Fast Fingerprints Classification Only Using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

A Mlp-Based Digit And Uppercase Characters Recognition System

A simple software solution for digit and uppercase handwritten characters recognition is presented. The proposed solution is based on a two-layer Multi Layer Perceptron (MLP) trained by a conjugate gradient descent (CGD) optimization algorithm. This neural network is embedded in a software tool for automatic processing of forms achieved using a scanner. The chosen solutions allow us to obtain good results both in terms of recognition rate and speed. In the paper are fully described design details and experimental results.

Image Processing Chain For Digital Still Cameras Based On The Simpil Architecture

The new generation of wireless devices herald the development of products for integrated portable image and video communication requiring to image and video applications high computing performance. Portable MultiMedia Supercomputers (PMMS), a new class of architectures, allow to combine high computational performance, needed by multimedia applications, and a big energy efficiency, needed by portable devices. Among PMMS, the SIMPil (SIMD processor pixel) architecture satisfies the above requirements, especially with video and digital images processing tasks. In this paper we, exploit the SIMPil computation and throughput efficiency to implement the whole image processing chain of a digital s…

MLP Neural Network Implementation on a SIMD Architecture

An Automatic Road Sign Recognition System {A(RS)2} is aimed at detection and recognition of one or more road signs from realworld color images. The authors have proposed an A(RS)2 able to detect and extract sign regions from real world scenes on the basis of their color and shape features. Classification is then performed on extracted candidate regions using Multi-Layer Perceptron neural networks. Although system performances are good in terms of both sign detection and classification rates, the entire process requires a large computational time, so real-time applications are not allowed. In this paper we present the implementation of the neural layer on the Georgia Institute of Technology …

Morphological Enhancement and Triangular Matching for Fingerprint Recognition

Among the principal problems for realizing a robust Automated Fingerprint Identification System (AFIS) there are the images quality and matching algorithms. In this paper a fingerprint enhancement algorithm based on morphological filter and a triangular matching are introduced. The enhancement phase is based on tree steps: directional decomposition, morphological filter and composition. For the matching phase a global transformation to overcame the effects of rotation, displacement and deformation between acquired and stored fingerprint is performed using the number of similar triangular, having fingerprint minutiae as vertexes. The performance of the proposed approach has been evaluated on…

Fast Fuzzy Fusion in Multimodal Biometric Systems

Artificial intelligence techniques for cancer treatment planning

An artificial intelligence system, NEWCHEM, for the development of new oncology therapies is described. This system takes into account the most recent advances in molecular and cellular biology and in cell-drug interaction, and aims to guide experimentation in the design of new optimal protocols. Further work is being carried out, aimed to embody in the system all the basic knowledge of biology, physiopathology and pharmacology, to reason qualitatively from first principles so as to be able to suggest cancer therapies.

An extended JADE-S based framework for developing secure Multi-Agent Systems

Agent communities are self-organized virtual spaces consisting of a large number of agents and their dynamic environments. Within a community, agents group together offering special e-services for effective, reliable, and mutual benefits. Usually, an agent community is composed of specialized agents performing one or more tasks in a single domain/sub-domain, or in highly intersecting domains. However, secure Multi- Agent Systems require severe mechanisms in order to prevent malicious attacks. Several limits affect exiting secure agents platform, such as the lack of a strong authentication system, the lack of a flexible distributed mechanism for access control and the lack of a system for st…

Hourly Forecasting of SO2 Pollutant Concentration Using an Elman Neural Network

In this paper the first results produced by an Elman neural network for hourly SO2 ground concentration forecasting are presented. Time series has been recorded between 1998 and 2001 and are referred to a monitoring station of SO2 in the industrial site of Priolo, Syracuse, Italy. Data has been kindly provided by CIPA (Consorzio Industriale per la Protezione dell'Ambiente, Siracusa, Italia). Time series parameters are the horizontal and vertical wind velocity, the wind direction, the stability classes of Thomas, the base level of the layer of the atmospheric stability, the gradient of the potential temperature and the difference of the potential temperature of reference.

An Embedded Biometric Sensor for Ubiquitous Authentication

Communication networks and distributed technologies move people towards the era of ubiquitous computing. An ubiquitous environment needs many authentication sensors for users recognition, in order to provide a secure infrastructure for both user access to resources and services and information management. Today the security requirements must ensure secure and trusted user information to protect sensitive data resource access and they could be used for user traceability inside the platform. Conventional authentication systems, based on username and password, are in crisis since they are not able to guarantee a suitable security level for several applications. Biometric authentication systems…

CliffoSor: A Parallel Embedded Architecture for Geometric Algebra and Computer Graphics

Geometric object representation and their transformations are the two key aspects in computer graphics applications. Traditionally, compute-intensive matrix calculations are involved to model and render 3D scenery. Geometric algebra (a.k.a. Clifford algebra) is gaining growing attention for its natural way to model geometric facts coupled with its being a powerful analytical tool for symbolic calculations. In this paper, the architecture of CliffoSor (Clifford Processor) is introduced. ClifforSor is an embedded parallel coprocessing core that offers direct hardware support to Clifford algebra operators. A prototype implementation on an FPGA board is detailed. Initial test results show more …

A Novel Iris Recognition System based on Micro-Features

In this paper a novel approach for iris recognition system based on iris micro-features is proposed. The proposed system follows the minutiae based approach developed for fingerprint recognition systems. The proposed system uses four iris microfeatures, considered as minutiae, for identification. The individualized characteristics are nucleus, collarette, valleys and radius. Iris recognition is divided in three main phases: image preprocessing, micro-features extraction and matching. The algorithm has been tested on CASIA v1.0 iris image database obtaining an high accuracy. The obtained experimental results have been analyzed and compared with the Daugman based approach.

