beyond the direct hazards of earthquakes, the deposited mass of earthquake-induced landslide (EQIL) in the riverbeds causes the river to thrust upward. The EQIL inventories are generated mostly by the traditional or semisupervised mapping approaches, which required a parameter's tuning or binary threshold decision in the practical application. In this study, we investigated the impact of optical data from the PlanetScope sensor and topographic factors from the ALOS sensor on EQIL mapping using a deep-learning convolution neural network (CNN). Thus, six training datasets were prepared and used to evaluate the performance of the CNN model using only optical data and using these data along with each and all topographic factors across the west coast of the Trishuli river in Nepal. For the first time, the Dempster-Shafer (D-S) model was applied for combining the resulting maps from each CNN stream that trained with different datasets. Finally, seven different resulting maps were compared against a detailed and accurate inventory of landslide polygons by a mean intersection-over-union (mIOU). Our results confirm that using the training dataset of the spectral information along with the topographic factor of the slope is helpful to distinguish the landslide bodies from other similar features, such as barren lands, and consequently increases the mapping accuracy. The improvement of the mIOU was a range from approximately zero to more than 17%. Moreover, the D-S model can be considered as an optimizer method to combine the results from different scenarios.

This article proposes a disturbance observer-based Sliding Mode Control (SMC) approach for the robust synchronization of uncertain delayed chaotic systems. This is done by, first, examining and analyzing the electronic behavior of the master and slave Sprott chaotic systems. Then, synthesizing a robust sliding mode control technique using a newly proposed sliding surface that encompasses the synchronization error between the master and slave. The external disturbances affecting the system were estimated using a disturbance observer. The proof of the semi-globally bounded synchronization between the master and slave was established using the Lyapunov stability theory. The efficiency of the proposed approach was first assessed using a simulation study, then, experimentally validated on a data security system. The obtained results confirmed the robust synchronization properties of the proposed approach in the presence of time-delays and external disturbances. The experimental validation also confirmed its ability to ensure the secure transfer of data.

This paper proposes a vector and a linear control strategy for wind turbines connecting to a DC collector grid. Two conversion schemes are considered for the wind turbine, (i) an active scheme where a dual rotor generator (DRG) is connected to a voltage source converter (VSC), and (ii) a passive scheme where a DRG is connected to a passive uncontrolled rectifier. The vector controller is implemented on VSC and DRG where the turbine rotor speed and output power are controlled via a VSC, and the wind turbine generator (WTG) rectified output DC voltage is controlled via a DRG DC filed current. A load-angle control and analytical modeling is discussed for the vector controller and evaluated via simulation results. In the passive scheme where the VSC is replaced with an uncontrolled rectifier the control is entirely shifted to DRG where a linear controller is implemented that controls the turbine rotor speed, power and voltage. A comparison of the vector and linear control is presented, and simulation results verify the effectiveness of the control strategy in steady-state and when turbine transitions between different wind velocities.

This paper proposes an adaptive integral-type terminal sliding mode approach for the attitude and position tracking control of a quadrotor UAV subject to model uncertainties and external disturbances. First, an integral-type terminal sliding tracker is designed to attain the quadrotor UAV tracking performance in finite time when the upper bound of perturbations and uncertainties are known. Next, an adaptation law is proposed and a modified parameter-tuning integral-type terminal sliding mode tracking control scheme is designed to compensate of the model uncertainties and external disturbances. The stability and finite time convergence of the proposed approach is verified using the Lyapunov theory. Its performance is assessed using a simulation study encompassing various scenarios. Low chattering dynamics, fast convergence rate, and absence of singularities are the main features of the proposed approach.

