sponsorship: This research is supported by iSEE-6G project under the Horizon Europe Research and Innovation program with Grant Agreement No. 101139291. (iSEE-6G project under the Horizon Europe Research and Innovation program|101139291)

In this paper, we present a spatially consistent A2G channel model based on probabilistic LOS/NLOS segmentation to parameterize the deterministic path loss and stochastic shadow fading model. Motivated by the limitations of existing Unmanned Aerial Vehicle (UAV) channel models that overlook spatial correlation, our approach reproduces LOS/NLOS transitions along ground user trajectories in urban environments. This model captures environment-specific obstructions by means of azimuth and elevation-dependent LOS probabilities without requiring a full detailed 3D representation of the surroundings. We validate our framework against a geometry-based simulator by evaluating it across various urban settings. The results demonstrate its accuracy and computational efficiency, enabling further realistic derivations of path loss and shadow fading models and thorough outage analysis.

Accurate Probability of Line-of-Sight$(P_{\text{LoS}})$modeling is important in evaluating the performance of Unmanned Aerial Vehicle (UAV)-based communication systems in urban environments, where real-time communication and low latency are often major requirements. Existing$P_{L o S}$models often rely on simplified Manhattan grid layouts using International Telecommunication Union (ITU)-defined builtup parameters, which may not reflect the randomness of real cities. Therefore, this paper introduces the Urban Line-ofSight Simulator (ULS) to model$P_{\text{LoS}}$for three random city layouts with varying building sizes and shapes constructed using ITU built-up parameters. Based on the ULS simulated data, we obtained the empirical$P_{L o S}$for four standard urban environments across three different city layouts. Finally, we analyze how well Manhattan grid-based models replicate$P_{L o S}$results from random and real-world layouts, providing insights into their applicability for time-critical communication systems in urban IoT networks.

Communications between unmanned aerial vehicles (UAVs) play an important role in deploying aerial networks. Although some studies reveal that drone-based air-to-air (A2A) channels are relatively clear and thus can be modeled as free-space propagation, such an assumption may not be applicable to drones flying in low altitudes of built-up environments. In practice, low-altitude A2A channel modeling becomes more challenging in urban scenarios since buildings can obstruct the line-of-sight (LOS) path, and multipaths from buildings lead to additional losses. Therefore, we herein focus on modeling low-altitude A2A channels considering a generic urban deployment, where we introduce the evidence of the small-size first Fresnel zone at the millimeter-wave (mmWave) band to approximately derive the LOS probability. Then, the path loss under different propagation conditions is investigated to obtain an integrated path loss model. In addition, we incorporate the impact of imperfect beam alignment on the path loss, where the relation between path loss fluctuation and beam misalignment level is modeled as an exponential form. Finally, comparisons with the 3GPP model show the effectiveness of the proposed analytical model. Numerical simulations in different environments and heights provide practical deployment guidance for aerial networks.

Brain-Computer Interfaces (BCIs) based on Steady-State Visually Evoked Potentials (SSVEPs) can be used as hand-free control device. To utilize this control method in a real life scenario, we created a system in which a smart home is controlled by BCI. Six devices in the smart home environment could be controlled with the BCI system: The entrance door, the wardrobe, the kitchens' worktop and drawers, the light system of all the rooms and a guide light. In the presented paper, the visual stimuli for the BCI were placed at multiple screens in the smart home (placed at different locations such as the kitchen and the living room). The processing was done on one computer, located in the living room. The placement of the visual stimuli corresponded to the actuators that were controlled, e.g. the kitchen drawers were linked to the stimuli displayed in the kitchen. An online experiment was conducted where participants went through a scenario consisting of thirteen SSVEP-BCI selections in total. Eight healthy participants took part in the experiments. For BCI signal acquisition, a mobile EEG amplifier was used. Participants walked freely around the rooms during the experiment. An average accuracy of 81 % was achieved, which suggests that the SSVEP-system is suitable to control the external devices in the smart home, and that the system can be expanded to involve more actuators.

Brain-Computer Interfaces (BCIs) based on code-modulated visual evoked potentials can be used as hand-free communication tool for severely disabled people. In this paper we propose a filter bank design for c-VEP BCIs based on alpha, beta and gamma sub-bands. The approach was tested using a dictionary driven spelling application utilizing flexible time-windows. The graphical user interface offers word suggestions that are updated after each selection. The system was tested with 18 healthy participants. Performance of a word and a sentence spelling task was analyzed. Remarkably, in the word spelling task, all participants reached 100 % accuracy. In the sentence spelling task the mean accuracy was still extremely high (97 %). Furthermore, to assess the speed of the system, information transfer rate (ITR) and output characters per minute (OCM) were calculated. Mean ITRs of 149.3 bpm and 93.1 bpm were reached in word and sentence spelling; the mean OCM was 29.9 chars/min and 32.1 chars/min.

