The document discusses a project to develop a mobile application for verifying identity documents using a C-One E-ID handheld device. The application extracts data like fingerprints from documents and ID cards, compares the extracted data to the holder's live fingerprints scanned using the device, and verifies their identity. The document outlines the technologies used including fingerprint biometrics, machine readable travel documents, smart cards, and security features to access identity data. It describes the challenges faced in extracting data from documents and the partial success in developing applications to verify e-passports and smart ID cards by reading selected data fields and fingerprints.
An insight into the E-Passport, aka Biometric Passport, the need for biometrics in travel documents, the ICAO regulations governing the information contained in the electronic chip, RFID technique, Privacy threats in the current design.
This document summarizes object tracking methods, including representations of objects, features for tracking, detection approaches, tracking algorithms, and future directions. It discusses representing objects as points, patches, or contours, using features like color, edges, texture, and optical flow for detection and tracking. Detection can be done through point detection, background subtraction, segmentation, and supervised learning. Tracking algorithms include point tracking, kernel tracking, and silhouette tracking. The document outlines challenges like occlusion, camera motion, and non-rigid objects that remain for future work in object tracking.
The seminar presentation introduced fog computing, which extends cloud computing and services to the edge of the network. Fog computing provides data, compute, and application services to end-users. It was developed to address limitations of cloud computing like high latency and lack of location awareness. Fog computing improves efficiency, latency, security, and supports real-time interactions through geographical distribution of resources at the edge of the network. The presentation covered fog computing characteristics, architecture, applications in areas like smart grids and vehicle networks, and concluded that fog computing will grow in helping network paradigms requiring fast processing.
Federated learning allows training of machine learning models across decentralized data by using a centralized aggregator. The document discusses IBM's approach to federated learning including its Python framework, supported machine learning models and libraries, communication methods, and security features. It provides an overview of the basic federated learning process and architecture with local models trained at each party and aggregated into a shared model.
The need for intelligent, personalized experiences powered by AI is ever-growing. Our devices are producing more and more data that could help improve our AI experiences. How do we learn and efficiently process all this data from edge devices while maintaining privacy? On-device learning rather than cloud training can address these challenges. In this presentation, we’ll discuss:
- Why on-device learning is crucial for providing intelligent, personalized experiences without sacrificing privacy
- Our latest research in on-device learning, including few-shot learning, continuous learning, and federated learning
- How we are solving system and feasibility challenges to move from research to commercialization
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
An insight into the E-Passport, aka Biometric Passport, the need for biometrics in travel documents, the ICAO regulations governing the information contained in the electronic chip, RFID technique, Privacy threats in the current design.
This document summarizes object tracking methods, including representations of objects, features for tracking, detection approaches, tracking algorithms, and future directions. It discusses representing objects as points, patches, or contours, using features like color, edges, texture, and optical flow for detection and tracking. Detection can be done through point detection, background subtraction, segmentation, and supervised learning. Tracking algorithms include point tracking, kernel tracking, and silhouette tracking. The document outlines challenges like occlusion, camera motion, and non-rigid objects that remain for future work in object tracking.
The seminar presentation introduced fog computing, which extends cloud computing and services to the edge of the network. Fog computing provides data, compute, and application services to end-users. It was developed to address limitations of cloud computing like high latency and lack of location awareness. Fog computing improves efficiency, latency, security, and supports real-time interactions through geographical distribution of resources at the edge of the network. The presentation covered fog computing characteristics, architecture, applications in areas like smart grids and vehicle networks, and concluded that fog computing will grow in helping network paradigms requiring fast processing.
Federated learning allows training of machine learning models across decentralized data by using a centralized aggregator. The document discusses IBM's approach to federated learning including its Python framework, supported machine learning models and libraries, communication methods, and security features. It provides an overview of the basic federated learning process and architecture with local models trained at each party and aggregated into a shared model.
The need for intelligent, personalized experiences powered by AI is ever-growing. Our devices are producing more and more data that could help improve our AI experiences. How do we learn and efficiently process all this data from edge devices while maintaining privacy? On-device learning rather than cloud training can address these challenges. In this presentation, we’ll discuss:
- Why on-device learning is crucial for providing intelligent, personalized experiences without sacrificing privacy
- Our latest research in on-device learning, including few-shot learning, continuous learning, and federated learning
- How we are solving system and feasibility challenges to move from research to commercialization
This document summarizes a student project using a neural network for character recognition. The project aims to develop software that can recognize English characters by processing input characters, training a neural network algorithm, and modifying the characters. The methodology involves 4 phases - pre-processing the image, segmenting the image into individual characters, extracting features, and performing classification and recognition using an artificial neural network. The literature review summarizes several papers on using neural networks for handwritten character recognition in various languages.
