Normal ECG Recognition for Express-Diagnostics Based on Scale-Space Representation and Dynamic Matching

A novel approach of normal ECG recognition based on scale-space signal representation is proposed. The approach utilizes curvature scale-space signal representation used to match visual objects shapes previously and dynamic programming algorithm for matching CSS representations of ECG signals. Extraction and matching processes are fast and experimental results show that the approach is quite robust for preliminary normal ECG recognition.

Improved Hybrid Model of HMM/GMM for Speech Recognition

In this paper, we propose a speech recognition engine using hybrid model of Hidden Markov Model (HMM) and Gaussian Mixture Model (GMM). Both the models have been trained independently and the respective likelihood values have been considered jointly and input to a decision logic which provides net likelihood as the output. This hybrid model has been compared with the HMM model. Training and testing has been done by using a database of 20 Hindi words spoken by 80 different speakers. Recognition rates achieved by normal HMM are 83.5% and it gets increased to 85% by using the hybrid approach of HMM and GMM.

"AVO-Polynom" Recognition Algorithm

Estimates Calculating Algorithms have a long story of application to recognition problems. Furthermore they have formed a basis for algebraic recognition theory. Yet use of ECA polynomials was limited to theoretical reasoning because of complexity of their construction and optimization. The new recognition method “AVO- polynom” based upon ECA polynomial of simple structure is described.

Self Evolving Character Recognition Using Genetic Operators

In this paper, a novel approach for character recognition has been presented with the help of genetic operators which have evolved from biological genetics and help us to achieve highly accurate results. A genetic algorithm approach has been described in which the biological haploid chromosomes have been implemented using a single row bit pattern of 315 values which have been operated upon by various genetic operators. A set of characters are taken as an initial population from which various new generations of characters are generated with the help of selection, crossover and mutation. Variations of population of characters are evolved from which the fittest solution is found by subjecting the various populations to a new fitness function developed. The methodology works and reduces the dissimilarity coefficient found by the fitness function between the character to be recognized and members of the populations and on reaching threshold limit of the error found from dissimilarity, it recognizes the character. As the new population is being generated from the older population, traits are passed on from one generation to another. We present a methodology with the help of which we are able to achieve highly efficient character recognition.

A stable graph-based representation for object recognition through high-order matching

Many Object recognition techniques perform some flavour of point pattern matching between a model and a scene. Such points are usually selected through a feature detection algorithm that is robust to a class of image transformations and a suitable descriptor is computed over them in order to get a reliable matching. Moreover, some approaches take an additional step by casting the correspondence problem into a matching between graphs defined over feature points. The motivation is that the relational model would add more discriminative power, however the overall effectiveness strongly depends on the ability to build a graph that is stable with respect to both changes in the object appearance and spatial distribution of interest points. In fact, widely used graph-based representations, have shown to suffer some limitations, especially with respect to changes in the Euclidean organization of the feature points. In this paper we introduce a technique to build relational structures over corner points that does not depend on the spatial distribution of the features. © 2012 ICPR Org Committee.

Human behaviour recognition in data-scarce domains

This paper presents the novel theory for performing multi-agent activity recognition without requiring large training corpora. The reduced need for data means that robust probabilistic recognition can be performed within domains where annotated datasets are traditionally unavailable. Complex human activities are composed from sequences of underlying primitive activities. We do not assume that the exact temporal ordering of primitives is necessary, so can represent complex activity using an unordered bag. Our three-tier architecture comprises low-level video tracking, event analysis and high-level inference. High-level inference is performed using a new, cascading extension of the Rao–Blackwellised Particle Filter. Simulated annealing is used to identify pairs of agents involved in multi-agent activity. We validate our framework using the benchmarked PETS 2006 video surveillance dataset and our own sequences, and achieve a mean recognition F-Score of 0.82. Our approach achieves a mean improvement of 17% over a Hidden Markov Model baseline.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

Call recognition and individual identification of fish vocalizations based on automatic speech recognition: An example with the Lusitanian toadfish

The study of acoustic communication in animals often requires not only the recognition of species specific acoustic signals but also the identification of individual subjects, all in a complex acoustic background. Moreover, when very long recordings are to be analyzed, automatic recognition and identification processes are invaluable tools to extract the relevant biological information. A pattern recognition methodology based on hidden Markov models is presented inspired by successful results obtained in the most widely known and complex acoustical communication signal: human speech. This methodology was applied here for the first time to the detection and recognition of fish acoustic signals, specifically in a stream of round-the-clock recordings of Lusitanian toadfish (Halobatrachus didactylus) in their natural estuarine habitat. The results show that this methodology is able not only to detect the mating sounds (boatwhistles) but also to identify individual male toadfish, reaching an identification rate of ca. 95%. Moreover this method also proved to be a powerful tool to assess signal durations in large data sets. However, the system failed in recognizing other sound types.

