—In this modern era, biometrics incorporate various mechanisms to recognize inimitable features of human beings by utilizing their biological and evident features. This paper proposes a novel technique for constructing a resilient and secure biometric recognition system. In this paper, an ECG-hash code of two distinct individuals has been formed by taking dot product of electrocardiogram (ECG) feature matrices of two persons located at two different sites at respective databases. The validity of the system increases as samples from both persons, between whom the transmission takes place, are essential. Besides, electrocardiogram is such a unique feature of an individual that could not be compromised at any circumstance as contradictory to other features like fingerprints, face recognition etc. Moreover, the ECG-hash code is encrypted using rule vector of cellular automata that gives better security in terms of randomness of generated cipher text.

/pdf/cellular-automata-based-encrypted-ecg-hash-code-generation-16o6ju2mpr.pdf

Cellular Automata based Encrypted ECG-hash Code Generation: An Application in Inter-human Biometric Authentication System

The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system’s architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results.

Inertial Motion Capture Costume Design Study.

List of Figures vii Preface ix Acknowledgments xv About the Authors xix Introduction 1 PART 1 Documenting Learning with ePortfolios 5 1 Why Document Learning? 7 2 A Stakeholder s Approach to Documenting Learning 25 3 Designing Effective ePortfolio Learning Activities 41 PART 2 Creating and Implementing ePortfolios 67 4 Engaging Today s Learners: Students and ePortfolios 69 5 ePortfolios Outside the Classroom: Involving Campus Partners 85 6 Using ePortfolios to Support Assessment 95 PART 3 Practical Considerations for Implementing ePortfolios 105 7 Faculty Development and ePortfolios 107 8 Selecting ePortfolio Technologies to Support Learning 121 9 Evaluating the Impact of ePortfolios 139 Conclusion 145 References 149 Index 155

Documenting learning with eportfolios: a guide for college instructors / Tracy Penny Light, Helen L. Chen, John C. Ittelson

In this paper, the modern symmetric stream ciphers Enocoro, Decim, Grain, HC, MUGI, Mickey, Rabbit, RC-4, Salsa20, SNOW20, Sosemanuk, Strumok, Trivium, and a block cipher AES, which can be used in streaming encryption modes, are being researched The main results of testing the rate of formation of a key stream with cross-platform implementation of encryption algorithms in the Java programming language on operating systems Windows 10 (x64), Debian (Kali) and Android (x64) are presented Testing was conducted on a portable personal computer (Intel Pentium 3550m 23ГГц, RAM 4Гб (1600MГц)) and on a mobile device, in particular, on a smartphone (Samsung galaxy S7)

Research of cross-platform stream symmetric ciphers implementation

The purpose of this paper is to document the diffusion of license plate readers (LPRs) in the USA, examining the variety, evolution and tracking of their uses through a national survey.,This study employs a national, stratified, representative survey of US law enforcement agencies with 100 or more officers.,LPR technology is currently used by at least two-thirds of larger police agencies, which represents a more than threefold increase in LPR acquisition in the last 10 years. The number of LPRs per agency, while small (about eight on average), has also more than doubled. Federal and state funding, advocacy by law enforcement leaders, and the intuitive appeal of LPRs have likely contributed to this rapid adoption. While LPRs are still primarily used to detect and recover stolen automobiles in patrol, their use has expanded into other types of investigative and security functions. Despite the increased use and numbers of LPRs in policing, their use is highly discretionary and infrequently tracked.,LPRs continue to be widely used in law enforcement, despite a lack of strong research evidence for their crime prevention benefits. Further studies are needed on the most effective ways for agencies to utilize small numbers of LPRs and the potential return on investment for acquiring larger numbers of the devices.,This study tracks the history of LPR diffusion and use and goes beyond prior law enforcement surveys by examining specific uses of LPRs and the extent to which agencies track their uses and outcomes.

