There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

The Pharmacological Basis of Therapeutics A Textbook of Pharmacology, Toxicology and Therapeutics for Physicians and Medical Students. By Prof. Louis Goodman and Prof. Alfred Gilman. Pp. xiii + 1383. (New York: The Macmillan Company, 1941.) 50s. net.

The Pharmacological Basis of Therapeutics

http://academic.oup.com/milmed/article-pdf/146/7/506/24126715/milmed-146-7-506.pdf

Goodman and Gilman's The Pharmacological Basis of Therapeutics

国際生活機能分類(International Classification of Functioning, Disability and Health: ICF)にみた福祉・保健・医療の専門職協働における連携に関する貢献と課題

The Electroencephalogram (EEG) is one of the useful biosignals detect the human emotions. This paper discusses on a research conducted to determine the changes in the electrical activity of the human brain related to distinct emotions. We designed a competent acquisition protocol for acquiring the EEG signals under audio-visual induction environment. The EEG data has been collected from 6 healthy subjects with in an age group of 21-27 using 63 biosensors. From the subjective analysis on each emotion, three emotions have been identified with higher agreement. After preprocessing the signals, discrete wavelet transform is employed to extract the EEG parameters. The feature vectors derived from the above feature extraction method on 63 biosensors form an input matrix for emotion classification. In this work, we have used Fuzzy C-Means (FCM) and Fuzzy k-Means (FKM) clustering methods for classifying the emotions. We have also analyzed the performance of FCM and FKM on reduced number of 24 biosensors model. Finally, we compared the performance of clustering the discrete emotions using FCM and FKM on both 64 biosensors and 24 biosensors. Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.

http://www.wseas.us/e-library/conferences/2008/meta_hangzhou/CD/meta-44.pdf

EEG feature extraction for classifying emotions using FCM and FKM

The goal of this paper is to discuss comparison of speech parameterization methods: Mel-Frequency Cepstrum Coefficients (MFCC) and Linear Prediction Cepstrum Coefficients (LPCC) for recognizing the stuttered events. Speech samples from UCLASS are used for our analysis. The stuttered events are identified through manual segmentation and used for feature extraction. Two simple classifiers are used for testing the proposed features. Conventional validation method is used for testing the reliability of the classifier. The experimental investigation elucidates MFCC and LPCC features which can be used for identifying the stuttered events and LPCC features were slightly outperformed than MFCC features.

Classification of speech dysfluencies with MFCC and LPCC features

This study presents the processes undertaken in the design and development of an intelligent omni-directional mobile robot using four custom-made mecanum wheels. The mecanum wheel developed consists of nine rollers made from delrin. All mecanum wheels are independently powered using four units of precisian gear DC motors and the wheel/motor assemblies were mounted directly to the robot chassis made using an aluminum frame. A four channel high power H-bridge using 2 units of LMD 18200 motor drivers IC circuit was design, built and interfaced to a BasicStamp (BS2) microcontroller board. Basic mobility algorithm using BasicStamp software was developed to test the basic mobility capabilities and test the qualitative view of the system's mobility performance. An experiment was set-up to analyze the motion characteristic of the mobile robot motion in Y-axis, X-axis and rotary motion. Data from the experiment will be used for mathematically model for mobile robot platform and speed controller modeling and design. Mecanum wheel mobile robot provides a convenient platform for further development in the mobile platform. The combination of mechanical design on the wheel and chassis, motion control and multiple input/output sensors allow the exploration of large number of control algorithm and software to be implemented to the robot for practical applications.

Designing Omni-Directional Mobile Robot with Mecanum Wheel

Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals. Emotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm. Analysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively. The results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

/pdf/classification-of-emotional-states-from-electrocardiogram-2va6akzp59.pdf

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

The main objective of this work is to develop an electronic travel aid to assist the blinds for obstacle identification in their navigation. This navigation assistance for visually impaired (NAVI) system presented in this paper consists of a single board processing system (SBPS), a vision sensor mounted headgear and a pair of stereo earphones. The image environment in front of the blind is captured by the vision sensor. The image is processed by a new real time image processing scheme using fuzzy clustering algorithms. The processed image is mapped onto a specially structured stereo acoustic patterns and transferred to the stereo earphones in the system. Blind individuals were trained with NAVI system and tested for obstacle identification. Suggestions from the blind volunteers regarding pleasantness and discrimination of sound pattern were also incorporated in the prototype. The proposed processing methodology is found to be effective for object identification and for producing stereo sound patterns in the NAVI system.

Sazali Yaacob

Papers

EEG feature extraction for classifying emotions using FCM and FKM

Classification of speech dysfluencies with MFCC and LPCC features

Designing Omni-Directional Mobile Robot with Mecanum Wheel

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

Fuzzy image processing scheme for autonomous navigation of human blind