High performance 2.5 GHz microwave identification system

2001 ◽  
Vol 118 (10) ◽  
pp. 525-526
Author(s):  
J. Gila ◽  
A. Renner ◽  
W. Konrad ◽  
P. Veith
Keyword(s):  
High Performance ◽  
Identification System

scholarly journals DISCRIMINANT ANALYSIS OF PURIFICATION ON TURMERIC (CURCUMA LONGA LINN) SAMPLES BASED ON PHYTO-PHARMACOGNOSTICAL AND MULTIVARIATE CHEMOMETRIC TECHNIQUE

2017 ◽  
Vol 9 (9) ◽  
pp. 156
Author(s):  
Anuj Kumar ◽  
Akanksha Thakur ◽  
Jayanta Kumar Maji ◽  
Prashant Bedarkar
Keyword(s):  
Image Processing ◽  
High Performance ◽  
Curcuma Longa ◽  
Color Space ◽  
Principal Component ◽  
Identification System ◽  
Microscopy Image ◽  
Chemometric Technique ◽  
Lab Color Space ◽  
Primary Substance

Objective: To establish an acceptable identification system of various purification effects in context classical based different media on turmeric samples and relates its altering pattern in favor of phyto-pharmacognostical, image processing and multivariate chemometric analysis.Methods: Authenticated turmeric samples purified through different processes by using different media such as cow’s urine, panchapllava (five different plants tender leaves), the inflorescence of alambusha (Sphaeranthus indicus, Linn) decoction, water and buttermilk. Resultant samples dried, pulverized and undertaken powder microscopy, image processing, physicochemical and chromatographic fingerprinting (HPTLC). The multivariate chemometric analysis, principal component analysis (PCA) analyzed with help of Unscrambler and image processing in Matlab software.Results: The addition of characters of medias drug with turmeric powders like the crystal of gomutra, pollen grain and starch grain of Alambusha, epidermis, fibre, the crystal of panchapllava. Identify different perceivable colors in variously processed turmeric by analyzing the Lab color space through the Image segmentation. PC1 and PC2 explained (90 + 9) % total variance in score plot of respective purify turmeric samples shown clear grouping in relation to the physicochemical constant. Quantification of curcumin in various treated turmeric samples displayed variation due to additive effect in high-performance thin layer chromatographic profile.Conclusion: This study proved that purification in ayurveda not only refers to the elimination of toxins and unwanted particles but also the transformation in the properties in the primary substance rendering it safe as well as many desired qualities are imbibed in it.

BIOMETRIC WRITER IDENTIFICATION: FEATURE ANALYSIS AND CLASSIFICATION

2006 ◽  
Vol 20 (04) ◽  
pp. 483-503 ◽  
Author(s):  
JOULIA CHAPRAN
Keyword(s):  
High Performance ◽  
Handwriting Recognition ◽  
Personal Identification ◽  
Small Scale ◽  
Identification System ◽  
Writer Identification ◽  
Dynamic Features ◽  
Digital Writing ◽  
The Subject ◽  
New Feature

This paper describes an efficient biometric writer identification system that can be used in a resource contained embedded environment. Writer identification (personal identification by general handwriting) is a relatively new area of handwriting research when compared to the handwriting recognition or signature verification areas. This work is aimed at exploring only small-scale handwriting samples, especially single handwritten words. A database of such samples has been collected dynamically using a digital writing tablet and a force sensitive pen. The dynamic approach adapted in this area is largely new, and only a few papers exist on the subject. Mainly dynamic features of handwriting connected with the writing process itself are considered, although some static features are also used. It is also shown how simple parameters can be used in embedded writer identification devices, and whether they can perform adequately in this type of application. A new feature selection algorithm based on likeness coefficients is proposed. The classifiers used are Minimum-Distance Classifier, Bayes Classifier, and finally their serial combination. The efficiency of this approach and its high performance together show the reasons for employing it in embedded biometric identification devices.

