Jeng-Wei Lin

Cr doping in the La1:2Sr1:8Mn2O7 system

The effect of doping Cr in the Mn site of the La1.2Sr1.8Mn2O7 system has been studied. Addition of Cr modifies the transport and magnetic properties of the parent phase. With increasing Cr, the insulator-metal transition observed in the parent phase is suppressed and insulating behaviour is induced. In the range of doping studied (25%), the compositions show ferromagnetic behaviour with the Curie temperature decreasing with increasing Cr. The unit cell volume also shows a decrease. However, the magnetoresistance ratio is not significantly affected. We compare these results with an earlier study of doping Cr in the three-dimensional LaMnO3 structure, where it was seen that the ferromagnetic and magnetoresistance characteristics are sensitive to Cr doping. The results of the present study suggest that the layered manganates are more accommodative to doping compared to the three-dimensional perovskites.

A Physiology-Based Seizure Detection System for Multichannel EEG

Background Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. Electroencephalogram (EEG) signals play a critical role in the diagnosis of epilepsy. Multichannel EEGs contain more information than do single-channel EEGs. Automatic detection algorithms for spikes or seizures have traditionally been implemented on single-channel EEG, and algorithms for multichannel EEG are unavailable. Methodology This study proposes a physiology-based detection system for epileptic seizures that uses multichannel EEG signals. The proposed technique was tested on two EEG data sets acquired from 18 patients. Both unipolar and bipolar EEG signals were analyzed. We employed sample entropy (SampEn), statistical values, and concepts used in clinical neurophysiology (e.g., phase reversals and potential fields of a bipolar EEG) to extract the features. We further tested the performance of a genetic algorithm cascaded with a support vector machine and post-classification spike matching. Principal Findings We obtained 86.69% spike detection and 99.77% seizure detection for Data Set I. The detection system was further validated using the model trained by Data Set I on Data Set II. The system again showed high performance, with 91.18% detection of spikes and 99.22% seizure detection. Conclusion We report a de novo EEG classification system for seizure and spike detection on multichannel EEG that includes physiology-based knowledge to enhance the performance of this type of system.

Epileptic Seizure Detection for Multichannel EEG Signals with Support Vector Machines

Epilepsy is a common chronic neurological disorder characterized by recurrent unprovoked seizures. The electroencephalogram (EEG) signals play an important role in the diagnosis of epilepsy. In addition, multi-channel EEG signals have much more discrimination information than a single channel. However, traditional recognition algorithms of EEG signals are lack of multichannel EEG signals. In this paper, we propose a new method of epileptic seizure detection based on multichannel EEG signals. Both unipolar and bipolar EEG signals are considered in our approach. We make use of approximate entropy (ApEn) and statistic values to extract features. Furthermore, we tested the performance of four different Support Vector Machines (SVMs). The results reveal that the grid SVM achieves the highest totally classification accuracy (98.91%).

FOS: A Funnel-Based Approach for Optimal Online Traffic Smoothing of Live Video

Traffic smoothing is an efficient means to reduce the bandwidth requirement for transmitting a variable-bit-rate video stream. Several traffic-smoothing algorithms have been presented to offline compute the transmission schedule for a prerecorded video. For live video applications, Sen present a sliding-window algorithm, referred to as SLWIN(k), to online compute the transmission schedule on the fly. SLWIN(k) looks ahead W video frames to compute the transmission schedule for the next k frametimes, where klesw. Note that W is upper bounded by the initial delay of the transmission. The time complexity of SLWIN(k) is O(W*N/k) for an N frame live video. In this paper, we present an O(N) online traffic-smoothing algorithm and two variants, denoted as FOS, FOS1 and FOS2, respectively. Note that O(N) is a trivial lower bound of the time complexity of the traffic-smoothing problem. Thus, the proposed algorithm is optimal. We compare the performance of our algorithms with SLWIN(k) based on several benchmark video clips. Experiment results show that FOS2, which adopts the aggressive workahead heuristic, further reduces the bandwidth requirement and better utilizes the client buffer for real-time interactive applications in which the initial delays are small

Bio-signal analysis system design with support vector machines based on cloud computing service architecture

Today, many bio-signals such as Electroencephalography (EEG) are recorded in digital format. It is an emerging research area of analyzing these digital bio-signals to extract useful health information in biomedical engineering. In this paper, a bio-signal analyzing cloud computing architecture, called BACCA, is proposed. The system has been designed with the purpose of seamless integration into the National Taiwan University Health Information System. Based on the concept of. NET Service Oriented Architecture, the system integrates heterogeneous platforms, protocols, as well as applications. In this system, we add modern analytic functions such as approximated entropy and adaptive support vector machine (SVM). It is shown that the overall accuracy of EEG bio-signal analysis has increased to nearly 98% for different data sets, including open-source and clinical data sets.

