Hybrid MIMO Architectures for Millimeter Wave Communications: Phase Shifters or Switches?

Hybrid analog/digital MIMO architectures were recently proposed as an alternative for fully-digitalprecoding in millimeter wave (mmWave) wireless communication systems. This is motivated by the possible reduction in the number of RF chains and analog-to-digital converters. In these architectures, the analog processing network is usually based on variable phase shifters. In this paper, we propose hybrid architectures based on switching networks to reduce the complexity and the power consumption of the structures based on phase shifters. We define a power consumption model and use it to evaluate the energy efficiency of both structures. To estimate the complete MIMO channel, we propose an open loop compressive channel estimation technique which is independent of the hardware used in the analog processing stage. We analyze the performance of the new estimation algorithm for hybrid architectures based on phase shifters and switches. Using the estimated, we develop two algorithms for the design of the hybrid combiner based on switches and analyze the achieved spectral efficiency. Finally, we study the trade-offs between power consumption, hardware complexity, and spectral efficiency for hybrid architectures based on phase shifting networks and switching networks. Numerical results show that architectures based on switches obtain equal or better channel estimation performance to that obtained using phase shifters, while reducing hardware complexity and power consumption. For equal power consumption, all the hybrid architectures provide similar spectral efficiencies.Comment: Submitted to IEEE Acces

Semiclassical statistico-dynamical description of polyatomic photo-dissociations: State-resolved distributions

An alternative methodology to investigate indirect polyatomic processes with quasi-classical trajectories is proposed, which effectively avoids any binning or weighting procedure while provides rovibrational resolution. Initial classical states are started in terms of angle-action variables to closely match the quantum experimental conditions and later transformed into Cartesian coordinates, following an algorithm very recently published [J. Chem. Phys. 130, 114103 (2009)]. Trajectories are then propagated using the 'association' picture, i.e. an inverse dynamics simulation in the spirit of the exit-channel corrected phase space theory of Hamilton and Brumer [J. Chem. Phys. 82, 595 (1985)], which is shown to be particularly convenient. Finally, an approximate quasi-classical formula is provided which under general conditions can be used to add possible rotational structures into the vibrationally-resolved quasi-classical distributions. To introduce the method and illustrate its capabilities, correlated translational energy distributions from recent experiments in the photo-dissociation of ketene at 308 nm [J. Chem. Phys. 124, 014303 (2006)] are investigated. Quite generally, the overall theoretical algorithm reduces the total number of trajectories to integrate and allows for fully theoretical predictions of experiments on polyatomics.Comment: 10 pages, 3 figures, submitted to Phys. Chem. Chem. Phys; v2: corrects Fig. 3 and its discussio

The angular spectrum of the scattering coefficient map reveals subsurface colorectal cancer

Abstract Colorectal cancer diagnosis currently relies on histological detection of endoluminal neoplasia in biopsy specimens. However, clinical visual endoscopy provides no quantitative subsurface cancer information. In this ex vivo study of nine fresh human colon specimens, we report the first use of quantified subsurface scattering coefficient maps acquired by swept-source optical coherence tomography to reveal subsurface abnormities. We generate subsurface scattering coefficient maps with a novel wavelet-based-curve-fitting method that provides significantly improved accuracy. The angular spectra of scattering coefficient maps of normal tissues exhibit a spatial feature distinct from those of abnormal tissues. An angular spectrum index to quantify the differences between the normal and abnormal tissues is derived, and its strength in revealing subsurface cancer in ex vivo samples is statistically analyzed. The study demonstrates that the angular spectrum of the scattering coefficient map can effectively reveal subsurface colorectal cancer and potentially provide a fast and more accurate diagnosis

Quasi-exactly Solvable Lie Superalgebras of Differential Operators

In this paper, we study Lie superalgebras of $2\times 2$ matrix-valued first-order differential operators on the complex line. We first completely classify all such superalgebras of finite dimension. Among the finite-dimensional superalgebras whose odd subspace is nontrivial, we find those admitting a finite-dimensional invariant module of smooth vector-valued functions, and classify all the resulting finite-dimensional modules. The latter Lie superalgebras and their modules are the building blocks in the construction of QES quantum mechanical models for spin 1/2 particles in one dimension.Comment: LaTeX2e using the amstex and amssymb packages, 24 page

Environmentally Friendly Thermoelectric Materials: High Performance from Inorganic Components with Low Toxicity and Abundance in the Earth

This review article gives an overview of the recent research directions in eco-friendly, non-toxic, and earth-abundant thermoelectric materials. It covers materials such as sulfides, tetrahedrites, earth-abundant oxides, silicides, copper iodine, Half-Heusler intermetallic compounds, nitrides, and other environmentally friendly thermoelectrics. In all cases, their history, structure, general characteristics, thermoelectric properties, synthesis methods, and related thermoelectric applications are compiled. It is also shown that they are starting to be an excellent alternative for producing cost-effective, sustainable, and non-toxic thermoelectric generators. This review does not try to include all possible materials, but to show that there are high zT thermoelectric materials that are starting to be an excellent alternative for producing cost-effective, sustainable, and non-toxic thermoelectric generators.O.C.-C. and M.M.-G. would like to acknowledge financial support from MAT2017-86450-C4-3-R and the 2D_MESES project from CSIC, and J.R.A., from RTI2018-099794-B-I100

A face recognition system for assistive robots

Assistive robots collaborating with people demand strong Human-Robot interaction capabilities. In this way, recognizing the person the robot has to interact with is paramount to provide a personalized service and reach a satisfactory end-user experience. To this end, face recognition: a non-intrusive, automatic mechanism of identification using biometric identifiers from an user's face, has gained relevance in the recent years, as the advances in machine learning and the creation of huge public datasets have considerably improved the state-of-the-art performance. In this work we study different open-source implementations of the typical components of state-of-the-art face recognition pipelines, including face detection, feature extraction and classification, and propose a recognition system integrating the most suitable methods for their utilization in assistant robots. Concretely, for face detection we have considered MTCNN, OpenCV's DNN, and OpenPose, while for feature extraction we have analyzed InsightFace and Facenet. We have made public an implementation of the proposed recognition framework, ready to be used by any robot running the Robot Operating System (ROS). The methods in the spotlight have been compared in terms of accuracy and performance in common benchmark datasets, namely FDDB and LFW, to aid the choice of the final system implementation, which has been tested in a real robotic platform.This work is supported by the Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech, the research projects WISER ([DPI2017-84827-R]),funded by the Spanish Government, and financed by European RegionalDevelopment’s funds (FEDER), and MoveCare ([ICT-26-2016b-GA-732158]), funded by the European H2020 program, and by a postdoc contract from the I-PPIT-UMA program financed by the University of Málaga