Ali Mirzaei

Bio: Ali Mirzaei is an academic researcher from Shiraz University of Technology. The author has contributed to research in topics: Catalysis & Fischer–Tropsch process. The author has an hindex of 43, co-authored 274 publications receiving 6437 citations. Previous affiliations of Ali Mirzaei include Cardiff University & University of Tehran.

Deep Learning-Based Channel Estimation

Abstract: In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

Resistive-based gas sensors for detection of benzene, toluene and xylene (BTX) gases: a review

Abstract: Benzene, toluene, and xylene gases, which are known collectively as BTX gases, are volatile organic compounds (VOCs) that are used extensively in many industrial products. Nevertheless, BTX gases are quite toxic and need to be detected by sensitive sensors as fast as possible. Among the various gas sensors available, resistive-based gas sensors are among the most promising candidates for the detection of these gases. On the other hand, it is difficult to realize resistive-based gas sensors with high sensitivity to BTX gases owing to their relatively low chemical reactivity. In addition, the selective detection of a single gas among the BTX gases is challenging because of their similar nature and structure. This review discusses the different strategies employed in resistive-based gas sensors for the realization of high performance BTX gas sensors.

Microwave-assisted synthesis of metal oxide nanostructures for gas sensing application: A review

Abstract: This review gives a comprehensive report on the microwave-assisted synthesis of metal oxides for applications in the field of gas sensing. In recent years, microwave heating technology has gained importance in the synthesis of metal oxides because of its faster, cleaner and cost effectiveness than conventional thermal heating. Further, due to the peculiarity of microwave heating mechanism, the synthesis of metal oxides in different nanostructured forms by microwave-assisted methods has been widely pursued and the nanomaterials thus obtained have been applied as sensing elements in chemoresistive gas sensors. Their gas sensing performances are here described and discussed in detail, emphasizing the improved characteristics compared with materials produced by conventional synthesis procedures.

Fast parallel algorithms for short-range molecular dynamics

Abstract: Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of inter-atomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently—those with short-range forces where the neighbors of each atom change rapidly. They can be implemented on any distributed-memory parallel machine which allows for message-passing of data between independently executing processors. The algorithms are tested on a standard Lennard-Jones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers--the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray Y-MP and C90 algorithm shows that the current generation of parallel machines is competitive with conventional vector supercomputers even for small problems. For large problems, the spatial algorithm achieves parallel efficiencies of 90% and a 1840-node Intel Paragon performs up to 165 faster than a single Cray C9O processor. Trade-offs between the three algorithms and guidelines for adapting them to more complex molecular dynamics simulations are also discussed.

The Design and Analysis of Experiments

Abstract: THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.