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Noisy intermediate-scale quantum (NISQ) computers face significant reliability challenges because they are vulnerable to quantum noise, which severely limits their fidelity in quantum applications. In particular, deep circuits with a large number of quantum gates are susceptible to errors as they are more likely to lose their states in a deep circuit. In this paper, we propose a quantum circuit for square root operation that generates correct results on NISQ devices. The square root operation is used in many applications such as complex number computations, computer graphics, etc. While there have been limited studies on quantum square root circuits, none of them can be implemented on NISQ devices. In this work, we simplify the structure of an exact quantum square root circuit and reduce the number of quantum gates. The proposed design reduces the complexity of the square root circuit while maintaining the same level of precision in outputs. In addition, we exploit approximate computing to simplify the circuit further and run it on a real quantum computer. Approximate computing is used in classical computers to enhance the power and/or performance in exchange for accuracy. We exploit approximate computing for a different purpose, which is reducing the depth as well as the number of quantum gates in the square root circuit. To validate the effectiveness of our approach, we conduct experiments on an IBM quantum computer, where our circuit produces meaningful results. Furthermore, we present examples of error-resilient applications to demonstrate the validity of our approximate circuit.

In this paper, a new synchronous rectified converter with ultra-low output current ripple and high voltage attenuation is proposed. The presented converter can achieve soft switching without requiring any additional active components, leading to high efficiency while maintaining the simplicity of conventional converters. Other main features of the proposed circuit are inherent shoot-through protection, reduced current stress of the semiconductors due to the coupled inductor, and low voltage stress across the low-side MOSFET. In addition, a pulse frequency modulation control scheme is used, which allows for an extended soft-switching range without imposing an excessive circulating current at light loads. The steady-state analysis and experimental results from a 120 W prototype are presented in this paper.

The existence of imbalanced single- and two-phase loads, coupled with short-circuit faults, contributes to voltage imbalance. It is inevitable to use LCL filters to remove harmonics caused by converter switching. While effective in removing harmonic components, these filters may inadvertently amplify and propagate resonance. Both voltage imbalance and active resonance significantly degrade power quality. This paper proposes a multi-level hierarchical control system to mitigate both phenomena. At Level 1, the controller incorporates proportional-resonant regulators for current and voltage. Level 2 employs common droop control to establish microgrid voltage and frequency reference values. Level 3 introduces a washout filter with an adjustable Kd gain, capable of attenuating active resonance harmonics originating from the LCL filter. Finally, Level 4 integrates a controller based on extracting positive and negative sequence components using the dual decoupled synchronous reference frame (DDSRF) algorithm. This controller effectively compensates for negative sequence components of voltage in the load bus, balancing load voltage. Simulation results for a standalone microgrid demonstrate that the proposed 4-level hierarchical control algorithm can reduce the load bus voltage imbalance from 10% to 2.5% under the most challenging conditions. It diminishes harmonic distortion caused by active resonance from 11% to 2.5%. The presented control algorithm can perform optimally under dynamic changes of active and reactive power of nonlinear and imbalanced loads. The main merit of this approach is not to use physical devices and Flexible Alternative Current Transmission Systems (FACTS), such as Active Power Filters (APF), Distribution Static Synchronous Compensators (DsSTATCOM), SVC, etc.

In the digital age, online reviews significantly influence consumer purchasing decisions. However, there is a growing concern about fake reviews undermining their credibility. While existing research underscores the significance of online reviews for consumers, there is a gap in the literature on developing a predictive model for assessing the credibility of online reviews that takes into account the consumer perspective as the end user of such information. To address this gap, this study combines human-driven and data-driven approaches by using two datasets with different labeling methods to develop a predictive model. The first dataset is labeled using the Yelp filtering algorithm, while the second dataset is labeled based on the perception of the crowd toward credibility. The results show that the predictive model can differentiate fake and credible reviews with an 82% AUC (Area Under the Curve) performance, using review attributes such as length, subjectivity, readability, extremity, external and internal consistency. Additionally, SHAP (SHapley Additive exPlanations) has been conducted to further explain important characteristics that differentiate fake and credible reviews.

