964 research outputs found
Pemilihan kerjaya di kalangan pelajar aliran perdagangan sekolah menengah teknik : satu kajian kes
This research is a survey to determine the career chosen of form four student
in commerce streams. The important aspect of the career chosen has been divided
into three, first is information about career, type of career and factor that most
influence students in choosing a career. The study was conducted at Sekolah
Menengah Teknik Kajang, Selangor Darul Ehsan. Thirty six form four students was
chosen by using non-random sampling purpose method as respondent. All
information was gather by using questionnaire. Data collected has been analyzed in
form of frequency, percentage and mean. Results are performed in table and graph.
The finding show that information about career have been improved in students
career chosen and mass media is the main factor influencing students in choosing
their career
Training Multi-layer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest for
supervised and unsupervised learning applications. This paper deals with the
problem of training multi-layer feedforward SNNs. The non-linear
integrate-and-fire dynamics employed by spiking neurons make it difficult to
train SNNs to generate desired spike trains in response to a given input. To
tackle this, first the problem of training a multi-layer SNN is formulated as
an optimization problem such that its objective function is based on the
deviation in membrane potential rather than the spike arrival instants. Then,
an optimization method named Normalized Approximate Descent (NormAD),
hand-crafted for such non-convex optimization problems, is employed to derive
the iterative synaptic weight update rule. Next, it is reformulated to
efficiently train multi-layer SNNs, and is shown to be effectively performing
spatio-temporal error backpropagation. The learning rule is validated by
training -layer SNNs to solve a spike based formulation of the XOR problem
as well as training -layer SNNs for generic spike based training problems.
Thus, the new algorithm is a key step towards building deep spiking neural
networks capable of efficient event-triggered learning.Comment: 19 pages, 10 figure
Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout
Heart-rate estimation is a fundamental feature of modern wearable devices. In
this paper we propose a machine intelligent approach for heart-rate estimation
from electrocardiogram (ECG) data collected using wearable devices. The novelty
of our approach lies in (1) encoding spatio-temporal properties of ECG signals
directly into spike train and using this to excite recurrently connected
spiking neurons in a Liquid State Machine computation model; (2) a novel
learning algorithm; and (3) an intelligently designed unsupervised readout
based on Fuzzy c-Means clustering of spike responses from a subset of neurons
(Liquid states), selected using particle swarm optimization. Our approach
differs from existing works by learning directly from ECG signals (allowing
personalization), without requiring costly data annotations. Additionally, our
approach can be easily implemented on state-of-the-art spiking-based
neuromorphic systems, offering high accuracy, yet significantly low energy
footprint, leading to an extended battery life of wearable devices. We
validated our approach with CARLsim, a GPU accelerated spiking neural network
simulator modeling Izhikevich spiking neurons with Spike Timing Dependent
Plasticity (STDP) and homeostatic scaling. A range of subjects are considered
from in-house clinical trials and public ECG databases. Results show high
accuracy and low energy footprint in heart-rate estimation across subjects with
and without cardiac irregularities, signifying the strong potential of this
approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at
Elsevier Neural Network
An optimised deep spiking neural network architecture without gradients
We present an end-to-end trainable modular event-driven neural architecture
that uses local synaptic and threshold adaptation rules to perform
transformations between arbitrary spatio-temporal spike patterns. The
architecture represents a highly abstracted model of existing Spiking Neural
Network (SNN) architectures. The proposed Optimized Deep Event-driven Spiking
neural network Architecture (ODESA) can simultaneously learn hierarchical
spatio-temporal features at multiple arbitrary time scales. ODESA performs
online learning without the use of error back-propagation or the calculation of
gradients. Through the use of simple local adaptive selection thresholds at
each node, the network rapidly learns to appropriately allocate its neuronal
resources at each layer for any given problem without using a real-valued error
measure. These adaptive selection thresholds are the central feature of ODESA,
