Earticle

This paper proposes two topics for a high performance fingerprint sensor. A modified capacitive detection circuit of charge sharing scheme is proposed, which shows 40% reduction of power consumption and 90% improvement of difference between a ridge and valley sensing voltage than typical scheme. This paper also proposes an effective fingerprint identification system with hardware unit (thinning processor) for thinning stage processing of a verification algorithm based on minutiae. Each step of a fingerprint algorithm is analyzed on FPGA and ARMulator. The proposed scheme just needs 56,000 cycles for a thinning processing in 160x192 pixel array and the layout area is 0.541 mm2 at 0.35μm CMOS process.

Green microalgae Chlorella vulgaris species was collected from Bhavani Lake at Erode District, Tamil Nadu State, India. The Fourier Transform Infrared spectroscopy and Scanning Electron Microscopic studies were utilized to examine morphology of Chlorella Vulgaris. This Chlorella vulgaris is attempted its efficiency in removing Nitrate and Phosphate from waste water medium. These experimental studies reveal that, Chlorella Vulgaris is having full-fledged efficiency in removing Nitrates and Phosphates from waste water medium. This microalgae specimen ‘Chlorella Vulgaris’ was attained 100% removal efficiency of Nitrates and Phosphates at a time intravel of 6 days period.

The incidence of hip fractures occurring at old age is gradually increasing. We analyzed he various factors affecting on the postoperative mortality of hip fractures for persons aging more than 65 years old. We hypothesized that the duration until surgery and the American Society of Anesthesiologists (ASA) classification were useful variables affecting on postoperative mortality rates. A retrospective review of 247 patients with hip fractures from January 2006 to June 2008 who had undergone operative treatment was performed. Overall postoperative one year mortality rate was 10.1%. The mortality rates who underwent surgery post injury for more than 3 days and less than 3 days were 11.7% and 5.9%, respectively. Higher mortality rates in ASA class 3 and 4 (9.7% and 22.2%, respectively) than in ASA class 2 (9.1%). In univariable analyses, sex and patients’ age at injury had statistical significances. In multivariable analyses, duration until surgery and ASA classification had strong correlation with postoperative mortality rate. Hip fractures in elderly patients should be fixed as early as possible. Patients with preoperative higher risk (in ASA class 3 or 4) should be closely managed and care must be taken following hip fracture surgery.

As growing concerns about food safety, consumers consider more about what to eat and how to buy groceries. The new term ‘locavore’ coined in the mid-2000s is a reflection of this recent phenomenon. Its meaning is a person interested in eating food that is locally produced. As an appropriate countermeasure for healthful food consumption, the locavore movement can sustain food supply system in a various way. The agricultural business in the rural areas has been on the decline due to a series of market openings caused by the FTAs. In this regard, it is time to bring up more effective feasible measures for farming. By putting the locavore movement into action, the rural community can boost its economy as well as enhance its social unity. In this study, Pocheon, Gyeonggi province is chosen to be a model to initiate the locavore movement in Korea. As a neighboring city of Seoul, Pocheon composes of a number of farmlands and livestock breeding farms as well as tourists’ attractions. The transportation system in Pocheon is so convenient that Seoul citizens easily visit the community. In this regard, Pocheon is most likely to be a starting point providing the urban areas nearby with good quality of local food.

Ovarian cancer is very malignant tumor because it doesn’t have any striking symptoms in its early stages. That’s why the early screening is really necessary in its clinics. We try to look for the optimal methodology to find out biomarker combination making its classification performance better than other cases. We evaluate 9 machine learning algorithms, those are Random Forest, Logistic, Multilayer Perceptron, Bagging, Classification Via Regression, LogitBoost, MultiClassifer, Simple Logistic, and Logistic Regression. The Area Under the Curve (AUC) of each algorithm is compared. We firstly select 15 biomarkers which are widely spread in the ovarian cancer diagnosis and find the best three combinations which composed of two, three and four biomarkers by using Logistic Regression which is well known for its reliable performance. Than we re-evaluate the best combinations with nine algorithms including Logistic Regression to find the optimal machine learning algorithm. In this research, we can find possibility to use another machine learning algorithm rather than Logistic Regression.

Various contents utilizing 3D holographic image have been developed and this led to the need of research on problems which are related to 3D syndrome. It is now more urgent to find the mechanism to sort out such problems as some people show such symptoms as fatigue, dizziness and vomiting after watching 3D contents. This research, in this light, has been conducted to suggest an indicator for detecting 3D syndrome by extracting and analyzing the changes in user’s electroencephalogram of which result can be utilized for implementing the system which prevent and mitigate the syndromes which occur after watching 3D holography.

In this paper, we propose and implement a leaf recognition system using the leaf vein and shape that can be used for plant classification. The proposed approach uses major main vein and frequency domain data by using Fast Fourier Transform (hereinafter, FFT) methods with distance between contour and centroid on the detected leaf image. Total 21 leaf features were extracted for the leaf recognition, which they include ① the distance feature between centroid and all points on the leaf contour, ② frequency domain data by FFT that was performed using the distances. In summary, 10 features of all the 21 leaf features were extracted using distance, FFT magnitude, and phase, the other 10 features were extracted using t the digital morphological features using four basic geometric features and five vein features, and the last 1 feature was extracted using the convex hull. To verify the validity of the approach, images of 1907 leaves apply to classify 32 kinds of plants. In the experimental results, the proposed leaf recognition system showed an average recognition rate of 97.19%, and we can confirm that the recognition rate of the proposed leaf recognition system was better than that of the existed leaf recognition method.