Design Space Exploration of Parallel Embedded Architectures for Native Clifford Algebra Operations

In the past few decades, Geometric or Clifford algebra (CA) has received a growing attention in many research fields, such as robotics, machine vision and computer graphics, as a natural and intuitive way to model geometric objects and their transformations. At the same time, the high dimensionality of Clifford algebra and its computational complexity demand specialized hardware architectures for the direct support of Clifford data types and operators. This paper presents the design space exploration of parallel embedded architectures for native execution of four-dimensional (4D) and five-dimensional (5D) Clifford algebra operations. The design space exploration has been described along wit…

Three Hours Ahead Prevision of SO2 Pollutant Concentration Using an Elman Neural based Forecaster

Abstract Indoor air quality near the industrial site is tightly joined to pollutant concentration level, since outdoor pollution heavily influences air quality and, consequently, inhabitants health. A pollution management system is essential for health protection. Automatic air quality management systems have became an important research issue with strong implications for inhabitants’ health. In this paper an automatic forecaster based on neural networks for SO 2 concentration prevision is proposed. The analyzed area covers different small towns near the industrial site of Priolo, in the south of the world. Among these towns, Melilli was the first town in Italy that was evacuated for high l…

Fingerprint and Smartcards in Embedded Authentication Systems

A Self-Contained Biometric Sensor for Ubiquitous Autentication

Embedded Knowledge-based Speech Detectors for Real-Time Recognition Tasks

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of automatic speech recognition (ASR) systems are comparable to human speech recognition (HSR) only under very strict working conditions, and in general much lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to raise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as de…

BioAnalysis: A Framework for Structural and Functional Robustness Analysis of Metabolic Networks

The main objective of this work is to analyze metabolic networks evolution in terms of their robustness and fault tolerance capabilities. In metabolic networks, errors can be seen as random removal of network nodes, while attacks are high-connectivity-degree node deletion aimed at compromising network activity. This paper proposes a software framework, namely BioAnalysis, used to test the robustness and the fault tolerance capabilities of real metabolic networks, when mutations and node deletions affect the network structure. The performed simulations are related to the central metabolic network of the well-known E. coli single-celled bacterium and involve either hub nodes or non-hub nodes,…

Fingerprint image enhancement using directional morphological filter

Fingerprint images quality enhancement is a topic phase to ensure good performance in an automatic fingerprint identification system (AFIS) based on minutiae matching. In this paper a new fingerprint enhancement algorithm based on morphological filter is introduced. The algorithm is based on three steps: directional decomposition, morphological filter and composition. The performance of the proposed approach has been evaluated on two sets of images: the first one is DB3 database from Fingerprint Verification Competition (FVC) and the second one is self collected using an optical scanner

Applications of a neural network to forecast the hourly pollutant’s concentrations (SO2) in the industrial site of Priolo (Sicily)

Neural Networks and Metabolic Networks: Fault Tolerance and Robustness Features

The main objective of this work is the comparison between metabolic networks and neural networks (ANNs) in terms of their robustness and fault tolerance capabilities. In the context of metabolic networks errors are random removal of network nodes, while attacks are failures in the network caused intentionally. In the contest of neural networks errors are usually defined configurations of input submitted to the network that are affected by noise, while the failures are defined as the removal of some network neurons. This study have proven that ANNs are very robust networks, with respect to the presence of noise in the inputs, and the partial removal of some nodes, until it reached a critical…

A Mobile Agent Based System for Documents Classification and Retrieval

A Programmable Networked Processing Node for 3D Brain Vessels Reconstruction

Real-time 3D imaging represents a developing trend in medical imaging. However, most of the 3D medical imaging algorithms are computationally intensive. In this paper, a programmable networked node for 3D brain vessels reconstruction is proposed. Starting from 2D PC-MRA (Phase-Contrast Magnetic Resonance Angiography) sequences, the node is able to generate the 3D brain vasculature using the MIP (Maximum Intensity Projection) algorithm. The node has been prototyped on the Celoxica RC203E board, equipped with a Virtex II FPGA, to get the advantages of an hardware implementation, reaching a better throughput with respect to analogous software implementations. Its generality and programmable ca…

Fingerprint and Iris based Authentication in Intercooperative Emerging e-Infrastructures

E-infrastructures must support the development of heterogeneous applications for workstation network, for mobile and portable systems and devices. In this context and relating to all collaborative and pervasive computational technology a very important role is played by security and authentication systems, which represent the first step of the whole process. Biometric authentication systems represent a valid alternative to conventional authentication systems providing robust procedures for user authentication. On the other hand, Internet of Things involves a heterogeneous set of interacting devices to enable innovative global and local applications and services for users. In this chapter fi…

Driver Assistance Systems: a Real-Time Road Signs Recognizer

Un Sistema ad Agenti Mobili per la Classificazione e il Recupero di Documenti - A Mobile Agent Based System for Documents Classification and Retrieval

METABOLIC NETWORKS ROBUSTNESS: THEORY, SIMULATIONS AND RESULTS

Metabolic networks are composed of several functional modules, reproducing metabolic pathways and describing the entire cellular metabolism of an organism. In the last decade, an enormous interest has grown for the study of tolerance to errors and attacks in metabolic networks. Studies on their robustness have suggested that metabolic networks are tolerant to errors, but very vulnerable to targeted attacks against highly connected nodes. However, many findings on metabolic networks suggest that the above classification is too simple and imprecise, since hub node attacks can be by-passed if alternative metabolic paths can be exploited. On the contrary, non-hub nodes attacks can affect cell …

Geometric and conceptual knowledge representation within a generative model of visual perception

A representation scheme of knowledge at both the geometric and conceptual levels is offered which extends a generative theory of visual perception. According to this theory, the perception process proceeds through different scene representations at various levels of abstraction. The geometric domain is modeled following the CSG (constructive solid geometry) approach, taking advantage of the geometric modelling scheme proposed by A. Pentland, based on superquadrics as representation primitives. Recursive Boolean combinations and deformations are considered in order to enlarge the scope of the representation scheme and to allow for the construction of real-world scenes. In the conceptual doma…

Efficient MLP Digital Implementation on FPGA

The efficiency and the accuracy of a digital feed-forward neural networks must be optimized to obtain both high classification rate and minimum area on chip. In this paper an efficient MLP digital implementation. The key features of the hardware implementation are the virtual neuron based architecture and the use of the sinusoidal activation function for the hidden layer. The effectiveness of the proposed solutions has been evaluated developing different FPGA based neural prototypes for the High Energy Physics domain and the automatic Road Sign Recognition domain. The use of the sinusoidal activation function decreases hardware resource employment of about 32% when compared with the standar…

