Subspace-based order estimation techniques in massive MIMO

Order estimation, also known as source enumeration, is a classical problem in array signal processing which consists in estimating the number of signals received by an array of sensors. In the last decades, numerous approaches to this problem have been proposed. However, the need of working with large-scale arrays (like in massive MIMO systems), low signal-to-noise- ratios, and poor sample regime scenarios, introduce new challenges to order estimation problems. For instance, most of the classical approaches are based on information theoretic criteria, which usually require a large sample size, typically several times larger than the number of sensors. Obtaining a number of samples several times larger than the number of sensors is not always possible with large-scale arrays. In addition, most of the methods found in literature assume that the noise is spatially white, which is very restrictive for many practical scenarios. This dissertation deals with the problem of source enumeration for large-scale arrays, and proposes solutions that work robustly in the small sample regime under various noise models. The first part of the dissertation solves the problem by applying the idea of subspace averaging. The input data are modelled as subspaces, and an average or central subspace is computed. The source enumeration problem can be seen as an estimation of the dimension of the central subspace. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. The proposed SA criterion is especially effective in high-dimensional scenarios with low sample support for uniform linear arrays in the presence of white noise. Further, the proposed SA method is extended for: i) non-white noises, and ii) non-uniform linear arrays. The SA criterion is sensitive with the chosen dimension of extracted subspaces. To solve this problem, we combine the SA technique with a majority vote approach. The number of sources is detected for increasing dimensions of the SA technique and then a majority vote is applied to determine the final estimate. Further, to extend SA for arrays with arbitrary geometries, the SA is combined with a sparse reconstruction (SR) step. In the first step, each received snapshot is approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l-0 norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. The second half of the dissertation introduces a completely different approach to the order estimation for uniform linear arrays, which is based on matrix completion algorithms. This part first discusses the problem of order estimation in the presence of noise whose spatial covariance structure is a diagonal matrix with possibly different variances. The diagonal terms of the sample covariance matrix are removed and, after applying Toeplitz rectification as a denoising step, the signal covariance matrix is reconstructed by using a low-rank matrix completion method adapted to enforce the Toeplitz structure of the sought solution. The proposed source enumeration criterion is based on the Frobenius norm of the reconstructed signal covariance matrix obtained for increasing rank values. The proposed method performs robustly for both small and large-scale arrays with few snapshots. Finally, an approach to work with a reduced number of radio–frequency (RF) chains is proposed. The receiving array relies on antenna switching so that at every time instant only the signals received by a randomly selected subset of antennas are downconverted to baseband and sampled. Low-rank matrix completion (MC) techniques are then used to reconstruct the missing entries of the signal data matrix to keep the angular resolution of the original large-scale array. The proposed MC algorithm exploits not only the low- rank structure of the signal subspace, but also the shift-invariance property of uniform linear arrays, which results in a better estimation of the signal subspace. In addition, the effect of MC on DOA estimation is discussed under the perturbation theory framework. Further, this approach is extended to devise a novel order estimation criterion for missing data scenario. The proposed source enumeration criterion is based on the chordal subspace distance between two sub-matrices extracted from the reconstructed matrix after using MC for increasing rank values. We show that the proposed order estimation criterion performs consistently with a very few available entries in the data matrix.This work was supported by the Ministerio de Ciencia e Innovación (MICINN) of Spain, under grants TEC2016-75067-C4-4-R (CARMEN) and BES-2017-080542

Selection of Gate Dielectrics for ZnO Based Thin-Film Transistors

The bulk of semiconductor technology has been based on silicon till today. But silicon has its own limitations. It is not transparent to visible light and hence it cannot be used in certain applications. ZnO is a material which is transparent to visible light. In this paper, we compare the electrical performance of ZnO Thin film Transistors using different gate insulators. Certain performance indices and material indices were considered as the selection criteria for electrical performance. A methodology known as Ashby’s approach was adopted to find out the best gate insulators and based on this methodology various charts were plotted to compare different properties of competing materials. This work concludes that Y2O3 is the best insulator followed by ZrO2 and HfO2

