In this paper a novel feature extraction technique for micro-Doppler classification and its real time implementation using SVM on an embedded low-cost DSP are presented. The effectiveness of the proposed technique is improved through the exploitation of the outlier rejection capabilities of the Robust PCA in place of the classic PCA.

Advanced forearm prosthetic devices employ classifiers to recognize different electromyography (EMG) signal patterns, in order to identify the user's intended motion gesture. The classification accuracy is one of the main determinants of real-time controllability of a prosthetic limb and hence the necessity to achieve as high an accuracy as possible. In this paper, we study the effects of the temporal and spatial information provided to the classifier on its off-line performance and analyze their inter-dependencies. EMG data associated with seven practical hand gestures were recorded from partial-hand and trans-radial amputee volunteers as well as able-bodied volunteers. An extensive investigation was conducted to study the effect of analysis window length, window overlap, and the number of electrode channels on the classification accuracy as well as their interactions. Our main discoveries are that the effect of analysis window length on classification accuracy is practically independent of the number of electrodes for all participant groups; window overlap has no direct influence on classifier performance, irrespective of the window length, number of channels, or limb condition; the type of limb deficiency and the existing channel count influence the reduction in classification error achieved by adding more number of channels; partial-hand amputees outperform trans-radial amputees, with classification accuracies of only 11.3% below values achieved by able-bodied volunteers.

Automatically detecting pathological voice disorders such as vocal cord paralysis or Reinke's edema is a challenging and important medical classification problem. While deep learning techniques have achieved significant progress in the speech recognition field there has been less research work in the area of pathological voice disorders detection. A novel system for pathological voice detection using convolutional neural network (CNN) as the basic architecture is presented in this work. The novel system uses spectrograms of normal and pathological speech recordings as the input to the network. Initially Convolutional deep belief network (CDBN) are used to pre-train the weights of CNN system. This acts as a generative model to explore the structure of the input data using statistical methods. Then a CNN is trained using supervised back-propagation learning algorithm to fine tune the weights. It will be shown that a small amount of data can be used to achieve good results in classification with this deep learning approach. A performance analysis of the novel method is provided using real data from the Saarbrucken Voice database.

The ever-increasing demand for artificial intelligence (AI) systems is underlining a significant requirement for new, AI-optimised hardware. Neuromorphic (brain-like) processors are one highly-promising solution, with photonic-enabled realizations receiving increasing attention. Among these, approaches based upon vertical cavity surface emitting lasers (VCSELs) are attracting interest given their favourable attributes and mature technology. Here, we demonstrate a hardware-friendly neuromorphic photonic spike processor, using a single VCSEL, for all-optical image edge-feature detection. This exploits the ability of a VCSEL-based photonic neuron to integrate temporally-encoded pixel data at high speed; and fire fast (100 ps-long) optical spikes upon detecting desired image features. Furthermore, the photonic system is combined with a software-implemented spiking neural network yielding a full platform for complex image classification tasks. This work therefore highlights the potential of VCSEL-based platforms for novel, ultrafast, all-optical neuromorphic processors interfacing with current computation and communication systems for use in future light-enabled AI and computer vision functionalities.

Acoustic analysis using signal processing tools can be used to extract voice features to distinguish whether a voice is pathological or healthy. The proposed work uses spectrogram of voice recordings from a voice database as the input to a Convolutional Neural Network (CNN) for automatic feature extraction and classification of disordered and normal voice. The novel classifier achieved 88.5%, 66.2% and 77.0% accuracy on training, validation and testing data set respectively on 482 normal and 482 organic dysphonia speech files. It reveals that the proposed novel algorithm on the Saarbruecken Voice Database can effectively been used for screening pathological voice recordings.

The combination of neuromorphic visual sensors and spiking neural network offers a high efficient bio-inspired solution to real-world applications. However, processing event- based sequences remains challenging because of the nature of their asynchronism and sparsity behavior. In this paper, a novel spiking convolutional recurrent neural network (SCRNN) architecture that takes advantage of both convolution operation and recurrent connectivity to maintain the spatial and temporal relations from event-based sequence data are presented. The use of recurrent architecture enables the network to have a sampling window with an arbitrary length, allowing the network to exploit temporal correlations between event collections. Rather than standard ANN to SNN conversion techniques, the network utilizes a supervised Spike Layer Error Reassignment (SLAYER) training mechanism that allows the network to adapt to neuromorphic (event-based) data directly. The network structure is validated on the DVS gesture dataset and achieves a 10 class gesture recognition accuracy of 96.59% and an 11 class gesture recognition accuracy of 90.28%.

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Taking inspiration from the structure and behaviour of the human visual system and using the Transposed Convolution and Saliency Mapping methods of Convolutional Neural Networks (CNN), a spiking event-based image segmentation algorithm, SpikeSEG is proposed. The approach makes use of both spike-based imaging and spike-based processing, where the images are either standard images converted to spiking images or they are generated directly from a neuromorphic event driven sensor, and then processed using a spiking fully convolutional neural network. The spiking segmentation method uses the spike activations through time within the network to trace back any outputs from saliency maps, to the exact pixel location. This not only gives exact pixel locations for spiking segmentation, but with low latency and computational overhead. SpikeSEG is the first spiking event-based segmentation network and over three experiment test achieves promising results with 96% accuracy overall and a 74% mean intersection over union for the segmentation, all within an event by event-based framework.