Towards Automatic License Plate Detection.

Automatic License Plate Detection (ALPD) is an integral component of using computer vision approaches in Intelligent Transportation Systems (ITS). An accurate detection of vehicles' license plates in images is a critical step that has a substantial impact on any ALPD system's recognition rate. In this paper, we develop an efficient license plate detecting technique through the intelligent combination of Faster R-CNN along with digital image processing techniques.

Content-Aware SLIC Super-Pixels for Semi-Dark Images (SLIC++).

Super-pixels represent perceptually similar visual feature vectors of the image. Super-pixels are the meaningful group of pixels of the image, bunched together based on the color and proximity of singular pixel. Computation of super-pixels is highly affected in terms of accuracy if the image has high pixel intensities, i.

Bimodal Data Fusion of Simultaneous Measurements of EEG and fNIRS during Lower Limb Movements.

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have temporal and spatial characteristics that may complement each other and, therefore, pose an intriguing approach for brain-computer interaction (BCI). In this work, the relationship between the hemodynamic response and brain oscillation activity was investigated using the concurrent recording of fNIRS and EEG during ankle joint movements. Twenty subjects participated in this experiment.

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.

Block Compressive Sensing (BCS) Based Low Complexity, Energy Efficient Visual Sensor Platform with Joint Multi-Phase Decoder (JMD).

Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN.

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.