Selection of Gate Dielectrics for ZnO Based Thin-Film Transistors

The bulk of semiconductor technology has been based on silicon till today. But silicon has its own limitations. It is not transparent to visible light and hence it cannot be used in certain applications. ZnO is a material which is transparent to visible light. In this paper, we compare the electrical performance of ZnO Thin film Transistors using different gate insulators. Certain performance indices and material indices were considered as the selection criteria for electrical performance. A methodology known as Ashby\u27s approach was adopted to find out the best gate insulators and based on this methodology various charts were plotted to compare different properties of competing materials. This work concludes that Y2O3 is the best insulator followed by ZrO2 and HfO2

Characterizing Patch Test Findings in African American ACD Patients

Introduction: Allergic Contact Dermatitis (ACD) in African Americans has not been well studied, and there is little information related to differences in patterns of ACD between African Americans and Caucasians. This paper aims to investigate relative differences in patterns of sensitization in African American ACD patients. Methods: This study is a retrospective descriptive study. Data of ACD patients patch tested from 2009-2019 by Dr. Gaspari and Dr. Brod at TJUH and HUP/UPHS were reviewed. Patch test findings for African American and Caucasian patients were compared. Outcomes such as positive allergens, strength, clinical relevance, patient occupation, and personal product use were measured. The exclusion criteria included patients that had undergone patch testing with inconclusive results (technical problem with patch tests, or the patient did not return for all patch test readings). Results: The total sample size is approximately 450-500 patients, depending on further exclusion. Data analysis of ACD in African American patients (n = 47) is complete, therefore results below pertain only to this subgroup. Gold, nickel, MI/MCI, disperse dyes, PPD, textile dyes, and fragrance mixes were the most common positive allergens. 21 patients had definite clinically relevant reactions, while 6 were probable. The rest were questionable or no relevance. More conclusive data analyses will be reported once the data is complete. Discussion: Different patterns of allergy may occur due to differing patterns of exposure to personal care products (cultural), genetic susceptibility, and/or healthcare disparities. Addressing this may be useful for preventative purposes and for offering safer alternatives to common allergens

Source enumeration in non-white noise and small sample size via subspace averaging

This paper addresses the problem of source enumeration by an array of sensors in the challenging conditions of: i) large uniform arrays with few snapshots, and ii) non-white or spatially correlated noises with arbitrary correlation. To solve this problem, we combine a subspace averaging (SA) technique, recently proposed for the case of independent and identically distributed (i.i.d.) noises, with a majority vote approach. The number of sources is detected for increasing dimensions of the SA technique and then a majority vote is applied to determine the final estimate. As illustrated by some simulation examples, this simple modification makes SA a very robust method of enumerating sources in these challenging scenarios.This work was supported by the Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grants TEC2016-75067-C4-4-R (CARMEN) and BES-2017-080542

Development of a smart grid for the proposed 33 KV ring main Distribution System in NIT Rourkela

The non-reliability of fossil fuels has forced the world to use energy efficiently. These days, it is being stressed to use the electrical power smartly so that energy does not go waste. And hence comes the concept of a Smart Grid. So it becomes necessary for reputed places of academics to develop the prototype of the same in their campus. National Institute of Technology (NIT) Rourkela intends to set up a 33KV Ring Main Distribution System including 33/0.433 KV substations in its campus. The present 11KV line will be discarded and replaced by the 33KV system. The main driving force behind this step by the management is to accommodate the stupendously increased power requirement of the institute. The above mentioned plan also includes, set up of Data Acquisition System (DAS) that intends to monitor the electrical equipment in the substations. This is being done not only to increase the accountability and reliability of the distribution system but also to encourage academic research in the distribution automation domain. All in all, an excellent step towards make the Grid, Smart. In this project work the focus is laid on getting load flow solution of the 33KV ring main system. Here the authors use a specialized algorithm for distribution network with high R/X value to obtain the load flow solution. Then using artificial neural networks computation, algorithms are implemented to do the load forecasting and dynamic tariff setting. At the end a Web Portal, the NITR e-Power Monitoring System is developed that will be an excellent interface to the public in general and will help the students of the institute to know their grid well. In short a conscious effort is put to make the grid more interactive