AI Personal Trainer Using Open CV and Media PipeIRJET Journal
This document summarizes a research paper that proposes an AI personal trainer system using computer vision techniques. The system uses OpenCV and MediaPipe to detect a user's body pose and angles in real-time video to correct their form during exercises. It aims to help users safely and effectively work out at home without a physical trainer. The system would also connect users with similar fitness goals to encourage motivation. The researchers believe this AI trainer could make exercise more accessible and convenient for users.
Multi-task learning (MTL) is a machine learning approach where a single model is trained to perform multiple tasks simultaneously by optimizing multiple loss functions. MTL can improve performance by leveraging commonalities between tasks through implicit data augmentation, attention focusing, and representation bias. MTL works well in applications like computer vision, natural language processing, and speech recognition. Key MTL methods for deep learning include hard and soft parameter sharing.
This document provides an overview of the Web of Things (WoT) and Cloud of Things. It defines WoT and how it differs from IoT, describing WoT's focus on integrating physical objects and systems onto the web. It then discusses standardization efforts for WoT architecture and middleware platforms for different application domains. Finally, it briefly introduces the Cloud of Things and how business intelligence can analyze sensor data from the WoT and Cloud.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
The document discusses the development of 40 Gigabit Ethernet and 100 Gigabit Ethernet standards. It notes that in 2006, the IEEE determined these faster speeds were needed - 40 Gbps for computing and 100 Gbps for network aggregation. The IEEE formed a task force in 2008 to develop these standards. Key aspects included preserving the Ethernet frame format while supporting faster speeds over fiber and copper cable. The physical coding sublayer implements a multilane distribution scheme to help meet engineering challenges, distributing data across multiple "lanes" to support various interface widths.
The Sky X technology uses Sky X gateways and the XTP protocol to improve bandwidth utilization over satellite networks. The Sky X gateway intercepts TCP connections and converts data to XTP for transmission over the satellite link. It then converts data back to TCP for delivery. This architecture enhances performance without any changes to end clients or servers. Using XTP and optimizations, Sky X can increase web and file transfer speeds by 3-100 times over standard satellite internet connections. It provides a fully transparent and reliable way to access more of the available satellite bandwidth.
This document discusses quantization techniques for convolutional neural networks to improve performance. It examines quantizing models trained with floating point precision to fixed point to reduce memory usage and accelerate inference. Tensorflow and Caffe Ristretto quantization approaches are described and tested on MNIST and CIFAR10 datasets. Results show quantization reduces model size with minimal accuracy loss but increases inference time, likely due to limited supported operations.
Transfer Learning and Fine-tuning Deep Neural NetworksPyData
This document outlines Anusua Trivedi's talk on transfer learning and fine-tuning deep neural networks. The talk covers traditional machine learning versus deep learning, using deep convolutional neural networks (DCNNs) for image analysis, transfer learning and fine-tuning DCNNs, recurrent neural networks (RNNs), and case studies applying these techniques to diabetic retinopathy prediction and fashion image caption generation.
Project Tango is a project by Google that aims to give mobile devices a 3D understanding of space using advanced sensors and computer vision. The Tango prototype is an Android device that tracks its own 3D motion and creates a 3D model of the surrounding environment in real-time. It uses motion tracking, depth perception, and area learning technologies. Potential applications include improved indoor navigation, more efficient shopping, emergency response, augmented reality gaming, and 3D modeling of objects.
This document discusses the 3D Internet and its potential uses and advantages. The 3D Internet combines 3D graphics with the Internet to allow for interactive and immersive virtual experiences. It can be used for education through virtual experiments and simulations, e-commerce through virtual product visualization and stores, and social networking. Advantages include content accessibility and user control. The document also outlines some technologies being developed to further the 3D Internet, such as virtual reality glasses and holograms, and speculates on it becoming a mainstream technology in the future to enhance how information is accessed and shared online.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Synopsis: A high-level technical introduction to ConfD. Introduction to ConfD architecture, data model driven paradigm, core engine features and northbound interfaces.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/event-based-neuromorphic-perception-and-computation-the-future-of-sensing-and-ai-a-keynote-presentation-from-ryad-benosman/
Ryad Benosman, Professor at the University of Pittsburgh and Adjunct Professor at the CMU Robotics Institute, presents the “Event-Based Neuromorphic Perception and Computation: The Future of Sensing and AI” tutorial at the May 2022 Embedded Vision Summit.
