DeepVision: Enhanced Drone Detection and Recognition in Visible Imagery through Deep Learning Networks.

Drones are increasingly capturing the world's attention, transcending mere hobbies to revolutionize areas such as engineering, disaster aid, logistics, and airport protection, among myriad other fascinating applications. However, there is growing concern about the risks that they pose to physical infrastructure, particularly at airports, due to potential misuse. In recent times, numerous incidents involving unauthorized drones at airports disrupting flights have been reported.

Medical ultrasound image speckle reduction and resolution enhancement using texture compensated multi-resolution convolution neural network.

Ultrasound (US) imaging is a mature technology that has widespread applications especially in the healthcare sector. Despite its widespread use and popularity, it has an inherent disadvantage that ultrasound images are prone to speckle and other kinds of noise. The image quality in the low-cost ultrasound imaging systems is degraded due to the presence of such noise and low resolution of such ultrasound systems.

Performance Evaluation of EEG Based Mental Stress Assessment Approaches for Wearable Devices.

Mental stress has been identified as the root cause of various physical and psychological disorders. Therefore, it is crucial to conduct timely diagnosis and assessment considering the severe effects of mental stress. In contrast to other health-related wearable devices, wearable or portable devices for stress assessment have not been developed yet.

ECM-LSE: Prediction of Extracellular Matrix Proteins Using Deep Latent Space Encoding of k-Spaced Amino Acid Pairs.

Extracelluar matrix (ECM) proteins create complex networks of macromolecules which fill-in the extracellular spaces of living tissues. They provide structural support and play an important role in maintaining cellular functions. Identification of ECM proteins can play a vital role in studying various types of diseases.

Beamforming Using Exact Evaluation of Leakage and Ergodic Capacity of MU-MIMO System.

Closed-form evaluation of key performance indicators (KPIs) of telecommunication networks help perform mathematical analysis under several network configurations. This paper deals with a recent mathematical approach of indefinite quadratic forms to propose simple albeit exact closed-form expressions of the expectation of two significant logarithmic functions. These functions formulate KPIs which include the ergodic capacity and leakage rate of multi-user multiple-input multiple-output (MU-MIMO) systems in Rayleigh fading channels.

Underwater Vehicle Positioning by Correntropy-Based Fuzzy Multi-Sensor Fusion.

The ability of the underwater vehicle to determine its precise position is vital to completing a mission successfully. Multi-sensor fusion methods for underwater vehicle positioning are commonly based on Kalman filtering, which requires the knowledge of process and measurement noise covariance. As the underwater conditions are continuously changing, incorrect process and measurement noise covariance affect the accuracy of position estimation and sometimes cause divergence.

Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman Filter.

The Kalman filter variants extended Kalman filter (EKF) and error-state Kalman filter (ESKF) are widely used in underwater multi-sensor fusion applications for localization and navigation. Since these filters are designed by employing first-order Taylor series approximation in the error covariance matrix, they result in a decrease in estimation accuracy under high nonlinearity. In order to address this problem, we proposed a novel multi-sensor fusion algorithm for underwater vehicle localization that improves state estimation by augmentation of the radial basis function (RBF) neural network with ESKF.

Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization.

Wireless networks are vulnerable to jamming attacks. Jamming in wireless communication becomes a major research problem due to ease in Unmanned Aerial Vehicle (UAV) launching and blocking of communication channels. Jamming is a subset of Denial of Service Attack (DoS) and an intentional interference where the malicious node disrupts the wireless communication by increasing the noise at the receiver node through transmission interference signal towards the target channel.

Real-Time Stress Assessment Using Sliding Window Based Convolutional Neural Network.

Mental stress has been identified as a significant cause of several bodily disorders, such as depression, hypertension, neural and cardiovascular abnormalities. Conventional stress assessment methods are highly subjective and tedious and tend to lack accuracy. Machine-learning (ML)-based computer-aided diagnosis systems can be used to assess the mental state with reasonable accuracy, but they require offline processing and feature extraction, rendering them unsuitable for real-time applications.

Differences in photosynthetic syndromes of four halophytic marsh grasses in Pakistan.

Salt-tolerant grasses of warm sub-tropical ecosystems differ in their distribution patterns with respect to salinity and moisture regimes. Experiments were conducted on CO fixation and light harvesting processes of four halophytic C grasses grown under different levels of salinity (0, 200 and 400 mM NaCl) under ambient environmental conditions. Two species were from a high saline coastal marsh (Aeluropus lagopoides and Sporobolus tremulus) and two were from a moderate saline sub-coastal draw-down tidal marsh (Paspalum paspalodes and Paspalidium geminatum).

Excreting and non-excreting grasses exhibit different salt resistance strategies.

The combination of traits that makes a plant successful under saline conditions varies with the type of plant and its interaction with the environmental conditions. Knowledge about the contribution of these traits towards salt resistance in grasses has great potential for improving the salt resistance of conventional crops. We attempted to identify differential adaptive response patterns of salt-excreting versus non-excreting grasses.

An adaptive learning rate for RBFNN using time-domain feedback analysis.

Radial basis function neural networks are used in a variety of applications such as pattern recognition, nonlinear identification, control and time series prediction. In this paper, the learning algorithm of radial basis function neural networks is analyzed in a feedback structure. The robustness of the learning algorithm is discussed in the presence of uncertainties that might be due to noisy perturbations at the input or to modeling mismatch.