Capacitor banks (CBs) play a crucial role in energy storage and frequency control within autonomous microgrids. However, the impact of internal capacitor configurations, varying in terms of equivalent series resistance (ESR), capacitance, and rated voltage, on CB degradation, reliability, and peak current remains an understudied aspect. Moreover, the absence of a capacitance degradation coefficient in the standard MIL-HDBK-217 equations for predicting the reliability of electrolytic capacitors poses a significant challenge. To address these issues, this study examines a microgrid composed of diverse renewable energy systems, featuring nine distinct CB arrangements. The design of CBs considers both capacitance and peak output current individually. An evaluation is conducted to compare construction costs, lifetimes, and peak output currents across all layouts. Additionally, a novel formula is introduced to estimate the reliability and lifetime of CBs, while an existing formula for calculating CB peak output current is enhanced. The research explores the impact of ambient temperature and capacitor voltage on the reliability of various capacitor designs, proposing a novel framework for assessing CB reliability based on MIL-HDBK-338B, which accounts for both short-circuit and open-circuit faults. The practicality of these findings is confirmed through a comparison of experimental and simulation results. The inverter operation video, simulation, and all production data including PCB and processor codes are also attached.

The impact of the COVID-19 pandemic and the introduction of artificial intelligence-based tools created significant job losses across various sectors in all countries around the world. A large portion of these job losses is permanent. Furthermore, the hidden unemployment numbers are higher than currently reported and the impact of Generative Pretrained Transformer (GPT) based tools will further increase the unemployed population in the coming years. Most businesses are likely to experience significant disruptions to their business-as-usual operations and will face business underperformance for long periods. To ensure business continuity and a smooth recovery process following severe disruptions, it is crucial to establish a recovery strategy. To provide enough workforce for the recovery strategy of various businesses, a large-scale rapid re-education of the workforce is required. Intelligent and virtual workplaces will replace traditional offices in various sectors in the upcoming years and many low-skilled jobs are in danger of being permanently lost. In this paper, an artificial intelligence-based framework for rapid work-skill re-education for evolving markets named Career-gAIde is presented. The proposed framework uses automatic analysis of the job resume of the workers for recommendations of a suitable new job with a higher salary and the best rapid re-education path toward that job. Custom build deep neural networks based on CNN-Random along with customized natural language processing tools are designed for large-scale automatic recommendation of a personalized education and career path to each job seeker. The proposed work is focused on software engineering job search and resume upgrades. There is also a book recommendation module for obtaining the knowledge of job seekers. Precision criteria were used to evaluate the job offer recommendations and the proposed framework achieves 67% in this measure. The Recall criteria were used to assess the required skills, with results of 84% and 79%, respectively. The experimental results show that the proposed framework can provide a solution for rapid work-skill re-adjustment for large-scale workforces.

Secure communication of medical images is essential to telemedicine. Message Authentication Codes (MAC) can be embedded inside medical images to protect their integrity. Fragile watermarking algorithms are suitable options since they can be used to detect any tampering attempt. In this paper, a novel fragile data-hiding algorithm based on Integer-to-Integer Discrete Wavelet Transforms (IIDWT) and $A_{5}$ Lattice Vector Quantization (LVQ) is proposed. In the proposed data-hiding algorithm, a combination of the medical image Metadata and a MAC is embedded into the medical image. The Metadata includes information about the patient such as name, family, birthday, the place where it is created such as the name of the hospital, and the physician’s name. To preserve the privacy of the patients and the physician/hospital, the Metadata is then replaced with fake information. The receiver can extract the Metadata and the MAC. If the extracted MAC is the same as the expected MAC, the integrity of the medical image is guaranteed. Otherwise, a tampering attempt is detected. The proposed algorithm can embed 50% more data than similar algorithms in medical images while keeping the Peak Signal to Noise Ratio (PSNR) in acceptable ranges. Furthermore, the proposed algorithm is applied to a dataset of medical images and high PSNR values above 53.88 dB are experienced.

