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In resource limited nations, cancer control is often a lower priority issue creating challenges for the prevention, early diagnosis, and treatment of cancer. Training and education are vital components of efforts to tackle this problem. A 3-day cancer control workshop was conducted at the Lagos State University Teaching Hospital (LASUTH), Nigeria, in 2013. The curriculum included didactic lectures,...
This paper presents a Nonlinear AutoRegressive with eXogenous input (NARX)-based approach for human-emotion recognition from an input video. The dynamics of facial expressions are first captured by performing a temporal-spatial analysis by extracting local and spatial features using a pyramid of histograms of oriented gradients (PHOG) descriptor. Then the temporal phases of facial expressions are...
This paper presents a real-time emotion recognition system (RTERS) as a first step towards developing a socially intelligent robot. The RTERS first localizes faces in a sequence of images, then features are extracted and passed to a recognition engine that codes facial expressions into one of seven different emotional states: happiness, sadness, fear, disgust, anger, surprise, and neutrality.We propose...
This paper presents a hierarchal, two-layer, connectionist-based human-action recognition system (CHARS) as a first step towards developing socially intelligent robots. The first layer is a K-nearest neighbor (K-NN) classifier that categorizes human actions into two classes based on the existence of locomotion, and the second layer consists of two multi-layer recurrent neural networks that distinguish...
Because the protection of perimeters in national, agricultural, airport, prison, military sites, and residential areas against dangerous approaching human and vehicles when using humans to provide security is expensive or unsafe, acoustic/vibration signature identification of approaching human and vehicles threats has attracted increased attention. This paper addresses the development and deployment...
This paper introduces a spike based pattern recognition method and applies it to a real world seismic event recognition application. The processing unit - building blocks - of the developed method is statistical and considers both temporal frequency of spikes and their timings. Upon a significant statistical change of the temporal timings of the input spikes, compared to the model it has been trained...
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