Preparation and Application of Electrodes in Capacitive Deionization (CDI): a State-of-Art Review

As a promising desalination technology, capacitive deionization (CDI) have shown practicality and cost-effectiveness in brackish water treatment. Developing more efficient electrode materials is the key to improving salt removal performance. This work reviewed current progress on electrode fabrication in application of CDI. Fundamental principal (e.g. EDL theory and adsorption isotherms) and process factors (e.g. pore distribution, potential, salt type and concentration) of CDI performance were presented first. It was then followed by in-depth discussion and comparison on properties and fabrication technique of different electrodes, including carbon aerogel, activated carbon, carbon nanotubes, graphene and ordered mesoporous carbon. Finally, polyaniline as conductive polymer and its potential application as CDI electrode-enhancing materials were also discussed

Effects on accuracy of Uyghur handwritten signature recognition

In this paper, an approach for off-line Uyghur signature recognition is proposed. The signature images were preprocessed using improved techniques adapted to the Uyghur signature. The preprocessing are included noise reduction, binarization, normalization and thinning. Two types of preprocessing steps were conducted with and without thinning. The directional features, global baseline, upper and lower line features, local central features were extracted respectively after the two kinds of preprocessing. Experiments were performed selecting Euclidean distance and Chi-square distance based measure methods and using K nearest neighbor classifier for Uyghur signature samples from 50 different people with 1000 signatures. A correct recognition rate of 96.0% was achieved with thinning. The experimental results indicated that thinning has significant importance to the extracted features and its effects to the accuracy were related with the nature of extracted features

Off-line Uyghur signature recognition based on modified grid information features

Many techniques have been published on handwriting signature recognition, but none of these techniques presented are about Uyghur handwritten signature due to its complex nature. In this paper, we propose methods for off-line signature recognition for Uyghur handwriting first time. The signature images were pre-processed based on the nature of Uyghur signature. The preprocessing included noise reduction, binarization and normalization. Then multi-dimensional modified grid information features were extracted according to the character of Uyghur signature and its writing style. Finally, three kinds of classifica

Real-time neurodegenerative disease video classification with severity prediction

In this paper, an automatic diagnosis system for neurodegenerative diseases is presented. Starting with an existing neurodegenerative diseases gait dataset, namely the NDDGD dataset, classification and regression algorithms have been trained, with the inter-patient dataset separation scheme (walking patterns used for training and testing, belong to different people), and integrated within a larger automatic diagnosis system which make use of videos in input or real-time streaming from cameras for predicting the neurodegenerative disease, if present, and its stage. The proposed system is capable of predicting among 3 neurodegenerative diseases, namely: amyotrophic lateral sclerosis disease (ALS), Parkinson’s disease (PD), Huntington’s disease (HUN) and differentiate among the severity (stage) level of the disease, if found. The system makes use of common cameras for the 2D pose estimation and features engineering. The system can be easily deployed in hospitals and houses in order to help physicians with the diagnosis. When used in conjunction with physicians, this system can be a valuable tool for neurodegenerative diseases prediction