Neural Network Coding

In this paper we introduce Neural Network Coding(NNC), a data-driven approach to joint source and network coding. In NNC, the encoders at each source and intermediate node, as well as the decoder at each destination node, are neural networks which are all trained jointly for the task of communicating correlated sources through a network of noisy point-to-point links. The NNC scheme is application-specific and makes use of a training set of data, instead of making assumptions on the source statistics. In addition, it can adapt to any arbitrary network topology and power constraint. We show empirically that, for the task of transmitting MNIST images over a network, the NNC scheme shows improvement over baseline schemes, especially in the low-SNR regime

On palimpsests in neural memory: an information theory viewpoint

The finite capacity of neural memory and the reconsolidation phenomenon suggest it is important to be able to update stored information as in a palimpsest, where new information overwrites old information. Moreover, changing information in memory is metabolically costly. In this paper, we suggest that information-theoretic approaches may inform the fundamental limits in constructing such a memory system. In particular, we define malleable coding, that considers not only representation length but also ease of representation update, thereby encouraging some form of recycling to convert an old codeword into a new one. Malleability cost is the difficulty of synchronizing compressed versions, and malleable codes are of particular interest when representing information and modifying the representation are both expensive. We examine the tradeoff between compression efficiency and malleability cost, under a malleability metric defined with respect to a string edit distance. This introduces a metric topology to the compressed domain. We characterize the exact set of achievable rates and malleability as the solution of a subgraph isomorphism problem. This is all done within the optimization approach to biology framework.Accepted manuscrip

APPLICATION OF ANN AND GA FOR TRANSFORMER WINDING/ INSULATION FAULTS

This report presents an application of Artificial Neural Network and Genetic Algorithm for transformer winding/insulation faults diagnosed using Dissolved Gas in Oil Analysis. A back propagation training method is applied in neural network to detect the faults without cellulose involvement. While, heuristic method of Genetic Algorithm is used to locate the optimal values to enhance the accuracy of fault detection. The dissolved gas in oil analysis is chosen to diagnosis the transformer faults in this project as the method is known to be an early fault detection method and enables to carry out during online operation of the transformer. Besides, the condition of the transformer could be monitored continuously by time to time. The project outcome is analyzed using Neural Network and Genetic Algorithm MATLAB Toolbox. Comparison between the real fault and predicted fault is made as to observe the accuracy rate of the system. As transformer faults detection concentrated more in conventional method such the stability of the voltage and current of the transformer. Therefore, hopefully the transformer winding and insulation faults could be studied from new point ofview and method

Machine Translation from Natural Language to Code using Long-Short Term Memory

Making computer programming language more understandable and easy for the human is a longstanding problem. From assembly language to present day's object-oriented programming, concepts came to make programming easier so that a programmer can focus on the logic and the architecture rather than the code and language itself. To go a step further in this journey of removing human-computer language barrier, this paper proposes machine learning approach using Recurrent Neural Network (RNN) and Long-Short Term Memory (LSTM) to convert human language into programming language code. The programmer will write expressions for codes in layman's language, and the machine learning model will translate it to the targeted programming language. The proposed approach yields result with 74.40% accuracy. This can be further improved by incorporating additional techniques, which are also discussed in this paper.Comment: 8 pages, 3 figures, conferenc

What the Success of Brain Imaging Implies about the Neural Code

The success of fMRI places constraints on the nature of the neural code. The fact that researchers can infer similarities between neural representations, despite limitations in what fMRI measures, implies that certain neural coding schemes are more likely than others. For fMRI to be successful given its low temporal and spatial resolution, the neural code must be smooth at the sub-voxel and functional level such that similar stimuli engender similar internal representations. Through proof and simulation, we evaluate a number of reasonable coding schemes and demonstrate that only a subset are plausible given both fMRI’s successes and its limitations in measuring neural activity. Deep neural network approaches, which have been forwarded as computational accounts of the ventral stream, are consistent with the success of fMRI, though functional smoothness breaks down in the later network layers. These results have implications for the nature of neural code and ventral stream, as well as what can be successfully investigated with fMRI

Image database retrieval using neural networks

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The broad objective of this work has been to achieve retrieval of images from large unconstrained databases using image content. The problem is typified by the need to locate a target image within a database where no numerical indexing terms exist. Here, retrieval is based on important features within in an image and uses sample images or user sketches to specify a query. A typical query might be framed as "Find all images similar to this one", for example. The aim of this work has been to show how neural networks can provide a practical, flexible and robust solution to this problem. A neural network is basically an adaptive information filter which can be used to extract the salient characteristics of a data set during a training phase. The transformation learnt by the network can map the images into compact indices which support very rapid fuzzy matching of images across the database. This learning process optimises the performance of the code with respect to the contents of the database. We assess the applicability of several neural network architectures and learning rules for a practical coding scheme and investigate how the system parameters affect the performance of the system. We introduce a novel learning law which has a number of advantages over existing paradigms. In-depth mathematical analysis and extensive empirical tests are used to corroborate the arguments presented throughout. This thesis aims to show the nature of the image retrieval problem, how current research trends attempt to tackle it and how neural networks can offer us a real alternative to conventional approaches

Discovering Student Learning Styles In Engineering Mathematic At Politeknik Merlimau Using Neural Network Techniques

The identification of students’ learning style in learning mathematics is important for educators in choosing an effective teaching approach/methodology. Students from different field of studies to complete were asked the Index Learning Styles questionnaire to identify the student’s learning style of learning DBM1013 - Engineering Mathematics. This technique is used to consider their learning styles and how to improve students’ performance in learning DBM1013 – Engineering, Mathematics, the questionnaires were evaluated to identify the best learning styles used by students in learning Engineering Mathematics. However, the problem with this method is the time spent by students in answering questions and the accuracy of the results obtained. If questionnaires are too long, students tend to choose both answers arbitrarily instead of thinking about the result of the student’s learning style observed through analysis. This research identified the classification of students learning styles based on the Felder Silverman Learning dimension. Four learning dimension has been classified by using backpropagation neural networks. The algorithm has been run on training, validation and testing, training process data and 20 neurons. The result shows that the neural network is able to identify the students' learning styles according to the dimension with satisfying result