The rapid diffusion of license plate readers in US law enforcement agencies

Automatic license plate recognition (ALPR) methods and software are used in toll collection, traffic monitoring and other areas of road transport industry. Majority of ALPR methods and almost all in industrial use, try to recognize a license plate identifier from a single image. However, in a sequence of images, recognition of a license plate in any frame can be improved by considering the information from preceding and succeeding frames, using video object tracking. A new approach is presented, for combining a video tracking and a single frame ALPR method to improve the recognition rate. Unlike earlier techniques which are tied to specific object tracking and identifier recognition methods, the new method can be used with almost any tracking and single frame ALPR methods. Its key part is a method for clustering and alignment of candidate license plate identifiers in a video track. The results from five video sequences taken from a surveillance camera under various weather and light conditions demonstrate the recognition rate improvements.

ALPR - Extension to Traditional Plate Recognition Methods

Methods of tracking of multiple objects or people in video sequences have applications in many fields such as surveillance, art, transport or biology. This, over four decades old area is still very active, with multiple new contributions presented every year. Tracking methods must solve intricate problems, for example occlusion of many objects, crowded scenes, illumination of different places and motion of camera. This paper presents a brief survey of recent developments in video tracking based methods, focused mainly on the last three years. The surveyed methods are divided into two groups: tracking by detection, which includes methods that solve the problem of time-linking objects detected in all video frames, and tracking by correlation, containing methods that follow a selected object using cross correlation. The reviewed methods are collected in a table that lists for each method the benchmark datasets used for its evaluation, implementation environment, and whether it can track single or multiple objects.

Recent Developments in Tracking Objects in a Video Sequence

The intelligent video monitoring system SAVA has been implemented as a prototype at the 9th Technology Readiness Level. The source of data are video cameras located in the public space that provide HD video streaming. The aim of the study is to present an overview of the SAVA system enabling identification and classification in the real time of such behaviors as: walking, running, sitting down, jumping, lying, getting up, bending, squatting, waving, and kicking. It also can identify interactions between persons, such as: greeting, passing, hugging, pushing, and fighting. The system has module-based architecture and is combined of the following modules: acquisition, compression, path detection, path analysis, motion description, action recognition. The effect of the modules operation is a recognized behavior or interaction. The system achieves a classification correctness level of 80% when there are more than ten classes.

https://link.springer.com/content/pdf/10.1007%2F978-3-319-75420-8_47.pdf

Intelligent Video Monitoring System with the Functionality of Online Recognition of People’s Behavior and Interactions Between People

In the paper we present two approaches based on application of neural networks and Gene Expression Programming (GEP) to detect SQL attacks. SQL attacks are those attacks that take the advantage of using SQL statements to be performed. The problem of detection of this class of attacks is transformed to time series prediction and classification problems. SQL queries are used as a source of events in a protected environment. To differentiate between normal SQL queries and those sent by an attacker, we divide SQL statements into tokens and pass them to our detection system based on recurrent neural network (RNN), which predicts the next token, taking into account previously seen tokens. In the learning phase tokens are passed to a recurrent neural network (RNN) trained by backpropagation through time (BPTT) algorithm. Then, two coefficients of the rule are evaluated. The rule is used to interpret RNN output. In the testing phase RNN with the rule is examined against attacks and legal data to find out how evaluated rule affects efficiency of detecting attacks. The efficiency of this method of detecting intruders is compared with the results obtained from GEP.

Soft computing techniques for intrusion detection of SQL-based attacks

The main concern of this paper is cryptosystems with a symmetric key, in particular block ciphers The most important components of block ciphers are substitution boxes (S-boxes) Developing methods of cryptanalysis make ciphers worked on classical S-boxes not safe enough Therefore, we propose a methodology of generation cellular automata (CA)-based S-boxes with enhanced quality We provide an exhaustive experimental analysis of the proposed CA-based S-boxes in terms of non-linearity, autocorrelation, balance and strict avalanche criterion We show that proposed S-boxes have high cryptographic quality The interesting feature of the proposed S-boxes is a dynamic flexible structure, fully functionally realized by CA, while the classical S-boxes are represented by fixed table structures

Aldona Drabik

Papers

ALPR - Extension to Traditional Plate Recognition Methods

Recent Developments in Tracking Objects in a Video Sequence

Intelligent Video Monitoring System with the Functionality of Online Recognition of People’s Behavior and Interactions Between People

Soft computing techniques for intrusion detection of SQL-based attacks

Application of Cellular Automata in Symmetric Key Cryptography