We say that today’s mainstream computer vision technologies enable machines to “see,” much as humans do. We refer to today’s image sensors as the “eyes” of these machines. And we call our most powerful algorithms deep “neural” networks. In reality, the principles underlying current mainstream computer vision are completely different from those underlying biological vision. Conventional image sensors operate very differently from eyes found in nature, and there’s virtually nothing “neural” about deep neural networks. Can we gain important advantages by implementing computer vision using principles of biological vision? Professor Ryad Benosman thinks so.
Mainstream image sensors and processors acquire and process visual information as a series of snapshots recorded at a fixed frame rate, resulting in limited temporal resolution, low dynamic range and a high degree of redundancy in data and computation. Nature suggests a different approach: Biological vision systems are driven and controlled by events within the scene in view, and not – like conventional techniques – by artificially created timing and control signals that have no relation to the source of the visual information.
The term “neuromorphic” refers to systems that mimic biological processes. In this talk, Professor Benosman — a pioneer of neuromorphic sensing and computing — introduces the fundamentals of bio-inspired, event-based image sensing and processing approaches, and explores their strengths and weaknesses. He shows that bio-inspired vision systems have the potential to outperform conventional, frame-based systems and to enable new capabilities in terms of data compression, dynamic range, temporal resolution and power efficiency in applications such as 3D vision, object tracking, motor control and visual feedback loops.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
Model-based reinforcement learning uses a learned model of the environment to improve sample efficiency compared to model-free methods. Early work combined model predictive control with neural network models, but struggled with long-term predictions. More recent methods like World Model avoid separate controllers and instead train a large world model and small controller together, allowing the agent to "dream" without interacting with the real environment. Iterative methods alternate between improving the world model and controller to address errors from modeling an imperfect environment. Overall, model-based methods show promise for improved learning efficiency but remain challenging to apply to complex, stochastic domains.
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
This document discusses methods for estimating human pose from images using deep learning. It covers several approaches including SMPLIFY and SCAPE. SMPLIFY uses a CNN to detect 2D joints then fits a statistical body model called SMPL to estimate 3D pose. SCAPE is a graphics model of human shape learned from 3D scans, capturing pose and shape variability. The document reviews similarities and differences between methods, including using priors, image features, and optimization. It also discusses improving methods by making them fully automatic using detected joints rather than manual inputs.
This document summarizes recent advances in human pose estimation using deep learning methods. It first discusses traditional approaches like pictorial structures. It then covers several deep learning methods including global/holistic view using joint regression, local appearance using body part detection, and combining global and local information. Other methods discussed are using motion features and pose estimation in videos. Evaluation metrics like PCP and PDJ are also introduced. The document outlines many key papers in this area and provides examples of network architectures and results.
Federated learning is a new machine learning approach that trains models across millions of mobile devices while keeping training data localized on devices. It works by having devices train on local data, send encrypted parameter updates to a server, which then aggregates the updates to improve the shared model. This allows models to be trained without collecting and storing private user data in a centralized location, improving privacy while reducing latency and power consumption compared to traditional centralized training.
IRJET- Securing E-Medical Documents using QR CodeIRJET Journal
This document discusses securing e-medical documents using QR codes. It proposes a system where hospitals can upload medical records to the cloud with a patient ID. This ID is then converted to a QR code using the QRDroid function. Patients can then scan the QR code to download their medical document. The system aims to securely transmit medical data between hospitals and patients in a paperless manner using encrypted QR codes. It describes modules for user registration, database creation, QR code generation, scanning and file uploading/downloading. The goal is to provide a more secure, flexible and easy way for patients to access medical records electronically.
קומדע קבוצה לפתרונות תקשורת ואבטחת מידע מובילה, משנת 1985, עובדים עם חברות הטכנולוגיה הגדולות בעולם כדי להוביל פרויקטים חכמים ומתקדמים שמשנים את העולם! פתרונות ביומטריים חכמים מאפשרים הזדהות חזקה למניעת גניבת זהות וכן התנהלות נוחה, מהירה ופשוטה (לא צריך לסחוב שום דבר חוץ מקרנית העין ;)
Multi-task learning (MTL) is a machine learning approach where a single model is trained to perform multiple tasks simultaneously by optimizing multiple loss functions. MTL can improve performance by leveraging commonalities between tasks through implicit data augmentation, attention focusing, and representation bias. MTL works well in applications like computer vision, natural language processing, and speech recognition. Key MTL methods for deep learning include hard and soft parameter sharing.
