This paper presents the comprehensive numerical analysis of four, second order sliding manifold control algorithms to estimate rotor angle/position, θe, sensorlessly in brushless DC synchronous machine, namely, twisting, super twisting, convergence law and quasi-continuous sliding controller. The Lyapunov stability and convergence along with trajectories in the discrete time is also analyzed for each of these algorithms for both continuous and discontinuous controls.

Publication reference: https://doi.org/10.1007/978-981-16-1740-9_46This paper investigates the utilization of a physics simulation environment as the imagination of a robot, where it creates a replica of the detected terrain in a physics simulation environment in its memory, and “imagines” a simulated version of itself in that memory, performing actions and navigation on the terrain. The physics of the environment simulates the movement of robot parts and its interaction with the objects in the environment and the terrain, thus avoiding the need for explicitly programming many calculations.

The evolution of generative artificial intelligence (GenAI) constitutes a turning point in reshaping the future of technology in different aspects. Wireless networks in particular, with the blooming of self-evolving networks, represent a rich field for exploiting GenAI and reaping several benefits that can fundamentally change the way how wireless networks are designed and operated nowadays. To be specific, large language models (LLMs), a subfield of GenAI, are envisioned to open up a new era of autonomous wireless networks, in which a multimodal large model trained over various Telecom data, can be fine-tuned to perform several downstream tasks, eliminating the need for dedicated AI models for each task and paving the way for the realization of artificial general intelligence (AGI)-empowered wireless networks. In this article, we aim to unfold the opportunities that can be reaped from integrating LLMs into the Telecom domain. In particular, we aim to put a forward-looking vision on a new realm of possibilities and applications of LLMs in future wireless networks, defining directions for designing, training, testing, and deploying Telecom LLMs, and reveal insights on the associated theoretical and practical challenges.

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance-based, to test algorithm performance under a wide set of conditions. There are also resource- and behaviour-based benchmarks to test the resource consumption and the behaviour of algorithms. In this article, we propose a novel behaviour-based benchmark toolbox: BIAS (Bias in Algorithms, Structural). This toolbox can detect structural bias per dimension and across dimension based on 39 statistical tests. Moreover, it predicts the type of structural bias using a Random Forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for structural bias in an early phase of development. Experiments with a large set of generated structural bias scenarios show that BIAS was successful in identifying bias. In addition we also provide the results of BIAS on 432 existing state-of-the-art optimisation algorithms showing that different kinds of structural bias are present in these algorithms, mostly towards the centre of the objective space or showing discretization behaviour. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.

Abstract:  In this paper, we propose two new provable algorithms for tracking online low-rank approximations of high-order streaming tensors with missing data. The first algorithm, dubbed adaptive Tucker decomposition (ATD), minimizes a weighted recursive least-squares cost function to obtain the tensor factors and the core tensor in an efficient way, thanks to the alternating minimization framework and the randomized sketching technique. Under the Canonical Polyadic (CP) model, the second algorithm called ACP is developed as a variant of ATD when the core tensor is imposed to be identity. Both algorithms are low-complexity tensor trackers that have fast convergence and low memory storage requirements. A unified convergence analysis is presented for ATD and ACP to justify their performance. Experiments indicate that the two proposed algorithms are capable of streaming tensor decomposition with competitive performance with respect to estimation accuracy and runtime on both synthetic and real data. Code: https://github.com/thanhtbt/tensor_tracking Comment: to appear in Elsevier Patterns

In today’s increasingly data-driven landscape, organizations are shifting their focus toward leveraging data analytics for strategic decision-making. As data becomes a cornerstone of operational and strategic activities, the quality of this data has emerged as a non-negotiable aspect for organizations. Lack of attention to data quality can not only result in considerable revenue losses but can also cripple the effectiveness of analytics, causing misinformed decisions and strategic errors. Against this backdrop, this empirical study delves into the innovative avenue of utilizing Generative Artificial Intelligence (AI) as a mechanism for enhancing data quality. The research aims to explore multiple facets of organizational operationsâ\euro”ranging from technical infrastructure to business strategyâ\euro”to ascertain the potential advantages offered by Generative AI. Utilizing a mix of qualitative and quantitative methods, we conducted in-depth interviews, case studies, and simulations to evaluate the impact of Generative AI on data quality. Our findings reveal a multi-layered benefit structure. Notably, we found that Generative AI is not a replacement for existing, traditional methods of data quality assurance but serves as a powerful supplement. It augments traditional methods by increasing the accuracy of data, thereby offering a more reliable foundation for analytics. Additionally, the use of Generative AI can streamline workflows, enhancing productivity among various roles including solution architects and software developers. Moreover, it facilitates a more nuanced and accurate requirement gathering process, enabling businesses to fine-tune their data analytics strategies more effectively. In conclusion, our study establishes that integrating Generative AI into data quality management processes can not only resolve immediate issues surrounding data accuracy but also lead to long-term organizational benefits, such as higher efficiency and more effective decision-making. This research serves as a pioneering step in the intersection of Generative AI and data quality, setting the stage for future studies and real-world applications.

