Digital revolution and recent advances in telecommunications technology enable to design communication systems which operate within the regions close to the theoretical capacity limits. Ever-increasing demand for wireless communications and emerging numerous high-capacity services and applications mandate providers to employ more bandwidth-oriented solutions to meet the requirements. Trend and predictions point out that marketplace targets data rates around 10Gbps or even more within the upcoming decade. It is clear that such rates could only be achieved by employing more bandwidth with the state-of-the-art technology. Considering the fact that bands in the range of 275GHz-3000GHz, which are known as Terahertz (THz) bands, are not allocated yet for specific active services around the globe, there is an enormous potential to achieve the desired data rates. Although THz bands look promising to achieve data rates on the order of several tens of Gbps, realization of fully operational THz communications systems obliges to carry out a multidisciplinary effort including statistical propagation and channel characterizations, adaptive transceiver designs, reconfigurable platforms, advanced signal processing algorithms and techniques along with upper layer protocols equipped with various security and privacy levels. Therefore, in this study, several important statistical parameters for line-of-sight (LOS) channels are measured. High resolution frequency domain measurements are carried out at single-sweep within a span of 60GHz. Impact of antenna misalignment under LOS conditions is also investigated. By validating exponential decay of the received power in both time and frequency domain, path loss exponent is examined for different frequencies along with the frequency-dependent path loss phenomenon. Furthermore, impact of humidity is also tested under LOS scenario. Measurement results are presented along with relevant discussions and future directions are provided as well.

High-speed, reliable, flexible, and cost-effective underwater wireless communication systems have a wide variety of use in scientific, civilian, and commercial domains. Visible light communication (VLC) seems to be a promising candidate in satisfying majority of the requirements listed above. From this perspective, VLC could easily be deployed in heterogeneous wireless networking scenarios with high quality of service. Therefore, in this paper, an adapted LTE frame structure is implemented for underwater VLC (UVLC) system. The performance of UVLC system employing asymmetrically clipped optical orthogonal frequency division multiplexing modulation technique is practically tested by taking channel estimation and synchronization into account. Also, the effects of varying orthogonal frequency division multiplexing parameters on the system's signal-to-noise ratio and the bit-error-rate (BER) performances are studied. The implementation is tested for quadrature phase shift keying and 16-quadrature amplitude modulation techniques supporting both 128- and 1024-points fast Fourier transform configurations, which correspond to data rates of 1.92 and 15.36 Mbps, respectively. Experimental results show that BER could be reduced down to a level on the order of 10 ^-6 .

In this work, a detection method is proposed for Global Positioning System (GPS) jamming attacks. GPS signals despite being an indispensable part of our life, come with a great weakness which is having a weak signal. This makes it vulnerable to external attacks like jamming and spoofing. The purpose of this study is to properly be able to identify the type of jammer affecting the GPS signal so that proper defensive measures can be applied to ensure the safety of the signal. This work is the second phase of a study based on the effect of Electronic Jammers (EJ) on GPS signals. Four different EJ are studied and the purpose is to be able to identify which one of them is affecting the signal in order to apply the appropriate defensive measure. In order to achieve this, the power of the signal is analyzed in order to achieve a threshold/interval to be able to identify the type of jammer. All of the simulations and measurements are carried on MatLAB.

Detecting and analyzing complex patterns in multivariate time-series data is crucial for decision-making in urban and environmental system operations. However, challenges arise from the high dimensionality, intricate complexity, and interconnected nature of complex patterns, which hinder the understanding of their underlying physical processes. Existing AI methods often face limitations in interpretability, computational efficiency, and scalability, reducing their applicability in real-world scenarios. This paper proposes a novel visual analytics framework that integrates two generative AI models, Temporal Fusion Transformer (TFT) and Variational Autoencoders (VAEs), to reduce complex patterns into lower-dimensional latent spaces and visualize them in 2D using dimensionality reduction techniques such as PCA, t-SNE, and UMAP with DBSCAN. These visualizations, presented through coordinated and interactive views and tailored glyphs, enable intuitive exploration of complex multivariate temporal patterns, identifying patterns’ similarities and uncover their potential correlations for a better interpretability of the AI outputs. The framework is demonstrated through a case study on power grid signal data, where it identifies multi-label grid event signatures, including faults and anomalies with diverse root causes. Additionally, novel metrics and visualizations are introduced to validate the models and assess the performance, efficiency, and consistency of latent maps generated by VAE, which have been utilized in prior studies for latent space cartography and used as a benchmark in this study, and the emerging TFT architecture under various configurations. These analyses provide actionable insights for model parameter tuning and reliability improvements. Comparative results highlight that TFT achieves shorter run times and superior scalability to diverse time-series data shapes compared to VAE. This work advances fault diagnosis in multivariate time series, fostering explainable AI to support critical system operations.

The use of social media has grown significantly, and businesses are now using these platforms to promote their products and services. To do this, companies have created business accounts on social networks. However, social media platforms can also be a breeding ground for unwanted behaviors such as cyberbullying, sexual content, and promotional comments. To address this issue, a study was conducted to create a system that could classify public accounts on Instagram by analyzing comments, profile pictures, bios, and posts shared by users with business accounts. First, a crawler was developed, and data were collected using this crawler and then anonymized. Next, the collected data were processed using natural language processing (NLP) techniques for text and image processing methods for images to extract features and create a dataset. Nearly 10 000 profiles and 30 000 comments from public accounts were manually tagged to create the classification model. The final model had an accuracy rate of 95% on the dataset, allowing for the effective identification of different types of business accounts on Instagram.

This paper analyzes the performance of a dual-hop hybrid radio frequency - visible light communications (VLCs) system. VLC exploits high frequencies for signal propagation for short-range and is highly susceptible to blockages. Hence, in this work, the sensitivity of the VLC toward the blockages is explored. This study examines outdoor-indoor communication for an urban scenario featuring an office environment. The VLC system is designed by considering multiple VLC access points serving the end users in the presence of human blockages. Selection combining is employed to select the access point which offers the maximum instantaneous signal-to-noise ratio at the user. To capture the outdoor scenario, path loss modeling is conducted to account for signal attenuation from outdoor to indoor spaces. The outdoor scenario is modeled using Nakagami-m fading channels while VLC is employed for the indoor scenario. System performance is assessed in closed-form expression for outage probability and the symbol error rate for binary phase shift keying and quadrature amplitude modulation. A deep learning-based approach detects the symbols by tracking dynamic changes in the channel. Simulation results corroborate the correctness of derived analytical expressions and reveal that blockages significantly impact both single and multiple LED based communication channels.