Model-based System Engineering (MBSE) of the Internet of Things (IoT) literature is broad, and analysis of this literature enables the identification of themes and potential future study topics that will influence system development. This paper reports on bibliometric literature analysis of MBSE of IoT. It considers conference and journal publication trends in the state-of-the-art to identify emerging research themes from the standpoint of trans/multi-disciplinary scholarship and technology. We used Elsevier’s Scopus database to find relevant publications from January 2018 to December 2022. Using publication citation ranking and other factors (e.g., publication venues), we selected 110 articles and then analyzed them using BibExcel and VOSviewer software tools. With a modest decline in 2021, this analysis shows an overall increase in publications during the time period. A thematic analysis of the abstracts revealed a strong focus on the introduction of reference architectures and integration of MBSE with business and management methodologies like Agile and BPMN 2.0. Model-driven engineering and machine learning techniques are essential among the enablers for realization of complex heterogeneous IoT systems in the realm of Industry 4.0. We highlight these findings to better understand and meet the enduring challenge of scaling MBSE of IoT across diverse sectors like health, manufacturing, and transportation.

In-person banking is still an important part of financial services around the world. Hybrid bank branches with service robots can improve efficiency and reduce operating costs. An efficient autonomous Know-Your-Customer (KYC) is required for hybrid banking. In this paper, an automated deep learning-based framework for interbank KYC in robot-based cyber-physical banking is proposed. A deep biometric architecture was used to model the customer’s KYC and anonymize the collected visual data to ensure the customer’s privacy. The symmetric-asymmetric encryption-decryption module in addition to the blockchain network was used for secure and decentralized transmission and validation of the biometric information. A high-capacity fragile watermarking algorithm based on the integer-to-integer discrete wavelet transform in combination with the Z6 and A6 lattice vector quantization for the secure transmission and storage of in-person banking documents is also proposed. The proposed framework was simulated and validated using a Pepper humanoid robot for the automated biometric-based collection of handwritten bank checks from customers adhering to COVID-19 pandemic safety guidelines. The biometric information of bank customers such as fingerprint and name is embedded as a watermark in the related bank documents using the proposed framework. The results show that the proposed security protection framework can embed more biometric data in bank documents in comparison with similar algorithms. Furthermore, the quality of the secured bank documents is 20% higher in comparison with other proposed algorithms. Also, the hierarchal visual information communication and storage module that anonymizes the identity of people in videos collected by robots can satisfy the privacy requirements of the banks. Overall, the proposed framework can provide a rapid, efficient, and cost-effective inter-bank solution for future in-person banking while adhering to the security requirements and banking regulations.

Power systems based on centralized production are facing two limitations: the lack of fossil fuels and the need to reduce pollution; Therefore, the importance of distributed generation resources (DGs) has increased by connecting renewable energy systems to the network. With the increasing penetration of renewable energies in the network, power quality challenges in low voltage and medium voltage distribution systems have become one of the main fields of studies. Since most of the renewable energy systems are connected to the network with the help of electronic power converters devices and the main purpose of these converters is to connect DGs to the network in compliance with power quality standards; Therefore, if the switching frequency of inverters is not implemented properly, major power quality problems will be created. On the other hand, the reduction in power quality causes acute problems in power networks, which include lack of proper performance, reduction in useful life and efficiency of electrical and electronic devices in the network, creation of series and parallel resonance in some harmonics due to the presence of inductors and the capacitor, and as a result, more distortion in the voltage of the distribution network. This paper actually provides a road-map for researchers to investigate power quality issues. In other words, the mentioned cases show the importance of research and providing the solutions to improving power quality in distribution networks.

Regardless of the application in which power electronic converters are deployed, their desired performances crucially depend on the controlling strategy while different impressive parameters are varied. This paper offers a novel controlling strategy originated from the mixture of two well-known controlling techniques, namely feedback (FBC) and model predictive (MPC) controllers. It uses the advantages of the above-mentioned controllers while their drawbacks or limitations are covered by each other using the mixture of experts (MoE) technique. Two neural networks for capturing the features of MPC and FBC along with a gating network as the main tool of MoE are employed in order to optimize the controlling of the DC-DC power electronic converters. These networks are trained through a set of pair data as the input vector and the target data. The results reveal that better performance can be obtained via benefit exploitation of both controlling techniques using a comprehensive MoE. The dynamic and steady state errors are decreased by 5% and 8%, respectively which demonstrate a global enhancement in the controlling of the DC-DC power electronic converters.