Order estimation via matrix completion for multi-switch antenna selection

This letter addresses the problem of order estimation for uniform linear arrays (ULAs) with multi-switch antenna selection in the small-sample regime. Multi-switch antenna selection results in a data matrix with missing entries, a scenario for which existing order estimation methods that build on the eigenvalues of the sample covariance matrix do not perform well. A direct application of the Davis-Kahan theorem allows us to show that the signal subspace is quite robust in the presence of missing entries. Based on this finding, this letter proposes a matrix completion (MC) subspace-based order estimation criterion that exploits the shift-invariance property of ULAs. A recently proposed shift-invariant matrix completion (SIMC) method is used for reconstructing the data matrix, and the proposed order estimation criterion is based on the chordal subspace distance between two submatrices extracted from the reconstructed matrix for increasing values of the dimension of the signal subspace. Our simulation results show that the method provides accurate order estimates with percentages of missing entries higher than 50 % .This work was supported by the Ministerio de Ciencia e Innovación (MICINN) of Spain, and AEI/FEDER funds of the E.U., under Grants PID2019-104958RB-C43/C41 (ADELE) and BES-2017-080542

Sparse subspace averaging for order estimation

This paper addresses the problem of source enumeration for arbitrary geometry arrays in the presence of spatially correlated noise. The method combines a sparse reconstruction (SR) step with a subspace averaging (SA) approach, and hence it is named sparse subspace averaging (SSA). In the first step, each received snapshot is approximated by a sparse linear combination of the rest of snapshots. The SR problem is regularized by the logarithm-based surrogate of the l0-norm and solved using a majorization-minimization approach. Based on the SR solution, a sampling mechanism is proposed in the second step to generate a collection of subspaces, all of which approximately span the same signal subspace. Finally, the dimension of the average of this collection of subspaces provides a robust estimate for the number of sources. Our simulation results show that SSA provides robust order estimates under a variety of noise models.This work was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under grants TEC2017-86921-C2-2-R (CAIMAN), PID2019-104958RB-C43 (ADELE), and BES-2017-080542, and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM

iRogue: Identifying Rogue Behavior from App Reviews

An app user can access information of other users or third parties. We define rogue mobile apps as those that enable a user (abuser) to access information of another user or third party (victim), in a way that violates the victim's privacy expectations. Such apps are dual-use and their identification is nontrivial. We propose iRogue, an approach for identifying rogue apps based on their reviews, posted by victims, abusers, and others. iRogue involves training on deep learning features extracted from their 1,884 manually labeled reviews. iRogue first identifies how alarming a review is with respect to rogue behavior and, second, generates a rogue score for an app. iRogue predicts 100 rogue apps from a seed dataset curated following a previous study. Also, iRogue examines apps in other datasets of scraped reviews, and predicts an additional 139 rogue apps. On labeled ground truth, iRogue achieves the highest recall, and outperforms baseline approaches that leverage app descriptions and reviews. A qualitative analysis of alarming reviews reveals rogue functionalities. App users, platforms, and developers should be aware of such apps and their functionalities and take measures to curb privacy risk

Subspace averaging and order determination for source enumeration

In this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yuejie Chi. The work of V. Garg and I. Santamaria was supported in part by the Ministerio de Economía y Competitividad (MINECO) of Spain, and in part by the AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN), TEC2015-69648-REDC, and BES-2017-080542. The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grants TEC2015-69868-C2-1-R (ADVENTURE) and TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICOCM). The work of L. L. Scharf was supported in part by the U.S. NSF under Contract CISE-1712788