Fingerprint Feature Compression Using Statistical Coding Techniques

2009 ◽  
Author(s):  
C. Saravanan ◽  
Sanjeev S. Malalur ◽  
Micahel T. Manry
Keyword(s):  
Fingerprint Feature ◽  
Statistical Coding

Violation of medical statistics as one of the reasons for the low incidence of occupational diseases

2021 ◽  
Vol 61 (8) ◽  
pp. 510-514
Author(s):  
Aleksandr R. Tukov
Keyword(s):  
Occupational Diseases ◽  
Medical Center ◽  
Poor Quality ◽  
Medical Statistics ◽  
Information Base ◽  
Social Rehabilitation ◽  
The People ◽  
Negative Role ◽  
Low Incidence ◽  
Statistical Coding

Many authors point to the low incidence of occupational diseases in our country. The reasons are poor-quality preventive examinations, the lack of interest of employers in identifying these diseases, and others. However, there are no works in the literature devoted to errors in the statistical coding of diagnoses of occupational pathology and their accounting. The study aims to improve the accounting of occupational diseases in Russia. We conducted the study using the information base of the Industry Register of Persons with Occupational Diseases, developed by Burnazyan State Medical Center of the FMBA, Russia, which operating in the industry since 2011. As of 31.12.2020, the register contains information on 2,056 patients with occupational diseases, from among the employees of enterprises and organizations served by healthcare institutions of the FMBA, Russia. Errors of incorrect coding of diagnoses for occupational diseases with violation of the classification principles in the used directive materials played a negative role. The development of plans for medical and social rehabilitation measures to reduce the incidence of occupational diseases among the people working in harmful conditions. It requires correct knowledge of morbidity indicators of this nosology. In order to improve the accounting of occupational diseases in Russia, it is necessary to switch to the system of coding diagnoses of this nosology, adopted in medical statistics, and make appropriate changes to the directive documents.

scholarly journals A Novel Unit-Based Personalized Fingerprint Feature Selection Strategy for Dynamic Functional Connectivity Networks

2021 ◽  
Vol 15 ◽  
Author(s):  
Feng Zhao ◽  
Zhiyuan Chen ◽  
Islem Rekik ◽  
Peiqiang Liu ◽  
Ning Mao ◽  
...  
Keyword(s):  
Feature Selection ◽  
Functional Connectivity ◽  
Evaluation Method ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Superior Performance ◽  
Selection Strategy ◽  
Dynamic Functional Connectivity ◽  
Discriminative Feature ◽  
Fingerprint Feature

The sliding-window-based dynamic functional connectivity networks (SW-D-FCN) derive from resting-state functional Magnetic Resonance Imaging has become an increasingly useful tool in the diagnosis of various neurodegenerative diseases. However, it is still challenging to learn how to extract and select the most discriminative features from SW-D-FCN. Conventionally, existing methods opt to select a single discriminative feature set or concatenate a few more from the SW-D-FCN. However, such reductionist strategies may fail to fully capture the personalized discriminative characteristics contained in each functional connectivity (FC) sequence of the SW-D-FCN. To address this issue, we propose a unit-based personalized fingerprint feature selection (UPFFS) strategy to better capture the most discriminative feature associated with a target disease for each unit. Specifically, we regard the FC sequence between any pair of brain regions of interest (ROIs) is regarded as a unit. For each unit, the most discriminative feature is identified by a specific feature evaluation method and all the most discriminative features are then concatenated together as a feature set for the subsequent classification task. In such a way, the personalized fingerprint feature derived from each FC sequence can be fully mined and utilized in classification decision. To illustrate the effectiveness of the proposed strategy, we conduct experiments to distinguish subjects diagnosed with autism spectrum disorder from normal controls. Experimental results show that the proposed strategy can select relevant discriminative features and achieve superior performance to benchmark methods.

Implicit Encryption of Timing Packet with Statistical Side Channel Coding

2014 ◽  
Vol 513-517 ◽  
pp. 1221-1226
Author(s):  
Bo Tao Zhu ◽  
Xiao Xiao Liu ◽  
Jun Steed Huang Huang ◽  
Zu Jue Chen
Keyword(s):  
Wireless Communications ◽  
Channel Coding ◽  
Ad Hoc ◽  
Side Channel ◽  
Security Issue ◽  
The Future ◽  
Future Extension ◽  
Channel Information ◽  
Precision Time ◽  
Statistical Coding

This paper proposes a statistical coding methodology using covert side channel information to solve timing packet security issue, the main purpose here is to enhance the security of the timing protocol with backward compatible capability. In wireless communications, either ad-hoc military/ industrial network, or LTE/ LTE-A networks, GPS is used to provide time and location; however, the hackers often trying to spoof the signal. The alternative way of providing such signal is using protocols like IEEE1588 Precision Time Protocol (PTP); unfortunately, current timing packet is not encrypted, it can be altered by the hackers. To maintain the simplicity of such protocols, most vendors are reluctant to add encryption on top of it; nevertheless, the end customer wishes to see it. To solve this dilemma, we propose a backward compatible solution here. The basic idea is demonstrated using Matlab FFT calculation tool. The future extension using Fractional FFT is also suggested at.

scholarly journals Generating a Cancellable Fingerprint using Matrices Operations and Its Fingerprint Processing Requirements

2018 ◽  
Vol 14 (6) ◽  
pp. 1 ◽  
Author(s):  
Riki Mukhaiyar
Keyword(s):  
Region Of Interest ◽  
Original Form ◽  
Biometric Data ◽  
Matrix Operations ◽  
The Matrix ◽  
Robust Image ◽  
Reciprocal Matrix ◽  
Processing Steps ◽  
Fingerprint Feature

Cancellable fingerprint uses transformed or intentionally distorted biometric data instead of the original biometric data for identifying person. When a set of biometric data is found to be compromised, they can be discarded, and a new set of biometric data can be regenerated. This initial principal is identical with a non-invertible concept in matrices operations. In matrix domain, a matrix cannot be transformed into its original form if it meets several requirements such as non-square form matrix, consist of one zero row/column, and no row as multiple of another row. These conditions can be acquired by implementing three matrix operations using Kronecker Product (KP) operation, Elementary Row Operation (ERO), and Inverse Matrix (INV) operation. KP is useful to produce a non-square form matrix, to enlarge the size of matrix, to distinguish and disguise the element of matrix by multiplying each of elements of the matrix with a particular matrix. ERO can be defined as multiplication and addition force to matrix rows. INV is utilized to transform one matrix to another one with a different element or form as a reciprocal matrix of the original. These three matrix operations should be implemented together in generating the cancellable feature to robust image. So, if once three conditions are met by imposter, it is impossible to find the original image of the fingerprint. The initial aim of these operations is to camouflage the original look of the fingerprint feature into an abstract-look to deceive an un-authorized personal using the fingerprint irresponsibly. In this research, several fingerprint processing steps such as fingerprint pre-processing, core-point identification, region of interest, minutiae extration, etc; are determined to improve the quality of the cancellable feature. Three different databases i.e. FVC2002, FVC2004, and BRC are utilized in this work.

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