Robots are increasingly expected to work alongside humans in unstructured environments and perform complex tasks that involve free and constrained motion. Accurate tracking in free motion and compliance during interaction become essential as control objectives for successful task performance and safety. This study presents a novel control structure that achieves these objectives without relying on force/torque (F/T) sensors or joint torque sensors. The proposed method modifies the nonlinear disturbance observer (DOB) structure to modulate disturbance compensation based on exogenous force monitoring, distinguishing between disturbance and interaction forces using a pre-estimated compensation zone. To ensure stable contact with unknown environments, a compensation modulation method based on passivity analysis is provided. In addition, we propose a velocity-dependent compensation zone estimation method using Gaussian process that reduces interaction forces without compromising tracking performance. Our stability analysis shows that it can achieve uniformly ultimately bounded (UUB) behavior. The effectiveness of our approach is validated using a 6-DOF collaborative robot.

Planning for eventual electrification of transport in sub-Saharan Africa requires accurate simulation of its unique transport systems. The few studies that attempt to model electric minibus taxis – vehicles extensively used in sub-Saharan Africa’s public transport systems – vary greatly in their results. In this paper, we analyse, compare and correct the only two existing studies that project energy efficiency values of electric minibus taxis in the region. One of the studies projected an efficiency of 0.39kWh/km, while the other projected 0.93kWh/km. We carefully analyse the simulation tools and models and cumulatively applying corrections from literature and scientific analyses. As a result, we eliminate the discrepancy between the two simulation tools for a given data input and obtain a final efficiency estimation that ranges from 0.49–0.52kWh/km, depending on the input data.

Hybrid amplifiers combine discrete transistors with operational amplifiers (OAs) achieving superior characteristics compared with commercially available OAs, while keeping simple structures and a reduced number of parts. A single-ended amplifier topology based on a junction field-effect transistor (JFET) is proposed as a means of achieving very low input noise levels, both in current and voltage. It is direct-coupled and allows to place a high-pass filter at its input to preserve input range, or at its output to optimize noise. The proposed topology results in a low input capacitance and low input-current noise, making it suitable for applications where multiple amplifiers are connected in parallel to achieve ultra-low noise levels. The amplifier’s gain is set by a resistor ratio, being suitable for instrumentation front ends. The circuit analysis and design equations are provided to adapt it for different applications and, as an example, an amplifier with a gain of 40 dB and a bandwidth from DC to 1 MHz was designed, built, and tested.

The ultra-wide band radio technology (UWB) is currently considered the de-facto standard for implementing precise indoor positioning systems. Several positioning algorithms are currently being investigated for finding the best implementation in terms of scalability, refresh rate, and energy requirements. Among all the proposed approaches, the Downlink Time Difference of Arrival (DTDoA) is currently considered one of the most promising techniques capable of tracking any number of assets without decreasing the measurement update rate. This paper proposes a model for the DTDoA and validates the algorithms on UWB data, using a motion capture system as ground truth. Results highlight the validity of the previously proposed model, showing that the proposed UWB indoor positioning system achieves a maximum 30 cm uncertainty with only a simple wireless synchronisation, avoiding wired procedure that limits the usability of the infrastructure.

QRAGE is a novel imaging sequence and reconstruction method for the simultaneous mapping of multiple quantitative MRI parameters, including water content, T1, T2*, and magnetic susceptibility at ultrahigh field strength. QRAGE employs a novel ME-MPnRAGE sequence that acquires 171 contrast images at different inversion and echo time points, enabling accurate and precise parameter estimation. It further incorporates a novel model-based reconstruction method, MIRAGE2, that leverages prior knowledge of temporal signal evolution represented as damped complex exponentials by minimizing local Block-Hankel and Casorati matrices. Parameter maps are derived from the reconstructed contrast images through postprocessing steps.

