An Architecture of a Scalable Wireless Monitoring System

Automatic patient monitoring is becoming an import part of any health care system. The challenge is to create a patient monitoring that is capable of providing a continuous, reliable and real time monitoring and data acquisition services while removing any restrictions on the patient whereabouts. We present a scalable wireless telemedicine system that is capable of simultaneously monitoring large number of patients (acquiring patient's ECG data). We present a simple but useful technique for overcoming temporary "surges" in the environment where large number of patients (beyond the normal capacity of the monitoring system) must be monitored. The solution is based on modification to data transmission over the wireless network

Moving Target Search with Subgoal Graphs

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Identifying Hazardous Shapes in the Plane

This paper explores the problem of identifying the shapes of hazardous entities in R2 by a set S = {s1, s2, . . . , sk} of mobile sensors (autonomous robots). A hazardous entity, H, is a region that affects the operation of robots that either penetrate the area or come in contact with it. In this paper, we propose algorithms for searching a rectangular region for a stationary hazardous entity, where some a priori geometrical knowledge is given (e.g., edge size range), and if such an entity exists, then determine the area that it occupies. We explore entities that are convex in nature such as line segment, circles (discs), and simple convex shapes. The objectives are to minimize the distance travelled by the robots during the search phase, and to minimize the number of robots that are required to identify the region covered by the hazardous entity. The number of robots required to locate H is three or four robots when H is a line segment, two or three robots when H is a circle, and seven robots are sufficient when H is a triangle. Our results extend to n-vertex convex shapes and we show that 2n + 1 robots are sufficient to determine the coverage of H

Disassembling two-dimensional composite parts via translations

This paper deals with the computational complexity of disassembling 2-dimensional composite parts (comprised of simple polygons) via collision-free translations. The first result of this paper is an O(Mn + M log M) algorithm for computing a sequence of translations (performed in a common direction) to disassemble composite parts. The algorithm improves on the O(Mn log Mn) bound previously established for this problem and is easily seen to be optimal. The algorithm solves the problem posed by Nurmi and by Toussaint. The second result of this paper is an Ω(Mn + M log M) lower bound proof for the problem of detecting whether a composite part can be disassembled, or contains interlocking subparts. Thus, detecting the existence of a disassembly sequence is as hard as computing one. As a consequence, the algorithm for computing a disassembly sequence is optimal also for the detecting problem

Spikes annihilation in the Hodgkin-Huxley neuron

The Hodgkin-Huxley (HH) neuron is a nonlinear system with two stable states: A fixed point and a limit cycle. Both of them co-exist. The behavior of this neuron can be switched between these two equilibria, namely spiking and resting respectively, by using a perturbation method. The change from spiking to resting is named Spike Annihilation, and the transition from resting to spiking is named Spike Generation. Our intention is to determine if the HH neuron in 2D is controllable (i.e., if it can be driven from a quiescent state to a spiking state and vice versa). It turns out that the general system is unsolvable. 1 In this paper, first of all,2 we analytically prove the existence of a brief current pulse, which, when delivered to the HH neuron during its repetitively firing state, annihilates its spikes. We also formally derive the characteristics of this brief current pulse. We then proceed to explore experimentally, by using numerical simulations, the properties of this pulse, namely the range of time when it can be inserted (the minimum phase and the maximum phase), its magnitude, and its duration. In addition, we study the solution of annihilating the spikes by using two successive stimuli, when the first is, of its own, unable to annihilate the neuron. Finally, we investigate the inverse problem of annihilation, namely the spike generation problem, when the neuron switches from resting to firing

Desynchronizing a chaotic pattern recognition neural network to model inaccurate perception

The usual goal of modeling natural and artificial perception involves determining how a system can extract the object that it perceives from an image that is noisy. The "inverse" of this problem is one of modeling how even a clear image can be perceived to be blurred in certain contexts. To our knowledge, there is no solution to this in the literature other than for an oversimplified model in which the true image is garbled with noise by the perceiver himself. In this paper, we propose a chaotic model of pattern recognition (PR) for the theory of "blurring." This paper, which is an extension to a companion paper demonstrates how one can model blurring from the view point of a chaotic PR system. Unlike the companion paper in which a chaotic PR system extracts the pattern from the input, in this case, we show that even without the inclusion of additional noise, perception of an object can be "blurred" if the dynamics of the chaotic system are modified. We thus propose a formal model and present an analysis using the Lyapunov exponents and the Routh-Hurwitz criterion. We also demonstrate experimentally the validity of our model by using a numeral data set. A byproduct of this model is the theoretical possibility of desynchronization of the periodic behavior of the brain (as a chaotic system), rendering us the possibility of predicting, controlling, and annulling epileptic behavior

Some analysis on the network of bursting neurons: Quantifying Behavioral Synchronization

There are numerous families of Neural Networks (NN) used in the study and development of the field of Artificial Intelligence (AI). One of the more re

Periodicity and stability issues of a chaotic pattern recognition neural network

Traditional pattern recognition (PR) systems work with the model that the object to be recognized is characterized by a set of features, which are treated as the inputs. In this paper, we propose a new model for PR, namely one that involves chaotic neural networks (CNNs). To achieve this, we enhance the basic model proposed by Adachi (Neural Netw 10:83-98, 1997), referred to as Adachi's Neural Network (AdNN), which though dynamic, is not chaotic. We demonstrate that by decreasing the multiplicity of the eigenvalues of the AdNN's control system, we can effectively drive the system into chaos. We prove this result here by eigenvalue computations and the evaluation of the Lyapunov exponent. With this premise, we then show that such a Modified AdNN (M-AdNN) has the desirable property that it recognizes various input patterns. The way that this PR is achieved is by the system essentially sympathetically "resonating" with a finite periodicity whenever these samples (or their reasonable resemblances) are presented. In this paper, we analyze the M-AdNN for its periodicity, stability and the length of the transient phase of the retrieval process. The M-AdNN has been tested for Adachi's dataset and for a real-life PR problem involving numerals. We believe that this research also opens a host of new research avenues

Investigating schizophrenia using local connectivity considerations within the piriform cortex

One of the two hypotheses which explain the cause of schizophrenia is the aberrant connectivity between neurons. For example, over 250,000 brain cells are generated every minute in a two months old fetus. These cells slither across the brain, seeking out their proper destination, and then send out billions of axons, similarly to new branches of massive trees in a forest. The axons make connections with other brain cells, and a single neuron may have 100,000 connections with other neurons. This connection building phase is followed by a pruning phase. Many of these synapses will die (e.g. only half of the 200 billions generated neurons will survive to adulthood). If the pruning of the synapses is not efficient, then the aberrant connectivity can lead to diseases like schizophrenia. The second hypothetical mechanism underlying schizophrenia can be the low level of local connections between neurons (excessive synoptic pruning). This two hypotheses are investigated experimentally in this paper. In order to do this investigation, we simulate the brain biological system by introducing a neural network model that embeds two subsystems (zones) within it. In this model, which tries to reproduce the piriform cortex, we perform changes to the number of connections (by increasing or decreasing them). By modifying the connectivity, we attempt to simulate the pruning process which may cause schizophrenia. The analyzed signals which describe these two zones are EEGs, We have investigated the level of chaos and the synchronization between these two zones. Both of these hypotheses have an impact on the level of chaos, but the excessive synaptic pruning hypothesis has a higher impact on the system dynamics regarding the nonlinear interdependence measure, than the insufficient pruning hypothesis