Adaptive Growth: Real-time CNN Layer Expansion

Deep Neural Networks (DNNs) have shown unparalleled achievements in numerous applications, reflecting their proficiency in managing vast data sets. Yet, their static structure limits their adaptability in ever-changing environments. This research presents a new algorithm that allows the convolutional layer of a Convolutional Neural Network (CNN) to dynamically evolve based on data input, while still being seamlessly integrated into existing DNNs. Instead of a rigid architecture, our approach iteratively introduces kernels to the convolutional layer, gauging its real-time response to varying data. This process is refined by evaluating the layer's capacity to discern image features, guiding its growth. Remarkably, our unsupervised method has outstripped its supervised counterparts across diverse datasets like MNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100. It also showcases enhanced adaptability in transfer learning scenarios. By introducing a data-driven model scalability strategy, we are filling a void in deep learning, leading to more flexible and efficient DNNs suited for dynamic settings. Code:(https://github.com/YunjieZhu/Extensible-Convolutional-Layer-git-version).Comment: Code: https://github.com/YunjieZhu/Extensible-Convolutional-Layer-git-versio

Effective Audio Classification Network Based on Paired Inverse Pyramid Structure and Dense MLP Block

Recently, massive architectures based on Convolutional Neural Network (CNN) and self-attention mechanisms have become necessary for audio classification. While these techniques are state-of-the-art, these works' effectiveness can only be guaranteed with huge computational costs and parameters, large amounts of data augmentation, transfer from large datasets and some other tricks. By utilizing the lightweight nature of audio, we propose an efficient network structure called Paired Inverse Pyramid Structure (PIP) and a network called Paired Inverse Pyramid Structure MLP Network (PIPMN). The PIPMN reaches 96\% of Environmental Sound Classification (ESC) accuracy on the UrbanSound8K dataset and 93.2\% of Music Genre Classification (MGC) on the GTAZN dataset, with only 1 million parameters. Both of the results are achieved without data augmentation or model transfer. Public code is available at: https://github.com/JNAIC/PIPM

Image Reconstruction for Multi-frequency Electromagnetic Tomography based on Multiple Measurement Vector Model

Imaging the bio-impedance distribution of a biological sample can provide understandings about the sample's electrical properties which is an important indicator of physiological status. This paper presents a multi-frequency electromagnetic tomography (mfEMT) technique for biomedical imaging. The system consists of 8 channels of gradiometer coils with adjustable sensitivity and excitation frequency. To exploit the frequency correlation among each measurement, we reconstruct multiple frequency data simultaneously based on the Multiple Measurement Vector (MMV) model. The MMV problem is solved by using a sparse Bayesian learning method that is especially effective for sparse distribution. Both simulations and experiments have been conducted to verify the performance of the method. Results show that by taking advantage of multiple measurements, the proposed method is more robust to noisy data for ill-posed problems compared to the commonly used single measurement vector model.Comment: This is an accepted paper which has been submitted to I2MTC 2020 on Nov. 201

Spectral and Spatial Dependence of Diffuse Optical Signals in Response to Peripheral Nerve Stimulation

Using non-invasive, near-infrared spectroscopy we have previously reported optical signals measured at or around peripheral nerves in response to their stimulation. Such optical signals featured amplitudes on the order of 0.1% and peaked about 100 ms after peripheral nerve stimulation in human subjects. Here, we report a study of the spatial and spectral dependence of the optical signals induced by stimulation of the human median and sural nerves, and observe that these optical signals are: (1) unlikely due to either dilation or constriction of blood vessels, (2) not associated with capillary bed hemoglobin, (3) likely due to blood vessel(s) displacement, and (4) unlikely due to fiber-skin optical coupling effects. We conclude that the most probable origin of the optical response to peripheral nerve stimulation is from displacement of blood vessels within the optically probed volume, as a result of muscle twitch in adjacent areas.National Institutes of Health (R01-NS059933); U.S. Army Medical Acquisition Activity (W81XWH-07-2-0011