Sorry, you need to enable JavaScript to visit this website.

It is well known that the empirical mode decomposition algorithm does not always return an appropriate decomposition due to problems like mode mixing. In this paper, we consider the problem of a component being split across several intrinsic mode functions (IMFs). We propose the use of a hidden Markov model (HMM) to track the dominant component across the set of IMFs returned by EMD.

This paper determined the best combination that maximizes the classification accuracy of single-channel electroencephalogram (EEG)-based motor imagery brain–computer interfaces (BCIs). BCIs allow people including completely locked-in patients to communicate with others without actual movements of body. Whereas EEGs are usually observed by multiple electrodes, single-channel measurement has been recently studied for gaining the simplicity of use.

This paper determined the best combination that maximizes the classification accuracy of single-channel electroencephalogram (EEG)-based motor imagery brain–computer interfaces (BCIs). BCIs allow people including completely locked-in patients to communicate with others without actual movements of body. Whereas EEGs are usually observed by multiple electrodes, single-channel measurement has been recently studied for gaining the simplicity of use.

Civic engagement platforms such as SeeClickFix and FixMyStreet have revolutionized the way citizens interact with local governments to report and resolve urban issues. However, recognizing which urban issues are important to the community in an accurate and timely manner is essential for authorities to prioritize important issues, allocate resources and maintain citizens’ satisfaction with local governments.

Accurate traffic accident detection is crucial to improving road safety conditions and route navigation, and to making informed decisions in urban planning among others. This paper proposes a Bayesian quickest change detection approach for accurate freeway accident detection in near–real–time based on speed sensor readings. Since post–accident conditions are hardly known, a maximum likelihood method is devised to track the relevant unknown parameters over time. Four aggregation schemes are designed to exploit the spatial correlation among sensors.

This work addresses the problem of Port-Starboard (PS) beamforming for low-frequency active sonar (LFAS) with a triplet receiver array.

The work presents a new algorithm for sub-bands beam-space adaptive beamforming with twist compensation and evaluates its performance with experimental data collected at sea.

Fine-grained recognition focuses on the challenging task of automatically identifying the subtle differences between similar categories. Current state-of-the-art approaches require elaborated feature learning procedures, involving tuning several hyper-parameters, or rely on expensive human annotations such as objects or parts location. In this paper we propose a simple method for fine-grained recognition that exploits a nearly cost-free attention-based focus operation to construct an ensemble of increasingly specialized Convolutional Neural Networks.

We present a primal-dual interior point method (IPM) with a novel preconditioner to solve the ℓ1-norm regularized least square problem for nonnegative sparse signal reconstruction. IPM is a second-order method that uses both gradient and Hessian information to compute effective search directions and achieve super-linear convergence rates. It therefore requires many fewer iterations than first-order methods such as iterative shrinkage/thresholding algorithms (ISTA) that only achieve sub-linear convergence rates.