Related papers: Dynamic Pooling Improves Nanopore Base Calling Acc…

Convolutional neural network (CNN) offers significant accuracy in image detection. To implement image detection using CNN in the internet of things (IoT) devices, a streaming hardware accelerator is proposed. The proposed accelerator…

Several of the next generation sequencers are limited in their sample preparation process by the need to make an absolute measurement of the number of template molecules in the library to be sequenced. As currently practiced, the practical…

A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether…

Brain-inspired machine learning is gaining increasing consideration, particularly in computer vision. Several studies investigated the inclusion of top-down feedback connections in convolutional networks; however, it remains unclear how and…

In convolutional neural networks (CNNs), downsampling operations are crucial to model performance. Although traditional downsampling methods (such as maximum pooling and cross-row convolution) perform well in feature aggregation, receptive…

We present an unique way to detect basepair mismatches in DNA leading to different epigenetic disorder by the method of nanopore sequencing. Based on a tight-binding formulation of graphene nanopore based device, using Greens function…

Parallelization techniques have become ubiquitous for accelerating inference and training of deep neural networks. Despite this, several operations are still performed in a sequential manner. For instance, the forward and backward passes…

Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial…

Discrete Fourier transforms provide a significant speedup in the computation of convolutions in deep learning. In this work, we demonstrate that, beyond its advantages for efficient computation, the spectral domain also provides a powerful…

Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to multiple hardware platforms, nor handle diverse architectural search-spaces. To remedy this, we present DONNA (Distilling Optimal Neural Network…

Solid-state nanopores, nm-sized holes in thin, freestanding membranes, are powerful single-molecule sensors capable of interrogating a wide range of target analytes, from small molecules to large polymers. Interestingly, due to their high…

Hand-sized Deoxyribonucleic acid (DNA) sequencing machines are of growing importance in several life sciences fields as their small footprints enable a broader range of use cases than their larger, stationary counterparts. However, as…

Recently, there has been an explosive growth of mobile and embedded applications using convolutional neural networks(CNNs). To alleviate their excessive computational demands, developers have traditionally resorted to cloud offloading,…

Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer.…

Temporary changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack…

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic…

Pooling is essentially an operation from the field of Mathematical Morphology, with max pooling as a limited special case. The more general setting of MorphPooling greatly extends the tool set for building neural networks. In addition to…

Pooling operations, which can be calculated at low cost and serve as a linear or nonlinear transfer function for data reduction, are found in almost every modern neural network. Countless modern approaches have already tackled replacing the…

Graphene nanopore based sensor devices are exhibiting the great potential for the detection of DNA. To understand the fundamental aspects of DNA translocating through a graphene nanopore, in this work, molecular dynamics (MD) simulations…

In modern computer vision tasks, convolutional neural networks (CNNs) are indispensable for image classification tasks due to their efficiency and effectiveness. Part of their superiority compared to other architectures, comes from the fact…