Electromechanical Probing of Ionic Currents in Energy Storage Materials

The electrochemical processes in energy storage materials are generally linked with changes of molar volume of the host compound. Here, the frequency dependent strain response of 1D electrochemically active systems to periodic electric bias is analyzed. The sensitivity and resolution of these electrochemical strain measurements are compared to the current-based electrochemical impedance spectroscopy. The resolution and detection limits of interferometric and atomic force microscopy based systems for probing electrochemical reactions on the nanoscale are analyzed.Comment: 12 pages, 4 figures, 2 tables, 2 appendices, submitted to Appl. Phys. Let

Pyroelectric response of ferroelectric nanoparticles: size effect and electric energy harvesting

The size effect on pyroelectric response of ferroelectric nanowires and nanotubes is analyzed. The pyroelectric coefficient strongly increases with the wire radius decrease and diverges at critical radius Rcr corresponding to the size-driven transition into paraelectric phase. Size-driven enhancement of pyroelectric coupling leads to the giant pyroelectric current and voltage generation by the polarized ferroelectric nanoparticles in response to the temperature fluctuation. The maximum efficiency of the pyroelectric energy harvesting and bolometric detection is derived, and is shown to approach the Carnot limit for low temperatures.Comment: 17 pages, 4 figures, 1 Appendi

FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations

Neural network-based methods for image processing are becoming widely used in practical applications. Modern neural networks are computationally expensive and require specialized hardware, such as graphics processing units. Since such hardware is not always available in real life applications, there is a compelling need for the design of neural networks for mobile devices. Mobile neural networks typically have reduced number of parameters and require a relatively small number of arithmetic operations. However, they usually still are executed at the software level and use floating-point calculations. The use of mobile networks without further optimization may not provide sufficient performance when high processing speed is required, for example, in real-time video processing (30 frames per second). In this study, we suggest optimizations to speed up computations in order to efficiently use already trained neural networks on a mobile device. Specifically, we propose an approach for speeding up neural networks by moving computation from software to hardware and by using fixed-point calculations instead of floating-point. We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations. We also show an example of how existing datasets can be modified and adapted for the recognition task in hand. Finally, we present the design and the implementation of a floating-point gate array-based device to solve the practical problem of real-time handwritten digit classification from mobile camera video feed

Experimental and Theoretical Investigation into the Effect of the Electron Velocity Distribution on Chaotic Oscillations in an Electron Beam under Virtual Cathode Formation Conditions

The effect of the electron transverse and longitudinal velocity spread at the entrance to the interaction space on wide-band chaotic oscillations in intense multiple-velocity beams is studied theoretically and numerically under the conditions of formation of a virtual cathode. It is found that an increase in the electron velocity spread causes chaotization of virtual cathode oscillations. An insight into physical processes taking place in a virtual cathode multiple velocity beam is gained by numerical simulation. The chaotization of the oscillations is shown to be associated with additional electron structures, which were separated out by constructing charged particle distribution functions.Comment: 9 pages, 8 figure