The electrical grid undergoes a fundamental change with the introduction of the Smart Grid; in particular, power substations have been evolving towards automated systems. Power substation communication networks evolved from legacy infrastructures mostly formed by serial devices to Ethernet-based digital communications networks, thanks to the introduction of the IEC 61850 standard. However, this evolution inherited the shortcomings of the traditional decentralized network management. This paper explores the upcoming evolution of IEC 61850 when it meets the ground-breaking programmable network technologies: Software Defined Networks (SDN) and Programmable Data Planes (PDP). Here, we describe how recent proposals leverage SDN to improve network management tasks such as topology discovery, multicast traffic management, QoS provisioning, among others in IEC 61850 based systems. We also outline potential improvements to critical network management tasks in power substations using PDP features such as in-band network telemetry. Finally, we discuss different challenges in the management of the communication network of Smart Power Substations and how they can be addressed with the implementation of programmable networks

This paper proposes a method to estimate the inertia constant of synchronous generators (SGs) and the virtual inertia provided by converter-interfaced generators (CIGs). It can be applied when the system is under normal operating conditions or after power imbalance events. The algorithm is based on a recursive system identification approach and uses the active power and bus frequency measurements proceeding from phasor measurement units. The measurements are preprocessed to improve the estimation accuracy. Subsequently, a model-independent rotor speed estimator is used to compute rotor speed variations (or internal frequency variations, in the case of CIGs). Afterwards, a recursive least-square equation error formulation is implemented to compute the SG and CIG parameters in the z-domain, which are later converted into their respective counterparts in the s-domain. The method is applied to several generation devices in the IEEE 39-bus and IEEE 118-bus test systems, including traditional SGs and CIGs providing virtual inertia. Numerical results show the accuracy and the low computational burden of the proposed method.

Future wireless networks \textcolor{black}{(5G and beyond),} also known as Next Generation or NextG, are the vision of forthcoming cellular systems, connecting billions of devices and people together. In the last decades, cellular networks have been dramatically growth with advanced telecommunication technologies for high-speed data transmission, high cell capacity, and low latency. The main goal of those technologies is to support a wide range of new applications, such as virtual reality, metaverse, telehealth, online education, autonomous and flying vehicles, smart cities, smart grids, advanced manufacturing, and many more. The key motivation of NextG networks is to meet the high demand for those applications by improving and optimizing network functions. Artificial Intelligence (AI) has a high potential to achieve these requirements by being integrated in applications throughout all layers of the network. However, the security concerns on network functions of NextG using AI-based models, i.e., model poising, have not been investigated deeply. It is crucial to protect the next-generation cellular networks against cyber attacks, especially adversarial attacks. Therefore, it needs to design efficient mitigation techniques and secure solutions for NextG networks using AI-based methods. This paper proposes a comprehensive vulnerability analysis of  deep learning (DL)-based channel estimation models trained with the dataset obtained from MATLAB’s 5G toolbox for adversarial attacks and defensive distillation-based mitigation methods. The adversarial attacks produce faulty results by manipulating trained DL-based models for channel estimation in NextG networks, while making models more robust against any attacks through mitigation methods. This paper also presents the performance of the proposed defensive distillation mitigation method for each adversarial attack against the channel estimation model. The results indicated that the proposed mitigation method can defend the DL-based channel estimation models against adversarial attacks in NextG networks.

In this paper, a super-broadband (SBB) rectifier using a wideband resistance compression network (WRCN) and harmonic cycling is proposed, simulated, fabricated, measured and analyzed in detail for the harvesting of ambient Radio frequency (RF) energy. Meanwhile, impedance compression and matching are achieved to implement the SBB using WRCN and impedance operation networks. Additionally, harmonic cycling structure and bending technology are employed to improve the power conversion efficiency (PCE) and reduce the size, respectively. For demonstration, a prototype of the proposed rectifier is fabricated and measured. The measured results are in good accordance with the simulated ones, indicating that the rectifier can provide high efficiency and ultra-broadband rectification characteristics. A PCE over 50% is realized at 3.5 GHz with an input power range of 6–24 dBm, and a PCE of 75% is obtained at an input power of 18 dBm. The PCE is greater than 50% at 17 dBm with an input frequency of 2.7–4.6 GHz. This work can find significant applications in the development of RF-harvesting.

