The ability to predict the behavior of a wireless channel in terms of the frame delivery ratio is quite valuable, and permits, e.g., to optimize the operating parameters of a wireless network at runtime, or to proactively react to the degradation of the channel quality, in order to meet the stringent requirements about dependability and end-to-end latency that typically characterize industrial applications. In this work, prediction models based on the exponential moving average (EMA) are investigated in depth, which are proven to outperform other simple statistical methods and whose performance is nearly as good as artificial neural networks, but with dramatically lower computational requirements. Regarding the innovation and motivation of this work, a new model that we called EMA linear combination (ELC), is introduced, explained, and evaluated experimentally. Its prediction accuracy, tested on some databases acquired from a real setup based on Wi-Fi devices, showed that ELC brings tangible improvements over EMA in any experimental conditions, the only drawback being a slight increase in computational complexity.

A document by Ying Chen. Click on the document to view its contents.

A document by Marika Kulmar. Click on the document to view its contents.

In recent years, neural network-based black-box modeling of nonlinear audio effects has improved considerably. Present convolutional and recurrent models can model audio effects with long-term dynamics, but the models require many parameters, thus increasing the processing time. In this paper, we propose KLANN, a Koopman-Linearised Audio Neural Network structure that lifts a one-dimensional signal (mono audio) into a high-dimensional approximately linear state-space representation with nonlinear mapping, and then uses differentiable biquad filters to predict linearly within the lifted state-space. Results show that the proposed models match the high performance of the state-of-the-art neural models while having a more compact architecture, reducing the number of parameters by tenfold, and having interpretable components.

The huge amount of data produced by billions of distributed Internet of Things (IoT) devices creates unprecedented opportunities for several fields and industries. These data can drive informed decision-making, enhance operational efficiency, and improve the quality of life for individuals and communities. IoT data marketplaces have evolved as a solution to allow trading data assets, where data consumers can acquire IoT data streams to improve data analytics and offer innovative services, while data providers can monetize their IoT data. The realization of trustworthy marketplace, however, faces significant impediments due to the lack of mutual trust between data providers and consumers with the absence of a central marketplace operator trusted by all entities. In this paper, we focus on developing a trustworthy and decentralized solution for real-time IoT data marketplaces. First, we propose Multi-authority Attribute-Based Encryption with Revocation and Accountability (MABERA), a novel trustworthy Multi-authority Attribute-Based Encryption (MA-ABE) that supports attribute-level revocation and accountability. Then, we propose a trustworthy and decentralized solution for real-time IoT data marketplaces that integrates MABERA with several cryptographic primitives to ensure data confidentiality, fair exchange, fine-grained access control, and accountability. We demonstrate that our solutions provide the desired security properties, and the simulation results show that our solutions are feasible and efficient.

In this research paper, threshold decomposition property (arising in the case of rank order order filters) is generalized to bipolar signals. By associating a square "threshold matrix" with a digital signal, its linear algebraic properties are investigated. Also, most general class of nonlinear filters for which the weak superposition property (based on threshold decomposition) holds (in the spirit of rank order filters) are identified. Partition theoretic interpretation of threshold decomposition is explored. It is reasoned that columnwise Boolean median filtering leads to faster implementation of median filter.

Applying VSLAM to mobile robots with limited computing power is the key to achieving autonomous navigation, and the cloud-edge collaborative VSLAM is a solution. However, the VSLAM data transmission in the working site with limited communication is still an open problem. To solve this problem, two core issues should be considered: the transmission frequency and the transmission data volume. In this paper, we propose an asynchronous submap building framework to reduce the transmission frequency. Also, we design an implicit representation-based transmission method to save the transmission data volume while satisfying the data association between the edge and the cloud. Through the experiments, our method shows advanced performance in terms of communication demand with low transmission frequency and small data volume. At the same time, comparable precision to the state-of-the-art is achieved, showing the effectiveness of the data association building. Thanks to the reduced transmission frequency and data volume, our system provides a feasible way to the cloud-based VSLAM under limited communication.

6G presents new opportunities to enrich the cellular ecosystem by introducing battery-less Zero-energy Internet of Things (ZE-IoT) devices, thus unleashing an era of massive, sustainable, and smart connectivity. This explains the increased interest in ZE-IoT in academia and industry. The road to a 6G future empowered by ZE-IoT entails cohesive efforts in the realm of standardization, academic research, and industrial trials, which are synergistic with the anticipated market demand and the dominant technology direction. In this article, we provide a holistic view of a 6G ZE-IoT future informed by the ongoing standardization activities in the 3rd generation partnership project (3GPP) for ZE-IoT, the role of the emerging technology trends such as digital twins and artificial intelligence, and the technical challenges in integrating ZE-IoT into the cellular ecosystem. Finally, we present some recent research results to address some of the discussed challenges.  

This paper quantitatively analysed orchestral conducting patterns, and detected variations as a result of extraneous body movement during conducting, in the first experiment of its kind. A novel live conducting system featuring data capture, processing, and analysis was developed. Reliable data of an expert conduc-tor's movements was collected, processed, and used to calculate average trajectories for different conducting techniques with various extraneous body movements; variations between extraneous body movement techniques and controlled technique were definitively determined in a novel quantitative analysis. A portable and affordable mechatronic system was created to capture and process live baton tip data, and was found to be accurate through calibration against a reliable reference. Experimental conducting field data was captured through the mechatronic system, and analysed against previously calculated average trajectories; the extraneous movement used during the field data capture was successfully identified by the system .

A document by Musrea Ghaseb . Click on the document to view its contents.