In this paper, an AC superconducting multi-phase symmetric-winding machine is designed for a wind power generator to improve its performance and reduce losses, where four handpicked topological designs were explored and compared. In particular, it is found that using high-phase order of unique phasors further improves the performance. The iron losses are reduced, and the rippling behaviour is reduced due to the smoother airgap magnetic flux density. Furthermore, a higher LCM is achieved due to a better slot-pole combination for fractional slot concentrated windings without having space sub-harmonics. Nonetheless, it is shown that creating a smooth air gap magnetic flux density does not improve the AC hysteretic superconducting losses; thus, further analysis with another approach must be done. Moreover, it is found that the Meisner effect is present in the machine and is inversely proportional to the AC hysteretic superconducting losses. Finally, it shows that a 13-phase AC-superconducting machine can achieve a theoretical limit approaching 101.7017 Nm/kg for the torque-to-weight (TTW) ratio, outperforming a classic winding layouts.

Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection using machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Additionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw attention to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.

Social media is a platform in which tons and tons of text are generated each and every day. The data is so large that cannot be easily understood, so this has paved a path to a new field in the information technology which is natural language processing. In this paper, the text data which is used for the classification is tweets that determines the state of the person according of the sentiments which is positive, negative and neutral. Emotions are the way of expression of the person’s feelings which has a high influence on the decision making tasks. Here we have proposed the text representation, Term Frequency Inverse Document Frequency (tfidf), Keras embedding along with the machine learning and deep learning algorithms for the purpose of the classification of the sentiments, out of which Logistics Regression machine learning based methods out performs well when the features is taken in the limited amount as the features increases Support Vector Machine (SVM) which is also one of the machine learning algorithm out performs well making a benchmark accuracy for this dataset as the 75.8%. For the research purpose the dataset has been made publically available.

Using graph theory to identify essential proteins is a hot topic at present. These methods are called network-based methods. However, the generalization ability of most network-based methods is not satisfactory. Hence, in this paper, we consider the identification of essential proteins as a multi-objective optimization problem and use a novel multi-objective optimization method to solve it. The optimization result is a set of Pareto solutions. Every solution in this set is a vector which has a certain number of essential protein candidates and is considered as an independent predictor or voter. We use a voting strategy to assemble the results of these predictors. To validate our method, we apply it on the protein-protein interactions (PPI) datasets of two species (Yeast and Escherichia coli). The experiment results show that our method outperforms state-of-the-art methods in terms of sensitive, specificity, F-measure, accuracy, and generalization ability.

This paper focuses on the common mistakes made by students at the early stages of research when writing scientific papers, thesis and reports. It points out mistakes to be avoided and the basic rules that can be inferred by reading a few scientific documents but are not usually clearly written and that we, as professors, end up teaching over and over again. The outcome of this paper will be, hopefully, that we will not need to correct the same common mistakes again and you, as a student, will have a faster lane to publishing. Starting with the abstract: it is composed of a single paragraph, does not contain acronyms or references and describes in short the work, main highlights and points out the results or main conclusions obtained from the work being presented. The abstract is an independent part of the paper and commonly has a character or word limit that you need to respect. It can be read as a “stand-alone” and the paper starts in the introduction, meaning that the introduction is not the sequence of the abstract and it can have some text in common if needed.

The traditional approach of radio frequency electromagnetic field exposure compliance assessment is highly conservative when applied to radio base station antennas implementing dynamic beamforming. In this paper, an analytical model based on the queuing theory with a hyper-exponential service distribution time is developed to assess the time-averaged actual maximum downlink exposure of 5G multi-column radio base station antennas by taking into account the effects of beam scanning over time in free space. Using the measured antenna radiation patterns, the 5G downlink antenna precoding codebook, and assuming a conservative user equipment distribution, the ratio of the actual maximum exposure to the theoretical maximum exposure with 100% traffic load and 75% time-division duplex downlink duty cycle is found to be less than 0.5 and 0.3 for four-transmitter and eight-transmitter radio base station antennas, respectively. These results show that assuming constant peak power transmission in a fixed direction leads to an overestimate of downlink exposure also from conventional antennas characterized by only a few transmitters in addition to massive multi-input multi-output products.