Traffic video analytics has become one of the core components in the evolution of transportation systems. Artificially intelligent traffic management systems apply computer vision techniques to alleviate the monotony of manually monitoring the video feed from surveillance cameras. Locating the objects of interest is the most crucial step in the pipeline of such video analytics systems. An abundance of research has been conducted to find the location of the targets in traffic scenes. This paper presents a comprehensive review of different algorithms used for object detection in traffic surveillance applications in addition to the recent trends and future directions. Based on the approaches used in the related studies, we categorize the object detection methods into motion-based and appearance-based techniques. We further classify each group of techniques into a number of subcategories and analyze the advantages and disadvantages of each method. The major challenges, limitations, and potential solutions are also discussed along with the future scope and directions.

Bulk-sensitive microscopic measurements of buried magnetic domains are reported. Utilizing a new magneto-optical effect in the hard X-ray regime, referred to as X-ray magnetic circularly polarized emission, the internal transverse domains in a grain-oriented electrical steel sheet are unambiguously observed. This allows the lateral shapes of the transverse domains to be successfully visualized. In addition, we discuss the magnetic domain structure of the supplementary domains below the surface from measurements performed at several pairs of incident and exit angles.

This paper introduces a paradigm shift regarding vocal learning simulations, in which the communicative function of speech acquisition determines the learning process and intelligibility is considered the main measure of learning success. Thereby, a novel approach for artificial early vocal learning is presented that utilizes deep neural network-based phoneme recognition in order to calculate the speech acquisition objective function. This function guides a learning framework that involves the state-of-the-art articulatory speech synthesizer VocalTractLab as the motor-to-acoustic forward model. It is shown that in this way an extensive set of German phonemes consisting of most German consonants and all stressed vowels can be produced successfully. The synthetic phonemes were rated as highly intelligible by human listeners in a listening experiment. Furthermore, it is shown that visual speech information, such as lip and jaw movements can be extracted from video recordings and be incorporated into the learning framework as an additional loss component during the optimization process. It was observed that this visual loss did not increase the overall intelligibility of phonemes. Instead, the visual loss acted as a regularization mechanism that facilitated the finding of more biologically plausible solutions in the articulatory domain.

To automate network operations and deployment of compute services, intent-driven service management (IDSM) is essential. It enables network users to express their service requirements in a declarative manner as intents. To fulfill the intents, closed control-loop operations carry out required configurations and deployments without human intervention. Despite the fact that intents are fulfilled automatically, conflicts may arise between user’s and service provider’s intents due to limited resources availability. This triggers IDSM system to initialize an intent negotiation process among conflicting actors. Intent negotiation involves generating one or more alternate intents based on the current state of the underlying physical/virtual resources, which are then presented to the intent creator for acceptance or rejection. In this way, the quality of services (QoS) can be improved significantly by maximizing the acceptance rate of service requests in the scenario of limited resources. However, intent negotiation systems are still in their infancy. The available solutions are platform dependent which poses various challenges in their adoption to diverse platforms. The main focus of this work is to draft and evaluate a comprehensive and generic intent negotiation framework which can be used to develop intent ne- gotiation solutions for diverse IDSM platforms. In this work, we have identified and defined various processes that are necessary for intent negotiation. Furthermore, a generic intent negotiation framework is presented representing interactions among the identified processes, while conflicting actors engage in the intent negotiation. The results demonstrated that the proposed intent negotiation framework increases the intent acceptance rate by up to 38% with processing overheads less than 10%.