The best marker combination for differentiating the ovarian cancer from benign is explored with the logistic regression. The serum samples from 81 patients with ovarian cancer and 216 patients with benign pelvic masses provided by 2 institutes were analyzed using Luminex assay test. The selection performance of the logistic regression was compared with three other methods such as t-test, genetic algorithm, and random forest. The evaluation of the four methods were performed also with three classification methods including logistic regression, linear discriminant analysis, and k-nearest neighbor method. The 4 marker combination from the logistic regression showed the best performance against the other selection methods in terms of the average accuracy.

This paper is designed to provide a framework to ensure security and reliability of the flow of biometric information for telebiometric applications using mobile devices. Also, we define twelve telebiometrics authentication models depending on the configuration of the biometric sensor, the mobile device, and the server. It also specifies threat in operating telebiometric systems based on the mobile device and proposes a general guideline for security countermeasures from both technical and managerial perspectives in order to establish a safe mobile environment for the use of telebiometric systems.

Missing, swapping, mixing, and illegal adoption of newborns is a global challenge and research done to solve this issue is minimal and least reported in the literature. To the best of our knowledge there is no multimodal database of newborns is available in public domain to test the algorithms for their identification. An attempt has been made to prepare a multimodal database of newborns to help the researchers to test their different algorithms for the recognition accuracy. The database includes physiological characteristics like face, ear, headprint and soft biometrics data like gender, height, weight and blood group of 280 newborns. With all these identity characteristics this database may be useful for authenticating the newborns using unimodal and multimodal biometric systems development for newborn.

Facial expression recognition, as one of the important topics in pattern recognition and computer vision, has broad applications in fields of human-computer interaction, psychological behavior analysis, image understanding. This paper presents a novel facial expression recognition method based on global and local features extraction and facial recognition using decision-level fusion. We first extract Local Directional Pattern (LDP) global features of the whole face which can guarantee basic expression difference and decrease the influence of no-facial region meanwhile, and then the Local Directional Pattern Variance (LDPv) descriptor is used to extract local features of regions of eyes and mouth to extrude their contribution on expression changes. After feature extraction, PCA technique is utilized to reduce dimension of input feature space. Finally, in order to avoid redundant feature repeat we don't use feature fusion with simple concatenation, a decision-level fusion for global LDP feature and local LDPv feature by Support Vector Machine (SVM) is selected to recognition respectively. Furthermore, we also research the optimal parameters for regions-dividing and weight of LDPv. The proposed method is investigated on two standard databases Cohn-Kanade and JAFFE, and extensive experimental results indicate the effectiveness.

The cleaner chromate production process using sub-molten salt has been successfully simulated using METSIM in order to evaluate the mass balance, energy consumption, and other conditions necessitate for the process scaling up. Based on the simulation results, the mass and heat balance of every operation unit are obtained, providing valuable reference for the process design. Further, the heat exchange network optimization using pinch technology is performed, enabling the reduction of the heat and cool energy consumption by 31.5% and 31.7%, respectively.

In recent years, comparative genomic hybridization arrays (aCGH) techniques have been developed rapidly, and aCGH data analysis can identify chromosomal aberrations that are related to the development of many complex diseases. Currently, Chronic Obstructive Pulmonary Disease (COPD) is predicted to become the third most common cause of death and the fifth most common cause of disability in the world by 2020. Unfortunately, So far the studies to COPD have not been well characterized despite the well-documented role that cigarette smoking plays in the genesis of COPD. Therefore, in this study, we used comparative genomic hybridization arrays (aCGH) techniques to detect COPD related susceptibility regions (potential genomic aberrations) which will provide the support for COPD clinical study. Furthermore, the SW-ARRAY algorithm was used to detect the copy number variable (CNV) regions, and these regions were compared between patients with COPD and patients without COPD. Our results can help understand the disease etiology of COPD.

OBJECTIVE: To verify the feasibility of identification of different phenotypes of breast cancer by metabolic pathways-based two-step selective cluster analysis. METHODS: Gene expression data of breast cancer (series number GSE10810) were available through Gene Expression Omnibus (GEO) database of National Center for Biotechnology Information (NCBI). Breast cancer samples served as study materials. Samples were analyzed by two-step selective clustering method based on several pathways. After the first clustering analysis, samples with fair results were retained; the other samples were chosen to do the second clustering analysis and got the final results. Cluster3.0 software was applied to clustering analysis. RESULTS AND CONCLUSION: Finally, breast cancer samples were well identified by analyzing of 3 date sets of KEGG pathways. The results confirm the feasibility of discernment of different phenotypes by this method.

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. This idea is very insightful for solving bioinformatics problems. Aligning distantly related protein sequences is a long-standing problem in bioinformatics and a key for successful protein structure prediction. A fast and valid algorithm can benefit the whole process of biology research. In this paper, we introduce an algorithm that given a certain evaluation function, will calculate the optimal alignment by dynamic programming.