In this paper, we proposed a home energy management (HEM) scheme for minimization in electricity bills and reduction in peak load. This can be achieved by scheduling the usage timings of appliances (APP) for shifting load from peak hours (PHs) to OFF-peak hours (OPHs). In this study we proposed a technique which is hybrid of two bio-inspired optimization techniques chicken swarm optimization (CSO) and bacterial foraging optimization (BFA). Simulation results shows that proposed hybrid technique reduces the cost and load peaks by shifting load from PHs to OPHs and reduction in load peaks.

Home Energy Management Systems (HEMS) have been widely used for energy management in smart homes. Energy management in a smart home is a challenging task, which require efficient scheduling of appliances. The main focus of HEMS is to schedule the operation of appliances in such a way that it gives us optimized performance in terms of Peak to Average Ratio (PAR), Electric Cost (EC) minimization, execution time and User Comfort (UC). The Time of Use (ToU) pricing scheme is used in this paper. We used Genetic Algorithm (GA), Biogeography-based optimization (BBO) and our proposed hybrid Genetic Biogeography-based Optimization (GBBO), techniques to schedule appliances in single home and for multiple homes. Simulations are carried out using eight different appliances. The results show that GA and GBBO execute better in case of PAR reduction and EC minimization. GBBO outperforms in terms of user comfort. We calculated the UC in terms of waiting time.

In this paper the influence of different backgrounds was investigated on SSVEP-based BCI performance, with a focus on measuring objective user fatigue. We tested three different backgrounds: a black screen, white noise, and a video, with a BCI spelling application (in which four distinct frequencies were used). We measured information transfer rate (ITR), accuracy, and several parameters which could indicate user fatigue levels. The data were recorded with EEG. The level of comfort before and after the experiment, as well as the subjective level of fatigue were assessed using questionnaires, while the objective evaluation was done using the measured indexes. Eight healthy participants were tested. With the used objective fatigue evaluation method, no significant differences were found between the different background scenarios. Evaluating the study as a whole showed significantly decreased alpha-band activity (p = 0.03) at the end and a significantly increased subjective user fatigue. This experiment was conducted to investigate the level of fatigue caused by SSVEP-based BCIs in noisy environments.

Many individuals require Aided Augmentative and Alternative Communication (AAAC) to communicate, especially when traditional AAC, like sign language, is not an option due to some movement impairments. This paper presents a high tech AAAC using Surface Electromyography (sEMG) signals as an input modality for the direct control of a speller presented on a Graphical User Interface (GUI). The GUI displays alphabetical letters, special characters, and a delete option. The aim of this study is to introduce and examine the performance of a three-steps sEMG-based speller which does not depend on the movement of a system cursor. Seven subjects participated in the experiment to test the spelling system. Each participant had to undergo a familiarization task first, after which the main spelling task took place. Experimental data were recorded and the resulted mean accuracy and information transfer rate (ITR) for the main spelling task were 89.10% and 55.10 bits/min, respectively, at an average spelling speed of 10.29 char/min. Results and users' feedback also indicated that the spelling performance could be further enhanced through training and the regular use of the speller.

Brain-Computer Interface (BCI) interprets brain signals, which are measured by an electroencephalogram (EEG), allowing people to communicate without the need for any muscular movement. One of the most commonly discussed applications in literature is BCI speller as a communication modality for people with disabilities. In Brain-Neural Computer Interface (BNCI) other biosignals are merged with the brain signals to combine the advantages of both. Consequently, the here-studied hybrid BCI system discusses the parallel usage of an Steady-State Visual Evoked Potential (SSVEP) BCI and surface-electromyographic (sEMG) activity to accomplish a faster performance for a more accurate spelling application. sEMG signal was used for the activation of target selection for an SSVEP-based speller. Eight participants carried out three copy-spelling tasks for each system (six in total) to compare between both paradigms: SSVEP alone and the hybrid speller. Results showed that the hybrid speller can achieve faster performances without affecting the accuracy (mean accuracy 92.37%, 93.75%, and 100%, mean ITR 37.37, 33.41, and 31.05 bits/min), verifying our hypothesis. In addition, participants who experienced difficulties controlling the SSVEP speller were able to control the hybrid system.