Efficient rapid prototyping of image and video processing algorithms

Image and video processing tasks are often confined for real-time execution on large size workstations or expensively custom designed hardware. The current availability of mature reconfigurable hardware, like Field Programmable Gate Arrays (FPGAs), coupled with the usage of hardware programming languages offers a good path for porting such applications on portable devices. This paper explores the rapid prototyping of a real-time road sign recognition system on a FPGA, using an algorithmic-like hardware programming language: the Handel-C language. We investigate the relationship between efficient Handel-C data, structures, constructs and the related high level C data, structures, constructs.…

An Embedded Fingerprint Recognizer

A Dual-Core Coprocessor with Native 4D Clifford Algebra Support

Geometric or Clifford Algebra (CA) is a powerful mathematical tool that is attracting a growing attention in many research fields such as computer graphics, computer vision, robotics and medical imaging for its natural and intuitive way to represent geometric objects and their transformations. This paper introduces the architecture of CliffordCoreDuo, an embedded dual-core coprocessor that offers direct hardware support to four-dimensional (4D) Clifford algebra operations. A prototype implementation on an FPGA board is detailed. Experimental results show a 1.6× average speedup of CliffordCoreDuo in comparison with the baseline mono-core architecture. A potential cycle speedup of about 40× o…

An FPGA Implementation of a Quadruple-Based Multiplier for 4D Clifford Algebra

Geometric or Clifford algebra is an interesting paradigm for geometric modeling in fields as computer graphics, machine vision and robotics. In these areas the research effort is actually aimed at finding an efficient implementation of geometric algebra. The best way to exploit the symbolic computing power of geometric algebra is to support its data types and operators directly in hardware. However the natural representation of the algebra elements as variable-length objects causes some problems in the case of a hardware implementation. This paper proposes a 4D Clifford algebra in which the variable-length elements are mapped into fixed-length elements (quadruples). This choice leads to a s…

Experimental studies on continuous speech recognition using neural architectures with “adaptive” hidden activation functions

The choice of hidden non-linearity in a feed-forward multi-layer perceptron (MLP) architecture is crucial to obtain good generalization capability and better performance. Nonetheless, little attention has been paid to this aspect in the ASR field. In this work, we present some initial, yet promising, studies toward improving ASR performance by adopting hidden activation functions that can be automatically learned from the data and change shape during training. This adaptive capability is achieved through the use of orthonormal Hermite polynomials. The “adaptive” MLP is used in two neural architectures that generate phone posterior estimates, namely, a standalone configuration and a hierarch…

Fingerprint Registration Using Specialized Genetic Algorithms

One of the most common problem to realize a robust matching algorithm in an Automated Fingerprint Identification System (AFIS) is the images registration. In this paper a fingerprints registration method based on a specialized genetic algorithm (GA) is proposed. A global transformation between two fingerprint images is performed using genetic data evolutions based on specialized mutation rate and solution refining. An AFIS including the above method has been developed and tested on two different fingerprint databases: NIST 4 ink-on-paper and self optical scanned. The obtained experimental results show that the proposed approach is comparable with literature systems working on medium quality…

Fast Fingerprints Classification only using the Directional Image

The classification phase is an important step of an automatic fingerprint identification system, where the goal is to restrict only to a subset of the whole database the search time. The proposed system classifies fingerprint images in four classes using only directional image information. This approach, unlike the literature approaches, uses the acquired fingerprint image without enhancement phases application. The system extracts only directional image and uses three concurrent decisional modules to classify the fingerprint. The proposed system has a high classification speed and a very low computational cost. The experimental results show a classification rate of 87.27%.

An Embedded, FPGA-based Computer Graphics Coprocessor with Native Geometric Algebra Support

The representation of geometric objects and their transformation are the two key aspects in computer graphics applications. Traditionally, computer-intensive matrix calculations are involved in modeling and rendering three-dimensional (3D) scenery. Geometric algebra (aka Clifford algebra) is attracting attention as a natural way to model geometric facts and as a powerful analytical tool for symbolic calculations. In this paper, the architecture of Clifford coprocessor (CliffoSor) is introduced. CliffoSor is an embedded parallel coprocessing core that offers direct hardware support to Clifford algebra operators. A prototype implementation on a programmable gate array (FPGA) board is detailed…

A Novel Embedded Fingerprints Authentication System Based on Singularity Points

In this paper a novel embedded fingerprints authentication system based on core and delta singularity points detection is proposed. Typical fingerprint recognition systems use core and delta singularity points for classification tasks. On the other hand, the available optical and photoelectric sensors give high quality fingerprint images with well defined core and delta points, if they are present. In the proposed system, fingerprint matching is based on singularity points position, orientation, and relative distance detection. As result, fingerprint matching involves the comparison between few features leading to a very fast system with recognition rates comparable to the standard minutiae…

Fingerprint Quality Evaluation in a Novel Embedded Authentication System for Mobile Users

The way people access resources, data and services, is radically changing using modern mobile technologies. In this scenario, biometry is a good solution for security issues even if its performance is influenced by the acquired data quality. In this paper, a novel embedded automatic fingerprint authentication system (AFAS) for mobile users is described. The goal of the proposed system is to improve the performance of a standard embedded AFAS in order to enable its employment in mobile devices architectures. The system is focused on the quality evaluation of the raw acquired fingerprint, identifying areas of poor quality. Using this approach, no image enhancement process is needed after the …

Fingerprint Traits and RSA Algorithm Fusion Technique

The present work deals with modern computing systems security issues, focusing on biometric based asymmetric keys generation process. Conventional PKI systems are based on private/public keys generated through RSA or similar algorithms. The present solution embeds biometric information on the private/public keys generation process. In addition the corresponding private key depends on physical or behavioural biometric features and it can be generated when it is needed. Starting from fingerprint acquisition, the biometric identifier is extracted, cyphered, and stored in tamper-resistant smart card to overcome the security problems of centralized databases. Biometric information is then used f…

Self-organizing maps: A new digital architecture

An original hardware architecture implementing the self-organizing feature maps, which is one of the most powerful and efficent neural network algorithm, is presented. The architecture, contrary to the most investigated hardware implementations of neural networks, is a full digital one and it may be easily built by using the standard VLSI techniques.