This document provides an overview of the Web of Things (WoT) and Cloud of Things. It defines WoT and how it differs from IoT, describing WoT's focus on integrating physical objects and systems onto the web. It then discusses standardization efforts for WoT architecture and middleware platforms for different application domains. Finally, it briefly introduces the Cloud of Things and how business intelligence can analyze sensor data from the WoT and Cloud.
Computer vision has received great attention over the last two decades.
This research field is important not only in security-related software but also in the advanced interface between people and computers, advanced control methods, and many other areas.
The document discusses the development of 40 Gigabit Ethernet and 100 Gigabit Ethernet standards. It notes that in 2006, the IEEE determined these faster speeds were needed - 40 Gbps for computing and 100 Gbps for network aggregation. The IEEE formed a task force in 2008 to develop these standards. Key aspects included preserving the Ethernet frame format while supporting faster speeds over fiber and copper cable. The physical coding sublayer implements a multilane distribution scheme to help meet engineering challenges, distributing data across multiple "lanes" to support various interface widths.
The Sky X technology uses Sky X gateways and the XTP protocol to improve bandwidth utilization over satellite networks. The Sky X gateway intercepts TCP connections and converts data to XTP for transmission over the satellite link. It then converts data back to TCP for delivery. This architecture enhances performance without any changes to end clients or servers. Using XTP and optimizations, Sky X can increase web and file transfer speeds by 3-100 times over standard satellite internet connections. It provides a fully transparent and reliable way to access more of the available satellite bandwidth.
This document discusses quantization techniques for convolutional neural networks to improve performance. It examines quantizing models trained with floating point precision to fixed point to reduce memory usage and accelerate inference. Tensorflow and Caffe Ristretto quantization approaches are described and tested on MNIST and CIFAR10 datasets. Results show quantization reduces model size with minimal accuracy loss but increases inference time, likely due to limited supported operations.
Transfer Learning and Fine-tuning Deep Neural NetworksPyData
This document outlines Anusua Trivedi's talk on transfer learning and fine-tuning deep neural networks. The talk covers traditional machine learning versus deep learning, using deep convolutional neural networks (DCNNs) for image analysis, transfer learning and fine-tuning DCNNs, recurrent neural networks (RNNs), and case studies applying these techniques to diabetic retinopathy prediction and fashion image caption generation.
Project Tango is a project by Google that aims to give mobile devices a 3D understanding of space using advanced sensors and computer vision. The Tango prototype is an Android device that tracks its own 3D motion and creates a 3D model of the surrounding environment in real-time. It uses motion tracking, depth perception, and area learning technologies. Potential applications include improved indoor navigation, more efficient shopping, emergency response, augmented reality gaming, and 3D modeling of objects.
This document discusses the 3D Internet and its potential uses and advantages. The 3D Internet combines 3D graphics with the Internet to allow for interactive and immersive virtual experiences. It can be used for education through virtual experiments and simulations, e-commerce through virtual product visualization and stores, and social networking. Advantages include content accessibility and user control. The document also outlines some technologies being developed to further the 3D Internet, such as virtual reality glasses and holograms, and speculates on it becoming a mainstream technology in the future to enhance how information is accessed and shared online.
The first lecture of expert system with python course.
Enjoy!
you can find the second lecture here:
https://www.slideshare.net/ahmadhussein45/expert-system-with-python-2
Synopsis: A high-level technical introduction to ConfD. Introduction to ConfD architecture, data model driven paradigm, core engine features and northbound interfaces.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2022/08/event-based-neuromorphic-perception-and-computation-the-future-of-sensing-and-ai-a-keynote-presentation-from-ryad-benosman/
Ryad Benosman, Professor at the University of Pittsburgh and Adjunct Professor at the CMU Robotics Institute, presents the “Event-Based Neuromorphic Perception and Computation: The Future of Sensing and AI” tutorial at the May 2022 Embedded Vision Summit.
We say that today’s mainstream computer vision technologies enable machines to “see,” much as humans do. We refer to today’s image sensors as the “eyes” of these machines. And we call our most powerful algorithms deep “neural” networks. In reality, the principles underlying current mainstream computer vision are completely different from those underlying biological vision. Conventional image sensors operate very differently from eyes found in nature, and there’s virtually nothing “neural” about deep neural networks. Can we gain important advantages by implementing computer vision using principles of biological vision? Professor Ryad Benosman thinks so.
Mainstream image sensors and processors acquire and process visual information as a series of snapshots recorded at a fixed frame rate, resulting in limited temporal resolution, low dynamic range and a high degree of redundancy in data and computation. Nature suggests a different approach: Biological vision systems are driven and controlled by events within the scene in view, and not – like conventional techniques – by artificially created timing and control signals that have no relation to the source of the visual information.