Brain-Computer Interface (BCI) provides a non-muscular communication by using the brain signals. During last decades, the BCI-systems provided various graphical user interfaces, especially the speller interfaces. Efforts had been made to increase the speller speed and user friendliness of the system. In this paper, we are introducing a browser-driven SSVEP-based BCI web speller (accessible at: https://bci-lab.hochschule-rhein-waal.de/en/speller/). This web speller can be used with the help of major existing web browsers. Thus, all the international researchers working in the field of BCI can access this web speller online free of charge, and they can run this speller using their own classifier applications. In the presented three-step web speller, the user can select a desired character by going through three different steps of the web interface. The browser-driven BCI speller was tested in this study by ten subjects, who were asked to spell the words "BCI_LAB" and "KLEVE". All of the subjects were able to perform the spelling tasks, with the mean accuracy of 94.5% and an average information transfer rate (ITR) of 12.7 bits/min.

Brain-Computer Interfaces (BCIs) allow communication and control of the environment without the use of peripheral muscles. One of the standard BCI paradigms is based on steady state visual evoked potentials (SSVEPs), brain signals induced by gazing at a constantly flickering target. In this article, a VR SSVEP-based steering simulation is presented and evaluated in comparison to a standard desktop version. Three control classes were used to control the application. The experimental task was to steer a virtual vacuum robot and collect 10 dust piles (for this, at least 31 command classifications were required). Participants were instructed to complete the task twice, using a head mounted display (HMD) and the laptop screen for visual stimulation. All participants were able to complete the task in both scenarios. Mean accuracies of 98.91% and 97.48% and mean ITRs of 23.96 and 20.71 bits/min were achieved for the HMD and desktop control, respectively. On average, the number of commands needed to complete the task in the online experiment was 32.00 and 32.75, for the HMD and desktop scenario, respectively.

Brain-Computer Interfaces (BCIs) allow users to communicate and to control their environment without the use of peripheral muscles. One of the commonly used BCI paradigms is called steady state visual evoked potentials (SSVEPs). It is controlled by brain signals induced through gazing at a constantly flickering light source. In this article, a VR-based SSVEP-controlled simulation is presented and evaluated, in comparison to the standard desktopbased version. Three control classes were used to control the application. The experimental task was to navigate (in first person view) through a virtual maze and reach the desired destination (the optimal path required at least 26 commands). Participants performed the task twice, using either a head mounted display (HMD), or the laptop screen, to compare the paths and investigate the spatial awareness with the HMD. During the first run, the participants needed to explore the maze (different corridors and doors on 3 levels) in order to reach the destination (a teleporter room). There were three commands available for the user: "Turn left", "Forward", and "Turn right". All participants were able to complete both versions (HMD and laptop screen). On average, the number of commands needed to complete the task in the online experiment was 51 and 62 for the HMD and laptop screen, respectively. The average time of the task for the HMD scenario was 287.16 seconds and for the laptop 435.79 seconds, almost 51.75% higher. We found, that the participants achieved a better spatial awareness with the HMD setup.

Brain-computer interfaces (BCIs) based on code-modulated potentials (cVEPs) identify a target usually in the synchronous way (i.e. after a preset time period the system will produce a command output). Hence, users have only a limited amount of time to fixate a desired target. For the practical usability of BCI spellers it is important to distinguish between intentional and unintentional fixations. In this paper we propose the use of threshold-based target identification methods for the cVEP paradigm. These methods were tested with a dictionary driven spelling application utilizing eight flashing targets. In this respect, an n-gram word prediction model was implemented. The performance of ten healthy participants was evaluated in an online experiment. All participants completed different German sentences using the cVEP BCI with a mean information transfer rate (ITR) of 31.08 bpm.

A new backoff scheme named Unique Backoff Algorithm (UBA) has been introduced in this paper for IEEE 802.15.6 Wireless Body Area Networks (WBANs). This scheme tries to eliminate the collision among nodes by assigning unique backoff values. UBA removes the large delays during transmissions between different nodes and hence increases overall system performance. Furthermore, the concept of Backup Slot ( BS) has also been introduced in the superframe to accommodate one failure of highest priority in the current superframe. For evaluation, proposed scheme has been simulated in comparison with the Binary Exponential Backoff (BEB) using different priorities. Results show that our scheme outperforms BEB in terms of throughput and superframe efficiency.