A User-Friendly Interface for Fingerprint Recognition Systems Based on Natural Language Processing

Biometric recognition systems represent a valid solution to the safety problem of internet accessibility, even if they do not always provide an environment easily comprehensible by users and operators with a mid-level of competence. This gap can be partially filled if, instead of using the conventional access routines to the authentication system, the user could simply write to the system through the interface and using high level sentences and requests be able to use its own natural language to reach the intended goal. On the other hand, biometrics features are widely used for recognition and identification all over the world, generating large databases. In this paper a user-friendly inter…

Neural Classification of HEP Experimental Data

High Energy Physics (HEP) experiments require discrimination of a few interesting events among a huge number of background events generated during an experiment. Hierarchical triggering hardware architectures are needed to perform this tasks in real-time. In this paper three neural network models are studied as possible candidate for such systems. A modified Multi-Layer Perception (MLP) architecture and a E alpha Net architecture are compared against a traditional MLP Test error below 25% is archived by all architectures in two different simulation strategies. E alpha Net performance are 1 to 2% better on test error with respect to the other two architectures using the smaller network topol…

An Advanced Technique for User Identification Using Partial Fingerprint

User identification is a very interesting and complex task. Invasive biometrics is based on traits uniqueness and immutability over time. In forensic field, fingerprints have always been considered an essential element for personal recognition. The traditional issue is focused on full fingerprint images matching. In this paper an advanced technique for personal recognition based on partial fingerprint is proposed. This system is based on fingerprint local analysis and micro-features, endpoints and bifurcations, extraction. The proposed approach starts from minutiae extraction from a partial fingerprint image and ends with the final matching score between fingerprint pairs. The computation o…

An Enhanced Autentication System for the JADES-S Platform

An Intelligent Sensor for Fingerprint Recognition

Embedded Coprocessors for Native Execution of Geometric Algebra Operations

Clifford algebra or geometric algebra (GA) is a simple and intuitive way to model geometric objects and their transformations. Operating in high-dimensional vector spaces with significant computational costs, the practical use of GA requires dedicated software and/or hardware architectures to directly support Clifford data types and operators. In this paper, a family of embedded coprocessors for the native execution of GA operations is presented. The paper shows the evolution of the coprocessor family focusing on the latest two architectures that offer direct hardware support to up to five-dimensional Clifford operations. The proposed coprocessors exploit hardware-oriented representations o…

Introducing Pseudo-Singularity Points for Efficient Fingerprints Classification and Recognition

Fingerprint classification and matching are two key issues in automatic fingerprint recognition. Generally, fingerprint recognition is based on a set of relevant local characteristics, such as ridge ending and bifurcation (minutiae). Fingerprint classification is based on fingerprint global features, such as core and delta singularity points. Unfortunately, singularity points are not always present in a fingerprint image: the acquisition process is not ideal, so that the fingerprint is broken, or the fingerprint belongs to the arch class. In the above cases, pseudo-singularity-points will be detected and extracted to make possible fingerprint classification and matching. As result, fingerpr…

Bright Pupil Detection in an Embedded, Real-Time Drowsiness Monitoring System

Driver’s drowsiness is stated as an important cause of road and highway accidents. Therefore, the development of a system for monitoring the driver’s level of fatigue is desirable in order to prevent accidents. The paper presents the design and the implementation of a system able to find and evidence the drowsiness level of a driver in an ordinary motor vehicle, in order to prevent car accidents. The system, made up of a car installed infrared video camera connected to the Celoxica RC203E FPGA based board, is able to perform a real time video stream processing. The system exploits the “bright pupil” phenomenon produced by the retina, that reflects the 90% of the incident light when a radiat…

A Specialized Architecture for Color Image Edge Detection Based on Clifford Algebra

Edge detection of color images is usually performed by applying the traditional techniques for gray-scale images to the three color channels separately. However, human visual perception does not differentiate colors and processes the image as a whole. Recently, new methods have been proposed that treat RGB color triples as vectors and color images as vector fields. In these approaches, edge detection is obtained extending the classical pattern matching and convolution techniques to vector fields. This paper proposes a hardware implementation of an edge detection method for color images that exploits the definition of geometric product of vectors given in the Clifford algebra framework to ex…

An Enhanced Autentication System for JADE-S Platform

An Heuristic Approach for the Training Dataset Selection in Fingerprint Classification Tasks

Fingerprint classification is a key issue in automatic fingerprint identification systems. It aims to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper an heuristic approach using only the directional image information for the training dataset selection in fingerprint classification tasks is described. The method combines a Fuzzy C-Means clustering method and a Naive Bayes Classifier and it is composed of three modules: the first module builds the working datasets, the second module extracts the training images dataset and, finally, the third module classifies fingerprint images in four classes. Unlike literature approaches using …

Clifford Algebra based Edge Detector for Color Images

Edge detection is one of the most used methods for feature extraction in computer vision applications. Feature extraction is traditionally founded on pattern recognition methods exploiting the basic concepts of convolution and Fourier transform. For color image edge detection the traditional methods used for gray-scale images are usually extended and applied to the three color channels separately. This leads to increased computational requirements and long execution times. In this paper we propose a new, enhanced version of an edge detection algorithm that treats color value triples as vectors and exploits the geometric product of vectors defined in the Clifford algebra framework to extend …

Topological Information, Flux Balance Analysis, and Extreme Pathways Extraction for Metabolic Networks Behaviour Investigation