The main problem in the operation of micro-grids is controlling the voltage and frequency. The inertia of the whole grid is low, so the operation of the system is interrupted by sudden changes in load or incidence in the absence of a proper control system. In order to solve this issue, various control structures have been proposed. In this paper, an optimal distributed control strategy for coordinating multiple distributed generation instances is presented in an islanded microgrid. A secondary frequency control method is implemented in order to eliminate voltage deviation and reduce the small signal error. In this layer, an optimized PID controller is used. PID controller optimization is carried out via the Honey Badger Algorithm, and results are obtained using the MATLAB software. According to the results, inadequate adjustment of a secondary loop leads to poor and unacceptable outcomes, and the necessary power quality is not achieved. However, by using the proposed method, a proper performance of the microgrid in the face of disturbances is achieved.

Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached.

This paper proposes a strategy to manage an electric vehicle charging station (EVCSs) with a grid-side interface based on a Modular Multilevel Converter (MMC). The MMC topology is studied due to its potential for reducing the footprint and the use of active material in the internal distribution system by allowing for transformer-less connection to the medium voltage distribution grid. However, heterogeneous charging demands and arrival-departure profiles of the electric vehicles (EVs) could lead to significant loading unbalances among the MMC arms and among the modules of a single arm. Nevertheless, the current in the grid interface must be kept balanced and sinusoidal. Furthermore, the voltages of the modules of an arm must be balanced. This work combines a load management (LM) algorithm with a power flow management (PFM) algorithm to achieve the required characteristics of grid current and module voltages under the heterogeneity of the charging demand in MMC-based EVCSs. The PFM algorithm controls the circulating currents to compensate the phase-to-phase, arm-to-arm and intra-arm unbalances of the given loading. To minimize the additional losses resulting from active balancing by the PFM, the LM optimizes the charging schedules and allocations of incoming EVs into charging units in order to minimize phase-to-phase and arm-to-arm unbalances in the system. The performance of the proposed optimization-based LM is compared with a rule-based benchmark LM by simulating the daily operation of an example shopping mall parking with MMC-based grid interface. In scenarios with pronounced unbalance limitations, the optimization-based LM increases the supplied energy significantly. Real-time (RT) simulations demonstrate a balanced and sinusoidal grid current profile and balanced module voltages in MMC arms over the daily scenarios. These results indicate that the proposed strategy combining LM and PFM is applicable for real-world deployments.

Cutting and punching of the steel used in power transformer core may cause edge burrs. This, along with the degradation of the lamination insulation, can lead to interlaminar short circuits. Analysing these faults helps understanding their effect on the transformer reliability and performance. In this light, the actual paper aims to experimentally simulate and analyse both faults using a 15 kVA three phase power transformer. Effects produced from both selected faults are experimentally investigated in this paper where different scenarios are considered such as the area of the affected regions and the number of short-circuited laminations. Various flux densities are considered ranging from 0.5 to 1.8 T. Of interest, the current at no load is recorded and the test is repeated for any given scenario. The obtained results are presented and discussed to study the effect of each fault on the transformer performance. Overall, the transformer current increases with the number of short-circuits between laminations for both faults. This increase is related to the flux density, which is dependent and sensitive to the short circuit location. Such findings represent a good indication of the severity of short circuits relative to their position in the transformer core, and can be exploited to discuss the power losses in the transformer core.

This paper proposes an improved salp swarm algorithm (ISSA) as an effective metaheuristic method for tackling global optimization issues and damping power system oscillations. In the suggested ISSA, new equations are introduced to update the location of the leader and followers. This modification improves the method’s exploration possibilities while also preventing it from converging prematurely. Benchmark test functions are used to confirm the proposed algorithm’s performance, and the results are compared to SSA and other effective optimization algorithms. According to the extensive comparisons, the enhanced ISSA algorithm has higher convergence accuracy and stability than the original SSA and other researched algorithms. Furthermore, the feasibility and efficiency of the proposed method were demonstrated by the simultaneous coordinated design of UPFC based damping controllers. For the two-area, four-machine system, the experimental findings are provided. Simulation experiments reveal that ISSA designed controllers outperform those created using other methods.

