A Cloud robotics architecture to foster individual child partnership in medical facilities

Robots and automation systems have become a valuable partner in several facets of human life: from learning and teaching, to daily working, including health monitoring and assistance. So far, these appealing robot-based applications are restricted to conduct repetitive, yet useful, tasks due to the reduced individual robots’ capabilities in terms of processing and computation. This concern prevents current robots from facing more complex applications related to understanding hu- man beings and perceiving their subtle feelings. Such hardware limitations have been already found in the computer science field. In this domain, they are currently being addressed using a new resource exploitation model coined as cloud computing, which is targeted at enabling massive storage and computation using smartly connected and inexpensive commodity hardware. The purpose of this paper is to propose a cloud-based robotics architecture to effectively develop complex tasks related to hospitalized children assistance. More specifically, this paper presents a multi-agent learning system that combines machine learning and cloud computing using low-cost robots to (1) collect and perceive children status, (2) build a human-readable set of rules related to the child-robot relationship, and (3) improve the children experience during their stay in the hos- pital. Conducted preliminary experiments proof the feasibility of this proposal and encourage practitioners to work towards this direction.Peer ReviewedPostprint (published version

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average