In this review, we describe historical and recent developments towards tackling a century-long challenge in teletraffic theory, namely the evaluation of blocking probability in overflow systems with mutual overflow. Such systems have many applications in a variety of telecommunications and service systems, including wireless communications, cloud computing, intensive care, and emergency services, and various methods have been developed over the past century to address this challenge. In particular, the recent development of the Information Exchange Surrogate Approximation (IESA) provides significantly increased accuracy and robustness compared to previous approximation methods of its kind while also providing high computational efficiency not available via simulation or exact analysis. To the best of our knowledge, IESA is the first approximation method to combine high levels of accuracy, robustness, and computational efficiency when evaluating blocking probability in overflow systems with mutual overflow, and thus forms a major breakthrough in this century-long effort.

Discriminating motor imagery with electroencephalogram (EEG)-based brain-computer interface (BCI) poses a challenge as it involves an extensive data acquisition phase that demands a substantial amount of effort from the user. To address this issue, one approach is to use unsupervised domain adaptation, where classification models are constructed using data from multiple subjects, and only the unlabeled data from the target user is used for model calibration. However, since brain patterns from motor imagery vary between individuals, the reliability of each subject must be considered when multiple subjects are used to build the classification model. Thus in this paper, we propose Selective-MDA that performs domain adaptation on each source subject and selectively limits influences based on their domain discrepancies. To evaluate our approach, we assess our results with two public datasets, BCI Competition IV IIa and the Autocalibration and Recurrent Adaptation datasets. We further investigate the effect of source selection by comparing the discrimination performance when different numbers of source domains are selected based on discrepancy measures. Our results demonstrate that Selective-MDA not only integrates multi-source domain adaptation to cross-subject motor imagery discrimination but also highlights the impact of source domain selection when using data from multiple subjects for model training.

Recent study approach with Ambient-Latent Space resembles a new possibility of simulating dynamics with more accuracy. This paper aims to suggest idea also by adding temporary dimension(like in case of SVM) in learning given dynamics by optimization in additional to the recent study.

Terahertz (THz) massive MIMO with wideband hybrid precoding has been considered one of the crucial techniques to compensate the high path loss in 6G high-data-rate Internet of Things (IoT). However, the beam split in wideband hybrid precoding makes the beam of different sub-carriers aim at different directions, which results in only partial channel state information (CSI) from the users to the base station. The efficiency of the CSI-based THz wideband beamforming scheme which is more efficient than the hardware-based scheme in the narrow band would degrade severely. To address the degradation, in this paper, we firstly propose a sensing-aided THz wideband hybrid precoding which restores the full CSI. Through sensing and deducing the angle-frequency information, we construct a channel selecting matrix and inverse the full CSI from our complete channel dictionary. Moreover, in order to satisfy the multi-user access requirements in IoT, we also propose dynamic RF chains and dynamic power allocation schemes to further enhance the performance in multi-user scenario based on a new precoding perspective in which each RF chains serves only one user. This benefits from the highly sparse THz channel characteristic. The spectral efficiency and energy efficiency are employed to validate that the proposed is efficient. The numerical results demonstrate that our proposed sensing-aided wideband hybrid precoding scheme achieves similar performance to the optimal precoding and much better performance to the true time delay scheme and the full CSI based scheme.

A document by Eslam Mounier . Click on the document to view its contents.

A document by Amjad Iqbal . Click on the document to view its contents.

This paper presents and compares two promising approaches for realizing liquid crystal (LC)-based phase shifting topologies for Reconfigurable Intelligent Surfaces (RIS). Main requirements for the phase shifters include high Figure of Merit (FoM), compact size, low response times and constant group delay. The first approach is based on defective ground structure inverted microstrip lines (DGS-IMSL) and shows high FoM above 74 °/dB between 25 and 30 GHz. In addition, it promises low response times due to its thin 4.6 µm LC layer and features moderate physical length below 0.45 λ0. However, it is limited by a non-constant group delay. The second approach, the bandpass inverted microstrip line (BP-IMSL), has a lower FoM of approximately 26 °/dB between 28 to 30 GHz with a thicker LC layer of 20 µm. Nevertheless, a shorter physical size below 0.25 λ0 is accomplished and potential for a flat group delay is given.