Renewable energy is becoming more popular due to environmental concerns about the previous energy source. Accurate solar photovoltaic (PV) system model parameters substantially impact the efficiency of solar energy conversion to electricity. In this matter, swarm and evolutionary optimization algorithms have been widely utilized in dealing with practical problems due to their more straightforward concepts, efficacy, flexibility, and easy-to-implement procedural frameworks. However, the nonlinearity and complexity of the PV parameter identification caused swarm and evolutionary optimizers to exhibit Immaturity in the obtained solutions. In this study, an effective metaheuristic algorithm based on tunicate swarm optimization (TSA) is proposed for parameter identification of PV models. The proposed improved algorithm (ITSA) has two main phases at each iteration: searching all around the search space based on a randomly selected tunicate and improving the search using the position of the best tunicate. This modification improves the algorithm’s exploration ability while also preventing premature convergence. The suggested algorithm’s performance is confirmed using ten mathematical test functions and the outcomes are compared with TSA as well as some effective optimization algorithms. The proposed ITSA optimally identifies various parameters in the PV model, such as single diode (SDM), double diode (DDM), and PV modules. Based on the comprehensive comparisons, results indicate that the improved ITSA algorithm has higher convergence accuracy and better stability than the original TSA and other studied algorithms.

This paper deals with the detection and classification of two types of lamination faults (i.e., edge burr and lamination insulation faults) in a three-phase transformer core. Previous experimental results are exploited, which are obtained by employing a 15 kVA transformer under healthy and faulty conditions. Different test conditions were considered such as the flux density, number of the affected laminations, and fault location. Indeed, the current signals were used where four features (Average, Fundamental, Total Harmonic Distortion (THD), and Standard Deviation (STD)) were extracted. Elaborating A total of 328 samples, these features are utilized as input vectors to train and test classification models based on SVM, KNN, and DT algorithms. Based on the selected features, the results confirmed that the transformer current can be used for the detection of lamination faults. An accuracy rate of more than 84% was obtained using three different classifiers. Such findings provided a promising step toward fault detection and classification in electrical transformers, helping to prevent the system and avoid other related issues such as the increase in power loss and temperature.

The intermittent and uncertain behavior of renewable energy sources; moreover, the increase in penetration of these resources causes some drawbacks in power grids, particularly in low-inertia microgrids. In addition to supply-side challenges, changing load rate has a considerable negative effect on small-size microgrids. Therefore, according to these challenges, the lack of balance between generation and demand is a vital issue in microgrids. One way to face these challenges would be using a suitable control strategy. In this research, an adaptive fuzzy model predictive control has been proposed as a novel control approach and has been compared with conventional controllers such as an optimal PI, and an adaptive optimal model predictive control. It is important to mention that, various types of load changing and time-varying parameters have been considered for a new model of small size microgrids in this study. The comparison and simulation results obviously indicate the effectiveness of the proposed control strategy.

As an increasing amount of data processing is done at the network edge, high energy costs and carbon emission of Edge Clouds (ECs) are becoming significant challenges. The placement of application components (e.g., in the form of containerized microservices) on ECs has an important effect on the energy consumption of ECs, impacting both energy costs and carbon emissions. Due to the geographic distribution of ECs, there is a variety of resources, energy prices and carbon emission rates to consider, which makes optimizing the placement of applications for cost and carbon efficiency even more challenging than in centralized clouds. This paper presents a Dynamic Energy cost and Carbon emission-efficient Application placement method (DECA) for ECs. DECA addresses both the initial placement of applications on ECs and the re-optimization of the placement using migrations. DECA considers geographically varying energy prices and carbon emission rates as well as optimizing the usage of both network and computing resources at the same time. By combining a prediction-based A* algorithm with a Fuzzy Sets technique, DECA makes intelligent decisions to optimize energy cost and carbon emissions. Simulation results show the ability of DECA in providing a tradeoff and optimizing energy cost and carbon emission at the same time.

