BiLSTM regression model for face sketch synthesis using sequential patterns

Neural Computing and Applications - Face sketch synthesis from an input photo is a challenging task for law enforcement applications. Prior methods generally have required neighbor selection...     

Markov random fields and facial landmarks for handling uncontrolled images of face sketch synthesis

Pattern Analysis and Applications - Face sketch synthesis has drawn great attention in many computer vision applications such as law enforcement and digital entertainment. The majority of existing...     

Molecular Simulation of Naphthenic Acid Removal on Acidic Catalyst (Ⅰ) Mechanism of Catalytic Decarboxylation

In this paper, the charge distribution, the chemical bond order and the reactive performance of carboxylic acid model compounds on acidic catalyst were investigated by using molecular simulation technology. The simulation results showed that the bond order of C-O was higher than that of C-C,and C-C bond connected to the carbon atom in the carboxyl radical had the lowest bond order. The charge distributions of model naphthenic acids were similar in characteristics that the negative charges were concentrated on carboxyls. According to the simulation results, the mechanisms of catalytic decarboxylation over acidic solid catalyst were proposed, and a new route was put forward regarding removal of the naphthenic acid from crude oil through catalytic decarboxylation.     

High performance accelerators for deep neural networks: A review

The availability of huge structured and unstructured data, advanced highly dense memory and high performance computing machines have provided a strong push for the development in artificial intelligence (AI) and machine learning (ML) domains. AI and machine learning has rekindled the hope of efficiently solving complex problems which was not possible in the recent past. The generation and availability of big-data is a strong driving force for the development of AI/ML applications, however, several challenges need to be addressed, like processing speed, memory requirement, high bandwidth, low latency memory access, and highly conductive and flexible connections between processing units and memory blocks. The conventional computing platforms are unable to address these issues with machine learning and AI. Deep neural networks (DNNs) are widely employed for machine learning and AI applications, like speech recognition, computer vison, robotics, and so forth, efficiently and accurately. However, accuracy is achieved at the cost of high computational complexity, sacrificing energy efficiency and throughput like performance measuring parameters along with high latency. To address the problems of latency, energy efficiency, complexity, power consumption, and so forth, a lot of state of the art DNN accelerators have been designed and implemented in the form of application specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs). This work provides the state of the art of all these DNN accelerators which have been developed recently. Various DNN architectures, their computing units, emerging technologies used in improving the performance of DNN accelerators will be discussed. Finally, we will try to explore the scope for further improvement in these accelerator designs, various opportunities and challenges for the future research.     

Intelligent Biometric Group Hand Tracking (IBGHT) database for visual hand tracking research and development

With the increase of innovations in vision-based hand gesture interaction system, new techniques and algorithms are being developed by researchers. However, less attention has been paid on the scope of dismantling hand tracking problems. There is also limited publicly available database developed as benchmark data to standardize the research on hand tracking area. For this purpose, we develop a versatile hand gesture tracking database. This database consists of 60 video sequences containing a total of 15,554 RGB color images. The tracking sequences are captured in different situations ranging from an easy indoor scene to extremely high challenging outdoor scenes. Complete with annotated ground truth data, this database is made available on the web for the sake of assisting other researchers in the related fields to test and evaluate their algorithms based on standard benchmark data.     

Molecular Simulation of Naphthenic Acid Removal on Acidic Catalyst （I） Mechanism of Catalytic Decarboxylation

In this paper, the charge distribution, the chemical bond order and the reactive performance of carboxylic acid model compounds on acidic catalyst were investigated by using molecular simulation technology. The simulation results showed that the bond order of C-O was higher than that of C-C, and C-C bond connected to the carbon atom in the carboxyl radical had the lowest bond order. The charge distributions of model naphthenic acids were similar in characteristics that the negative charges were concentrated on carboxyls. According to the simulation results, the mechanisms of catalytic decarboxylation over acidic solid catalyst were proposed, and a new route was put forward regarding removal of the naphthenic acid from crude oil through catalytic decarboxylation.     

高酸原油催化脱酸工艺研究进展

介绍了高酸原油催化加氢脱酸法、催化热解脱酸法和催化化学反应脱酸法,展望了该工艺的应用前景。提出了根据高酸原油的特点及其腐蚀特性,开发催化脱酸工艺的发展方向。