It is well known that the metaverse, also named general cyberspace, is virtual-real fusion spaces, consisting of a virtual space namely cyberspace and virtual-real spaces namely cyber-enabled physical, social, and thinking (cyber-enabled) spaces. This paper discusses the open issues of the metaverse in terms of science and technology and proposes a new discipline and inter-discipline hierarchy for the metaverse first. Then, it ex?plores various relevant standards of discipline classification and a discipline and inter-discipline hierarchy based on physical, social and thinking spaces, and investigates the cyberspace and cyber?enabled spaces. On the basis of the above research, this paper proposes the detailed metaverse discipline and inter-discipline hierarchy based on cyber-physical-social-thinking spaces and enriches the content of cyberology from the aspects of cyberspace and cyber-enabled spaces. Finally, this paper predicts the future development of cyberology in terms of society, enterprise and humankind.

We present optimized modulation and coding for the recently introduced dual modulated QR (DMQR) codes that extend traditional QR codes to carry additional secondary data in the orientation of elliptical dots that replace black modules in the barcode images. By dynamically adjusting the dot size, we realize gains in embedding strength for both the intensity modulation and the orientation modulation that carry the primary and secondary data, respectively. Furthermore, we develop a model for the coding channel for the secondary data that enables soft-decoding via 5G NR (new radio) codes already supported by mobile devices. The performance gains for the proposed optimized designs are characterized via theoretical analysis, simulations, and actual experiments using smartphone devices. The theoretical analysis and simulations inform our design choices for the modulation and coding, and the experiments characterize the overall improvement in performance for the optimized design over the prior unoptimized designs. Importantly, the optimized designs significantly increase usability of DMQR codes with commonly used QR code beautification that cannibalizes a portion of the barcode image area for the insertion of a logo or image. In experiments with a capture distance of 15 inches, the optimized designs increase the decoding success rates between 10% and 32% for the secondary data while also providing gains for primary data decoding at larger capture distances. When used with beautification in typical settings, the secondary message is decoded with a high success rate for the proposed optimized designs, whereas it invariably fails for the prior unoptimized designs.

With the development of 5G communications, location-based services (LBS) mobile applications have already become ubiquitous. As a result, applications like foursquare and Twitter have generated a massive amount of semantic trajectory data, which can no longer be efficiently processed by a single machine due to computing power limitations. Among the processing and analysis of semantic trajectory data, the similarity join query is a general yet computationally complex query operation. It is widely used in different applications, such as route planning, personalized carpooling, and geographic location recommendations. This paper proposes a distributed join framework to process similarity join over large sets of semantic trajectories. The framework supports the similarity join of semantic trajectories in the textual, temporal, and spatial domains. Experimental results on real datasets show that in comparison with baselines, the distributed framework in this paper has excellent scalability and query efficiency.

Abstract—Artificial Intelligence (AI)-Based Security Intelligence Modelling can be used to prevent, detect, and manage cyber threats. Data-driven AI solutions are currently undergoing rigorous research and design in their own field, but few scholars or practitioners frame Authorised Push Payment (APP) Scams as a unique cybersecurity concern, or tailor technical solutions based on the local regulatory context. Drawing on a recent consultation publication by the UK Payment Systems Regulator on APP scams (November 2021), this paper shows how AI can be leveraged to manage APP scams and explores some of the opportunities and risks one should consider when adopting such an approach. We highlight three scenarios: 1) Liability on Payment Service Provider; 2) Liability on Payor; and 3) Liability on Payor with Substantial Public Sector Involvement. These examples illustrate how socio-technical systems can play a design role, and consequently assist industry leaders and engineering management in prioritizing investment focus, strategic approaches, and technical solutions.

By extending the context of the Game Theory to the understanding of P vs NP Problem, fundamental principles of observation can be explained to the very limit of existence.