With the increasing penetration of distributed generations, preserving the protection system coordination is of great importance for the distribution network operation. This paper presents a supplementary protection strategy for the multi-agent system (MAS)-based hierarchical protection scheme (HPS). The primary level of MAS-based HPS clears the fault and preserves the protection coordination while the higher levels update the relay settings. The key advantage of the proposed supplementary scheme is that the primary level can autonomously and quickly preserve the protection coordination when the main relay is during the time-consuming process of updating settings in the case of network operating condition change. By adopting converter-based DGs as the only MAS agents, the proposed supplementary scheme divides the distribution network into several zones that communicate together using point-to-point communication. Without communicating with higher protection levels, the supplementary protection limits the influence of DGs on the fault current by controlling the produced power of each zone. The effectiveness of the proposed strategy is validated through several simulation case studies on the Isfahan distribution network.

Significant attention has been paid to robotic rehabilitation using various types of actuator and power transmission. Amongst those, cable-driven rehabilitation robots (CDRRs) are relatively newer and their control strategies have been evolving in recent years. CDRRs offer several promising features, such as low inertia, lightweight, high payload-to-weight ratio, large work-space and configurability. In this paper, we categorize and review the cable-driven rehabilitation robots in three main groups concerning their applications for upper limb, lower limb, and waist rehabilitation. For each group, target movements are identified, and promising designs of CDRRs are analyzed in terms of types of actuators, controllers and their interactions with humans. Particular attention has been given to robots with verified clinical performance in actual rehabilitation settings. A large part of this paper is dedicated to comparing the control strategies and techniques of CDRRs under five main categories of: Impedance-based, PID-based, Admittance-based, Assist-as-needed (AAN) and Adaptive controllers. We have carefully contrasted the advantages and disadvantages of those methods with the aim of assisting the design of future CDRRs.

We provide a concept of operations and a corresponding implementation of a long-range air traffic flow management in the Asia-Pacific region. This management will provide an appropriate demand-capacity balancing considering both aircraft sequencing by local arrival management procedures and flow optimization to prevent over-demand in the approach area around the airport. Thus, coordination of long-range international flights demands collaboration between different flight information regions and local regulations. As Singapore Changi Airport is a central element of the Asia-Pacific flow management high share of long-haul air traffic, we use this airport to demonstrate our approach. To derive the operational conditions and actual traffic patterns at the airport, ADS-B messages and flight plan information are processed. The data are cleaned, analyzed, and filtered to provide information about arrival flows within given distances to the airport. We provide an efficacy analysis of the long-range air traffic flow management using two approaches. First, we applied a mixed-integer optimization of time shifts of normal distributed flight times. Here, the regulation of long-range flights by time shifts (e.g., achieved by speed advisories) shows a significant relief from periods of over-demand at the airport approach sector. Second, we implement a reference and a test case scenario in an agent-based simulation environment including the local arrival management procedures. Here, the number of holdings and the associated holding time could be reduced by at least 26%.

This study presents a multi-objective policy for harmonic distortion and loss mitigation in micro-grid and active distribution grid. The implementation of this policy in networks including Distributed Generation (DG), is put forward based on the determination of the locational marginal price of each DG bus, considering their impacts on the loss and harmonic mitigation. In this process, each DG receives an incentive in the form of price increment, based on the Aumann-Shapley value game-theoretic method as an iterative algorithm, considering the effectiveness of reducing loss or harmonic distortion. Furthermore, as a decision-making tool, the operator can change the contribution of the incentives used to mitigate the loss or harmonic distortion and make an appropriate estimation for the next step conditions. The simulations are organized based on real data on the modified Taiwan Power Company grid (TPC). The modified TPC consists of renewable energy sources (RES) and fossil-fuel based DG units. Also, to achieve a realistic framework, generation of RES, loads, and market prices are considered as uncertain parameters. The results demonstrate the competence of the proposed method for the TPC network in terms of triple pricing methods comparison, total loss reduction, harmonic mitigation, and merchandising surplus controlling.