In recent years biological processes modeling and simulation have become two key issues in analyzing complex cellular systems. Information about metabolic networks is often incomplete, since a large portion of available data is ignored by its probabilistic nature. The main objective of this work is to investigate metabolic networks behavior in terms of their fault tolerance capabilities as random removal of network nodes and high-connectivity-degree node deletion aimed at compromising or modifying network activity. This paper proposes a software framework, namely CEllDataLaB, containing three tasks to perform the structural and functional analysis: topological analysis, flux balance analysi…

Using the Hermite Regression Formula to Design a Neural Architecture with Automatic Learning of the “Hidden” Activation Functions

The value of the output function gradient of a neural network, calculated in the training points, plays an essential role for its generalization capability. In this paper a feed forward neural architecture (αNet) that can learn the activation function of its hidden units during the training phase is presented. The automatic learning is obtained through the joint use of the Hermite regression formula and the CGD optimization algorithm with the Powell restart conditions. This technique leads to a smooth output function of αNet in the nearby of the training points, achieving an improvement of the generalization capability and the flexibility of the neural architecture. Experimental results, ob…

An Embedded Real-Time Lane-Keeper for Automatic Vehicle Driving

Automatic vehicle driving involves several issues, such as the capability to follow the road and keep the right lane, to maintain the distance between vehicles, to regulate vehiclepsilas speed, to find the shortest route to a destination. In this paper a real-time automatic lane-keeper is proposed. The main features of the system are the lane markers location process as well as the computation of the vehiclepsilas steering lock. The above techniques require high elaboration speed to execute, check and complete an operation before a prearranged time. Clearly if system processing exceeds the deadline, the whole operation became meaningless or, in the meantime, the vehicle can reach a critical…

A system based on neural architectures for the reconstruction of 3-D shapes from images

The connectionist approach to the recovery of 3-D shape information from 2-D images developed by the authors, is based on a system made up by two cascaded neural networks. The first network is an implementation of the BCS, an architecture which derives from a biological model of the low level visual processes developed by Grossberg and Mingolla: this architecture extracts a sort of brightness gradient map from the image. The second network is a backpropagation architecture that supplies an estimate of the geometric parameters of the objects in the scene under consideration, starting from the outputs of the BCS. A detailed description of the system and the experimental results obtained by si…

ConformalALU: A Conformal Geometric Algebra Coprocessor for Medical Image Processing

Medical imaging involves important computational geometric problems, such as image segmentation and analysis, shape approximation, three-dimensional (3D) modeling, and registration of volumetric data. In the last few years, Conformal Geometric Algebra (CGA), based on five-dimensional (5D) Clifford Algebra, is emerging as a new paradigm that offers simple and universal operators for the representation and solution of complex geometric problems. However, the widespread use of CGA has been so far hindered by its high dimensionality and computational complexity. This paper proposes a simplified formulation of the conformal geometric operations (reflections, rotations, translations, and uniform …

Multi-modal biometric authentication systems

The main goal of a biometric system is to discriminate automatically subjects in a reliable and dependable way, accordingly to a specific target application. The discrimination is based on one or more types of information derived from physical or behavioural traits, such as fingerprint, face, iris, voice, hand, or signature. Applications of biometrics range from homeland security and border control to e-commerce and e-banking, including secure networking and authentication. Traditionally, biometric systems working on a single biometric feature, have many limitations, such as, trouble with data sensors, where captured sensor data are often affected by noise, distinctiveness ability, because …

A Novel Technique for Fingerprint Classification based on Fuzzy C-Means and Naive Bayes Classifier

Fingerprint classification is a key issue in automatic fingerprint identification systems. One of the main goals is to reduce the item search time within the fingerprint database without affecting the accuracy rate. In this paper, a novel technique, based on topological information, for efficient fingerprint classification is described. The proposed system is composed of two independent modules: the former module, based on Fuzzy C-Means, extracts the best set of training images, the latter module, based on Fuzzy C-Means and Naive Bayes classifier, assigns a class to each processed fingerprint using only directional image information. The proposed approach does not require any image enhancem…

A Study of Perceptron Mapping Capability to Design Speech Event Detectors

Event detection is a fundamental yet critical component in automatic speech recognition (ASR) systems that attempt to extract knowledge-based features at the front-end level. In this context, it is common practice to design the detectors inside well-known frameworks based on artificial neural network (ANN) or support vector machine (SVM). In the case of ANN, speech scientists often design their detector architecture relying on conventional feed-forward multi-layer perceptron (MLP) with sigmoidal activation function. The aim of this paper is to introduce other ANN architectures inside the context of detection-based ASR. In particular, a bank of feed-forward MLPs using sinusoidal activation f…

CliifoSor: a Parallel embedded architecture for Geometric Algebra and Computer Graphics

A Neural Solution for a Mobile Robot Navigation into Unknown Indoor Environments Using Visual Landmarks

In this paper we present a neural solution for a mobile robot navigation into unknown indoor environments by using landmarks. Robot navigation task is implemented by two groups of processes based on MLP neural networks classifiers: a Low Level Vision System performs obstacle avoidance and corridor following, while an High Level Vision System extracts landmarks contents and performs goal directed navigation. A path-planner manages the two navigation systems and interacts with the robot hardware. The proposed solution is very strong and flexible and can be used to drive a mobile robot in real indoor environments. In the paper experimental results are also reported.

A Monitoring Framework Exploiting the Synergy between Actual and Virtual Wireless Sensors

This paper describes a framework that allows realistic monitoring of a wireless sensor network in order to assess its behavior before actually deploying all the nodes. Designing a wireless sensor network for a specific application typically involves a preliminary phase of simulations that rely on specialized software, whose behavior does not necessarily reproduce what will be experienced by an actual network. On the other hand, delaying the test phase until deployment may not be advisable due to unreasonable costs. This paper suggests the adoption of a hybrid approach that involves coupling an actual wireless sensor network composed of a minimal set of actual nodes with a simulated one; we …

Reti metaboliche: studio e simulazione delle variazioni dei parametri strutturali al variare della temperatura