Human kinetics, specifically joint moments and ground reaction forces (GRFs) can provide important clinical information and can be used to control assistive devices. Traditionally, collection of kinetics is mostly limited to the lab environment because it relies on data that measured from a motion capture system and floor-embedded force plates to calculate the dynamics via musculoskeletal models. This spatially limited method makes it extremely challenging to measure kinetics outside the laboratory in a variety of walking conditions due to the expensive device setup and large space required. Recently, employing machine learning with IMU sensors are suggested as an alternative method for biomechanical analyses. Although these methods enable estimating human kinetic data outside the laboratory by linking IMU sensor data with kinetics dataset, they were limited to show inaccurate kinetic estimates even in highly repeatable single walking conditions due to the employment of generic deep learning algorithms. Thus, this paper proposes a novel deep learning model, KineticsFM-DLR-Ensemble-Net to estimate the hip, knee, and ankle joint moments in the sagittal plane and 3 dimensional ground reaction forces (GRFs) using three IMU sensors on the thigh, shank, and foot under several representative walking conditions in daily living, such as treadmill, level-ground, stair, and ramp with different walking speeds. This is the first study that implements both joint moments and GRFs in multiple walking conditions using IMU sensors via deep learning. Our deep learning model is versatile and accurate for identifying human kinetics across diverse subjects and walking conditions and our model outperforms state-of-the-art deep learning model for kinetics estimation by a large margin.

Rotor position information obtained through a position sensor is crucial for proper field-oriented control (FOC) of permanent magnet synchronous machine based high dynamic energy conversion applications. In applications such as transportation and industrial, PMSM energy conversion systems are subjected to harsh environmental conditions such as vibration, shock, and thermal shock causing mechanical interfaces holding positions sensors to fail, introducing error to the position measurement. A dynamic position sensor offset error (DPSOE), being such a failure mode has the potential to degrade system torque output or more adversely, reverse machine torque output that may cause catastrophic outcomes. This paper evaluates different DPSOE scenarios, presents an approach to model the failure mode for analysis and proposes two novel and robust DPSOE detection methods for PMSM drive systems. The proposed detection methods are analytically proven and supported by simulation and experimental results under multiple operating conditions proving robustness.

This work reports a class of silicon micromachined millimeter scale electrostatic actuator capable of both axial and lateral in-plane displacement with a relatively high energy density. The actuator is comprised of a large-scale integrated array of individual parallel-plate electrostatic cells. The force and displacement of cells within the array add up providing relatively large actuation force and displacement output for the actuators. The non-conventional fabrication process allows realization of submicron transduction airgaps within the actuator cells lowering the actuation voltage. The fabrication process also allows electrical isolation of different cells within the array so that different sections of the actuator ribbon can be actuated independently enabling lateral deformation of the actuator ribbon in addition to axial contraction. Axial in-plane displacement of up to 20.50 µm has been shown for a 2010 µm × 1000 µm fabricated actuator. The actuator output force is as high as 10.3 mN demonstrating maximum energy density of 1.11 mJ/cm3. The same actuator ribbon demonstrated lateral displacement of 10.11 um.

Temporal coherence and spectral regularity are critical cues for human auditory streaming processes and considered in many sound separation models. Some examples include the Conv-tasnet model, which focuses on temporal coherence using short length kernels to analyze sound, and the dual path convolution recurrent network (DPCRN) model, which uses two RNNs to analyze general patterns along the temporal and spectral dimensions on a spectrogram. In this paper, we propose a harmonic-aware tri-path convolution recurrent network (HATPCRN) model to separate singing voices on a spectrogram via the addition of an inter-band RNN, designed specifically for the harmonic structure. Evaluation results on the DSD100 and MUSDB18 datasets show that this addition can further boost separation performances of the DPCRN.

This paper presents the design and characterization of a fully-integrated receiver based on single-photon avalanche diodes (SPADs) with nanosecond dead time for high-speed high-sensitivity optical wireless communication (OWC).