Sensor arrays are ubiquitous. They capture images in digital cameras, record the swipes of our fingers on the screens of our phones and tablets, or map pressure distribution over an area. Soft capacitive sensor arrays have been proposed to make electronic pressure-sensing skins capable of identifying the location and intensity of touch. However, large arrays of those sensors remain challenging to produce, as they require high-resolution patterning of electrodes and routing of long and thin electrical connections.


Abstract—A novel approach is proposed in this article to boost the energy efficiency (EE) of an AoI-aware IoT network. In particular, we propose a new approach that is based a combination of simultaneous wireless information and power


A new approach addressing the spectrum scarcity challenge in 6G networks by implementing an enhanced licensed shared access (LSA) framework is considered. The proposed mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism in which the determination of weights is based on the results of the previous auctions.


In this paper, we develop a hierarchical aerial computing framework composed of high altitude platform (HAP) and unmanned aerial vehicles (UAVs) to compute the fully offloaded tasks of terrestrial mobile users which are connected through an uplink non-orthogonal multiple access (UL-NOMA).


In this paper, we propose a novel cooperative resource sharing in a multi-tier edge slicing networks which is

robust to imperfect channel state information (CSI) caused by user equipments’ (UEs) mobility. Due to the mobility

of UEs, the dynamic requirements of their tasks, and the limited resources of the network, we propose a smart joint

dynamic pricing and resources sharing (SJDPRS) scenario that can incentivize the infrastructure provider (InP) and

mobile network operators (MNOs). Aiming to maximize the profits of UEs, MNOs and the InP under the task


Abstract— Objective: Recently, pupil oscillation synchronized with a steady visual stimulus was employed for an input of an interface. The system is inspired by steady-state visual evoked potential (SSVEP) BCIs, but it eliminates the need for contact with the participant because it does not need electrodes to measure electroencephalography. However, the stimulation frequency is restricted to being below 2.5 Hz because of the mechanics of pupillary vibration and information transfer rate (ITR) is lower than SSVEP BCIs.


Abstract—Network slicing (NwS) is one of the main technologies

in the €…h-generation of mobile communication and

beyond (5G+). One of the important challenges in the NwS

is information uncertainty which mainly involves demand

and channel state information (CSI). Demand uncertainty is

divided into three types: number of users requests, amount

of bandwidth, and requested virtual network functions workloads.

Moreover, the CSI uncertainty is modeled by three

methods: worst-case, probabilistic, and hybrid. In this paper,


An example of the implementation of the SCQP method using the lin operator is included in the MAIN_example.py file. This file details an optimal control problem involving an inverted pendulum, solved with the sequential convex quadratic programming (SCQP) method, and is based on the example presented in the publication: R. Verschueren, N. van Duijkeren, R. Quirynen, M. Diehl, Moritz, "Exploiting Convexity in Direct Optimal Control: A Sequential Convex Quadratic Programming Method," Proceedings of the 2016 Conference on Decision and Control, 2016, pp. 1099 - 1104.


In this paper, we consider that the unmanned aerial vehicles (UAVs) with attached intelligent reflecting surfaces (IRSs) play the role of flying reflectors that reflect the signal of users to the destination, and utilize the power-domain non-orthogonal multiple access (PD-NOMA) scheme in the uplink. We investigate the benefits of the UAV-IRS on the internet of things (IoT) networks that improve the freshness of collected data of the IoT devices via optimizing power, sub-carrier, and trajectory variables, as well as, the phase shift matrix elements.


Brain-Computer Interface (BCI) has become an established technology to interconnect a human brain and an external device. One of the most popular protocols for BCI is based on the extraction of the so-called P300 wave from EEG recordings. P300 wave is an event-related potential with a latency of 300 ms after the onset of a rare stimulus. In this paper, we used deep learning architectures, namely convolutional neural networks (CNNs), to improve P300-based BCIs.