Many outstanding studies have reported promising results in seizure forecasting, one of the most challenging predictive data analysis problems. This is mainly because electroencephalogram (EEG) bio-signal intensity is very small, in $\mu \text{V}$ range, and there are significant sensing difficulties given physiological and non-physiological artifacts. Today the process of accurate epileptic seizure identification and data labeling is done by neurologists. The current unpredictability of epileptic seizure activities together with the lack of reliable treatment for patients living with drug resistant forms of epilepsy creates an urgency for research into accurate, sensitive and patient-specific seizure forecasting. Most seizure forecasting algorithms use only labeled data for training purposes. As the seizure data is labeled manually by neurologists, preparing the labeled data is expensive and time consuming, making the best use of the data critical. In this article, we propose an approach that can make use of not only labeled EEG signals but also the unlabeled ones which are more accessible. We use the short-time Fourier transform on 28-s EEG windows as a pre-processing step. A generative adversarial network (GAN) is trained in an unsupervised manner where information of seizure onset is disregarded. The trained Discriminator of the GAN is then used as a feature extractor. Features generated by the feature extractor are classified by two fully-connected layers (can be replaced by any classifier) for the labeled EEG signals. This semi-supervised patient-specific seizure forecasting method achieves an out-of-sample testing area under the operating characteristic curve (AUC) of 77.68%, 75.47% and 65.05% for the CHB-MIT scalp EEG dataset, the Freiburg Hospital intracranial EEG dataset and the EPILEPSIAE dataset, respectively. Unsupervised training without the need for labeling is important because not only it can be performed in real-time during EEG signal recording, but also it does not require feature engineering effort for each patient. To the best of our knowledge, this is the first application of GAN to seizure forecasting.

Electric Vehicles' Controller Area Network (CAN) bus serves as a legacy protocol for in-vehicle network communication. Simplicity, robustness, and suitability for real-time systems are the salient features of CAN bus. Unfortunately, the CAN bus protocol is vulnerable to various cyberattacks due to the lack of a message authentication mechanism in the protocol itself, paving the way for attackers to penetrate the network. This paper proposes a new effective anomaly detection model based on a modified one-class support vector machine in the CAN traffic. The proposed model makes use of an improved algorithm, known as the modified bat algorithm, to find the most accurate structure in the offline training. To evaluate the effectiveness of the proposed method, CAN traffic is logged from an unmodified licensed electric vehicle in normal operation to generate a dataset for each message ID and a corresponding occurrence frequency without any attacks. In addition, to measure the performance and superiority of the proposed method compared to the other two famous CAN bus anomaly detection algorithms such as Isolation Forest and classical one-class support vector machine, we provided Receiver Operating Characteristic (ROC) for each method to quantify the correctly classified windows in the test sets containing attacks. Experimental results indicate that the proposed method achieved the highest rate of True Positive Rate (TPR) and lowest False Positive Rate (FPR) for anomaly detection compared to the other two algorithms. Moreover, in order to show that the proposed method can be applied to other datasets, we used two recent popular public datasets in the scope of CAN bus traffic anomaly detection. Benchmarking with more CAN bus traffic datasets proves the independency of the proposed method from the meaning of each message ID and data field that make the model adaptable with different CAN datasets.

Nowadays, utilities aim to find methods for improving the reliability of distribution systems and satisfying the customers by providing the continuity of power supply. Different methodologies exist for utilities to improve the reliability of network. In this paper, demand response (DR) programs and smart charging/discharging of plug-in electric vehicles (PEVs) are investigated for improving the reliability of radial distribution systems adopting particle swarm optimization (PSO) algorithm. Such analysis is accomplished due to the positive effects of both DR and PEVs for dealing with emerging challenges of the world such as fossil fuel reserves reduction, urban air pollution and greenhouse gas emissions. Additionally, the prioritization of DR and PEVs is presented for improving the reliability and analyzing the characteristics of distribution networks. The reliability analysis is performed in terms of loss of load expectation (LOLE) and expected energy not served (EENS) indexes, where the characteristics contain load profile, load peak, voltage profile and energy loss. Numerical simulations are accomplished to assess the effectiveness and practicality of the proposed scheme.

Plasmonic filters are excellent candidates for spectral filters in the near infrared spectrum. Such filters require only a single nanoscale thickness perforated metal film that can be tailored to tune the location of the transmission maximum. Here, we demonstrate using computational methods a double coaxial aperture array (DAA) in a hexagonal geometry that can be tuned to produce plasmon resonances accompanied by strong transmission in the near-infrared spectrum and at telecommunication wavelengths with a peak wavelength insensitive to angle of incidence. The DAA consists of two concentric annular apertures and radial distance between the two apertures is varied to tune the resonant wavelength. The presented geometry finds potential applications in modulators for telecommunication, spectroscopy, solar cells, high-resolution CMOS image sensors, chemical sensors, and biosensors.

