SEGMENTASI KARAKTER TULISAN TANGAN ONLINE MENGGUNAKAN FILTER IIR

Segmentasi karakter merupakan proses yang sangat penting dalam analisa dan pengenalan karakter tulisan tangan. Paper ini adalah mengembangkan suatu metode segmentasi yang dapat menghasilkan segmen karakter tulisan tangan online sesuai dengan segmentasi acuan. Beberapa algoritma segmentasi telah dikembangkan. Sebagian menggunakan pendekatan wavelet dan sebagian lagi menggunakan pendekatan filter. Karakteristik data yang digunakan pada kedua pendekatan tersebut adalah kecepatan linear. Penggunaan karakteristik ini masih menghasilkan derau yang tinggi, sehingga mempersulit proses segmentasi. Hal ini disebabkan karena adanya perbedaan kecepatan menulis dan kecepatan sampling. Sulitnya proses segmentasi terjadi karena adanya lokal maksimum dan minimum yang bukan sebenarnya. Akibatnya, titik potong segmentasi menjadi tidak tepat. Secara keseluruhan proses segmentasi menjadi tidak akurat dan tidak sesuai dengan segmen acuan. Untuk menghilangkan atau memfilter derau tersebut digunakan filter smoothing IIR (infinite impulse response filters). Filter ini memiliki kemampuan yang baik dalam menghilangkan atau memfilter derau. Penghilangan derau pada data karakter tulisan tangan online ini untuk mempermudah proses segmentasi. Selain itu, penggunaan filter IIR ini dapat meningkatkan akurasi posisi pemotongan segmen. Paper ini menggunakan 52 data karakter tulisan tangan online yang terdiri dari dua set data karakter a-z. Hasil eksperimen yang diperoleh menunjukan bahwa filter IIR menghasilkan proses smoothing yang baik. Hal ini dibuktikan dengan sedikitnya lokal maksimum dan minimum yang dihasilkan sehingga memudahkan melakukan pemotongan pada titik segmen dan diperoleh ketepatan jumlah segmen dan posisi pemotongan segmen

The Lognometer: A New Normalized and Computerized Device for Assessing the Neurodevelopment of Fine Motor Control in Children

Motor skills are fundamental for the development of children. Neurodevelopmental tests currently used by professionals for measuring motor control maturity exhibit several limitations. To address some of these, we have designed the Lognometer, a tablet-based device that can run computerized neuromotor tests. To normalize this tool against a representative population, we collected handwritten triangles from 780 children. We used the Sigma-Lognormal model and a prototype-based parameter estimation algorithm to analyze these movements. To ensure clinical acceptance, we developed an explainable solution relying on statistical regression. We evaluated how well the proposed lognormal decomposition captures the motor control maturation between 6 to 13 years of age by plotting the biological age versus the age estimated using movement kinematics. To provide an equivalent to growth curves, we further overlaid percentile lines that can be used by clinicians to evaluate the neuromotor development of children

An Immune Clonal Selection Algorithm for Synthetic Signature Generation

The collection of signature data for system development and evaluation generally requires significant time and effort. To overcome this problem, this paper proposes a detector generation based clonal selection algorithm for synthetic signature set generation. The goal of synthetic signature generation is to improve the performance of signature verification by providing more training samples. Our method uses the clonal selection algorithm to maintain the diversity of the overall set and avoid sparse feature distribution. The algorithm firstly generates detectors with a segmented r-continuous bits matching rule and P-receptor editing strategy to provide a more wider search space. Then the clonal selection algorithm is used to expand and optimize the overall signature set. We demonstrate the effectiveness of our clonal selection algorithm, and the experiments show that adding the synthetic training samples can improve the performance of signature verification

Vulnerabilities and attack protection in security systems based on biometric recognition

Tesis doctoral inédita. Universidad Autónoma de Madrid, Escuela Politécnica Superior, noviembre de 200

Potentiels évoqués associés au temps d'occurrence du modèle delta-lognormal pour un mouvement volontaire induit

