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In this paper, a region-based deep convolutional neural network
(R-DCNN) is proposed to detect and classify gestures
measured by a frequency-modulated continuous wave radar
system. Micro-Doppler (μD) signatures of gestures are exploited,
and the resulting spectrograms are fed into a neural
network. We are the first to use the R-DCNN for radar-based
gesture recognition, such that multiple gestures could be automatically
detected and classified without manually clipping
the data streams according to each hand movement in advance.

In this paper, we adapt Recurrent Neural Networks with Stochastic Layers, which are the state-of-the-art for generating text, music and speech, to the problem of acoustic novelty detection. By integrating uncertainty into the hidden states, this type of network is able to learn the distribution of complex sequences. Because the learned distribution can be calculated explicitly in terms of probability, we can evaluate how likely an observation is then detect low-probability events as novel.

We introduce a novel technique for the automatic detection of word boundaries within continuous sentence expressions in Japanese Sign Language from three-dimensional body joint positions. First, the flow of signed sentence data within a temporal neighborhood is determined utilizing the spatial correlations between line segments of inter-joint pairs. Next, a frame-wise binary random forest classifier is trained to distinguish word and non-word frame content based on the extracted spatio-temporal features.

Recent years have assisted a widespreading of Radio-Frequency-based tracking and mapping algorithms for a wide range of applications, ranging from environment surveillance to human-computer interface.

We present a novel event embedding algorithm for crime data that can jointly capture time, location, and the complex free-text component of each event. The embedding is achieved by regularized Restricted Boltzmann Machines (RBMs), and we introduce a new way to regularize by imposing a ℓ1 penalty on the conditional distributions of the observed variables of RBMs. This choice of regularization performs feature selection and it also leads to efficient computation since the gradient can be computed in a closed form.

Conventional image discrimination tasks are performed on fully observed images. In challenging real imaging scenarios, where sensing systems are energy demanding or need to operate with limited bandwidth and exposure-time budgets, or defective pixels, where the data collected often suffers from missing information, and this makes the task extremely hard. In this paper, we leverage Convolutional Neural Networks (CNNs) to extract information from partially observed images.

Efficient deployment of Internet of Things (IoT) sensors primarily depends on allowing the adjustment of sensor power consumption according to the radio frequency (RF) propagation channel which is dictated by the type of the surrounding indoor environment. This paper develops a machine learning approach for indoor environment classification by exploiting support vector machine (SVM) based on RF signatures computed from real-time measurements.

Principal component analysis (PCA) and linear discriminant analysis (LDA) are the most well-known methods to reduce the dimensionality of feature vectors. However, both methods face challenges when used on multilabel data—each data point may be associated to multiple labels. PCA does not take advantage of label information thus the performance is sacrificed. LDA can exploit class information for multiclass data, but cannot be directly applied to multilabel problems. In this paper, we propose a novel dimensionality reduction method for multilabel data.