Blockchain-enabled Internet of Vehicles provides a reliable collaboration environment for traffic entities and promotes road safety and traffic efficiency through real-time sharings between vehicles and infrastructure. However, this is usually confronted by a bottleneck as the Internet of Vehicles scales up. In this paper, we propose a novel Graphical Consensus-based Sharding (GCS) framework, which is underpinned by four important strategies: 1) A graphical consensus is adopted as its intra-shard consensus, where the consensus group is set up according to its maximal connected subgraph, and the leader is elected by its reliability weight. The consensus group is refreshed intermittently by alternating the role of leader. 2) Within GCS, the intra-shard data are stored in the local chain, while a block-based directed acyclic graph, rather than a chained structure, is employed as the main chain. The local chain is used to respond to requests within each shard, and the main chain supports the cross-shard sharings. GCS will parallelly optimize the throughput of the blockchain-enabled Internet of Vehicles. 3) GCS further introduces the shard backup and node scheduling to handle shard failure and overheating by using new backup strategies and temporal-spatial graph convolutional network prediction model, respectively. 4) An off-chain transmission algorithm is presented for secure sharings between the infrastructure and the vehicles. Simulation results show that the number of Transactions Per Second is 1.69 times higher than that of the non-sharding blockchain, and the pending time is dramatically reduced compared to the mainstreaming sharding approach, which is 1.02s.

This paper introduces a unified framework for developing graph-based change detection algorithms in remote sensing, which is based on signal feasibility problems and variational inequalities. We argue that signal feasibility problems provide a natural way to frame the change detection problem, while variational inequalities, core elements of modern data science and signal processing methods, enable us to find efficient, stable, and reliable solutions to the proposed feasibility problems. We demonstrate the design of both semi-supervised and unsupervised change detection schemes from our perspective, establishing connections with graph Laplacian filtering and graph convolutional networks. In contrast to specialized methods that rely on composite objective functions with multiple penalty parameters, our approach greatly simplifies hyperparameter selection, as the hyperparameters are both bounded and can form convex combinations (i.e., they are non-negative and sum up to one). We evaluate our approach on various real heterogeneous and homogeneous datasets, demonstrating its capabilities compared to traditional and modern change detection methods. Additionally, our ablation studies confirm the consistency of our solutions under variations in the number of nodes and graph structure learning methods. We conclude by discussing the advantages, limitations, and promising future research directions, with connections to graph filtering, sampling set selection, and self-supervised learning. The source code to replicate the experiments and explore the approach further is available on GitHub at https://github.com/jfflorez/Exploiting-variational-inequalities-for-generalized-change-detection-on-graphs.git This article has been accepted for publication in the IEEE Transactions on Geoscience and Remote Sensing. For the latest version of this work, please visit: https://ieeexplore.ieee.org/document/10272641

As a specific category of artificial intelligence (AI), generative artificial intelligence (GenAI) generates new content that resembles what is created by humans. The rapid development of GenAI systems has created a huge amount of new data on the Internet, posing new challenges to current computing and communication frameworks. Currently, GenAI services rely on the traditional cloud computing framework due to the need for large computation resources. However, such services will encounter high latency because of data transmission and a high volume of requests. On the other hand, edge-cloud computing can provide adequate computation power and low latency at the same time through the collaboration between edges and the cloud. Thus, it is attractive to build GenAI systems at scale by leveraging the edge-cloud computing paradigm.  In this overview paper, we review recent developments in GenAI and edge-cloud computing, respectively.  Then, we use two exemplary GenAI applications to discuss technical challenges in scaling up their solutions using edge-cloud collaborative systems. Finally, we list design considerations for training and deploying GenAI systems at scale and point out future research directions.Â