The term “neuromorphic” refers to systems that mimic biological processes. In this talk, Professor Benosman — a pioneer of neuromorphic sensing and computing — introduces the fundamentals of bio-inspired, event-based image sensing and processing approaches, and explores their strengths and weaknesses. He shows that bio-inspired vision systems have the potential to outperform conventional, frame-based systems and to enable new capabilities in terms of data compression, dynamic range, temporal resolution and power efficiency in applications such as 3D vision, object tracking, motor control and visual feedback loops.
Deep neural networks have boosted the convergence of multimedia data analytics in a unified framework shared by practitioners in natural language, vision and speech. Image captioning, lip reading or video sonorization are some of the first applications of a new and exciting field of research exploiting the generalization properties of deep neural representation. This tutorial will firstly review the basic neural architectures to encode and decode vision, text and audio, to later review the those models that have successfully translated information across modalities. The contents of this tutorial are available at: https://telecombcn-dl.github.io/2019-mmm-tutorial/.
Model-based reinforcement learning uses a learned model of the environment to improve sample efficiency compared to model-free methods. Early work combined model predictive control with neural network models, but struggled with long-term predictions. More recent methods like World Model avoid separate controllers and instead train a large world model and small controller together, allowing the agent to "dream" without interacting with the real environment. Iterative methods alternate between improving the world model and controller to address errors from modeling an imperfect environment. Overall, model-based methods show promise for improved learning efficiency but remain challenging to apply to complex, stochastic domains.
Big Data Helsinki v 3 | "Federated Learning and Privacy-preserving AI" - Oguz...Dataconomy Media
"Machine learning algorithms require significant amounts of training data which has been centralized on one machine or in a datacenter so far. For numerous applications, such need of collecting data can be extremely privacy-invasive. Recent advancements in AI research approach this issue by a new paradigm of training AI models, i.e., Federated Learning.
In federated learning, edge devices (phones, computers, cars etc.) collaboratively learn a shared AI model while keeping all the training data on device, decoupling the ability to do machine learning from the need to store the data in the cloud. From personal data perspective, this paradigm enables a way of training a model on the device without directly inspecting users’ data on a server. This talk will pinpoint several examples of AI applications benefiting from federated learning and the likely future of privacy-aware systems."
This document discusses methods for estimating human pose from images using deep learning. It covers several approaches including SMPLIFY and SCAPE. SMPLIFY uses a CNN to detect 2D joints then fits a statistical body model called SMPL to estimate 3D pose. SCAPE is a graphics model of human shape learned from 3D scans, capturing pose and shape variability. The document reviews similarities and differences between methods, including using priors, image features, and optimization. It also discusses improving methods by making them fully automatic using detected joints rather than manual inputs.
This document summarizes recent advances in human pose estimation using deep learning methods. It first discusses traditional approaches like pictorial structures. It then covers several deep learning methods including global/holistic view using joint regression, local appearance using body part detection, and combining global and local information. Other methods discussed are using motion features and pose estimation in videos. Evaluation metrics like PCP and PDJ are also introduced. The document outlines many key papers in this area and provides examples of network architectures and results.
Federated learning is a new machine learning approach that trains models across millions of mobile devices while keeping training data localized on devices. It works by having devices train on local data, send encrypted parameter updates to a server, which then aggregates the updates to improve the shared model. This allows models to be trained without collecting and storing private user data in a centralized location, improving privacy while reducing latency and power consumption compared to traditional centralized training.
IRJET- Securing E-Medical Documents using QR CodeIRJET Journal
This document discusses securing e-medical documents using QR codes. It proposes a system where hospitals can upload medical records to the cloud with a patient ID. This ID is then converted to a QR code using the QRDroid function. Patients can then scan the QR code to download their medical document. The system aims to securely transmit medical data between hospitals and patients in a paperless manner using encrypted QR codes. It describes modules for user registration, database creation, QR code generation, scanning and file uploading/downloading. The goal is to provide a more secure, flexible and easy way for patients to access medical records electronically.
קומדע קבוצה לפתרונות תקשורת ואבטחת מידע מובילה, משנת 1985, עובדים עם חברות הטכנולוגיה הגדולות בעולם כדי להוביל פרויקטים חכמים ומתקדמים שמשנים את העולם! פתרונות ביומטריים חכמים מאפשרים הזדהות חזקה למניעת גניבת זהות וכן התנהלות נוחה, מהירה ופשוטה (לא צריך לסחוב שום דבר חוץ מקרנית העין ;)
Improvement of a PIN-Entry Method Resilient to ShoulderSurfing and Recording ...IJRTEMJOURNAL
The scope of this work extends to system components (for example service providers, networks,
servers, hosts, applications, processes and personnel) which are used to exchange PIN-related data. The PIN
Guidelines in this document encompass PIN security within any one system or sub-system and between systems.