In questo lavoro, si sono analizzate le correlazioni tra la temperatura e i parametri strutturali delle reti metaboliche. L’obiettivo che si è posto, è stato quello di determinare con quali leggi matematiche variano i parametri strutturali delle reti metaboliche, rispetto alla temperatura, e alla forte pressione dei vincoli selettivi a cui essi sono sottoposti. Al fine di comprendere meglio l’analisi di questo obiettivo, dal punto di vista analitico, si sono mostrate l’equazioni che regolano i parametri strutturali delle reti e si sono mostrati i grafici che descrivono gli andamenti dei vari parametri strutturali delle reti metaboliche al variare della temperatura. Successivamente, dopo ave…

Motion analysis using the novelty filter

Abstract An original approach to the motion analysis, based on the novelty filter, is proposed. The novelty filter stresses the novelties occurring in a pattern representing an image of the scene under consideration with respect to patterns representing previous images of the same scene, so that visual information about the motion of the objects is obtained. The novelty filter may be implemented by a neural network architecture, taking advantage of the capabilities of massive parallelism, adaptive learning and noise robustness. The novelty filter may learn the entire trajectory of an object, through an incremental learning of a sequence of images capturing the scene, thus emphasizing if the…

A New Directional Morphological Filter in a Automated Fingerprint Identification System

A New Min-Max Optimisation Approach for Fast Learning Convergence of Feed-Forward Neural Networks

One of the most critical aspect for a wide use of neural networks to real world problems is related to the learning process which is known to be computational expensive and time consuming.

QUALITATIVE MODELING OF CELL GROWTH PROCESSES

In this paper we present a qualitative physics model to reason about cell growth processes and cell-drug interactions, to be used in the knowledge base of NEWCHEM, an expert system intended to guide experimentation in the design of new optimal protocols in cancer treatment, After a brief discussion of the contributions that artificial intelligence techniques could make in cancer research and a brief presentation of some currently developed expert systems, some details of the proposed model based on the Forbus and Kuipers approaches to qualitative physics are given and its implementation as a LISP program is briefly discussed.

Design and Implementation of an Efficient Fingerprint Features Extractor

Biometric recognition systems are rapidly evolving technologies and their use in embedded devices for accessing and managing data and resources is a very challenging issue. Usually, they are composed of three main modules: Acquisition, Features Extraction and Matching. In this paper the hardware design and implementation of an efficient fingerprint features extractor for embedded devices is described. The proposed architecture, designed for different acquisition sensors, is composed of four blocks: Image Pre-processor, Macro-Features Extractor, Micro- Features Extractor and Master Controller. The Image Pre- processor block increases the quality level of the input raw image and performs an a…

A Family of Embedded Coprocessors with Native Geometric Algebra Support

Clifford Algebra or Geometric Algebra (GA) is a simple and intuitive way to model geometric objects and their transformations. Operating in high-dimensional vector spaces with significant computational costs, the practical use of GA requires, however, dedicated software and/or hardware architectures to directly support Clifford data types and operators. In this paper, a family of embedded coprocessors for the native execution of GA operations is presented. The paper shows the evolution of the coprocessor family focusing on the latest two architectures that offer direct hardware support to up to five-dimensional Clifford operations. The proposed coprocessors exploit hardware-oriented represe…

An Embedded Fingerprints Classification System based on Weightless Neural Networks

Automatic fingerprint classification provides an important indexing scheme to facilitate efficient matching in large-scale fingerprint databases in Automatic Fingerprint Identification Systems (AFISs). The paper presents a new fast fingerprint classification module implementing on embedded Weightless Neural Network (RAM-based neural network). The proposed WNN architecture uses directional maps to classify fingerprint images in the five NIST classes (Left Loop, Right Loop, Whorl, Arch and Tented Arch) without anyone enhancement phase. Starting from the directional map, the WNN architecture computes the fingerprint classification rate. The proposed architecture is implemented on Celoxica RC20…

A New Embedded Coprocessor for Clifford Algebra based Software Intensive Systems

Computer graphics applications require efficient tools to model geometric objects and their transformations. Clifford algebra (also known as geometric algebra) is receiving a growing attention in many research fields, such as computer graphics, machine vision and robotics, as a new, interesting computational paradigm that offers a natural and intuitive way to perform geometric calculations. At the same time, compute-intensive graphics algorithms require the execution of million Clifford operations. Clifford algebra based software intensive systems need therefore the support of specialized hardware architectures capable of accelerating Clifford operations execution. In this paper the archite…

Urban Daily Peak-Forecasting NOx Using Recurrent Neural Network

A Self-Contained Biometric Sensor for Ubiquitous Authentication

This paper describes a real-life behavior framework in simulation game based on Probabilistic State Machine (PSM) with Gaussian random distribution. According to the dynamic environment information, NPC can generate behavior planning autonomously associated with defined FSM. After planning process, we illuminate Gaussian probabilistic function for real-life action simulation in time and spatial domains. The expected value of distribution is estimated during behavior planning process and variance is determined by NPC personality in order to realize real life behavior simulation. We experiment the framework and Gaussian PSM on a restaurant simulation game. Furthermore we give some suggestions…

An Embedded Real-Time Automatic Lane-Keeping System

Advanced Driver Assistance Systems (ADAS) are safety systems capable of identifying an unavoidable dangerous scenario and reacting coherently. An automatic lane-keeping system is designed to prevent dangerous events when the driver left inadvertently his/her own lane. In this paper a real-time automatic lane-keeping system is proposed. The main features of the system are the lane markers location process as well as the generation of the vehicle's steering angle. The system has been prototyped using the Celoxica RC203 board, equipped with a Xilinx Virtex II FPGA, for real-time processing of real motorway scenes coming from a CCD camera on a moving vehicle. The required processing time is 25,…

A real-time network architecture for biometric data delivery in Ambient Intelligence

Ambient Intelligent applications involve the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs (Ambi- ent Intelligence-AmI). Biometrics plays a key role in surveillance and security applications. Fingerprint, iris and voice/speech traits can be acquired by contact, contact-less, and at-a-distance sensors embedded in the environment. Biometric traits transmission and delivery is very critical and it needs real-time transmission net- work with guaranteed performance and QoS. Wireless networks become suitable for AmI if they are able to satisfy real-time communication and security system requi…

