Annotation alignment method on P2P-TV system

Peer-to-Peer Networking and Applications - P2P-TV is a TV system that receives content through a peer-to-peer network. Content is stored in the distributed manner then to be serviced to users, and...     

Droplet transportation using a pre-charging method for digital microfluidics

A new droplet-driving scheme for digital microfluidics termed the “pre-charging of a droplet” is demonstrated. In this method, a droplet is initially charged by applying “pre-charging” voltage between the droplet and an electrode buried under dielectric layers. The droplet is then driven to the next electrode by applying “driving” voltage between two adjacent buried electrodes. The concept of pre-charging was proved by the polarity of the charge stored in the droplet. When the droplet is pre-charged with positive voltage, it is driven with negative voltage and vice versa. Therefore, the magnitudes of the pre-charging and driving voltages are identical, but only with the opposite polarity. A 2.5-μL deionized water droplet is pre-charged and driven at a minimal voltage of 12 V. The charge stored in the droplet by this pre-charging method remained for more than 2 min, and the driving actuation could be repeated more than 150 times while the droplet remained its charged state. This method suggests a new means of driving a droplet for digital microfluidics at a relatively low voltage by utilizing both the electrostatic and dielectrophoretic force in the droplet transport process with a simpler structure compared to other single-plate structured devices.     

Blockchain Network Based Topic Mining Process for Cognitive Manufacturing

Wireless Personal Communications - Cognitive manufacturing has brought about an innovative change to the 4th industrial revolution based technology in combination with blockchain distributed...     

Associative Feature Information Extraction Using Text Mining from Health Big Data

Wireless Personal Communications - With the development of big data computing technology, most documents in various areas, including politics, economics, society, culture, life, and public health,...     

Performance Evaluation of Silence-Feature Normalization Model using Cepstrum Features of Noise Signals

Speech enhancement algorithms play an important role in speech signal processing. Over the past several decades, many algorithms have been studied for speech enhancement. A speech enhancement algorithm uses a noise removal method and a statistical model filter to analyze the speech signal in the frequency domain. Spectral subtraction and Wiener filters have been used as representative algorithms. These algorithms have excellent speech enhancement performance, but suffer from deterioration in performance due to specific noise or low signal-to-noise ratio (SNR) environments. In addition, according to estimations of erroneous noise, a noise existing in a voice signal is maintained so that a spectrum corresponding to a voice signal is distorted, or a frame corresponding to a voice signal cannot be retrieved, and voice recognition performance deteriorates. The problem of deterioration in speech recognition performance arises from the difference between speech recognition and training model. We use silence-feature normalization model as a methodology to improve the recognition rate resulting from the difference in the noisy environments. Conventional silence-feature normalization has a problem in that the silent part of the energy increases, which affects recognition performance due to unclear boundaries categorizing the voice. In this study, we use the cepstrum feature of the noise signals in the silence-feature normalization model to improve the performance of silence-feature normalization in a signal with a low SNR by setting a reference value for voiced and unvoiced classification. As a result of recognition rate confirmation, the recognition rates improve in performance, compared with other methods.     

Mining health-risk factors using PHR similarity in a hybrid P2P network

In an era of many diseases and increased longevity, more attention has been paid to chronic diseases that require constant health care. Under this circumstance, the development of research and development (R&D) for smart-device-based constant health care has drawn great attention. With the emergence of wearable devices, personal health devices (PHDs), and smartphones, various contents for constant health care have been developed. By using these devices, the users are able to collect personal health records (PHRs) that include data such as activity amount, heart rate, stress, and blood sugar. The range of the collected PHRs can be limited depending on the equipment or the surrounding environment. To overcome this problem, it is necessary to make a comparison with similar users in a cluster. Also, it is necessary to provide a service that can analyze and visually display the collected personal-health information. In this paper, we propose the mining of health-risk factors using the PHR similarity in a hybrid P2P network. This is a method of predicting a user’s health status using similarity-based data mining, where the PHRs are employed in a hybrid P2P environment consisting of a peer, a server, and a gateway. In a hybrid P2P environment, a user receives feedback on the result of a structured-data analysis. A peer searches for a different peer and gateway through a server and exchanges information. Depending on the data type, the PHR is divided into medical health examination, self-diagnosis, and personal-health data. The medical health examination contains the personal-health data that are generated regularly by a medical institution. Self-diagnosis represents the data of mental health, pains, and fatigue that can be changed often but cannot be collected by devices. Personal-health data mean the data that can be collected by individuals in everyday life. For the PHR-data analysis, an index is given to each attribute, and preprocessing is performed after a binary-code conversion. To predict a user’s health status, the PHR data are clustered on the basis of similarity in a hybrid P2P environment. The similarity between a user’s PHR and a PHR that is searched for in the network is measured. After the measurement, an index is given to the PHR that meets the minimum similarity and the PHR is incorporated into a Similarity PHR Group. The Similarity PHR Group flexibly changes depending on a user’s PHR status and the statuses of the users who have accessed the hybrid P2P network. A representative value of the Similarity PHR Group is extracted and is then compared with the user’s PHR to judge the user’s health status. The proposed method is suitable for a smart health service for chronic diseases requiring constant care, elderly health, and aftercare. This is a user-oriented health-care and promotion service wherein a user’s health status can be predicted through the mining of the health-risk factors of PHRs.