This process designs 10 digit keypad with random RGB color SCHEME using a Fast Finite-State Algorithm for
Generating RGB Palettes of Color. In this work, we propose a color finite-state LBG (CFSLBG) algorithm that
reduces the computation time by exploiting the correlations of palette entries between the current and previous
iterations.
3.2.qr code based information access system in shopping mall (1)Tejas Lalwani
This document proposes a QR code-based billing system for shops using Android smartphones. It involves using multiplexing and demultiplexing to encode and decode product information from a single QR code scanned by a smartphone. This would allow customers to scan QR codes of products to view authenticity and select items, sending the list to a server for the cashier. The proposed system aims to provide a simple, accurate way to capture product data and address limitations of traditional barcodes like damage or blockage issues.
Security for automation in Internet of Things by using one time passwordSHASHANK WANKHADE
The document discusses improving security for automation in the Internet of Things using one-time passwords. It proposes generating one-time passwords using AES algorithms to provide highly authorized authentication and access to equipment. The proposal includes using elliptic curve cryptography to generate one-time passwords and extending the AES symmetric encryption scheme with Lamport's one-time password algorithm. It analyzes the performance and security of the proposed scheme, finding it can be implemented in IoT networks to provide two-factor authentication between devices, applications, and their communications securely and efficiently with a smaller key size and lower computational requirements compared to other existing one-time password schemes.
Bank Locker System Using Fingerprint Authentication & Image ProcessingIRJET Journal
This document proposes a bank locker system using fingerprint authentication and image processing. It begins with an abstract that describes increasing bank and locker theft as motivation for improving security. The current system of using two keys (one for the user and one for the bank) is described as having drawbacks like lost keys enabling unauthorized access.
The proposed system introduces a locker security system based on face recognition and fingerprint technology for banks, security offices, and homes. It would only allow authorized persons to access valuables from the locker. Face recognition would be done using active appearance model algorithm with CNN prediction on a Raspberry Pi processor. Fingerprint authentication would also be used to securely access the locker. When an authorized person tries to access the locker, the
Fingerprint Authentication for ATM was about the biometric authentication security system for ATM which enabled the fingerprint authentication for traditional cash machines.
# Synopsis
https://www.slideshare.net/ParasGarg14/project-synopsis-68167417
# Report
https://github.com/ParasGarg/Fingerprint-Authentication-for-ATM/blob/master/Reports/Project%20Report.pdf
# Code
https://github.com/ParasGarg/Fingerprint-Authentication-for-ATM
Nowaday, embedded systems are widely used and connected to networks, especially the Internet. This become the Internet of Things (IoT) era. When a device is on the Internet, it may be attacked or intentionally used by an unauthorized persons. How can we make IoT devices secure under the limited resources?
This presentation will explain the lesson learned from banking and card payment industry how the embedded systems process financial transaction reliably and securely.
This document summarizes a student tracking and management system using RFID technology. The system uses RFID readers and tags to identify students. Readers are connected to a monitoring computer via GSM modules to transmit identification data via SMS, GPRS, or email. The monitoring computer stores tracking data and can notify parents. Algorithms are provided for reading tags, checking a database, and transmitting data. The system provides a way to identify and track students both inside and outside of a facility.
Design and Analysis of Ignition based on RFID by Arduino Nano CompilerIRJET Journal
This document describes a design for an ignition system for motorcycles that is activated using RFID technology. The system uses an Arduino Nano microcontroller connected to an RFID reader to read RFID tags. When a valid, authorized tag is scanned by the reader, the microcontroller activates a relay which starts the ignition. This provides a keyless security system to prevent theft. The system aims to provide a higher level of security than password-based systems by using unique RFID tags. It also allows new tags to be authorized and deactivated remotely through software. The document outlines the components, methodology, and technical specifications of the prototype ignition system based on RFID authentication.
Security and Authentication of Internet of Things (IoT) DevicesSanjayKumarYadav58
The proposed scheme deals with an authentication and security model for IoT applications. It is based on protecting the network from the intruders, decrease the authentication complexity and increase the communication efficiency of network devices. A signature based authentication scheme proposed for mutual authentication among users and devices in the network. The output of proposed scheme gives the better output compare to existing solutions in terms of End-To-End (E2E), Throughput, and Packet Delivery ratio. The proposed scheme implemented on Network Simulator (NS2).
