I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

As one important component of sulfur cathodes, the carbon host plays a key role in the electrochemical performance of lithium-sulfur (Li-S) batteries. In this paper, a mesoporous nitrogen-doped carbon (MPNC)-sulfur nanocomposite is reported as a novel cathode for advanced Li-S batteries. The nitrogen doping in the MPNC material can effectively promote chemical adsorption between sulfur atoms and oxygen functional groups on the carbon, as verifi ed by X-ray absorption near edge structure spectroscopy, and the mechanism by which nitrogen enables the behavior is further revealed by density functional theory calculations. Based on the advantages of the porous structure and nitrogen doping, the MPNC-sulfur cathodes show excellent cycling stability (95% retention within 100 cycles) at a high current density of 0.7 mAh cm −2 with a high sulfur loading (4.2 mg S cm −2 ) and a sulfur content (70 wt%). A high areal capacity (≈3.3 mAh cm −2 ) is demonstrated by using the novel cathode, which is crucial for the practical application of Li-S batteries. It is believed that the important role of nitrogen doping promoted chemical adsorption can be extended for development of other high performance carbon-sulfur composite cathodes for Li-S batteries.

Nitrogen-Doped Mesoporous Carbon Promoted Chemical Adsorption of Sulfur and Fabrication of High-Areal-Capacity Sulfur Cathode with Exceptional Cycling Stability for Lithium-Sulfur Batteries

Differential Evolution: A review of more than two decades of research

The operational efficiency of remote environmental wireless sensor networks (EWSNs) has improved tremendously with the advent of Internet of Things (IoT) technologies over the past few years. EWSNs require elaborate device composition and advanced control to attain long-term operation with minimal maintenance. This article is focused on power supplies that provide energy to run the wireless sensor nodes in environmental applications. In this context, EWSNs have two distinct features that set them apart from monitoring systems in other application domains. They are often deployed in remote areas, preventing the use of mains power and precluding regular visits to exchange batteries. At the same time, their surroundings usually provide opportunities to harvest ambient energy and use it to (partially) power the sensor nodes. This review provides a comprehensive account of energy harvesting sources, energy storage devices, and corresponding topologies of energy harvesting systems, focusing on studies published within the last 10 years. Current trends and future directions in these areas are also covered.

Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review.

https://hal.archives-ouvertes.fr/hal-01323841/document

Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate

Electrical impedance tomography (EIT) imaging method is gaining its popularity due to ease of use and also non-invasiveness. The inner distribution of resistivity, which corresponds to different resistivity properties of different tissues, is estimated from voltage potentials measured on the boundary of inspected object. The major problem of EIT is how to reconstruct the image of inner resistivity. There are many approaches to solve this issue, which require more computational demands. The use of neural network to solve this non-linear problem addresses the demand to ease the implementation and lower the computational demands. In this article we adopted the use of Radial Basis Function (RBF) neural network for image reconstruction and compared it to reconstructed images obtained using EIDORS software. RBF network was created and trained using the Matlab and neural network toolbox. As training data the simulated measurement voltages and EIDORS difference reconstruction gained values of model elements were used as input and output vectors. Then we performed testing onto 100 images and compared them with images reconstructed with EIDORS difference reconstruction. To calculate the error we used Mean Square Error algorithm.

Image reconstruction in electrical impedance tomography using neural network

Graphical abstractDisplay Omitted HighlightsFuzzy control is used to manage energy consumption in wireless sensor device.Control is based on energy available in storage and from the environment.Differential evolution tailors system parameters for intended deployment site.Validity of the proposed approach is confirmed using real weather forecast data.Comparison with other common optimization approaches is provided. Environmentally-powered wireless sensors use ambient energy from their environment to support their own energy needs. As such, they must operate without significant maintenance or user supervision. Due to the stochastic availability of ambient energy, its harvesting, storage and consumption must be managed by an efficient and robust controller that maintains data collection and transmission rates at desired levels, while maximizing the useful operational time of the system. To accomplish this task, the control system must observe the state of charge of an internal energy storage device, and consider the amount of energy available for harvest in the future. At the same time, the complexity of the controller must be limited so that it can be implemented on the simple embedded system of the sensor hardware. This paper presents a comprehensive synthesis of desired behavior of such controllers, and describes procedures for their design and optimization through an evolutionary fuzzy approach. The main contribution is the formalization of design objectives and development of the fitness function that drives the optimization process. Additional contributions include a comprehensive evaluation of several soft computing optimization approaches, thorough analysis of the optimized controller, its comparison to baseline control strategies, and validation of its operation with real energy availability forecasts.

Differential evolution of fuzzy controller for environmentally-powered wireless sensors

The paper describes methods of comparing calculated ECG signal with real signal. Five examination methods of ECG-signal reconstruction from reduced system were proposed in this description. These methods prove reconstruction and evaluate results. They are based on statistic methods, nonparametric approaches and autoregressive models.

Methods of Comparing ECG Reconstruction

This paper describes a long-term simulation of an environmental monitoring system. This innovative approach combines harvesting-aware power management with primary batteries used as a back-up. It significantly extends the operational life of the device, while avoiding loss of data due to insufficient solar energy during winter in the harsh Arctic environment. The simulation considers the device to be located in the Arctic environment. Its main operation modes involve measurement from sensor interface, data storage and transmission. To perform an effective data-for-energy exchange, the device is controlled by a fuzzy energy management strategy. The new structure of the fuzzy rule-based system independently controls two separate variables related to data collection and the operation of a data buffer. The simulator uses meteorological data from Inuvik, Northwest Territories, Canada, to estimate the amount of energy available for solar harvesting. This site, located above the polar circle, receives very limited amounts of solar radiation during winter. Operation of the device is evaluated over a two-year period. The simulation results are described both numerically and using time-series plots of energy- and data-related variables. The performance is adequate for unsupervised operation of the system with annual maintenance visits to replace batteries. DOI: http://dx.doi.org/10.5755/j01.eee.20.7.8020

/pdf/powering-environmental-monitoring-systems-in-arctic-regions-140cwdoewd.pdf

Michal Prauzek

Papers

Energy Harvesting Sources, Storage Devices and System Topologies for Environmental Wireless Sensor Networks: A Review.

Image reconstruction in electrical impedance tomography using neural network

Differential evolution of fuzzy controller for environmentally-powered wireless sensors

Methods of Comparing ECG Reconstruction

Powering Environmental Monitoring Systems in Arctic Regions: A Simulation Study