Silicon photonic micro-ring resonators (MRR) developed on the silicon-on-insulator (SOI) platform, owing to their high sensitivity and small footprint, show great potential for many chemical and biological sensing applications such as label-free detection in environmental monitoring, biomedical engineering, and food analysis. In this tutorial, we provide the theoretical background and give design guidelines for SOI-based MRR as well as examples of surface functionalization procedures for label-free detection of molecules. After introducing the advantages and perspectives of MRR, fundamentals of MRR are described in detail, followed by an introduction to the fabrication methods, which are based on a complementary metal-oxide semiconductor (CMOS) technology. Optimization of MRR for chemical and biological sensing is provided, with special emphasis on the optimization of waveguide geometry. At this point, the difference between chemical bulk sensing and label-free surface sensing is explained, and definitions like waveguide sensitivity, ring sensitivity, overall sensitivity as well as the limit of detection (LoD) of MRR are introduced. Further, we show and explain chemical bulk sensing of sodium chloride (NaCl) in water and provide a recipe for label-free surface sensing.

This paper aims to discuss the standard guidelines of the development process of secure software and will give justification on different types and ways of the software development processes. Additionally, a survey is conducted, the aim of which is to observe user behavior towards software system usage, user attitude in terms of privacy and policy awareness, security and privacy concerns. This is followed by discussion on how to secure software systems in development stage.

In this paper, internet of things (IoT) connectivity in rural areas is investigated. Both fronthaul and backhaul considerations are studied. First, intelligent radio resource management (RRM) and network planning techniques are discussed for IoT access/fronthaul networks. The proposed RRM scheduling approach was shown to lead to good performance in scheduling IoT devices. Then, several backhauling techniques for providing connectivity to rural areas are investigated and their cost efficiency is analyzed. Techniques based on free space optics with solar powered devices are found to be a suitable backhaul solution.

A document by Said Mikki . Click on the document to view its contents.

Kinematic redundancy in robots makes it possible to execute several control tasks simultaneously. As some tasks are usually more important than others, it is reasonable to dynamically decouple them in order to ensure their execution in a hierarchical way or even without any interference at all. The most widely used technique is to decouple the system by feedback linearization. However, that requires to actively shape the inertia and consequently modify the natural dynamics of the robot. Here we propose a passivity-based multi-task tracking controller that preserves these inertial properties but fully compensates for task-space cross-couplings using external force feedback. Additionally, three formal proofs are provided: uniform exponential stability for trajectory tracking, passivity during physical interaction, and input-to-state-stability. The controller is validated in simulations and experiments and directly compared with the hierarchical PD+ approach and the feedback linearization. The proposed approach is well suited for safe physical human-robot interaction and dynamic trajectory tracking if measurements or estimations of the external forces are available.

In this work, we design an elastic open radio access network (O-RAN) slicing for the industrial Internet of things (IIoT). Unlike IoT, IIoT poses additional challenges such as severe communication environment, network-slice resource demand variations, and on-time information update from the IIoT devices during industrial production. First, we formulate the O-RAN slicing problem for on-time industrial monitoring and control where the objective is to minimize the cost of fresh information updates (i.e., age of information (AoI)) from the IIoT devices (i.e., sensors) while maintaining the energy consumption of those devices with the energy constraint as well as O-RAN slice isolation constraints. Second, we propose the intelligent ORAN framework based on game theory and machine learning to mitigate the problem’s complexity. We propose a two-sided distributed matching game in the O-RAN control layer that captures the IIoT channel characteristics and the IIoT service priorities to create IIoT device and small cell base station (SBS) preference lists. We then employ an actor-critic model with a deep deterministic policy gradient (DDPG) in the O-RAN service management layer to solve the resource allocation problem for optimizing the network slice configuration policy under time varying slicing demand. While the matching game helps the actor-critic model, the DDPG enforces the long-term policy-based guidance for resource allocation that reflects the trends of all IIoT devices and SBSs satisfactions with the assignment. Finally, the simulation results show that the proposed solution enhances the performance gain for the IIoT services by serving an average of 50% and 43.64% more IIoT devices than the baseline approaches.