It is essential to establish precise times in future communication networks. Any real-time task’s function depends on the system’s ability to synchronise time. Time synchronisation is critical in the current communication network and must be maintained to transmit data packets. The functionality of 6G, the Tactile Internet, Time-Sensitive Networking, and ultra-reliable low-latency communications is highly susceptible to time synchronisation. We investigated the idea of employing time synchronisation across different communication network nodes. The current state-of-the-art employs network protocols like Precision-Time Protocol for synchronising clocks across different nodes. These network protocols are not robust and can generate jitters in data transmission. In this paper, we suggested synchronising the time of the node clocks at three different places using quantum technology. Notably, the oscillation frequencies of each qubit (or oscillator) located at these nodes can be synchronised using the quantum synchronisation technique. This set of three oscillators will work as a single clock and will be the master clock of the network. We propose distributing precise time and frequency standards using quantum synchronisation on node clocks. We can synchronise the three qubits (each placed at one node) to oscillate at an identical frequency by applying an external field of a wavelength of $813.32$ nm. We analysed our model for different coupling constants and dissipation rates to provide an analysis of the behaviour of the amount of synchronisation in different experimental configurations. The optimal accuracy for our system is $1.6 \times 10^{15}$ signals per second. Further, we used the Allan deviation to examine the stability of our system for various noise strengths.

Remote sensing image change captioning (RSICC) is a novel task that aims to describe the differences between bi-temporal images by natural language. Previous methods ignore a significant specificity of the task: the difficulty of RSICC is different for unchanged and changed image pairs. They process the unchanged and changed image pairs in a coupled way, which usually causes confusion for change captioning. In this paper, we decouple the task into two issues to ease it: whether and what changes have occurred. An image-level classifier performs binary classification to address the first issue. A feature-level encoder contributes to extracting discriminative features to help the caption generation module address the second issue. For caption generation, we utilize prompt learning to introduce pre-trained large language models (LLMs) into the RSICC task. A multi-prompt learning strategy is proposed to generate a set of unified prompts and a class-specific prompt conditioned on the image-level classifier’s results. It can prompt a pre-trained LLM to know whether changes exist and generate captions. Finally, the multiple prompts and the features of the feature-level encoder are fed into a frozen LLM for captioning. Compared with previous methods, our method can leverage the powerful abilities of the pre-trained LLM in language to generate plausible captions, which is free of training. Extensive experiments show that our method is effective and achieves state-of-the-art performance. Besides, an additional experiment demonstrates that our decoupling paradigm is more promising than the previous coupled paradigm for the RSICC task. We will make our codebase publicly available to facilitate future research at https://github.com/Chen-Yang-Liu/PromptCC

Metaverse is envisioned to operate on top of the Internet of Things (IoT) in which numerous IoT sensors and wearable devices continuously collect real-world data to reflect the physical world into its virtual counterpart. As a result, the metaverse not only inherits various security threats from the IoT network, but also faces far more sophisticated attacks related to virtual reality technology. As traditional security approaches show various limitations in the large-scale distributed metaverse such as single point of failure (SPoF) and scalability issue, this paper proposes MetaCIDS, a novel collaborative intrusion detection (CID) framework that leverages metaverse devices to collaboratively protect the metaverse. In MetaCIDS, a federated learning (FL) scheme based on unsupervised autoencoder and an attention-based supervised classifier enables metaverse users to train a CID model using their local network data, while the blockchain network allows metaverse users to utilize an on-chain model to detect intrusion network flows over their monitored local network traffic, then submit verifiable intrusion alerts to the blockchain to earn metaverse tokens. Security analysis shows that MetaCIDS can efficiently detect zero-day attacks (i.e., attacks unseen in the training data), while the training process is resistant to SPoF, data tampering, and up to 33% poisoning nodes thank to the blockchain consensus, reputation, and incentive mechanisms. Performance evaluation illustrates the efficiency of MetaCIDS with 96% to 99% detection accuracy on four different network intrusion datasets, supporting both multi-class detection using labeled data and anomaly detection trained on unlabeled data.