RÉSUMÉ Comprendre et reproduire le mouvement humain est l'un des objectifs que s'est fixe la communauté scientifique d'aujourd'hui. Pour y arriver, les chercheurs se basent sur deux philosophies : la première consiste à observer et décrire les phénomènes en place grâce a des outils d'observation très performants; la seconde consiste à développer des modèles mathématiques très utilisés pour la représentation des principes qui régissent l'exécution du mouvement. Plamondon, en 1995, représente le profil de vitesse asymétrique par une équation Delta-Lognormale. Son modèle pose que le mouvement s'effectue en trois étapes : préparation - émission d'une commande - exécution de la commande. II prédit également l'instant auquel le Système Nerveux Central émet la commande - ce temps est appelé temps d'occurrence. L'objectif de ce mémoire est de vérifier expérimentalement cette prédiction. Pour atteindre cet objectif, nous avons utilisé une des techniques d'observation du mouvement humain: l'électroencéphalographie. Nous présentons dans un premier temps le modèle Delta-Lognormal à travers ses hypothèses, ses paramètres et ses prédictions. Nous décrivons ensuite la technique des potentiels évoqués, qui est la dérivée de l'électroencéphalographie que nous avons utilisée dans le cadre de cette recherche. La réalisation d'une expérience nécessite une planification et l'exécution d'un protocole expérimental. Dans notre cas, nous avons choisi de demander à des sujets d'effectuer des traits rapides de crayon sur une tablette à numériser pendant qu'on enregistre leur électroencéphalogramme au moyen d'électrodes placées sur leur crane. Les sujets doivent réagir à deux types de stimuli: les stimuli visuels et les stimuli auditifs. À partir des données brutes recueillies, nous avons extrait les paramètres du modèle Delta-Lognormal notamment les temps d'occurrence. Nous avons également déduit les potentiels évoqués associés. De l'analyse de ces potentiels, nous avons observé une composante positive des potentiels évoqués issus des essais a stimuli visuels dont le temps de latence est statistiquement identique au temps d'occurrence. II en ressort donc qu'il existe une activité électrique, caractérisée par une composante positive des potentiels évoqués issus des essais a stimuli visuels, qui se produit à l'instant prédit par le modèle Delta-Lognormal comme étant le temps d'occurrence. En d'autres termes, nous avons montré que la prédiction du temps d'occurrence est justifiée. Nous avons également essayé d'associer les diverses électrodes a des zones correspondant aux trois étapes de la production du mouvement. Par ailleurs, nous avons utilisé le temps d'occurrence pour analyser les signaux électroencéphalographiques. Cette opération nous a permis d'observer une composante négative pour les potentiels évoqués issus des stimuli auditifs, alors qu'on n'observait rien dans la première partie de l'analyse. La confirmation de la prédiction du temps d'occurrence permet d'utiliser ce paramètre pour améliorer le traitement des signaux comme ce fut le cas dans cette recherche. On peut également approfondir l'étude de la composante associée au temps d'occurrence notamment examiner l'impact de diverses maladies sur la valeur du temps de latence et de l'amplitude. De telles études peuvent aboutir à l'établissement d'outils de diagnostic utilisant le modèle Delta-Lognormal comme principe de base.--------------------ABSTRACT Understanding and reproducing human movements is one of the assert goals of the scientific community. To reach this goal, researchers use two main philosophies: the first one is to observe and describe the mechanisms applied during the production of the movement using sophisticated devices or techniques; the second one is to develop mathematical models to replicate the movement. In 1995, Plamondon represents the asymmetric velocity profile by a DeltaLognormal equation. In his model, he supposes that the movement is executed in three phases: preparation - emission of a command - execution of the command. He also predicts the instant when the Central Nervous System emits the command. This instant is called the occurring time. The goal of this dissertation is to verify this prediction. To reach this goal, we used the electroencephalography technique. In first hand, we present the Delta-Lognormal model through his hypothesis, his parameters and his predictions. Then, we describe the evoked potentials techniques, which is the derivate of the electroencephalography used during this research. The fulfilment of an experiment needs a good planning and the execution of an experimental protocol. In our case, we chose to ask our subjects to draw quick line with a pencil on a numeric plate while we register their electroencephalograms using electrodes placed on their head. The subjects have to react after identifying two types of stimuli: visual ones and audible ones. With the data acquired, we extracted the parameters of the Delta-Lognormal model, particularly the occurring time. Additionally, we deduced the associated evoked potentials. From the analysis of these data, we found a positive component in the evoked potentials deduced from the trials using the visual stimuli. The latency of this component has been proved to be statistically identical to the occurring time. We, then, concluded that there's an electrical activity in the brain occurring at the exact time predicted by the Delta-Lognormal model as the moment when the Central Nervous System emits his command. In other words, we showed that the prediction of the occurring time of the Delta-Lognormal model is true. We, then, tried to associate each electrode with a zone corresponding to one of the three steps of the production of the movement. Furthermore, we used the occurring time to analyse the electroencephalographic signals. This operation allowed us to observe a negative component in the evoked potentials deduced from the trials using the audible stimuli. We have to mention that this component was not visible in the first part of the analysis. The confirmation of the prediction of the occurring time allows us to use this parameter to ameliorate the signal treatment as it was the case in this research. We can, as well, deepen the study of the positive component associated to the occurring time, by looking at the impact of diverse kinds of illness on the value of his latency or his amplitude. Such works can lead to the establishment of diagnosis devices using the Delta-Lognormal model as reference.-----------CONTENU Théorie cinématique : modèle Delta-Lognormal -- Contrôle moteur : des muscles à la cinématique -- Hypothèses du modèle Delta-Lognormal -- Modèle Delta-Lognormal -- Interprétation des paramètres -- Extraction des paramètres -- Électroencéphalographie : potentiels évoqués -- Expérience de vérification du temps d'occurrence : matériel et méthodes -- Sujets -- Enregistrement des profils de vitesse -- Enregistrement des électroencéphalogrammes -- Synchronisation des signaux EEG et du profil de vitesse -- Design de l'expérience Procédure de collecte de données -- Tâches -- Expérience de vérification du temps d'occurrence : analyses -- Extraction des paramètres du modèle Delta-Lognormal -- Potentiels évoqués -- Analyse et discussion des résultats -- Analyse des potentiels évoqués obtenus pour les essais avec stimuli auditifs -- Prédiction du temps d'occurrence -- Hypothèses sur la localisation des différentes étapes de l'exécution d'un mouvement volontaire induit -- Utilisation du temps d'occurrence dans l'analyse des potentiels évoqués -- Nouvelle composante de potentiels évoqués : composante du temps d'occurrence -- Hypothèses sur l'extraction des paramètres