A single-sided NMR approach to study structural differences of bovine articular tissue

This thesis work has been developed in collaboration between the Department of Physics and Astronomy of the University of Bologna and the IRCCS Rizzoli Orthopedic Institute during an internship period. The study aims to investigate the sensitivity of single-sided NMR in detecting structural differences of the articular cartilage tissue and their correlation with mechanical behavior. Suitable cartilage indicators for osteoarthritis (OA) severity (e.g., water and proteoglycans content, collagen structure) were explored through four NMR parameters: T2, T1, D, and Slp. Structural variations of the cartilage among its three layers (i.e., superficial, middle, and deep) were investigated performing several NMR pulses sequences on bovine knee joint samples using the NMR-MOUSE device. Previously, cartilage degradation studies were carried out, performing tests in three different experimental setups. The monitoring of the parameters and the best experimental setup were determined. An NMR automatized procedure based on the acquisition of these quantitative parameters was implemented, tested, and used for the investigation of the layers of twenty bovine cartilage samples. Statistical and pattern recognition analyses on these parameters have been performed. The results obtained from the analyses are very promising: the discrimination of the three cartilage layers shows very good results in terms of significance, paving the way for extensive use of NMR single-sided devices for biomedical applications. These results will be also integrated with analyses of tissue mechanical properties for a complete evaluation of cartilage changes throughout OA disease. The use of low-priced and mobile devices towards clinical applications could concern the screening of diseases related to cartilage tissue. This could have a positive impact both economically (including for underdeveloped countries) and socially, providing screening possibilities to a large part of the population.

Test vector generation for the phase II ATLAS event filter trigger upgrade

In the near future, the LHC experiments will continue to be upgraded as the LHC luminosity will increase from the design 1034 to 7.5 × 1034, with the HL-LHC project, to reach 3000 × f b−1 of accumulated statistics. After the end of a period of data collection, CERN will face a long shutdown to improve overall performance by upgrading the experiments and implementing more advanced technologies and infrastructures. In particular, ATLAS will upgrade parts of the detector, the trigger, and the data acquisition system. It will also implement new strategies and algorithms for processing and transferring the data to the final storage. This PhD thesis presents a study of a new pattern recognition algorithm to be used in the trigger system, which is a software designed to provide the information necessary to select physical events from background data. The idea is to use the well-known Hough Transform mathematical formula as an algorithm for detecting particle trajectories. The effectiveness of the algorithm has already been validated in the past, independently of particle physics applications, to detect generic shapes in images. Here, a software emulation tool is proposed for the hardware implementation of the Hough Transform, to reconstruct the tracks in the ATLAS Trigger and Data Acquisition system. Until now, it has never been implemented on electronics in particle physics experiments, and as a hardware implementation it would provide overall latency benefits. A comparison between the simulated data and the physical system was performed on a Xilinx UltraScale+ FPGA device.

Data-driven Signal Processing per Jamming Detection in Sistemi Satellitari

In questo elaborato vengono analizzate differenti tecniche per la detection di jammer attivi e costanti in una comunicazione satellitare in uplink. Osservando un numero limitato di campioni ricevuti si vuole identificare la presenza di un jammer. A tal fine sono stati implementati i seguenti classificatori binari: support vector machine (SVM), multilayer perceptron (MLP), spectrum guarding e autoencoder. Questi algoritmi di apprendimento automatico dipendono dalle features che ricevono in ingresso, per questo motivo è stata posta particolare attenzione alla loro scelta. A tal fine, sono state confrontate le accuratezze ottenute dai detector addestrati utilizzando differenti tipologie di informazione come: i segnali grezzi nel tempo, le statistical features, le trasformate wavelet e lo spettro ciclico. I pattern prodotti dall’estrazione di queste features dai segnali satellitari possono avere dimensioni elevate, quindi, prima della detection, vengono utilizzati i seguenti algoritmi per la riduzione della dimensionalità: principal component analysis (PCA) e linear discriminant analysis (LDA). Lo scopo di tale processo non è quello di eliminare le features meno rilevanti, ma combinarle in modo da preservare al massimo l’informazione, evitando problemi di overfitting e underfitting. Le simulazioni numeriche effettuate hanno evidenziato come lo spettro ciclico sia in grado di fornire le features migliori per la detection producendo però pattern di dimensioni elevate, per questo motivo è stato necessario l’utilizzo di algoritmi di riduzione della dimensionalità. In particolare, l'algoritmo PCA è stato in grado di estrarre delle informazioni migliori rispetto a LDA, le cui accuratezze risentivano troppo del tipo di jammer utilizzato nella fase di addestramento. Infine, l’algoritmo che ha fornito le prestazioni migliori è stato il Multilayer Perceptron che ha richiesto tempi di addestramento contenuti e dei valori di accuratezza elevati.