X-ray absorption near edge structure studies of colossal magnetoresistance ferromagnet (La1.4Sr1.6)Mn2O7

Abstract The Mn valence of a two-dimensional ferromagnet (La 1.4 Sr 1.6 )Mn 2 O 7 with colossal magnetoresistance has been determined to be around 3.3 by using both Mn K- and L 23 -edge X-ray absorption near edge structure (XANES) spectra. This suggests that the chemical substitution of Sr 2+ for La 3+ is an effective route of hole-doping in (La 1.4 Sr 1.6 )Mn 2 O 7 resulting in a metallic ferromagnetic transition at the temperature of around 148 K and giving rise to the colossal magnetoresistance effect at high magnetic field.

Epilepsy analytic system with cloud computing

Biomedical data analytic system has played an important role in doing the clinical diagnosis for several decades. Today, it is an emerging research area of analyzing these big data to make decision support for physicians. This paper presents a parallelized web-based tool with cloud computing service architecture to analyze the epilepsy. There are many modern analytic functions which are wavelet transform, genetic algorithm (GA), and support vector machine (SVM) cascaded in the system. To demonstrate the effectiveness of the system, it has been verified by two kinds of electroencephalography (EEG) data, which are short term EEG and long term EEG. The results reveal that our approach achieves the total classification accuracy higher than 90%. In addition, the entire training time accelerate about 4.66 times and prediction time is also meet requirements in real time.

A Multiclass Classification Tool Using Cloud Computing Architecture

Multiclass classification is an important technique to many complex biomedicine problems. Genetic algorithms (GA) are proven to be effective to select features prior to multiclass classification by support vector machines (SVM). However, their use is computation intensive. Based on SOA (Service Oriented Architecture) design principles, this paper proposes a cloud computing framework that exploits the inherent parallelism of GA-SVM classification to speed up the work. The performance evaluations on an mRNA benchmark cancer dataset have shown the effectiveness and efficiency of the framework. With a user-friendly web interface, the framework provides researchers an easy way to investigate the unrevealed secrets in the fast-growing repository of biomedical data.

Complete font generation of Chinese characters in personal handwriting style

Since a complete Chinese font has typically several thousand or more Chinese characters and symbols, and most of them are much more complicated than English alphabets, it takes a lot of time and efforts for even professional font engineers to create a Chinese font. Although several attempts had been made to synthesize Chinese characters from strokes and components, it is still not easy to synthesize so many Chinese characters at one time. In this paper, we present an easy and fast solution for an ordinary user to create a Chinese font of his or her handwriting style. We adopt the approach: to synthesize Chinese characters using components extracted from the users handwritings. In the preprocessing phase, we built a Web interface for crowds to label the positions and sizes of components of every Chinese character in the target character set. The standard Kai font was selected as a reference. We also devised an algorithm to find a small subset of Chinese characters having all required components to synthesize other Chinese characters. To create a personal handwriting font, with commonly-used 3,914 traditional Chinese characters, a user only has to handwrite 400 or so Chinese characters on a pad. One character by one character, our system can track every stroke, recognize and extract components from the users handwritings. Then, every target Chinese character is synthesized from the extracted components, by placing them properly according to their position and size information. The experiment results show that although manually fine-tune is still required for few synthesized Chinese characters, users can create a Chinese font of their personal handwriting styles more easily and quickly.

Particle Swarm Optimization Combined with Query-Based Learning Using MapReduce

Particle swarm optimization (PSO) has shown its effectiveness to solve many complex optimization problems. However, PSO sometimes may fall into a local optimal solution rather than the global optimal solution of a given problem. For large-scale optimization problems, several parallelized PSOs have been proposed in the literature. In this paper, a query-based learning (QBL) approach is adopted to help PSO jump out of a local optimum. An Oracle is introduced that answers PSO whether there are too many particles in a flat region of the solution space. If yes, the algorithm will redistribute some of the particles in the flat region to somewhere else. A parallelized implementation, referred to as MRPSO-QBL, is developed in the Apache Hadoop MapReduce framework, as the framework provides a simpler and better parallel programming paradigm. The experiment results on several benchmark functions have demonstrated that MRPSO-QBL can find better solutions and converge faster.