Fingerprint in User Authentication Process and Agent Ownership

Fingerprints and Smartcards in Embedded Authentication Systems

A neural network based automatic road signs recognizer

Automatic road sign recognition systems are aimed at detection and recognition of one or more road signs from real-world color images. In this research, road signs are detected and extracted from real world scenes on the basis of their color and shape features. A dynamic region growing technique is adopted to enhance color segmentation results obtained in the HSV color space. The technique is based on a dynamic threshold that reduces the effect of hue instability in real scenes due to external brightness variation. Classification is then performed on extracted candidate regions using multilayer perceptron neural networks. The obtained results show good detection and recognition rates of the…

A MAS Security Framework Implementing Reputation Based Policies and Owners Access Control

Multi-agent systems expose users to risks related to lack of knowledge above interacting users. Such systems should provide tools to protect their own resources from illegal accesses by unauthorized users. This paper describes a security framework for Multi-agent systems preventing a trusted agent to interact with malicious agents and granting agent and platform resources. This feature is obtained adding an access control mechanism that joins the benefits of Credential Based Access Control, Role Based Access Control and Mandatory Access Control. Authorizations and access control policies are set by XML based policy files. A case study on a distributed document retrieval system is also illus…

Biometric Authentication Technologies

An Integrated Neural and Algorithmic System for Optical Flow Computation

Motion detection plays a central role in several visual environments: knowledge of object velocities and trajectories is fundamental in scene interpretation and segmentation. This task appears a simple problem, but detecting moving objects is very difficult, in fact this is a problem that cannot be considered completely solved today [1] [2] [3].

Using the Hermite Regression Algorithm to Improve the Generalization Capability of a Neural Network

In this paper it is shown that the ability of classification and the ability of approximating a function are correlated to the value (in the training points) of the gradient of the output function learned by the network.

An Embedded Module for Iris Micro-Characteristics Extraction

In this paper a new approach, based on iris micro-characteristics, has been used to make possible an embedded biometric extractor. This recognition approach is based on ophthalmologic studies that have proven the existence of different micro-characteristics as well as fingerprint minutiae. These micro-characteristics are permanent and immutable and they can be used to create strong and robust identification systems.Biometric recognition systems are critical components of our everyday lives. Since such electronic products evolve to software intensive systems, where software, becoming larger, more complex and prevalent, introduces many problems in the development phases. The development of em…

Daily Urban NOx Peak Forecasting Using Recurrent Neural Network

Two-days ahead prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the urban area of Palermo, Italy

Abstract Artificial neural networks are functional alternative techniques in modelling the intricate vehicular exhaust emission dispersion phenomenon. Pollutant predictions are notoriously complex when using either deterministic or stochastic models, which explains why this model was developed using a neural network. Neural networks have the ability to learn about non-linear relationships between the used variables. In this paper a recurrent neural network (Elman model) based forecaster for the prediction of daily maximum concentrations of SO2, O3, PM10, NO2, CO in the city of Palermo is proposed. The effectiveness of the presented forecaster was tested using a time series recorded between …

Embedded access points for trusted data and resources access in HPC systems

Biometric authentication systems represent a valid alternative to the conventional username-password based approach for user authentication. However, authentication systems composed of a biometric reader, a smartcard reader, and a networked workstation which perform user authentication via software algorithms have been found to be vulnerable in two areas: firstly in their communication channels between readers and workstation (communication attacks) and secondly through their processing algorithms and/or matching results overriding (replay attacks, confidentiality and integrity threats related to the stored information of the networked workstation). In this paper, a full hardware access poi…

An Intelligent Sensor for Fingerprint Recognition

In this paper an intelligent sensor for fingerprint recognition is proposed. The sensor has the objective to overcome some limits of the fingerprint recognition software systems, as elaboration time and security issues related to fingerprint transmission between sensor and processing unit. Intelligent sensor has been prototyped using the Hamster Secugen sensor for image acquisition and the Celoxica RC1000 board, employing a Xilinx VirtexE2000 FPGA, for image processing and analysis. Resources used, elaboration time as well the recognition rates in both verification and identification modes are reported in the paper. To the best of our knowledge, this is the first implementation for a full h…

An Embedded Processor for Metabolic Networks Optimization

In recent years biological processes modelling and simulation have become two key issues in analyzing complex cellular systems. The computational requirements suggest to investigate alternative solutions to the common supercomputers and clusters in order to optimize and overcome computational bottleneck. The goal of this work is the design and the realization of an embedded processor for metabolic networks optimization in order to examine their behaviour and robustness under malfunctions of one or more nodes. The embedded processor has been prototyped on the Celoxica RC203E board, equipped with programmable FPGA technologies. A case studied outlining the E. Coli bacteria metabolic network i…

Multimodal fruition of 3D virtual environment for Cultural Heritage sites

4D Clifford algebra based on fixed-size representation

Geometric algebra (also known as Clifford algebra) is a powerful mathematical tool that offers a natural and direct way to model geometric objects and their transformations. It is gaining growing attention in different research fields as physics, robotics, CAD/CAM and computer graphics. In particular, 4D geometric algebra implements homogeneous coordinates, which are used to model 3D scenery in most computer graphics applications. The research work on Clifford algebra is actually aimed at finding efficient implementations of the algebra. This paper wants to give a contribution to this research effort by proposing a direct hardware support for geometric algebra operators. The paper introduce…

A Novel Iris Recognition System based on Microfeature

Fixed-size Quadruples for a New, Hardware-Oriented Representation of the 4D Clifford Algebra

Clifford algebra (geometric algebra) offers a natural and intuitive way to model geometry in fields as robotics, machine vision and computer graphics. This paper proposes a new representation based on fixed-size elements (quadruples) of 4D Clifford algebra and demonstrates that this choice leads to an algorithmic simplification which in turn leads to a simpler and more compact hardware implementation of the algebraic operations. In order to prove the advantages of the new, quadruple-based representation over the classical representation based on homogeneous elements, a coprocessing core supporting the new fixed-size Clifford operands, namely Quad-CliffoSor (Quadruple-based Clifford coproces…

Multi-Platform Agent Systems with Dynamic Reputation Policy Management

Open, distributed multi-platform agent systems require new management approaches for resources and data secure access. In this paper a Jade-S based multi-platform agent system implementing dynamic reputation policy management is proposed. The implemented extension deals with biometrics, X-Security, DES cryptography and agent reputation. With more details, the proposed reputation management system helps to assess the agent's behavior and reliability, in order to select trusted agents. This is made possible by the knowledge that agents are able to acquire, over time, and that allows them to choose the best solution using own intelligence in total autonomy.

