Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Novel Relay Selection Rules for Average Interference-Constrained Cognitive AF Relay Networks

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio (CR) network. However, unlike conventional relaying, the state of the links between the relay and the primary receiver affects the choice of the relay. Further, while the optimal amplify-and-forward (AF) relay selection rule for underlay CR is well understood for the peak interference-constraint, this is not so for the less conservative average interference constraint. For the latter, we present three novel AF relay selection (RS) rules, namely, symbol error probability (SEP)-optimal, inverse-of-affine (IOA), and linear rules. We analyze the SEPs of the IOA and linear rules and also develop a novel, accurate approximation technique for analyzing the performance of AF relays. Extensive numerical results show that all the three rules outperform several RS rules proposed in the literature and generalize the conventional AF RS rule.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Design and implementation of spectrum sensing for cognitive radios with a frequency-hopping primary system

In this work, spectrum sensing for cognitive radios is considered in the presence of multiple Primary Users (PU) using frequency-hopping communication over a set of frequency bands. The detection performance of the Fast Fourier Transform (FFT) Average Ratio (FAR) algorithm is obtained in closed-form, for a given FFT size and number of PUs. The effective throughput of the Secondary Users (SU) is formulated as an optimization problem with a constraint on the maximum allowable interference on the primary network. Given the hopping period of the PUs, the sensing duration that maximizes the SU throughput is derived. The results are validated using Monte Carlo simulations. Further, an implementation of the FAR algorithm on the Lyrtech (now, Nutaq) small form factor software defined radio development platform is presented, and the performance recorded through the hardware is observed to corroborate well with that obtained through simulations, allowing for implementation losses. (C) 2015 Elsevier B.V. All rights reserved.

SYNGAP1: Mind the Gap

A cardinal feature of early stages of human brain development centers on the sensory, cognitive, and emotional experiences that shape neuronal-circuit formation and refinement. Consequently, alterations in these processes account for many psychiatric and neurodevelopmental disorders. Neurodevelopment disorders affect 3-4% of the world population. The impact of these disorders presents a major challenge to clinicians, geneticists, and neuroscientists. Mutations that cause neurodevelopmental disorders are commonly found in genes encoding proteins that regulate synaptic function. Investigation of the underlying mechanisms using gain or loss of function approaches has revealed alterations in dendritic spine structure, function, and plasticity, consequently modulating the neuronal circuit formation and thereby raising the possibility of neurodevelopmental disorders resulting from synaptopathies. One such gene, SYNGAP1 (Synaptic Ras-GTPase-activating protein) has been shown to cause Intellectual Disability (ID) with comorbid Autism Spectrum Disorder (ASD) and epilepsy in children. SYNGAP1 is a negative regulator of Ras, Rap and of AMPA receptor trafficking to the postsynaptic membrane, thereby regulating not only synaptic plasticity, but also neuronal homeostasis. Recent studies on the neurophysiology of SYNGAP1, using Syngapl mouse models, have provided deeper insights into how downstream signaling proteins and synaptic plasticity are regulated by SYNGAP1. This knowledge has led to a better understanding of the function of SYNGAP1 and suggests a potential target during critical period of development when the brain is more susceptible to therapeutic intervention.

SYNGAP1: Mind the Gap

A cardinal feature of early stages of human brain development centers on the sensory, cognitive, and emotional experiences that shape neuronal-circuit formation and refinement. Consequently, alterations in these processes account for many psychiatric and neurodevelopmental disorders. Neurodevelopment disorders affect 3-4% of the world population. The impact of these disorders presents a major challenge to clinicians, geneticists, and neuroscientists. Mutations that cause neurodevelopmental disorders are commonly found in genes encoding proteins that regulate synaptic function. Investigation of the underlying mechanisms using gain or loss of function approaches has revealed alterations in dendritic spine structure, function, and plasticity, consequently modulating the neuronal circuit formation and thereby raising the possibility of neurodevelopmental disorders resulting from synaptopathies. One such gene, SYNGAP1 (Synaptic Ras-GTPase-activating protein) has been shown to cause Intellectual Disability (ID) with comorbid Autism Spectrum Disorder (ASD) and epilepsy in children. SYNGAP1 is a negative regulator of Ras, Rap and of AMPA receptor trafficking to the postsynaptic membrane, thereby regulating not only synaptic plasticity, but also neuronal homeostasis. Recent studies on the neurophysiology of SYNGAP1, using Syngapl mouse models, have provided deeper insights into how downstream signaling proteins and synaptic plasticity are regulated by SYNGAP1. This knowledge has led to a better understanding of the function of SYNGAP1 and suggests a potential target during critical period of development when the brain is more susceptible to therapeutic intervention.

Direct Link-Aware Relay Selection for Average Interference-Constrained Underlay Cognitive Radio

Cooperative relaying combined with selection exploits spatial diversity to significantly improve the performance of interference-constrained secondary users in an underlay cognitive radio network. We present a novel and optimal relay selection (RS) rule that minimizes the symbol error probability (SEP) of an average interference-constrained underlay secondary system that uses amplify-and-forward relays. A key point that the rule highlights for the first time is that, for the average interference constraint, the signal-to-interference-plus-noise-ratio (SINR) of the direct source-to-destination (SI)) link affects the choice of the optimal relay. Furthermore, as the SINR increases, the odds that no relay transmits increase. We also propose a simpler, more practical, and near-optimal variant of the optimal rule that requires just one bit of feedback about the state of the SD link to the relays. Compared to the SD-unaware ad hoc RS rules proposed in the literature, the proposed rules markedly reduce the SEP by up to two orders of magnitude.