Today, the US healthcare industry alone can save $300 B per year by using machine intelligence to analyze a rich set of existing medical data; results from these analyses can lead to breakthroughs such as more accurate medical diagnoses, discovery of new cures for diseases, and cost savings in the patient admission process at healthcare organizations. Because healthcare applications intrinsically imply a vast amount of data, the execution of any algorithm on medical data is computationally intensive. Significant advancements made in computational power in the past decade have provided the opportunity for many researchers to successfully implement various machine intelligence-based healthcare applications, which didn't run efficiently on earlier computational platforms. In this paper, we provide a survey of machine intelligence algorithms within the context of healthcare applications; our survey includes a comprehensive list of the most commonly used computational models and algorithms. We view the application of these algorithms in multiple steps, namely, data acquisition, feature extraction, and aggregation, modeling, algorithm training, and algorithm execution and provide details-as well as representative case studies-for each step. We provide a set of metrics that are used to evaluate modeling and algorithmic performance, which facilitate the comparison of the presented models and algorithms. Medical cyber-physical systems are presented as an emerging application case study of machine intelligence in healthcare. We conclude our paper by providing a list of opportunities and challenges for incorporating machine intelligence in healthcare applications and provide an extensive list of tools and databases to help other researchers.

Physical unclonable functions (PUFs) are increasingly used for authentication and identification applications as well as the cryptographic key generation. An important feature of a PUF is the reliance on minute random variations in the fabricated hardware to derive a trusted random key. Currently, most PUF designs focus on exploiting process variations intrinsic to the CMOS technology. In recent years, progress in emerging nanoelectronic devices has demonstrated an increase in variation as a consequence of scaling down to the nanoregion. To date, emerging PUFs with nanotechnology have not been fully established, but they are expected to emerge. Initial research in this area aims to provide security primitives for emerging integrated circuits with nanotechnology. In this paper, we review emerging nanotechnology-based PUFs.

We consider a multi-cell multiple-input multiple-output full-duplex (FD) system, where multiple FD capable base stations (BSs) serve multiple mobile users operating in FD mode. The self-interference at the BSs and users, and co-channel interference (CCI) between all the nodes (BSs and users) in the system are both taken into account. We consider the transmit and receive filter design for sum-rate maximization problem subject to sum-power constraints at the BSs and individual power constraints at each user of the system under the limited dynamic range considerations at the transmitters and the receivers. By reformulating this non-convex problem as an equivalent multi-convex optimization problem with the addition of two auxiliary variables, we propose a low complexity alternating algorithm that converges to a stationary point. Since this sum-rate maximization results in starvation of users in terms of resources depending on the power of the self-interference and CCI channels, we modify the sum-rate maximization problem by adding target data rate constraints on each user, and propose an algorithm based on Lagrangian relaxation of the rate constraints.

recent developments in semiconductor manufacturing have seen feature sizes reduce to as small as 3 nm. It is predicted that 2 nm, or even 1 nanometer will be achieved soon. Improving the level of cleanliness of the wafer mask during manufacturing can lead to improved product yield and quality. The quality of lithography technology and the reticle is one of the most important items in the wafer manufacturing process. The cleanliness of this process directly affects the wafer quality and yield. Because the wafer manufacturing process involves the stacking of multiple reticles through lithography technology, semiconductor factories mostly use a reticle stocker room to store the photomasks. However, the reticle is susceptible to defects caused by moisture, particles, and molecular contaminants in the air. Therefore, the reticle stocker room environment requires high cleanliness and humidity control. In this study, the flow stream lines, velocity and humidity fields associated with a flow isolation device (FID) installed in a reticle stocker room were analyzed with the assistance of computational fluid dynamics (CFD) software developed by ANSYS Fluent. Different velocity (V=1 m/s, 1.5 m/s, 2 m/s) of the flow isolation device were examined. The results show that under the same velocity (V=1 m/s), the wider the outlet width of the flow isolation device (W ${=}0$.2 m), the higher the isolation efficiency ($\eta {=}83.9$ %). The results also show that the faster the velocity of the flow isolation device (V =2 m/s), the better the isolation efficiency ($\eta {=}88.2$ %) under the same outlet width (W ${=}0$.1 m). The use of the flow isolation device can effectively reduce the supply of clean dry air (CDA) by up to 40%, greatly reducing energy consumption during semiconductor manufacturing. According to the results of this study, when using both a hollow fiber adsorption dryer and a flow isolation device with a width of 0.1 m and an outlet wind speed of 2 m/s, it can save 118,514 kWh per year, and its energy saving rate is 92.03%. [ABSTRACT FROM AUTHOR]