Application of EαNets to Feature Recognition of Articulation Manner in Knowledge-Based Automatic Speech Recognition

Speech recognition has become common in many application domains. Incorporating acoustic-phonetic knowledge into Automatic Speech Recognition (ASR) systems design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as detectors for manner of articulation attributes starting from representations of speech signal frames. In this paper, a set of six detectors for the above mentioned attributes is designed based on the E-αNet model of neural networks. This model was chosen for its capability to learn hidden acti…

A neural multi-agent based system for smart html pages retrieval

A neural based multi-agent system for smart HTML page retrieval is presented. The system is based on the EalphaNet architecture, a neural network capable of learning the activation function of its hidden units and having good generalization capabilities. System goal is to retrieve documents satisfying a query and dealing with a specific topic. The system has been developed using the basic features supplied by the Jade platform for agent creation, coordination and control. The system is composed of four agents: the trainer agent, the neural classifier mobile agent, the interface agent, and the librarian agent. The sub-symbolic knowledge of the neural classifier mobile agent is automatically …

A Frequency-based Approach for Features Fusion in Fingerprint and Iris Multimodal Biometric Identification Systems

The basic aim of a biometric identification system is to discriminate automatically between subjects in a reliable and dependable way, according to a specific-target application. Multimodal biometric identification systems aim to fuse two or more physical or behavioral traits to provide optimal False Acceptance Rate (FAR) and False Rejection Rate (FRR), thus improving system accuracy and dependability. In this paper, an innovative multimodal biometric identification system based on iris and fingerprint traits is proposed. The paper is a state-of-the-art advancement of multibiometrics, offering an innovative perspective on features fusion. In greater detail, a frequency-based approach result…

Fuzzy Fusion in Multimodal Biometric Systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy…

Fuzzy Fusion in Multimodal Biometric Systems

Multimodal authentication systems represent an emerging trend for information security. These systems could replace conventional mono-modal biometric methods using two or more features for robust biometric authentication tasks. They employ unique combinations of measurable physical characteristics: fingerprint, facial features, iris of the eye, voice print, hand geometry, vein patterns, and so on. Since these traits are hardly imitable by other persons, the aim of these multibiometric systems is to achieve a high reliability to determine or verify person's identity. In this paper a multimodal biometric system using two different fingerprints is proposed. The matching module integrates fuzzy…

Biometric sensors rapid prototyping on field-programmable gate arrays

AbstractBiometric user authentication in large-scale distributed systems involves passive scanners and networked workstations and databases for user data acquisition, processing, and encryption. Unfortunately, traditional biometric authentication systems are prone to several attacks, such as Replay Attacks, Communication Attacks, and Database Attacks. Embedded biometric sensors overcome security limits of conventional software recognition systems, hiding its common attack points. The availability of mature reconfigurable hardware technology, such as field-programmable gate arrays, allows the developers to design and prototype the whole embedded biometric sensors. In this work, two strong an…

Design and implementation of an embedded coprocessor with native support for 5D, quadruple-based Clifford algebra

Geometric or Clifford algebra (CA) is a powerful mathematical tool that offers a natural and intuitive way to model geometric facts in a number of research fields, such as robotics, machine vision, and computer graphics. Operating in higher dimensional spaces, its practical use is hindered, however, by a significant computational cost, only partially addressed by dedicated software libraries and hardware/software codesigns. For low-dimensional algebras, several dedicated hardware accelerators and coprocessing architectures have been already proposed in the literature. This paper introduces the architecture of CliffordALU5, an embedded coprocessing core conceived for native execution of up t…

Application of Enets to Feature Recognition of Articulation Manner in Knowledge-based Automatic Speech Recognition

A Multimodal Technique for an Embedded Fingerprint Recognizer in Mobile Payment Systems

The development and the diffusion of distributed systems, directly connected to recent communication technologies, move people towards the era of mobile and ubiquitous systems. Distributed systems make merchant-customer relationships closer and more flexible, using reliable e-commerce technologies. These systems and environments need many distributed access points, for the creation and management of secure identities and for the secure recognition of users. Traditionally, these access points can be made possible by a software system with a main central server. This work proposes the study and implementation of a multimodal technique, based on biometric information, for identity management a…

A real-time non-intrusive FPGA-based drowsiness detection system

Automotive has gained several benefits from the Ambient Intelligent researches involving the deployment of sensors and hardware devices into an intelligent environment surrounding people, meeting users’ requirements and anticipating their needs. One of the main topics in automotive is to anticipate driver needs and safety, in terms of preventing critical and dangerous events. Considering the high number of caused accidents, one of the most relevant dangerous events affecting driver and passengers safety is driver’s drowsiness and hypovigilance. This paper presents a low-intrusive, real-time driver’s drowsiness detection system for common vehicles. The proposed system exploits the ‘‘bright p…

Efficient FPGA Implementation of a Knowledge-Based Automatic Speech Classifier

Speech recognition has become common in many application domains, from dictation systems for professional practices to vocal user interfaces for people with disabilities or hands-free system control. However, so far the performance of Automatic Speech Recognition (ASR) systems are comparable to Human Speech Recognition (HSR) only under very strict working conditions, and in general far lower. Incorporating acoustic-phonetic knowledge into ASR design has been proven a viable approach to rise ASR accuracy. Manner of articulation attributes such as vowel, stop, fricative, approximant, nasal, and silence are examples of such knowledge. Neural networks have already been used successfully as dete…

Real-time image processing on an embedded simd architecture