Blue Eyes technology uses cameras and microphones to identify a user's actions and emotions in order to build machines that can understand human feelings and behaviors. It was developed by IBM to create computers with human-like perceptual and sensory abilities. The technology utilizes several components and techniques including emotion mouse, expression glasses, speech recognition, and eye tracking to analyze physiological data and determine a user's emotional state. It aims to develop more natural human-computer interaction and reduce human limitations. Potential applications of Blue Eyes technology include customer analysis in retail, vehicle safety systems, and medical operating systems.
IRJET-An Interline Dynamic Voltage Restorer (IDVR)IRJET Journal
This document summarizes a research paper on developing a biometric e-license system using fingerprints for driver identification and vehicle verification. The system aims to digitize driver's licenses and vehicle documents so that individuals do not need to carry physical documents. It involves developing Android and web applications to extract fingerprint minutiae and match them against a database to retrieve a person's driving records and vehicle details. The system architecture, hardware requirements, algorithm used and benefits of increasing efficiency and reducing documentation are discussed in less than 3 sentences.
Android Malware Detection Literature ReviewAhmed Sabbah
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This document describes a RFID-based automatic door locking system for home security. The system uses an Arduino nano microcontroller, RFID reader and tags, wireless transmitter and receiver modules, NodeMCU for WiFi connectivity, a 3x4 keypad, and servo motor. The system provides three ways to unlock the door - using a password via keypad, detecting an authorized RFID tag, or using a mobile application. It aims to remotely control the door unlock via a web connection and message passing between the owner and door lock for increased security and convenience.
A card reader is a device that reads data from cards with embedded storage media such as magnetic strips, computer chips, or barcodes. Modern card readers can read various types of cards including memory cards, smart cards, and magnetic stripe cards like credit cards. Card readers support the input of data from cards into computer systems and have evolved from early punched card readers to current electronic devices that interface with computer systems through common connections like USB.
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Similar to Document Verification through C-One E-Id - Copy (20)
2. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
2
3. Objective and Project Definition
Current users are able to do many different tasks
on the go using just a small pocket device.
Implement eGovernment mechanisms for
documents.
How?
Using handheld device, we will be able to identify
a person based on their personal ID or E-
Passport.
3
A mobile android application that read an ID
document, extract the fingerprint data and
compare it to the scanned fingerprint using
the readers integrated in C-One E-ID device.
4. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
4
9. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
9
10. Machine Readable Passport (MRP)
Travel document specified by International
Civil Aviation Organization
E-passport and Smart cards developed by
Inkript are types of MRP.
Lebanon was forced to apply ICAO standards
on civil documents to facilitate citizen travelling
10
11. Machine Readable Zone
Mandatory zone located on the MRP’s
data page
Used to store information used for the
BAC mechanism to read
files of the MRP :
◦ Passport Number
◦ Date of Birth
◦ Expiry date
11
12. Logical Data Structure
For both IC integrated in E-passport and
in Residency permits
Structured data as files called Data
Groups.
◦ DG1 : Personal Info
◦ DG2 : Owner Photo
◦ DG3 : Fingerprint (optional)
Elementary files required to validate
integrity ( EFcom ; EFSoD )
12
14. Communication with the IC/Chip
IC or Chip will be connected to a Card
Acceptance Device (CAD)
Chip speaks to the outside world using its
own data packages:APDU
APDU contains Command or a
Response message
Master- Slave model.
The Chip always waits for a command
APDU from the terminal
14
15. E-passport Security Features
while reading the chip
Gain Access to the contactless
Authentication of the data
Authentication of the IC
Additional access control mechanism
15
16. E-passport Security Features (2)
Gain Access to the contactless
To prevent eavesdropping
Chip Access Control mechanism :
◦ Only authorized access.
◦ Using cryptographic protocol
Info are needed from the MRZ to derive the keys.
Two Chip Access Control mechanism:
◦ BAC: Basic Access control
◦ PACE: Password authenticated connection
establishment
16
17. Read the
MRZ_Information
visually from MRZ
SHA-1 Hash of
MRZ_Information
Take the most
significant 16 bytes
of SHA-1 Hash as
Key Seed
Derive KEnc and
KMAc
Setup a secure
connection with
the IC
Granted access to
non sensitive data
(Personal info and
Photo)
17
E-passport Security Features (3)
Gain Access to the contactless (2) – BAC Mechanism
18. Content of Data security object (SOD)
and LDS are authentic.
Execute the hash of the LDS and compare
it to the existing hash in SOD file.
It’s a passive authentication.
