Robust Long-Term Hand Grasp Recognition With Raw Electromyographic Signals Using Multidimensional Uncertainty-Aware Models.

Hand grasp recognition with surface electromyography (sEMG) has been used as a possible natural strategy to control hand prosthetics. However, effectively performing activities of daily living for users relies significantly on the long-term robustness of such recognition, which is still a challenging task due to confused classes and several other variabilities. We hypothesise that this challenge can be addressed by introducing uncertainty-aware models because the rejection of uncertain movements has previously been demonstrated to improve the reliability of sEMG-based hand gesture recognition.

Reliability Analysis for Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks.

Hand gesture recognition with surface electromyography (sEMG) is indispensable for Muscle-Gesture-Computer Interface. The usual focus of it is upon performance evaluation involving the accuracy and robustness of hand gesture recognition. However, addressing the reliability of such classifiers has been absent, to our best knowledge.

Novel CE-CBCE feature extraction method for object classification using a low-density LiDAR point cloud.

Low-end LiDAR sensor provides an alternative for depth measurement and object recognition for lightweight devices. However due to low computing capacity, complicated algorithms are incompatible to be performed on the device, with sparse information further limits the feature available for extraction. Therefore, a classification method which could receive sparse input, while providing ample leverage for the classification process to accurately differentiate objects within limited computing capability is required.

A normalisation approach improves the performance of inter-subject sEMG-based hand gesture recognition with a ConvNet.

Recently, the subject-specific surface electromyography (sEMG)-based gesture classification with deep learning algorithms has been widely researched. However, it is not practical to obtain the training data by requiring a user to perform hand gestures many times in real life. This problem can be alleviated to a certain extent if sEMG from many other subjects could be used to train the classifier.

Link-prediction to tackle the boundary specification problem in social network surveys.

Diffusion processes in social networks often cause the emergence of global phenomena from individual behavior within a society. The study of those global phenomena and the simulation of those diffusion processes frequently require a good model of the global network. However, survey data and data from online sources are often restricted to single social groups or features, such as age groups, single schools, companies, or interest groups.

Oscillations and spiking pairs: behavior of a neuronal model with STDP learning.

In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models.

Comments on "Pruning error minimization in least squares support vector machines".

In this letter, we comment on "Pruning Error Minimization in Least Squares Support Vector Machines" by B. J. de Kruif and T.

Analysis of an evolving email network.

In this paper we study an evolving email network model first introduced by Wang and De Wilde, to the best of our knowledge. The model is analyzed by formulating the network topology as a random process and studying the dynamics of the process. Our analytical results show a number of steady state properties about the email traffic between different nodes and the aggregate networking behavior (i.

Robustness and regularity of oscillations in neuronal populations.

We study a biologically plausible but computationally simplified integrate-and-fire neuronal model. Oscillatory activity is analyzed in the networks with and without self-connections. We perform a detailed scan of four major parameters that represent the properties of neurons and synapses: connection ratio, connection strengths, post-synaptic potential decay rate and soma's potential decay rate.

Properties of evolving e-mail networks.

Computer viruses spread by attaching to an e-mail message and sending themselves to users whose addresses are in the e-mail address book of the recipients. Here we investigate a simple model of an evolving e-mail network, with nodes as e-mail address books of users and links as the records of e-mail addresses in the address books. Within specific periods, some new links are generated and some old links are deleted.

Familial pericentric inversion of chromosome 18: behavioral abnormalities in patients heterozygous for either the dup(18p)/del(18q) or dup(18q)/del(18p) recombinant chromosome.

We describe a family in which the largest hitherto reported pericentric inversion of chromosome 18, inv(18)(p11.22q23), segregates. Individuals heterozygous for the nonrecombinant inversion were unaffected.

Fuzzy utility and equilibria.

A decision maker is frequently confronted with fuzzy constraints, fuzzy utility maximization, and fuzziness about the state of competitors. In this paper we present a framework for fuzzy decision-making, using techniques from fuzzy logic, game theory, and micro-economics. In the first part, we study the rationality of fuzzy choice.