A MapReduce Clone Car Identification Model over Traffic Data Stream

2013 ◽  
Vol 346 ◽  
pp. 117-122
Author(s):  
Wen Chuan Yang ◽  
Guang Jie Lin ◽  
Jiang Yong Wang
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Ad Hoc ◽  
Image Data ◽  
Traffic Information ◽  
Identification System ◽  
Traffic Data ◽  
Traffic Surveillance ◽  
Fine Grain ◽  
Large Scale Dataset

Accompany the widely use of Intelligent Traffic in China, all traffic input data streams to the Traffic Surveillance Center (TSC). Some metropolitan TSC, such as in Beijing, produces up to 18 million records and 1T image data arriving every hour. Normally, the job of the TSC is to monitor and retain data. There is a tendency to put more capability into the TSC, such as ad-hoc query for clone car identification and feedback abnormal traffic information. Thus we definitely need to think about what can be kept in working storage and how to analysis it. Obviously, the ordinary database cannot handle the massive dataset and complex ad-hoc query. MapReduce is a popular and widely used fine grain parallel runtime, which is developed for high performance processing of large scale dataset. In this paper, we propose CarMR, a MapReduce Clone Car Identification system based on Hive/Hadoop frameworks. A distributed file system HDFS is used in CarMR for fast data sharing and query. CarMR supports fast locating clone car and also optimizes the route to catch fugitive. Our results show that the model achieves a higher efficiency.

Ship Surveillance by Integration of Space-borne SAR and AIS – Further Research

2013 ◽  
Vol 67 (2) ◽  
pp. 295-309 ◽  
Author(s):  
Zhi Zhao ◽  
Kefeng Ji ◽  
Xiangwei Xing ◽  
Huanxin Zou ◽  
Shilin Zhou
Keyword(s):  
Data Fusion ◽  
High Performance ◽  
Automatic Identification ◽  
Identification System ◽  
Performance Quality ◽  
Source Data ◽  
Fusion Methods ◽  
Feature Based ◽  
Global Coverage ◽  
Processing Techniques

Many countries are making increased efforts to improve marine security and safety and develop ship surveillance techniques to satisfy the increasing demands. Space-borne Synthetic Aperture Radar (SAR) delivers high performance day/night all weather capabilities and a space-based Automatic Identification System (AIS) can give near real time and global coverage. Limited by the development of sensors and data processing techniques, the integration of space-borne SAR and AIS has much to offer ship surveillance. State-of-the-art data fusion methods have generally provided satisfactory performance. However, in high-density shipping or high sea-states, performance quality is less assured. This paper firstly investigates improved data association methods. The association methods based on the position feature are improved, and multi-feature-based association methods are proposed. Then, ship identification and tracking by the integration of space-borne SAR and AIS are researched further. Multi-source data fusion strategy is also investigated. Finally, the discussion is presented and the future works are emphasized in the conclusion.

scholarly journals Hybrid Biometric Recognition using Stacked Auto Encoder with Random Forest Classifier

2020 ◽  
Vol 6 (2) ◽  
pp. 20-26
Author(s):  
Amreen Khan ◽  
Dr. Abhishek Bhatt
Keyword(s):  
Random Forest ◽  
High Performance ◽  
Research Work ◽  
Personal Data ◽  
Recognition System ◽  
Identification System ◽  
Optimum Approach ◽  
Vein Recognition ◽  
Effective Models ◽  
Result Analysis

In recent years, the need for security of personal data is becoming progressively important. A biometric system is an evolving technology that is used in various fields like forensics, secured area and security system. With respect to this concern, the identification system based on the fusion of multibiometric values is the most recommended in order to significantly improve and obtain high performance accuracy. The main purpose of this research work is to design and propose a hybrid system of combining the effect of three effective models: Retinex Algorithm, Stacked Deep Auto Encoder and Random forest (RF) classifier based on multi-biometric fingerprint as well as finger-vein recognition system. According to literature several fingerprint as well as fingervein recognition system are designed that uses various techniques in order to reduce false detection rate and to enhance the performance of the system. A comparative study of different recognition technique along with their limitations is also summarized and optimum approach is proposed which may enhance the performance of the system. In order to gain above mentioned objectives, fingerprint and fingervein dataset is taken for training and testing. The result analysis shows approx. 97% accuracy, 92% precision rate as well as 0.04 EER that shows enhancement over existing work.

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