IEEE Journal of Selected Topics in Signal Processing

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This paper presents a novel approach for accurate barcodes detection in real and challenging environments using compact deep neural networks. Our approach is based on Convolutional Neural Network ( CNN ) and neural network compression, which can detect the four vertexes coordinates of a barcode accurately and quickly. Our approach consists of four stages: ( i ) feature extraction by a base network, ( ii ) region proposal network ( RPN ) training, ( iii ) barcode classification and coordinates regression, and ( iv ) weights pruning and recoding.

Visual food recognition on mobile devices has attracted increasing attention in recent years due to its roles in individual diet monitoring and social health management and analysis. Existing visual food recognition approaches usually use large server-based networks to achieve high accuracy. 

We consider the problem of reliable information propagation in the brain using biologically realistic models of spiking neurons. Biological neurons use action potentials, or spikes, to encode information. Information can be encoded by the rate of asynchronous spikes or by the (precise) timing of synchronous spikes. Reliable propagation of synchronous spikes is well understood in neuroscience and is relatively easy to implement by biologically-realistic models of neurons. 

Solving visual question answering (VQA) task requires recognizing many diverse visual concepts as the answer. These visual concepts contain rich structural semantic meanings, e.g., some concepts in VQA are highly related (e.g., red & blue), some of them are less relevant (e.g., red & standing).

Deep learning methods haverevolutionized speech recognition, image recognition, and natural language processing since 2010. Each of these tasks involves a single modality in their input signals. However, many applications in the artificial intelligence field involve multiple modalities.

Speech analysis could provide an indicator of Alzheimer's disease and help develop clinical tools for automatically detecting and monitoring disease progression. While previous studies have employed acoustic (speech) features for characterisation of Alzheimer's dementia, these studies focused on a few common prosodic features, often in combination with lexical and syntactic features which require transcription.

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