Emotion extraction based on multi bio-signal using back-propagation neural network

This study proposes a system that can recognize human emotional state from bio-signal. The technology is provided to improve the interaction between humans and computers to achieve an effective human–machine that is capable for intelligent interaction. The proposed method is able to recognize six emotional states, such as joy, happiness, fear, anger, despair, and sadness. These set of emotional states are widely used for emotion recognition purposes. The result shows that the proposed method can distinguish one emotion compared to all other possible emotional states. The method is composed of two steps: 1) multi-modal bio-signal evaluation and 2) emotion recognition using artificial neural network. In the first step, we present a method to analyze and fix human sensitivity using physiological signals, such as electroencephalogram, electrocardiogram, photoplethysmogram, respiration, and galvanic skin response. The experimental analysis shows that the proposed method has good accuracy performance and could be applied on many human–computer interaction devices for emotion detection.

     

Real-time video watermarking techniques robust against re-encoding

This paper presents a real-time watermarking codec that is robust against re-encoding attacks for high-definition videos. The codec uses a segmentation function and texture detector techniques for applying real-time watermarking to human visual systems. Experimental results confirm that the proposed scheme satisfies the requirements of invisibility, real-time processing, and robustness against format conversion and low bit-rate encoding. The proposed algorithm has the advantages of simplicity, flexibility, and low computational burden; thus, it is a suitable candidate for many novel and interesting applications such as video fingerprinting for set-top boxes, Internet protocol television, personal video recorders, and satellite boxes.     

Improving Lipid Production of Yarrowia lipolytica by the Aldehyde Dehydrogenase-Mediated Furfural Detoxification

Yarrowia lipolytica, the non-conventional yeast capable of high lipogenesis, is a microbial chassis for producing lipid-based biofuels and chemicals from renewable resources such as lignocellulosic biomass. However, the low tolerance of Y. lipolytica against furfural, a major inhibitory furan aldehyde derived from the pretreatment processes of lignocellulosic biomass, has restricted the efficient conversion of lignocellulosic hydrolysates. In this study, the furfural tolerance of Y. lipolytica has been improved by supporting its endogenous detoxification mechanism. Specifically, the endogenous genes encoding the aldehyde dehydrogenase family proteins were overexpressed in Y. lipolytica to support the conversion of furfural to furoic acid. Among them, YALI0E15400p (FALDH2) has shown the highest conversion rate of furfural to furoic acid and resulted in two-fold increased cell growth and lipid production in the presence of 0.4 g/L of furfural. To our knowledge, this is the first report to identify the native furfural detoxification mechanism and increase furfural resistance through rational engineering in Y. lipolytica. Overall, these results will improve the potential of Y. lipolytica to produce lipids and other value-added chemicals from a carbon-neutral feedstock of lignocellulosic biomass.     

The placement position optimization of a biosensor array for wearable healthcare systems

Journal of Mechanical Science and Technology - Advancement in wearable healthcare technology allows a digital revolution in healthcare that is intended to extend life length and improve life...     

Enhanced Deep-Learning Prediction of Molecular Properties via Augmentation of Bond Topology

Deep learning has made great strides in tackling chemical problems, but still lacks full-fledged representations for three-dimensional (3D) molecular structures for its inner working. For example, the molecular graph, commonly used in chemistry and recently adapted to the graph convolutional network (GCN), is inherently a 2D representation of 3D molecules. Herein we propose an advanced version of the GCN, called 3DGCN, which receives 3D molecular information from a molecular graph augmented by information on bond direction. While outperforming state-of-the-art deep-learning models in the prediction of chemical and biological properties, 3DGCN has the ability to both generalize and distinguish molecular rotations in 3D, beyond 2D, which has great impact on drug discovery and development, not to mention the design of chemical reactions.     

Strategic Advances in Formation of Cell‐in‐Shell Structures: From Syntheses to Applications

Single‐cell nanoencapsulation, forming cell‐in‐shell structures, provides chemical tools for endowing living cells, in a programmed fashion, with exogenous properties that are neither innate nor naturally achievable, such as cascade organic‐catalysis, UV filtration, immunogenic shielding, and enhanced tolerance in vitro against lethal factors in real‐life settings. Recent advances in the field make it possible to further fine‐tune the physicochemical properties of the artificial shells encasing individual living cells, including on‐demand degradability and reconfigurability. Many different materials, other than polyelectrolytes, have been utilized as a cell‐coating material with proper choice of synthetic strategies to broaden the potential applications of cell‐in‐shell structures to whole‐cell catalysis and sensors, cell therapy, tissue engineering, probiotics packaging, and others. In addition to the conventional “one‐time‐only” chemical formation of cytoprotective, durable shells, an approach of autonomous, dynamic shellation has also recently been attempted to mimic the naturally occurring sporulation process and to make the artificial shell actively responsive and dynamic. Here, the recent development of synthetic strategies for formation of cell‐in‐shell structures along with the advanced shell properties acquired is reviewed. Demonstrated applications, such as whole‐cell biocatalysis and cell therapy, are discussed, followed by perspectives on the field of single‐cell nanoencapsulation.     

Single-Cell Nanoencapsulation: From Passive to Active Shells

Single-cell nanoencapsulation is an emerging field in cell-surface engineering, emphasizing the protection of living cells against external harmful stresses in vitro and in vivo. Inspired by the cryptobiotic state found in nature, cell-in-shell structures are formed, which are called artificial spores and which show suppression or retardation in cell growth and division and enhanced cell survival under harsh conditions. The property requirements of the shells suggested for realization of artificial spores, such as durability, permselectivity, degradability, and functionalizability, are demonstrated with various cytocompatible materials and processes. The first-generation shells in single-cell nanoencapsulation are passive in the operation mode, and do not biochemically regulate the cellular metabolism or activities. Recent advances indicate that the field has shifted further toward the formation of active shells. Such shells are intimately involved in the regulation and manipulation of biological processes. Not only endowing the cells with new properties that they do not possess in their native forms, active shells also regulate cellular metabolism and/or rewire biological pathways. Recent developments in shell formation for microbial and mammalian cells are discussed and an outlook on the field is given.     

Response Threshold Model Based UAV Search Planning and Task Allocation

This paper addresses a search planning and task allocation problem for a Unmanned Aerial Vehicle (UAV) team that performs a search and destroy mission in an environment where targets with different values move around. The UAVs are heterogeneous having different sensing and attack capabilities, and carry limited amount of munitions. The objective of the mission is to find targets and eliminate them as quickly as possible considering the values of the targets. In this context, there are two distinct issues that need to be addressed simultaneously: search planning and task allocation. The search plan generates an efficient search path for each UAV to facilitate a fast target detection. The task allocation assigns UAVs attack tasks over detected targets such that each UAV’s attack capability is respected. We model these two issues in one framework and propose a distributed approach that utilizes a probabilistic decision making mechanism based on response threshold model. The proposed approach accounts for natural uncertainties in the environment, and provides flexibility, resulting in efficient exploration in the environment and effective allocation of attack tasks. The approach is evaluated in simulation experiments in comparison with other methods, of which results show that our approach outperforms the other methods.