Recent observations of significant errors in static electricity meter readings have created a need for specification of new standard immunity test waveforms. This paper provides an overview of ongoing signal analysis work towards parsimonious specification of suitable test waveforms including candidate methods for signal representation, feature extraction and feature selection.

All existing solutions to distributed consensus are organised around a Paxos-like structure wherein processes contend for exclusive leadership in one phase, and then either use their dominant position to propose a value in the next phase or elect an alternate leader. This approach may be characterised as adversarial and phase-asymmetric, requiring distinct message schemas and process behaviours for each phase. In over three decades of research, no algorithm has diverged from this basic model, alluding to it perhaps being the only viable solution to consensus. This paper presents a new consensus algorithm named Spire, characterised by a phase-symmetric, cooperative structure. Processes do not contend for leadership; instead, they collude to iteratively establish a dominant value and may do so concurrently without conflicting. Each successive iteration is structured identically to the previous, employing the same messages and invoking the same behaviour. By these characteristics, Spire buckles the trend in protocol design, proving that at least two disjoint cardinal solutions to consensus exist. The resulting phase symmetry halves the number of distinct messages and behaviours, offering a clear intuition and an approachable foundation for learning consensus and building practical systems.

Deep learning (DL) techniques are being widely researched for their effectiveness in detecting cyber intrusions against the Internet of Things (IoT). However, time sensitive Critical Infrastructures (CIs) that rely on IoT require quicker detection of cyber intrusions close to the constrained devices in order to prevent service delays. FoG layer with higher computing resources and close proximity to IoT devices makes them suitable to deploy deep learning techniques for effective intrusion detection. However, communication overheads due to large volume of IoT data and computation requirements for deep learning models prevents effective use of FoG layer and raises scalability issues. To counter these issues, we propose a novel IoT intrusion detection framework incorporates feature selection step on time-series IoT data, followed by a deep learning Recurrent Neural Network (SimpleRNN and Bi-directional Long Short-Term Memory (LSTM)) based IoT attack detection. The effectiveness of the proposed approach was evaluated using the high-dimensional BoT-IoT dataset which contains large volumes of realistic IoT attack traffic. Results show that feature selection methods significantly ( 90%) reduced the dataset size and still achieved an increased recall rate compared to full feature set without loosing class differentiation ability. The SimpleRNN and Bi-LSTM models also did not suffer any underfitting or overfitting with the reduced feature space. The proposed deep learning based IoT intrusion detection framework is suitable for FoG layer deployment and can scale well with effective use of sub-divided dataset.

Recommender systems aim to personalize the experience of a user and are critical for businesses like retail portals, e-commerce websites, book sellers, streaming movie websites and so on. The earliest personalized algorithms use matrix factorization or matrix completion using algorithms like the singular value decomposition (SVD). There are other more advanced algorithms, like factorization machines, Bayesian personalized ranking (BPR), and a more recent Hebbian graph embeddings (HGE) algorithm. In this work, we implement BPR and HGE and compare our results with SVD, Non-negative matrix factorization (NMF) using the MovieLens dataset.

The conventional power distribution network is being transformed drastically due to high penetration of renewable energy sources (RES) and energy storage. The optimal scheduling and dispatch is important to better harness the energy from intermittent RES. Traditional centralized optimization techniques limit the size of the problem and hence distributed techniques are adopted. The distributed optimization technique partitions the power distribution network into sub-networks which solves the local sub problem and exchanges information with the neighboring sub-networks for the global update. This paper presents an adaptive spectral graph partitioning algorithm based on vertex migration while maintaining computational load balanced for synchronization, active power balance and sub-network resiliency. The parameters that define the resiliency metrics of power distribution networks are discussed and leveraged for better operation of sub-networks in grid connected mode as well as islanded mode. The adaptive partition of the IEEE 123-bus network into resilient sub-networks is demonstrated in this paper.

This paper investigates an average secrecy rate (ASR) maximization problem for an unmanned aerial vehicle (UAV) enabled wireless communication system, wherein a UAV is employed to deliver confidential information to a ground destination in the presence of a terrestrial passive eavesdropper. By employing an artificial noise (AN) injection based secure two-phase transmission protocol, we aim at jointly optimizing the UAV’s trajectory, network transmission power, and AN power allocation over a given time horizon to enhance the ASR performance. Specifically, we divide the original non-convex problem into four subproblems, and propose a successive convex approximation based efficient iterative algorithm to solve it suboptimally with guaranteed convergence. Simulation results demonstrate significant security advantages of our designed scheme over other known benchmarks, particularly for stringent flight durations.

Objective.Modeling the brain as a white box is vital for investigating the brain. However, the physical properties of the human brain are unclear. Therefore, BCI algorithms using EEG signals are generally a data-driven approach and generate a black- or gray-box model. This paper presents the first EEG-based BCI algorithm (EEGBCI using Gang neurons, EEGG) decomposing the brain into some simple components with physical meaning and integrating recognition and analysis of brain activity. Approach. Independent and interactive components of neurons or brain regions can fully describe the brain. This paper constructed a relationship frame based on the independent and interactive compositions for intention recognition and analysis using a novel dendrite module of Gang neurons. A total of 4,906 EEG data of left- and right-hand motor imagery(MI) from 26 subjects were obtained from GigaDB. Firstly, this paper explored EEGG’s classification performance by cross-subject accuracy. Secondly, this paper transformed the trained EEGG model into a relation spectrum expressing independent and interactive components of brain regions. Then, the relation spectrum was verified using the known ERD/ERS phenomenon. Finally, this paper explored the previously unreachable further BCIbased analysis of the brain. Main results. (1) EEGG was more robust than typical “CSP+” algorithms for the poorquality data. (2) The relation spectrum showed the known ERD/ERS phenomenon. (3) Interestingly, EEGG showed that interactive components between brain regions suppressed ERD/ERS effects on classification. This means that generating fine hand intention needs more centralized activation in the brain. Significance. EEGG decomposed the biological EEG-intention system of this paper into the relation spectrum inheriting the Taylor series (in analogy with the data-driven but human-readable Fourier transform and frequency spectrum), which offers a novel frame for analysis of the brain.

