This article presents a comparison of the entropy production in a laminar and transitional boundary layer flow with the spectral entropy produced in a region of instability induced by an imposed periodic disturbance. The objective of the study is exploratory in nature by computing a boundary-layer environment with well-established computer techniques and comparing the predictions of the maximum...

Deep belief network (DBN) is a probabilistic generative model with multiple layers of hidden nodes and a layer of visible nodes, where parameterizations between layers obey harmonium or restricted Boltzmann machines (RBMs). In this paper we present restricted deep belief network (RDBN) for multi-view learning, where each layer of hidden nodes is composed of view-specific and shared hidden nodes...

Convolutional neural networks (CNNs) are a popular and highly performant choice for pixel-wise dense prediction or generation. One of the commonly required components in such CNNs is a way to increase the resolution of the network’s input. The lower resolution inputs can be, for example, low-dimensional noise vectors in image generation [7] or low resolution (LR) feature maps for network vis...

This paper presents an algorithm for efficient distribution of completion detection blocks in a dualrail self-timed circuit to ensure correct computation of the completion signal. Layer-wise optimisation technique is used with the width of layers selected so as to satisfy timing constrains and use the least possible number of completion detection blocks.

Tailor-made metal oxide (MO) thin films with controlled compositions, electronic structures, and architectures are obtained via molecular layer deposition (MLD) and solution treatment. Step-wise formation of permeable hybrid films by MLD followed by chemical modification in solution benefits from the versatility of gas phase reactivity on surfaces while maintaining flexibility which is more com...

Designing a robust tracking control for a non-linear MIMO system with uncertainty is one of the most complicated control problems. In this paper, sliding mode changed to non-linear controllable canonical form by input-output linearization. This, sliding surfaces can be defined in a way that we can de-couple equations and indicate the sliding conditions of multi-variable controller system. The u...

Deep neural networks have been applied in many applications exhibiting extraordinary abilities the field of computer vision. However, complex network architectures challenge efficient real-time deployment and require significant computation resources energy costs. These challenges can be overcome through optimizations such as compression. Network compression often realized with little loss accu...

Regional neuraxial analgesia for pain relief during labor is a universally accepted, safe, and effective procedure involving administering medication into the epidural. Still, an adequate assessment requires continuous patient monitoring after catheter placement. This research introduces cutting-edge semantic thermal image segmentation method emphasizing superior interpretability regional monit...

Deep neural networks have been used in various machine learning applications and achieved tremendous empirical successes. However, training deep is a challenging task. Many alternatives proposed place of end-to-end back-propagation. Layer-wise one them, which trains single layer at time, rather than the whole layers simultaneously. In this paper, we study layer-wise using block coordinate gradi...