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Modular Neural Networks for Low Power Image Classification on Embedded Devi

Recent advances in the field of low-power embedded computing have allowed modular neural networks to be implemented on small, low-power processors. In particular, they have been used for the task of image classification. The authors of this paper are Abhinav Goel, Sara Aghajanzadeh, Caleb Tung, Shuo-Han Chen, and Yung-Hsiang Lu.


MFCC is a machine learning technique that combines spectral features and nonlinear transformation. It has been successful in speaker recognition due to its additive property. The technique is used to characterize recorded speech from a cell phone by processing its cepstrum and weighted equivalent impulse response. These signals contain a great deal of device-specific information and can be processed for identification.

MFCC is a feature that has attracted significant interest in speech-related research. It combines a number of statistical techniques, including GMMs, to produce a more accurate model of fundamental frequency and MFCC joint density. This approach also allows for localized modeling of fundamental frequency and MFCC joint density, a feature that can be used to improve speech quality.

8-bit Audio Signal Quantization Depth

This study reports that a CNN implementation with 8-bit audio signal quantization depth is more accurate than the Mel spectrogram and requires lower computational, memory, and power resources. The results show that MFCC can effectively reduce power consumption by up to 85%. Additionally, the implementation reduces the likelihood of misclassification.

In order to reduce the computational complexity of the neural network, it uses a pre-processing technique to reduce the dime using 256 FFT values and a Mel-scaled filter bank with 32 filters. The resulting 16-bit audio signal representation is then sliced into multiple, overlapping windows of 4 s each.

Reduced Invariance and Dimensionality of Neural Networks

The reduced invariance and dimensionality of modular deep neural networks enable the development of models with low computational costs, such as for image classification. The approach involves convolution on dimension-reduction blocks and downsampling strategies. In addition, attention modules improve feature distributions and facilitate discrimination between the front and background features. Reduced invariance and dimensionality are important considerations when building a neural network.

An ideal structure would embed the geometric structure of the data manifold in a high-dimensional space, and the distribution of the data points should be dependent on the data, task, and loss. Additionally, architecture-specific constraints should be taken into account. Randomness during initialization and training phases, for example, may lead to incoherent latent spaces. This can cause a generalization bound to change.

Self-sustaining Nature of Modular Neural Networks

The self-sustaining nature of modular neural networks for image classification on embedded Devi has been demonstrated using two methods. The first method involves randomly selecting n x m patches from an image and masking them with random values. This method reduced the error rate from 5.17 percent to 4.31%. It also proved to be the best patch fill method. It requires hand-designing only one parameter during implementation.

A second approach is to use Transfer Learning, which avoids overfitting. In Transfer Learning, the network is trained on a large dataset using weights from convolutional layers but does not duplicate the entire network. As many image datasets share similar low-level spatial characteristics, understanding how to transfer learned models to different datasets remains a challenging research problem. However, researchers Yosinski et al. have found that transferability is adversely affected by specialization, as it is hard to split co-adapted neurons.

Detection of Illegal Tree-cutting Activity

Detection of illegal tree-cutting activity in forests is a challenging problem that requires a novel approach. To achieve this task, we used modular neural networks. These networks are capable of learning from several independent input data sets. Their output data can be further analyzed to find patterns.

We trained our neural network by iterating 300 times. In addition, we used 15- dimensional data. We examined the modularity of the trained model by measuring the community structure in the network. This modular representation is key to understanding the trained result. It extracts the community structure of the input, hidden, and output layers.

Hamza Ahmed
Hamza Ahmed
Hamza Ahmed is graduated from the NED faculty of Software Engineering Karachi. This website is owned and operated by Hamza Ahmed


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