We propose a methodology of variable-temperature broadband noise characterization for cryogenic MOSFETs. A DUT is mounted on a reusable PCB vehicle with a built-in low-noise amplifier, and loaded into a cryogenic chamber. Using the vehicle, we measured flicker (low frequency) and white noise, and have successfully revealed dominance of shot noise in the temperature range from 300 to 120 K for the first time.

This paper presents a novel mechanism implemented in the SW to identify and authorize secure service call from the NW. While the current solution needs changes of the non-secure software (typically an RTOS), our solution exploits available hardware (i.e., the memory protection unit or MPU) to handle clients identification and authorization in a transparent way to non-secure software.

A highly miniaturized three-dimensional spherical folded dipole antenna has been reported, for which inherent impedance matching is achieved with respect to a practical source impedance by employing a simple series-LC loading combination, thereby engineering its input impedance. In order to maximize its bandwidth, the miniaturized antenna employs a spherical helix structure as the folded arm that occupies the full volume of the corresponding Chu sphere. A bifilar (two folded arms) and a quadrifilar (four folded arms) helix loaded folded dipole antenna are designed, and full-wave simulations show that both the resulting antennas demonstrate excellent impedance matching when miniaturized by 85% in comparison to a resonant dipole operating at the same frequency. Despite the high degree of miniaturization, the resulting radiation efficiencies for the bifilar and quadrifilar antennas are found to be 87.1% and 90.6%, respectively. Furthermore, various quality-factor definitions are explored for the quadrifilar antenna, and it is observed that the resulting quality factor is around 1.83 (1.22) times that predicted by the Chu (Thal) lower bound.

The development of a country and the well-being of its citizens rely heavily on the availability of electricity. Further needs for the improvements of power quality and availability have been the cause for numerous studies and research efforts. During this expansion of energy availability, renewable energy (RE) sources have become popular. The widespread use of RE has however been delayed by its unreliability and fluctuations in output. This problem has however partly been solved by the implementation of energy storage systems (ESS).

This paper describes a new method for measurements of signals in the 2--150 kHz frequency range, as required to support the regulation of conducted emissions on the power grid. The digital method is based on heterodyning, decimation and multi resolution analysis.

The use of Energy storage systems is becoming more widespread around the world due to the coincidental increase in available intermittent renewable energy. The main uses for energy storage are the balancing of supply and demand and increasing the reliability of the energy grid, while also offering other services, such as, cooling and heating for buildings and enabling the creation and use of portable electric devices and electric vehicles.

We present a computationally improved heuristic algorithm for transmission switching (TS) to recover load shed. Research from the past showed that changing power system topology may control power flows and remove line congestion. Hence, TS may reduce the required load shed. One of the main challenges is to find a potential TS candidate in a suitable time. Here, we propose a novel heuristic method that is capable of finding the potential TS candidate faster than existing algorithms in literature. The proposed method is compatible with both the AC and DC optimal power flows (OPF). Three metrics are used to compare the proposed algorithm with the state-of-the-art from literature to show the speedup and accuracy achieved. The proposed method is implemented on the IEEE 30-bus system, PEGASE 89-bus system, IEEE 118-bus system, and Polish 2383-bus system. The results on the large-scale Polish 2383-bus system shows that the proposed algorithm is scalable to large real-world systems. Parallel computing is implemented to further improve the computational performance of the proposed algorithm.

This is about the overall functionality and complexity (size) of the open source event stream processing system or StreamEPS for short. The elements of the platform will be functional if the design follows application interfaces as described in this work. The engine architecture details the overall functionality in terms of engine core, engine context, engine processing and of itself.

Quantum Computing presents an interesting paradigm where it can possibly offer certain improvements and additions to a classical network while training. This method is particularly prevalent in the current Noisy Intermediate-Scale Quantum era, where we can test these theories using libraries such as Pennylane in conjunction with robust ML frameworks such as TensorFlow. This paper presents a proof-of-concept for the same, using a hybrid quantum-classical model to solve a text classification problem on the IMDB Movie Sentiment Dataset. These hybrid models utilize precalculated embeddings and dense layers alongside a variational quantum circuit layer. We created 4 such models, utilizing various kinds of embeddings, namely NNLM-128, NNLM-50, Swivel and USE, using TFHub and Pennylane. We also trained classical versions of these models, without the variational quantum layer to evaluate the performances. All models were trained on the same data, keeping the parameters constant.

As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), and enabling technologies. First, we attempt to answer the question of “Is there any need for 6G?” by shedding light on the key driving factors of 6G, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of “What 6G may look like?”. This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

This work proposes a new input-based event-triggered control for a discrete-time singularly perturbed system, where latest measurements are transmitted when a pre-defined condition is satisfied on input. The other significant contribution of the proposed work is a separation of control updates for slow and fast dynamics.