ensuring network stability and remarkable robustness to noise as well as to the
selection of initial system parameters. Network activations are inherently
sparse due to a hard Winner-Take-All (WTA) constraint at each layer. We
evaluate the architecture on existing spatio-temporal datasets, including the
spike-encoded IRIS and TIDIGITS datasets, as well as a novel set of tasks based
on International Morse Code that we created. These tests demonstrate the
hierarchical spatio-temporal learning capabilities of ODESA. Through these
tests, we demonstrate ODESA can optimally solve practical and highly
challenging hierarchical spatio-temporal learning tasks with the minimum
possible number of computing nodes.Comment: 18 pages, 6 figure
Brain-Inspired Spatio-Temporal Associative Memories for Neuroimaging Data Classification: EEG and fMRI
Humans learn from a lot of information sources to make decisions. Once this information is learned in the brain, spatio-temporal associations are made, connecting all these sources (variables) in space and time represented as brain connectivity. In reality, to make a decision, we usually have only part of the information, either as a limited number of variables, limited time to make the decision, or both. The brain functions as a spatio-temporal associative memory. Inspired by the ability of the human brain, a brain-inspired spatio-temporal associative memory was proposed earlier that utilized the NeuCube brain-inspired spiking neural network framework. Here we applied the STAM framework to develop STAM for neuroimaging data, on the cases of EEG and fMRI, resulting in STAM-EEG and STAM-fMRI. This paper showed that once a NeuCube STAM classification model was trained on a complete spatio-temporal EEG or fMRI data, it could be recalled using only part of the time series, or/and only part of the used variables. We evaluated both temporal and spatial association and generalization accuracy accordingly. This was a pilot study that opens the field for the development of classification systems on other neuroimaging data, such as longitudinal MRI data, trained on complete data but recalled on partial data. Future research includes STAM that will work on data, collected across different settings, in different labs and clinics, that may vary in terms of the variables and time of data collection, along with other parameters. The proposed STAM will be further investigated for early diagnosis and prognosis of brain conditions and for diagnostic/prognostic marker discovery
FusionSense: Emotion Classification using Feature Fusion of Multimodal Data and Deep learning in a Brain-inspired Spiking Neural Network
Using multimodal signals to solve the problem of emotion recognition is one of the emerging trends in affective computing. Several studies have utilized state of the art deep learning methods and combined physiological signals, such as the electrocardiogram (EEG), electroencephalogram (ECG), skin temperature, along with facial expressions, voice, posture to name a few, in order to classify emotions. Spiking neural networks (SNNs) represent the third generation of neural networks and employ biologically plausible models of neurons. SNNs have been shown to handle Spatio-temporal data, which is essentially the nature of the data encountered in emotion recognition problem, in an efficient manner. In this work, for the first time, we propose the application of SNNs in order to solve the emotion recognition problem with the multimodal dataset. Specifically, we use the NeuCube framework, which employs an evolving SNN architecture to classify emotional valence and evaluate the performance of our approach on the MAHNOB-HCI dataset. The multimodal data used in our work consists of facial expressions along with physiological signals such as ECG, skin temperature, skin conductance, respiration signal, mouth length, and pupil size. We perform classification under the Leave-One-Subject-Out (LOSO) cross-validation mode. Our results show that the proposed approach achieves an accuracy of 73.15% for classifying binary valence when applying feature-level fusion, which is comparable to other deep learning methods. We achieve this accuracy even without using EEG, which other deep learning methods have relied on to achieve this level of accuracy. In conclusion, we have demonstrated that the SNN can be successfully used for solving the emotion recognition problem with multimodal data and also provide directions for future research utilizing SNN for Affective computing. In addition to the good accuracy, the SNN recognition system is requires incrementally trainable on new data in an adaptive way. It only one pass training, which makes it suitable for practical and on-line applications. These features are not manifested in other methods for this problem.Peer reviewe
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