A Review on Deep Learning Techniques for Video Prediction.

The ability to predict, anticipate and reason about future outcomes is a key component of intelligent decision-making systems. In light of the success of deep learning in computer vision, deep-learning-based video prediction emerged as a promising research direction. Defined as a self-supervised learning task, video prediction represents a suitable framework for representation learning, as it demonstrated potential capabilities for extracting meaningful representations of the underlying patterns in natural videos. Motivated by the increasing interest in this task, we provide a review on the deep learning methods for prediction in video sequences. We first define the video prediction fundamentals, as well as mandatory background concepts and the most used datasets. Next, we carefully analyze existing video prediction models organized according to a proposed taxonomy, highlighting their contributions and their significance in the field. The summary of the datasets and methods is accompanied with experimental results that facilitate the assessment of the state of the art on a quantitative basis. The paper is summarized by drawing some general conclusions, identifying open research challenges and by pointing out future research directions.

High speed CMOS acquisition system based on FPGA embedded image processing for electro-optical measurements.

Electro-optical measurements, i.e., optical waveguides and plasmonic based electrochemical impedance spectroscopy (P-EIS), are based on the sensitive dependence of refractive index of electro-optical sensors on surface charge density, modulated by an AC electrical field applied to the sensor surface. Recently, P-EIS has emerged as a new analytical tool that can resolve local impedance with high, optical spatial resolution, without using microelectrodes. This study describes a high speed image acquisition and processing system for electro-optical measurements, based on a high speed complementary metal-oxide semiconductor (CMOS) sensor and a field-programmable gate array (FPGA) board. The FPGA is used to configure CMOS parameters, as well as to receive and locally process the acquired images by performing Fourier analysis for each pixel, deriving the real and imaginary parts of the Fourier coefficients for the AC field frequencies. An AC field generator, for single or multi-sine signals, is synchronized with the high speed acquisition system for phase measurements. The system was successfully used for real-time angle-resolved electro-plasmonic measurements from 30 Hz up to 10 kHz, providing results consistent to ones obtained by a conventional electrical impedance approach. The system was able to detect amplitude variations with a relative variation of ±1%, even for rather low sampling rates per period (i.e., 8 samples per period). The PC (personal computer) acquisition and control software allows synchronized acquisition for multiple FPGA boards, making it also suitable for simultaneous angle-resolved P-EIS imaging.