Wearable computing and contextual awareness

Thesis (Ph.D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 1999.Includes bibliographical references (leaves 231-248).Computer hardware continues to shrink in size and increase in capability. This trend has allowed the prevailing concept of a computer to evolve from the mainframe to the minicomputer to the desktop. Just as the physical hardware changes, so does the use of the technology, tending towards more interactive and personal systems. Currently, another physical change is underway, placing computational power on the user's body. These wearable machines encourage new applications that were formerly infeasible and, correspondingly, will result in new usage patterns. This thesis suggests that the fundamental improvement offered by wearable computing is an increased sense of user context. I hypothesize that on-body systems can sense the user's context with little or no assistance from environmental infrastructure. These body-centered systems that "see" as the user sees and "hear" as the user hears, provide a unique "first-person" viewpoint of the user's environment. By exploiting models recovered by these systems, interfaces are created which require minimal directed action or attention by the user. In addition, more traditional applications are augmented by the contextual information recovered by these systems. To investigate these issues, I provide perceptually sensible tools for recovering and modeling user context in a mobile, everyday environment. These tools include a downward-facing, camera-based system for establishing the location of the user; a tag-based object recognition system for augmented reality; and several on-body gesture recognition systems to identify various user tasks in constrained environments. To address the practicality of contextually-aware wearable computers, issues of power recovery, heat dissipation, and weight distribution are examined. In addition, I have encouraged a community of wearable computer users at the Media Lab through design, management, and support of hardware and software infrastructure. This unique community provides a heightened awareness of the use and social issues of wearable computing. As much as possible, the lessons from this experience will be conveyed in the thesis.by Thad Eugene Starner.Ph.D

Proceedings of the 7th Sound and Music Computing Conference

Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010

WiFi-Based Human Activity Recognition Using Attention-Based BiLSTM

Recently, significant efforts have been made to explore human activity recognition (HAR) techniques that use information gathered by existing indoor wireless infrastructures through WiFi signals without demanding the monitored subject to carry a dedicated device. The key intuition is that different activities introduce different multi-paths in WiFi signals and generate different patterns in the time series of channel state information (CSI). In this paper, we propose and evaluate a full pipeline for a CSI-based human activity recognition framework for 12 activities in three different spatial environments using two deep learning models: ABiLSTM and CNN-ABiLSTM. Evaluation experiments have demonstrated that the proposed models outperform state-of-the-art models. Also, the experiments show that the proposed models can be applied to other environments with different configurations, albeit with some caveats. The proposed ABiLSTM model achieves an overall accuracy of 94.03%, 91.96%, and 92.59% across the 3 target environments. While the proposed CNN-ABiLSTM model reaches an accuracy of 98.54%, 94.25% and 95.09% across those same environments