Motor Unit Spike Trains Recontruction from sEMG Signals for Gesture Classification on a Low-Power Processing Platform

Hand gesture recognition based on surface electromyography (sEMG) signals is a promising approach for the development of intuitive human-machine interfaces (HMIs) in domains such as robotics and prosthetics. The sEMG signal arises from the muscles' electrical activity, and can thus be used to recognize hand gestures. The decoding from sEMG signals to actual control signals is non-trivial; typically, control systems map sEMG patterns into a set of gestures using machine learning, failing to incorporate any physiological insight. This master thesis aims at developing a bio-inspired hand gesture recognition system based on neuromuscular spike extraction rather than on simple pattern recognition. The system relies on a decomposition algorithm based on independent component analysis (ICA) that decomposes the sEMG signal into its constituent motor unit spike trains, which are then forwarded to a machine learning classifier. Since ICA does not guarantee a consistent motor unit ordering across different sessions, 3 approaches are proposed: 2 ordering criteria based on firing rate and negative entropy, and a re-calibration approach that allows the decomposition model to retain information about previous sessions. Using a multilayer perceptron (MLP), the latter approach results in an accuracy up to 99.4% in a 1-subject, 1-degree of freedom scenario. Afterwards, the decomposition and classification pipeline for inference is parallelized and profiled on the PULP platform, achieving a latency < 50 ms and an energy consumption < 1 mJ. Both the classification models tested (a support vector machine and a lightweight MLP) yielded an accuracy > 92% in a 1-subject, 5-classes (4 gestures and rest) scenario. These results prove that the proposed system is suitable for real-time execution on embedded platforms and also capable of matching the accuracy of state-of-the-art approaches, while also giving some physiological insight on the neuromuscular spikes underlying the sEMG.

Sviluppo e sperimentazione di una tecnica a base regionale per la generazione di serie temporali di portata in sezioni fluviali non strumentate

La conoscenza del regime dei deflussi di un corso d’acqua è uno strumento imprescindibile in diverse applicazioni tecniche, dalla progettazione di opere idrauliche alla calibrazione di modelli afflussi-deflussi. Tuttavia, poiché questa informazione non è sempre disponibile, si sono sviluppati in letteratura metodi regionali in grado di trasferire il dato di portata disponibile su sezioni idrologicamente simili alla sezione di interesse. Nel presente lavoro di Tesi, è stato sviluppato un algoritmo di generazione di serie di deflussi sintetici per sezioni non strumentate a partire da serie osservate in sezioni ad esse sincrone nei deflussi. L’algoritmo sfrutta una curva di durata regionale relativa al sito di interesse, stimata attraverso il metodo della portata indice: la portata indice è valutata da un modello multiregressivo mentre la curva regionale adimensionale è ottenuta dalle osservazioni di portata in sezioni strumentate, applicando il criterio della Regione di Influenza. La tecnica è stata verificata su una sezione dell’asta principale del Fiume Marecchia (nell’Italia settentrionale), caratterizzata da limitate osservazioni idrometriche, sfruttando i dati osservati su bacini orientali della Regione Emilia-Romagna. Per l’applicazione del metodo del deflusso indice, si è reso necessario anche il reperimento di indici morfologici e climatici, estraendo solo quelli più rappresentativi del dataset. Inoltre, vista la brevità degli eventi di piena nel bacino del Marecchia, si è messa a punto una procedura per discretizzare a passo orario le portate ricostruite degli eventi più significativi, sulla base delle osservazioni orarie nei bacini strumentati. L’algoritmo mostra buone prestazioni nel replicare le portate osservate, specialmente le piene, mentre sottostima le portate medio-basse. L’accordo tra osservazioni e simulazioni si è rivelato pienamente soddisfacente per la sezione del Fiume Marecchia considerata nelle indagini.

Classificação de água de coco processada e natural por meio de HCA, PCA e teores de íons metálicos determinados por ICP OES

Inductively Coupled Plasma Optical Emission Spectrometry was used to determine Ca, Mg, Mn, Fe, Zn and Cu in samples of processed and natural coconut water. The sample preparation consisted in a filtration step followed by a dilution. The analysis was made employing optimized instrumental parameters and the results were evaluated using methods of Pattern Recognition. The data showed common concentration values for the analytes present in processed and natural samples. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) indicated that the samples of different kinds were statistically different when the concentrations of all the analytes were considered simultaneously.

25 anos de quimiometria no Brasil

Chemometric activities in Brazil are described according to three phases: before the existence of microcomputers in the 1970s, through the initial stages of microcomputer use in the 1980s and during the years of extensive microcomputer applications of the ´90s and into this century. Pioneering activities in both the university and industry are emphasized. Active research areas in chemometrics are cited including experimental design, pattern recognition and classification, curve resolution for complex systems and multivariate calibration. New trends in chemometrics, especially higher order methods for treating data, are emphasized.