18
E-passport Security Features (4)
Authentication of Data
19. Against Chip substitution
Active Authentication mechanism
Based on challenge-response protocol
19
E-passport Security Features (5)
Authentication of the IC/Chip
20. Access fingerprint (and IRIS) file should be
more restricted.
Extended Access Control mechanism is
used.
◦ EAC = Chip Authentication + Terminal Authentication
Terminal authentication: two move
challenge response protocol
20
E-passport Security Features (6)
Additional control access mechanism
21. Used Smart Card Security Features
specifically in this project
Same structure of internal chip.
◦ LDS
◦ Apdu commands
Smart Card: another confidential info instead of the
MRZ_Information to perform BAC mechanism
21
E-passport Smart Card
Standard ICAO ICAO
Extract BAC key- and thus
accessing DG1 and DG2 -
using
MRZ Another Confidential
info
Security Features to access
DG1,DG2
ICAO Standard ICAO Standard
Security Feature to access
DG3 (Fingerprints)
EAC – Mentioned and
explained by ICAO
No security
22. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
22
23. Project Development
Read the MRZ
• OCR Tesseract
• Regula Document Reader
Read E-Passport
or Smart Card
Scan fingerprint
Compare the
two fingerprints
23
24. Project Development (2)
MRZ Project
OCRTesseract Project:
◦ Open source project /Use online trained data.
Regula Document Reader:
◦ Proprietary project for Regula Forensic.
24
Unsuccessful trials which leads to:
Enter manually the MRZ_information
needed for BAC mechanism
25. Project Development (3)
Read the
MRZ
• OCRTesseract
• Regula Document Reader
• Enter It Manually
Read E-
Passport or
Smart Card
• JMRTD Solution
• Coppernic Solution
• The integration of two solutions
25
26. Project Development (4)
Smart Card and E-passport projects
Java Machine ReadableTravel Document
Most popular to read
E-passport.
◦ Android supported :AJMRTD
◦ Uses NFC to read E-passport.
◦ Read DG1 and DG2.
26
Incompatibility between NFC and
RFID technology
27. Project Development (5)
Smart Card and E-passport projects
Coppernic solution:
◦ Able to read DG1 and DG2 file from the E-
passport.
◦ Complexity of integrating the EAC
mechanism to read DG3. (Fingerprint DG)
◦ Unsuccessful trial to read Fingerprint from E-
passport
27
We managed to develop a similar
application that reads only Smart Card
28. Coppernic Sample E-Passport Smart Card
Power Management Power up the RFId Power Up the Smart Card Reader
Keys for BAC mechanism MRZ_Information Another Confidential Info
Reading DG1 (Personal
Information
Extracting these info using
Coppernic methodology
Implementing JMRTD to extract the response
Reading DG2(Display
Picture)
Implementing JMRTD to parse the response
Reading DG3 Not supported yet due
the need of additional
security mechanisms
I managed to read DG3 since it does not
require any additional security and I
extracted the fingerprint template using
JMRTD
28
Project Development (6)
Smart Card and E-passport projects
29. Project Development (7)
Read the
MRZ
• OCR Tesseract
• Regula Document Reader
• Enter It Manually
Read E-
Passport or
Smart Card
• JMRTD Solution
• Coppernic Solution
• The integration of 2
Scan
fingerprint
• Neurotechnology
Compare
the two
fingerprints
• Neurotechnology
29
30. Fingerprint Sample
Neurotech Solution
Features:
◦ Reading fingerprint
◦ Extracting its minutias
◦ One to One verification
One finger to another finger (Ex:Thumb toThumb)
One finger to the 2 hands (Ex: Index to a person’s finger)
◦ One to Many verification
One finger to a database of fingers (Ex:Thumb to many
Thumbs)
30
31. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
31
37. Content
Objective and Project Definition
Device Used: C-One E-ID
Biometrics
◦ Fingerprint
Machine Readable Passport
◦ Machine Readable Zone (MRZ)
◦ Logical Data Structure
◦ Communication with IC/Chip
◦ E-Passport Security features
◦ Smart Card Security features
Project Development
◦ MRZ Project
◦ Smart Card and E-passport projects:
◦ Fingerprint project
Document verification application
Conclusion and Recommendations
37
38. Conclusion
Importance of such a device with these
advanced capabilities lies in the increased
need to control borders and critical areas
in such a country.
Enhance catching terrorists and forgers
over borders controls.
38
39. Recommendations
More research to read E-passports using
C-One E-ID
Reading MRZ visually and using the
camera by a well trained data.
Compare the fingerprint of any person
remotely with the database available on
the server
One level of security can be added to
prevent non authorized agents to use the
device.
39