Safety, security, and privacy are three critical concerns affiliated with the use of drones in everyday life. Considering their ever-shrinking sizes and capabilities, being aware of drone activities in the vicinity becomes an important surveillance item. Therefore, keeping track of drones and preferably their controllers should be included into the already-existing security measures. In this study, a frequency hopping spread spectrum (FHSS) type drone controller signal detection and emitter direction finding framework is proposed to achieve aforementioned goals. Since drone communications signals generally coexist with other FHSS signals in 2.4 GHz industrial, scientific, and medical (ISM) band, first, a method based on cyclostationarity analysis is proposed to distinguish the drone radio controller signals from other signals utilizing 2.4 GHz ISM band. Then, a variant of short-term Fourier transform is introduced to extract the parameters of detected drone remote controller signals. The correct hopping signals are then aggregated based on the clustered parameters to obtain their combined baseband equivalent signal. Furthermore, the resampling process is applied to reduce the unrelated samples in the spectrum and represent the spectrum with the reconstructed signal, which has a much lower bandwidth than the spread bandwidth. Finally, two different multiple signal classification algorithms are utilized to estimate the direction of the drone controller relative to the receiving system. In order to validate the overall performance of the proposed method, the introduced framework is implemented on hardware platforms and tested under real-world conditions. A uniform linear antenna array is utilized to capture over-the-air signals in hilly terrain suburban environments by considering both line-of-sight and non-line–of-sight cases. Direction estimation performance is presented in a comparative manner and relevant discussions are provided.

Wireless Local Area Network (WLAN) positioning is a challenging task indoors due to environmental constraints and the unpredictable behavior of signal propagation, even at a fixed location. The aim of this work is to develop deep learning-based approaches for indoor localization and tracking by utilizing Received Signal Strength (RSS). The study proposes Multi-Layer Perceptron (MLP), One and Two Dimensional Convolutional Neural Networks (1D CNN and 2D CNN), and Long Short Term Memory (LSTM) deep networks architectures for WLAN indoor positioning based on the data obtained by actual RSS measurements from an existing WLAN infrastructure in a mobile user scenario. The results, using different types of deep architectures including MLP, CNNs, and LSTMs with existing WLAN algorithms, are presented. The Root Mean Square Error (RMSE) is used as the assessment criterion. The proposed LSTM Model 2 achieved a dynamic positioning RMSE error of $1.73\mathrm{m}$, which outperforms probabilistic WLAN algorithms such as Memoryless Positioning (RMSE: $10.35\mathrm{m}$) and Nonparametric Information (NI) filter with variable acceleration (RMSE: $5.2\mathrm{m}$) under the same experiment environment.

In this paper, we propose an efficient automatic modulation classification (AMC) scheme for a group of narrowband and digitally modulated signals such as quadrature phase-shift keying (QPSK), 16-PSK, 64-PSK, 4-quadratic-amplitude modulation (QAM), 16-QAM, and 64-QAM. The classification was performed by analyzing the probability density distribution for the real and imaginary parts of the modulated signals. To simplify the complexity of the proposed approach, we performed the classification in two stages: first, we classified the modulation between QAM and PSK signaling, and then, we determined the M-ary order of the modulation by developing kernel density estimation, which is typically used in nonparametric methods for the estimation of the probability density function of a random variable with finite data samples. Simulations were carried out to evaluate the performance of the proposed scheme for flat channels. It is observed that this simple efficient technique can find applications in blind AMC, as the performance comparison with the state of the art is promising.

Cancer is the second leading cause of death worldwide. The etiology of the disease has remained elusive, but mutations causing aberrant RNA splicing have been considered one of the significant factors in various cancer types. The association of aberrant RNA splicing with drug/therapy resistance further increases the importance of these mutations. In this work, the impact of the splicing factor 3B subunit 1 (SF3B1) K700E mutation, a highly prevalent mutation in various cancer types, is investigated through molecular dynamics simulations. Based on our results, K700E mutation increases flexibility of the mutant SF3B1. Consequently, this mutation leads to i) disruption of interaction of pre-mRNA with SF3B1 and p14, thus preventing proper alignment of mRNA and causing usage of abnormal 3’ splice site, and ii) disruption of communication in critical regions participating in interactions with other proteins in pre-mRNA splicing machinery. We anticipate that this study enhances our understanding of the mechanism of functional abnormalities associated with splicing machinery, thereby, increasing possibility for designing effective therapies to combat cancer at an earlier stage.

Despite their reliability, on-site measurements are time-consuming and costly actions for the evaluation of new devices. Channel emulators are widely utilized measurement instruments to generate desired environmental channel effects in laboratory environments. Within these instruments, baseband emulators are expensive, and reverberation chambers provide limited control of the channel. However, radio-frequency (RF) circuit implementation of channel emulators provides an affordable and easy tool to test performances of new systems and methods under different channel effects. In this paper, a new RF domain Doppler emulator, which is compact and easy to control, is presented for measuring signal characteristics under frequency dispersive channel conditions. The circuit has been implemented using variable attenuators, switches, and power splitters to emulate the Doppler spread of air-ground channels, and the performance is evaluated through measurements. It is observed that the emulator indeed generates the desired Doppler model close enough to replicate environmental channels for mobile applications in laboratory environments.

In recent decades, there have been significant research efforts focusing on wireless indoor localization systems, with fingerprinting techniques based on received signal strength leading the way. The majority of the suggested approaches require challenging and laborious Wi-Fi site surveys to construct a radio map, which is then utilized to match radio signatures with particular locations. In this paper, a novel next-generation cyber-physical wireless indoor positioning system is presented that addresses the challenges of fingerprinting techniques associated with data collection. The proposed approach not only facilitates an interactive digital representation that fosters informed decision-making through a digital twin interface but also ensures adaptability to new scenarios, scalability, and suitability for large environments and evolving conditions during the process of constructing the radio map. Additionally, it reduces the labor cost and laborious data collection process while helping to increase the efficiency of fingerprint-based positioning methods through accurate ground-truth data collection. This is also convenient for working in remote environments to improve human safety in locations where human access is limited or hazardous and to address issues related to radio map obsolescence. The feasibility of the cyber-physical system design is successfully verified and evaluated with real-world experiments in which a ground robot is utilized to obtain a radio map autonomously in real-time in a challenging environment through an informed decision process. With the proposed setup, the results demonstrate the success of RSSI-based indoor positioning using deep learning models, including MLP, LSTM Model 1, and LSTM Model 2, achieving an average localization error of $\le 2.16$ m in individual areas. Specifically, LSTM Model 2 achieves an average localization error as low as $1.55$ m and $1.97$ m with $83.33\%$ and $81.05\%$ of the errors within 2 m for individual and combined areas, respectively. These outcomes demonstrate that the proposed cyber-physical wireless indoor positioning approach, which is based on the application of dynamic Wi-Fi RSS surveying through human feedback using autonomous mobile robots, effectively leverages the precision of deep learning models, resulting in localization performance comparable to the literature. Furthermore, they highlight its potential for suitability for deployment in real-world scenarios and practical applicability.