A self-adaptive data analysis for fault diagnosis of an automotive air-conditioner blower

This paper presents a fault diagnosis system for an automotive air-conditioner blower based on a noise emission signal using a self-adaptive data analysis technique. The proposed diagnosis system consists of feature extraction using the empirical mode decomposition (EMD) method and fault classification using the artificial neural network technique. The EMD method has been developed quite recently to adaptively decompose the non-stationary and non-linear signals. It sifts the complex signal of time series without losing its original properties and then obtains some useful intrinsic mode function (IMF) components. Calculating the energy of each component can reduce the computation dimensions and enhance classification performance. These energy features of various fault conditions are used as inputs to train the artificial neural network. In the fault classification, the probabilistic neural network (PNN) is used to verify the performance of the proposed system and compare with the traditional technique, back-propagation neural network (BPNN). The experimental results indicated the proposed technique performed well for quickly and accurately estimating fault conditions.     

Selective Ozonolysis of Cis-Crotamiton: Free Catalyzed Oxidative Synthesis of N-ethyl-N-(o-tolyl)formamide as a New Compound

A novel method for the synthesis of a N-ethyl-N-(o-tolyl)formamide was described which involves ozonolysis of a cis-crotamiton. The reaction proceeds via ozone attack on the enolic double bond, cleavage, and aldehyde formation through the free hydroxyl group. The present study establishes the conditions for selective ozonolysis of cis-crotamiton, leading to N-ethyl-N-(o-tolyl)formamide 2 (55.7%). The N-ethyl-N-(o-tolyl)formamide was characterized by spectroscopical techniques.     

Image encryption with a Josephson junction model embedded in FPGA

Multimedia Tools and Applications - The dynamical analysis, field programmable gate array (FPGA) implementation and image encryption of a linear piecewiseresistive-capacitive-inductive shunted...     

Review of medical image authentication techniques and their recent trends

Multimedia Tools and Applications - There is always a need for an updated systematic review of a special subject area because of its importance for the researchers and the interested audience. This...     

A Partial CSI Estimation Approach for Downlink FDD massive-MIMO System with Different Base Transceiver Station Topologies

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtained. In order to achieve the aforementioned objectives, this paper presents a feasible DL training sequence design based on a partial CSI estimation approach for an FDD massive-MIMO system with a shorter coherence time. To this end, a threshold-based approach is proposed for a suitable DL pilot selection by exploring the statistical information of the channel covariance matrix. The mean square error of the proposed design is derived, and the achievable sum rate and bit-error-rate for maximum ratio transmitter and regularized zero forcing precoding is investigated over different BTS topologies with uniform linear array and uniform rectangular array. The results show that a feasible performance in the DL FDD massive-MIMO systems can be achieved even when a large number of antenna elements are deployed by the BTS and a shorter coherence time is considered.

     

Chemical and Physical Properties Investigation as Indicators for the Ozonation Reaction Completion of Palm Olein

Palm olein is subjected to an ozonation reaction process to cleave the double bond; this process was investigated and optimized by applying different ranges of temperature. The completion of ozonation reaction and double bond cleavage was indicated by monitoring some chemical and physical properties, e.g., iodine value, peroxide value and viscosity, which were influenced by increasing the ozonation reaction temperature. It was seen in this investigation that the iodine value and the viscosity of the ozonated samples were decreased, while the peroxide value was increased with the increase of ozonation reaction temperature, whereas the palm olein double bonds were totally cleaved at the ozonation temperature of 150 °C.