With the rise and widespread deployment of a vast array of wireless radio access technologies (e.g., 3G, 4G/LTE, 802.11, Bluetooth, and Femto) and the growth of interest in potential 5G technologies such as millimeter-wave radio, coupled with the rapid increase in the number of network edge devices with multiple radio interfaces, the question of network control and client-to-base-station association becomes an important issue. Older, well-studied centralized control schemes where a single computational entity harvests channel information from individual clients in order to determine optimal resource allocations for each client is no longer tenable: such methods require significant signaling overhead which does not scale well with the expected number of hundreds of thousands of smart client devices with multiple radio interfaces capable of leveraging many different radio access technologies (RATs). With the rise of these smart devices, which come with significant computational power, it is now possible to ask the question: can the network allow the client control over RAT selection and association in order to meet some client-driven or network-driven objective, and to what degree does the network assist the client in making these choices? This question becomes particularly important given the increasing interest in standardization and deployment of client-controlled edge networking, or Fog networking. In this paper, we explore the spectrum of client-controlled HetNets for 5G networks: from the fully devolved distributed local control approach, where clients make local decisions without any assistance from the network, to the hybrid control approach where clients may make decisions given some global information provided by the network.

Information-centric networking (ICN) is a promising solution for most of Internet applications where the content represents the core of the application. However, the proposed solutions for the ICN architecture are associated with many complexities including pervasive caching in the Internet and incompatibility with legacy IP networks, so the deployment of ICN in real networks is still an open problem. In this paper, we propose a backward-compatible ICN architecture to address the caching issue in particular. The key idea is implementing edge caching in ICN, using a coalition of end clients and edge servers. Our solution can be deployed in IP networks with HTTP requests. We performed a trace-driven simulation for analyzing peer-assisted information-centric networking (PICN) benefits using IRCache and Berkeley trace files. The results show that on average, PICN decreases the latency for 78% and increases the content retrieval speed for 69% compared with a direct download from the original Web servers. When comparing PICN with a solution based on central proxy servers, we show that the hit ratio obtained using a small cache size in each PICN client is almost 14% higher than the hit ratio obtained with a central proxy server using an unlimited cache storage.

Networks of weakly coupled oscillators are special cases of Hopfield networks and as such may be used in building physical systems capable of associative memory recall and pattern recognition. However, the existing architectures are not suitable for hardware implementation mainly due to the complexity of required couplings between the oscillators. In this paper, we propose an alternative way of using coupled oscillators in what we call “discriminant circuits” in analogy to the concept of “discriminant functions” in the field of artificial intelligence and machine learning. The main advantage of our system is in the simplicity of its architecture, which relies only on local couplings between adjacent oscillators. Using this architecture, we design a network of coupled CMOS oscillators as the core of a physical non-Boolean pattern recognition engine. The simplicity of the proposed circuit makes it readily implementable on any standard CMOS technology.

In this paper, we describe a general-purpose dialogue management framework used to design SOPHIE (Standardized Online Patient for Healthcare Interaction Education). SOPHIE simulates a virtual standardized cancer patient that allows physicians to practice skills such as empathy and patient empowerment in end-of-life communication. To provide the user with an opportunity to practice these skills, SOPHIE must produce a natural, emotionally appropriate conversation, yet handle topic shifts and open-ended questions from the user. To accomplish this, our approach to dialogue management loosely follows schemas – explicit representations of the typical flows of dialogue in end-of-life communication – while also using flexible pattern-driven methods for interpretation and generation. We conduct a crowdsourced evaluation of conversations between medical students and SOPHIE. Our agent is judged to produce responses that are natural, emotionally appropriate, and consistent with her role as a cancer patient. Furthermore, it significantly outperforms an end-to-end neural model fine-tuned on a human standardized patient corpus, attesting to the advantages of a schema-guided approach in this domain. However, the system is currently limited in its ability to generate responses that are judged to demonstrate deep understanding of the user, suggesting that future work should place focus on integrating this framework with robust natural language understanding and commonsense reasoning methods. [ABSTRACT FROM AUTHOR]