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This paper proposes a fast technique for matching a query image to numerous database images under geometric variations in rotation, scale, and translation. Our proposed method extracts the Fourier-Mellin phase features from the images for invariant matching. The online matching process in our method is fast because it directly determines identification based on the correlation value between those features without the geometric alignment.

Human action recognition has a wide range of applications including biometrics and surveillance. Existing methods mostly focus on a single modality, insufficient to characterize variations among different motions. To address this problem, we present a CNN-based human action recognition framework by fusing depth and skeleton modalities. The proposed Adaptive Multiscale Depth Motion Maps (AM-DMMs) are calculated from depth maps to capture shape, motion cues. Moreover, adaptive temporal windows ensure that AM-DMMs are robust to motion speed variations.

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.

In the 3.5 GHz Citizens Broadband Radio Service (CBRS), 100 MHz of spectrum will be shared between commercial users and federal incumbents. Dynamic use of the band relies on a network of sensors dedicated to detecting the presence of federal incumbent signals and triggering protection mechanisms when necessary. This paper uses field-measured waveforms of incumbent signals in and adjacent to the band to evaluate the performance of matched-filter detectors for these sensors.

Several researchers recently demonstrated that it is feasible to locate building occupants solely from measured footstep vibrations. The research reported here applies that capability to track multiple building occupants in motion. In contrast to many indoor tracking methods based on wireless technology, this method frees individuals from the need to carry a device, and, furthermore, permits localization and tracking in facilities that restrict or prohibit wireless devices or cameras.