Real-Time Analysis of Correlations Between On-Body Sensor Nodes

The topology of a body sensor network has, until recently, often been overlooked; either because the layout of the network is deemed to be sufficiently static (”we always know well enough where sensors are”), we always know exactly where the nodes are or because the location of the sensor is not inherently required (”as long as the node stays where it is, we do not need its location, just its data”). We argue in this paper that, especially as the sensor nodes become more numerous and densely interconnected, an analysis on the correlations between the data streams can be valuable for a variety of purposes. Two systems illustrate how a mapping of the network’s sensor data to a topology of the sensor nodes’ correlations can be applied to reveal more about the physical structure of body sensor networks

Hacia el modelado 3d de tumores cerebrales mediante endoneurosonografía y redes neuronales

Las cirugías mínimamente invasivas se han vuelto populares debido a que implican menos riesgos con respecto a las intervenciones tradicionales. En neurocirugía, las tendencias recientes sugieren el uso conjunto de la endoscopia y el ultrasonido, técnica llamada endoneurosonografía (ENS), para la virtualización 3D de las estructuras del cerebro en tiempo real. La información ENS se puede utilizar para generar modelos 3D de los tumores del cerebro durante la cirugía. En este trabajo, presentamos una metodología para el modelado 3D de tumores cerebrales con ENS y redes neuronales. Específicamente, se estudió el uso de mapas auto-organizados (SOM) y de redes neuronales tipo gas (NGN). En comparación con otras técnicas, el modelado 3D usando redes neuronales ofrece ventajas debido a que la morfología del tumor se codifica directamente sobre los pesos sinápticos de la red, no requiere ningún conocimiento a priori y la representación puede ser desarrollada en dos etapas: entrenamiento fuera de línea y adaptación en línea. Se realizan pruebas experimentales con maniquíes médicos de tumores cerebrales. Al final del documento, se presentan los resultados del modelado 3D a partir de una base de datos ENS.Minimally invasive surgeries have become popular because they reduce the typical risks of traditional interventions. In neurosurgery, recent trends suggest the combined use of endoscopy and ultrasound (endoneurosonography or ENS) for 3D virtualization of brain structures in real time. The ENS information can be used to generate 3D models of brain tumors during a surgery. This paper introduces a methodology for 3D modeling of brain tumors using ENS and unsupervised neural networks. The use of self-organizing maps (SOM) and neural gas networks (NGN) is particularly studied. Compared to other techniques, 3D modeling using neural networks offers advantages, since tumor morphology is directly encoded in synaptic weights of the network, no a priori knowledge is required, and the representation can be developed in two stages: off-line training and on-line adaptation. Experimental tests were performed using virtualized phantom brain tumors. At the end of the paper, the results of 3D modeling from an ENS database are presented

An efficient and straightforward online quantization method for a data stream through remove-birth updating

The growth of network-connected devices is creating an explosion of data, known as big data, and posing significant challenges to efficient data analysis. This data is generated continuously, creating a dynamic flow known as a data stream. The characteristics of a data stream may change dynamically, and this change is known as concept drift. Consequently, a method for handling data streams must efficiently reduce their volume while dynamically adapting to these changing characteristics. This paper proposes a simple online vector quantization method for concept drift. The proposed method identifies and replaces units with low win probability through remove-birth updating, thus achieving a rapid adaptation to concept drift. Furthermore, the results of this study show that the proposed method can generate minimal dead units even in the presence of concept drift. This study also suggests that some metrics calculated from the proposed method will be helpful for drift detection

A survey on machine learning for recurring concept drifting data streams

The problem of concept drift has gained a lot of attention in recent years. This aspect is key in many domains exhibiting non-stationary as well as cyclic patterns and structural breaks affecting their generative processes. In this survey, we review the relevant literature to deal with regime changes in the behaviour of continuous data streams. The study starts with a general introduction to the field of data stream learning, describing recent works on passive or active mechanisms to adapt or detect concept drifts, frequent challenges in this area, and related performance metrics. Then, different supervised and non-supervised approaches such as online ensembles, meta-learning and model-based clustering that can be used to deal with seasonalities in a data stream are covered. The aim is to point out new research trends and give future research directions on the usage of machine learning techniques for data streams which can help in the event of shifts and recurrences in continuous learning scenarios in near real-time

Building environmentally-aware classifiers on streaming data

The three biggest challenges currently faced in machine learning, in our estimation, are the staggering quantity of data we wish to analyze, the incredibly small proportion of these data that are labeled, and the apparent lack of interest in creating algorithms that continually learn during inference. An unsupervised streaming approach addresses all three of these challenges, storing only a finite amount of information to model an unbounded dataset and adapting to new structures as they arise. Specifically, we are motivated by automated target recognition (ATR) in synthetic aperture sonar (SAS) imagery, the problem of finding explosive hazards on the sea oor. It has been shown that the performance of ATR can be improved by, instead of using a single classifier for the entire ATR task, creating several specialized classifers and fusing their predictions [44]. The prevailing opinion seems be that one should have different classifiers for varying complexity of sea oor [74], but we hypothesize that fusing classifiers based on sea bottom type will yield higher accuracy and better lend itself to making explainable classification decisions. The first step of building such a system is developing a robust framework for online texture classification, the topic of this research. xi In this work, we improve upon StreamSoNG [85], an existing algorithm for streaming data analysis (SDA) that models each structure in the data with a neural gas [69] and detects new structures by clustering an outlier list with the possibilistic 1-means [62] (P1M) algorithm. We call the modified algorithm StreamSoNGv2, denoting that it is the second version, or verse, if you will, of StreamSoNG. Notable improvements include detection of arbitrarily-shaped clusters by using DBSCAN [37] instead of P1M, using growing neural gas [43] to model each structure with an adaptive number of prototypes, and an automated approach to estimate the n parameters. Furthermore, we propose a novel algorithm called single-pass possibilistic clustering (SPC) for solving the same task. SPC maintains a fixed number of structures to model the data stream. These structures can be updated and merged based only on their "footprints", that is, summary statistics that contain all of the information from the stream needed by the algorithm without directly maintaining the entire stream. SPC is built on a damped window framework, allowing the user to balance the weight between old and new points in the stream with a decay factor parameter. We evaluate the two algorithms under consideration against four state of the art SDA algorithms from the literature on several synthetic datasets and two texture datasets: one real (KTH-TIPS2b [68]) and xii one simulated. The simulated dataset, a significant research effort in itself, is of our own construction in Unreal Engine and contains on the order of 6,000 images at 720 x 720 resolution from six different texture types. Our hope is that the methodology developed here will be effective texture classifiers for use not only in underwater scene understanding, but also in improving performance of ATR algorithms by providing a context in which the potential target is embedded.Includes bibliographical references