Diabetic autonomic neuropathy

Neuromorphic computing has come to refer to a variety of brain-inspired computers, devices, and models that contrast the pervasive von Neumann computer architecture This biologically inspired approach has created highly connected synthetic neurons and synapses that can be used to model neuroscience theories as well as solve challenging machine learning problems The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to finding materials and engineering breakthroughs to build devices to support these models, to creating a programming framework so the systems can learn, to creating applications with brain-like capabilities In this work, we provide a comprehensive survey of the research and motivations for neuromorphic computing over its history We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning approaches, hardware and devices, supporting systems, and finally applications We conclude with a broad discussion on the major research topics that need to be addressed in the coming years to see the promise of neuromorphic computing fulfilled The goals of this work are to provide an exhaustive review of the research conducted in neuromorphic computing since the inception of the term, and to motivate further work by illuminating gaps in the field where new research is needed

A Survey of Neuromorphic Computing and Neural Networks in Hardware.

https://www.internationaljournalofcardiology.com/article/S0167-5273(12)00326-9/pdf

How accurate is pulse rate variability as an estimate of heart rate variability?: A review on studies comparing photoplethysmographic technology with an electrocardiogram

Dairy industries have shown tremendous growth in size and number in most countries of the world. These industries discharge wastewater which is characterized by high chemical oxygen demand, biological oxygen demand, nutrients, and organic and inorganic contents. Such wastewaters, if discharged without proper treatment, severely pollute receiving water bodies. In this article, the various recent advancements in the treatment of dairy wastewater have been discussed and the areas where further research is needed have been identified.

An Overview of Various Technologies for the Treatment of Dairy Wastewaters

Underwater robot designs inspired by the behavior, physiology, and anatomy of fishes can provide enhanced maneuverability, stealth, and energy efficiency. Over the last two decades, robotics researchers have developed and reported a large variety of fish-inspired robot designs. The purpose of this review is to report different types of fish-inspired robot designs based upon their intended locomotion patterns. We present a detailed comparison of various design features like sensing, actuation, autonomy, waterproofing, and morphological structure of fish-inspired robots reported in the past decade. We believe that by studying the existing robots, future designers will be able to create new designs by adopting features from the successful robots. The review also summarizes the open research issues that need to be taken up for the further advancement of the field and also for the deployment of fish-inspired robots in practice.

https://iopscience.iop.org/article/10.1088/1748-3190/11/3/031001/pdf

Fish-inspired robots: design, sensing, actuation, and autonomy--a review of research.

The conventional Poincare plot for heart rate variability (HRV) analysis is a scatterplot of successive (lag 1) pairs of RR intervals (intervals between heartbeats), and its width (SD1) is considered a measure of short-term variability It has been shown that SD1 correlates better with HF than with LF (high- and low-frequency bands of the spectrum, respectively) Our aim was to assess how these correlations were affected when SD1 was obtained for longer lags 10 min ECGs were used to construct Poincare plots with lags of 1–10 heartbeats in two groups of subjects, one with normal HRV and the other with impaired HRV (control and diabetic groups respectively, N = 15 each) SD1 was quantified for these subjects and HRV spectral indices were estimated The diabetic group had lower LF, HF and SD1 than the control group (p 09), while SD1 for lags ≥ 4 correlated better with LF (rs ≥ 09) than with HF (065 ≤ rs ≤ 073) However, in the diabetic group, the correlation results did not change in that way for different lags (correlation results between HF and SD1: rs ≥ 095 for lags 1–10) In conclusion, the comparative strength of the correlations between lagged Poincare widths and spectral indices might be useful to distinguish normal from pathological HRV

https://iopscience.iop.org/article/10.1088/0967-3334/28/1/008/pdf

Correlations between frequency-domain HRV indices and lagged Poincaré plot width in healthy and diabetic subjects.

Convergence analysis of the least-squares identification algorithm with a variable forgetting factor the time-varying linear systems

An FPGA implementation of a multilayer perceptron neural network is presented. The system is parameterized both in network related aspects (e.g.: number of layers and number of neurons in each layer) and implementation parameters (e.g.: word width, pre-scaling factors and number of available multipliers). This allows to use the design for different network realizations, or to try different area-speed trade-offs simply by recompiling the design. Fixed point arithmetic with pre-scaling configurable in a per layer basis was used. The system was tested on an ARC-PCI board from altera/spl trade/ several examples from different application domains were implemented showing the flexibility and ease of use of the obtained circuit. Even with the rather old board used, an appreciable speed-up was obtained compared with a software-only implementation based on Matlab neural network toolbox.

NeuroFPGA-implementing artificial neural networks on programmable logic devices

Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through their skin. In weakly electric fish, these currents arise from a self-generated field (the electric organ discharge), depending on the electrical properties of the surrounding medium. The electric image can be defined as the pattern of transepidermal voltage distributed over the receptive surface. To understand electrolocation it is necessary to know how electric image of objects are generated. In pulse mormyrids, the electric organ is localized at the tail, far from the receptors and fires a short biphasic pulse. Consequently, if all the elements in the environment are resistive, the stimulus at every point on the skin has the same waveform. Then, any measure of the amplitude (for example, the peak to peak amplitude) could be the unique parameter of the stimulus at any point of the skin. We have developed a model to calculate the image, corroborating that images are spread over the whole sensory surface and have an opposite center-surround, "Mexican-hat" shape. As a consequence, the images of different objects superimpose. We show theoretically and by simulation that the image of a pair of objects is not the simple addition of the individual images of these objects.

Electric images of two low resistance objects in weakly electric fish

Background: The definitive incorporation of heart rate variability (HRV) as a clinical tool depends on the development of more confident techniques of measurement. The length of the studies is a critical issue. Whereas Holter studies allow the monitorization at different hours and activities, short-term recordings allow the control of environmental conditions. Recording length is also strongly related to the procedure of analysis; for instance, some time-domain indexes are strongly affected by the duration of the study. Meanwhile, spectral analyses require stationary conditions, only achieved in short-term studies. Our main goal was to determine if HRV indexes obtained from short-term analyses were as useful as those from Holter monitoring for diagnosis of reduced HRV in diabetes.



Methods: We studied two groups: one with impaired HRV (15 diabetic patients) and another with normal HRV (15 healthy subjects). HRV indexes obtained from 24-hour Holter recordings (SDNN, rMSSD, and the power of LF and HF bands), were correlated with analog indexes obtained from 10-minute digital acquired studies within each group. Besides, we compared the diabetic and control groups using the indexes obtained with both methodologies.



Results: The correlation was high (0.70≤r ≤ 0.85, P ≤ 0.0032) in the diabetic group, but was poor in the control group. HRV values were significantly lower in the diabetic group either for 24-hour or short-term studies (P ≤ 0.0113).



Conclusion: We conclude that short-term studies are at least as powerful as Holter to differentiate the diabetic group (impaired HRV) from the control group.

/pdf/heart-rate-variability-short-term-studies-are-as-useful-as-5a6upwwb6v.pdf

Rafael Canetti

Papers

Correlations between frequency-domain HRV indices and lagged Poincaré plot width in healthy and diabetic subjects.

Convergence analysis of the least-squares identification algorithm with a variable forgetting factor the time-varying linear systems

NeuroFPGA-implementing artificial neural networks on programmable logic devices

Electric images of two low resistance objects in weakly electric fish

Heart rate variability: short-term studies are as useful as holter to differentiate diabetic patients from healthy subjects.