The development of large language models, such as ChatGPT (GPT-3.5) and GPT-4, has revolutionized natural language processing (NLP) and opened up new possibilities in various fields. These models demonstrate remarkable capabilities in generating coherent and contextually relevant text, making them suitable for a wide range of applications. This work focuses on automatic text annotation in subjective problems and person-alization using ChatGPT. The primary objective is to investigate the ChatGPT generative capabilities and evaluate its performance in classification and regression NLP tasks. Furthermore, the work also contributes a novel methodology for evaluating personalized ChatGPT and adapting it to address specific problem domains. The results obtained from multiple experimental setups showcase the potential of the method to automatically exploit ChatGPT to generate text annotations. However, the conclusions drawn from the research highlight the need for a further detailed and more extensive analysis across multiple problem domains and diverse datasets.

The Internet of Things (IoT) has revolutionized various sectors by enabling seamless interaction between devices. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fall short in addressing these concerns due to the unique characteristics of IoT networks such as heterogeneity, scalability, and resource constraints. To address these challenges, this survey paper first explores the intersection of quantum computing, federated learning, and 6G wireless networks as a novel approach to enhancing IoT security and privacy. In order to enable several secure intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among a vast array of IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, key performance metrics, and summarizing the vital insights, challenges, and trends in the realm of securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks, suggesting potential integration with 7G, and summarizing the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.

We propose a method to extract the equivalent material parameters and circuit models for all-metallic tensor metamaterials. The metamaterials consist of an arrangement of sub-wavelength metallic pins affixed to the bottom plate of an air-filled parallel plate waveguide (PPW). Isotropic and anisotropic configurations are studied, which are implemented with cylindrical and elliptical pins, respectively. The effective permittivity and permeability tensor of an equivalent lossless, homogeneous PPW are extracted. An equivalent circuit model consisting of series inductances and a shunt capacitance is also extracted. An analytical procedure to perform the extraction is outlined. The procedure is validated using full-wave simulations and circuit theory. Finally, representative designs are presented to show the effectiveness and potential applications of the proposed homogenization technique. The extraction of effective material parameters enables the control of the properties of all-metal metamaterials, enabling the design of perfectly-matched, anisotropic unit cells. Ultimately, the accurate mapping between all-metal metamaterials and their equivalent circuit networks could be exploited to accelerate the inverse design of multi-functional metastructures.

The ongoing development of Quantum Computing (QC) is a breakthrough paradigm that holds the power to revolutionize the way we process information and solve complex problems today. Quantum Computation works on the fundamental principles of Superposition and Entanglement, enabling Quantum Computers to Leverage the power of Physics in Computing. QC has the potential to revolutionize fields such as Drug Development, Financial Modeling, Optimization, and Cryptography. Shor’s algorithm, a quantum algorithm used to factorize large numbers, poses significant threats to RSA, a widely used public key cryptosystem. RSA relies on the difficulty of factoring large semi-primes to keep its security. Shor's algorithm's ability to factorize quickly on a quantum computer undermines RSA's security assumptions, necessitating the exploration of post-quantum cryptographic solutions to ensure secure communication in the quantum era. The goal of this paper is to assess the present state of Shor's algorithm implementation and efficiency, specifically its application in the context of compromising public key cryptosystems like RSA.

In this article, a compact 3D beam-steering MIMO antenna operating at 5.75 GHz is proposed for on-body Internet of Things (IoT) applications. The planar antenna comprises a central patch and two concentric annular rings. The theory of spherical modes is used to discuss the beam-steering concept, where the free space measured beam-scanning covers the entire xy-plane, while a 44° range is realized in xz and yz-planes. The on-body performance is tested using a multi-layer phantom, and the measured peak realized gain is 6.41 dBi. The antenna is characterized by compact size (0.77λ), low profile (0.03λ), specific absorption rate below the established limits, good efficiency and 3D beam-steering characteristics while operating in an on-body setup. Therefore, the proposed solution can enable advanced wireless applications like localization and physical layer security in emerging size-constrained on-body IoT devices.

The Edge-Cloud continuum refers to the dynamic provisioning of distributed computing and network resources that can scale to support the creation of secure and resource-efficient digital ecosystems. Trusted interaction and orchestration of distributed edge-cloud resources is a fundamental principle of distributed network infrastructures and service provisioning. The Zero Trust Architecture (ZTA) paradigm is gaining momentum on the basis of being able to ensure trusted and secure interaction for edge-cloud networks. However, ZTA’s strict authentication policy mandates devices to be authenticated for every session, leading to significant overhead for resource-constrained devices engaged in multiple sessions. To address this challenge, this paper proposes a modified ZTA that integrates a reputation-based trust assessment mechanism, allowing a higher number of consecutive sessions without the need for costly authentication/authorisation while preserving system integrity. Reputation serves as a metric to gauge the trustworthiness of a node, considering its past behaviour and interactions. The proposed approach is evaluated for its feasibility in edge computing environments with limited resources, and simulation results demonstrate the practicality of utilising this technique in zero trust environments.

A document by Masood Khan . Click on the document to view its contents.

In this letter, we analyze the performance of a downlink non-orthogonal multiple access (NOMA) network that uses co-phasing at an intelligent reflecting surface (IRS) to communicate to a near-user (NU) and a far-user (FU) concurrently. Both users combine the direct and reflected signals. Considering cross-channel interference (CI) and a finite number of phase levels at the IRS, we derive closed-form expressions for the outage probability. Closed form expressions are also presented for throughput, energy efficiency (EE) and optimal power allocation. A low complexity procedure is suggested to determine the optimum partitioning so as to maximize the FU throughput while ensuring a desired throughput at the NU. The proposed IRS-NOMA (IN) system outperforms conventional NOMA (CN) and orthogonal multiple access (OMA) systems in terms of throughput and EE, and permits a broader range of NOMA power allocations. Simulations validate the derived expressions.