We describe a novel constrained least-squares (LS) optimization problem and propose an iterative solution that deals with the constraints of placing variables on the complex unit circle. We reformulate the LS problem with unit magnitude constraints so that we obtain efficient closed-form local updates based on Procrustes orthogonal solutions that monotonically improve the objective function. We show the performance of the proposed algorithm in a synthetic experiment and an application to hybrid precoding/combining in mmWave MIMO communications.

Many applications like audio and image processing show that sparse representations are a powerful and efficient signal modeling technique. Finding an optimal dictionary that generates at the same time the sparsest representations of data and the smallest approximation error is a hard problem approached by dictionary learning (DL). We study how DL performs in detecting abnormal samples in a dataset of signals. In this paper we use a particular DL formulation that seeks uniform sparse representations model to detect the underlying subspace of the majority of samples in a dataset, using a K-SVD-type algorithm. Numerical simulations show that one can efficiently use this resulted subspace to discriminate the anomalies over the regular data points.

In this paper, we describe a new algorithm to build a few sparse principal components from a given data matrix. Our approach does not explicitly create the covariance matrix of the data and can be viewed as an extension of the Kogbetliantz algorithm to build an approximate singular value decomposition for a few principal components. We show the performance of the proposed algorithm to recover sparse principal components on various datasets from the literature and perform dimensionality reduction for classification applications.

Separable, or Kronecker product, dictionaries provide natural decompositions for 2D signals, such as images. In this paper, we describe a highly parallelizable algorithm that learns such dictionaries which reaches sparse representations competitive with the previous state of the art dictionary learning algorithms from the literature but at a lower computational cost. We highlight the performance of the proposed method to sparsely represent image and hyperspectral data, and for image denoising.

In this paper, we propose a solution to the sensor management problem over multiple time instances that balances the accuracy of the sensor network estimation with its utilization. We show how this problem reduces to a binary optimization problem for which we give a convex relaxation based solution that involves the minimization of a regularized ℓ∞reweighted ℓ1norm. We show experimentally the behavior of the proposed algorithm and compare it with previous methods from the literature.

This paper considers the design of hybrid precoders and combiners for mmWave MIMO systems with per-antenna power constraints and the additional limitations introduced by the phase-shifting network in the analog processing stage. Previous hybrid designs were obtained using a total power constraint, but in practical implementations per-antenna constraints are more realistic, specially at mmWave, given the large number of power amplifiers used in the transmit array. Assuming perfect channel knowledge, we obtain first an approximation to the all-digital solution for the precoder and the combiner given the per-antenna constraints. Then, we develop a new method for the design of the hybrid precoder and combiner which attempts to match such all- digital approximation. Simulation results show the effectiveness of the proposed approach, which performs close to the all-digital solution.

This paper addresses the problem of designing thinned arrays with minimized side lobe levels for antenna selection in millimeter wave MIMO systems. We propose a new optimization solution based on compressed sensing techniques and convex optimization relaxation which we show to be a heuristic that solves the original binary optimization problem of side lobe level minimization. We compare the proposed method with other approaches from the literature like simulated annealing and genetic algorithms showing the superiority of the method in terms of performance, running time and ease of parameter tuning. The simulation results cover a wide range of dimensions and situations.

In this paper we analyze the mean squared error (MSE) for one-bit compressed sensing schemes based on measurement matrices that correspond to unit norm tight frames. We show that, as in the unquantized case, sensing with unit norm tight frames improves the MSE in the reconstruction of sparse vectors from one-bit measurements using l\ and thresholding algorithms. From our analytical and experimental results we conclude that when implementing one-bit compressed sensing schemes with fixed measurement matrices unit norm tight frames are the measurements of choice.

Millimeter communication systems use large antenna arrays to provide good average received power and to take advantage of multi-stream MIMO communication. Unfortunately, due to power consumption in the analog front-end, it is impractical to perform beamforming and fully digital precoding at baseband. Hybrid precoding/combining architectures have been proposed to overcome this limitation. The hybrid structure splits the MIMO processing between the digital and analog domains, while keeping the performance close to that of the fully digital solution. In this paper, we introduce and analyze several algorithms that efficiently design hybrid precoders and combiners starting from the known optimum digital precoder/combiner, which can be computed when perfect channel state information is available. We propose several low complexity solutions which provide different trade-offs between performance and complexity. We show that the proposed iterative solutions perform better in terms of spectral efficiency and/or are faster than previous methods in the literature. All of them provide designs which perform close to the known optimal digital solution. Finally, we study the effects of quantizing the analog component of the hybrid design and show that even with coarse quantization, the average rate performance is good.

Multiple input multiple output (MIMO) systems employing large antenna arrays are the basic architecture for millimeter wave (mmWave) systems. Due to the higher bandwidths to be used at mmWave, the corresponding sampling rates of high-resolution analog-to-digital converters (ADCs) are also very high, so that ADCs become the most power hungry devices in the reception chain. One solution is to employ low resolution, i.e. one-bit, ADCs. We develop an adaptive one-bit compressed sensing scheme that can be used at low-resolution mmWave receivers for channel estimation. The simulation results show that the adaptive one-bit compressed sensing scheme outperforms the fixed one in the context of mmWave channel estimation.

We propose a low complexity method for estimating direction of arrival (DOA) when the positions of the array sensors are affected by errors with known magnitude bound. This robust DOA method is based on solving an optimization problem whose solution is obtained in two stages. First, the problem is relaxed and the corresponding power estimation has an expression similar to that of standard beamforming. If the relaxed solution does not satisfy the magnitude bound, an approximation is made by projection. Unlike other robust DOA methods, no eigenvalue decomposition is necessary and the complexity is similar to that of MVDR. For low and medium SNR, the proposed method competes well with more complex methods and is clearly better than MVDR.

Antenna subset modulation (ASM) is a physical layer security technique that is well suited for millimeter wave communication systems. The key idea is to vary the radiation pattern at the symbol rate by selecting one from a subset of patterns with a similar main lobe and different side lobes. This paper shows that ASM is not robust to an eavesdropper that makes multiple simultaneous measurements at multiple angles. The measurements are combined and used to formulate an estimation problem to undo the effects of the side lobe randomization. Simulations show the performance of the estimation algorithms and how the eavesdropper can effectively recover the information if the signal-to-noise ratio exceeds a certain threshold. Using fewer active radio frequency chains makes it harder for the attacker to recover the transmit symbol, at the expense of more grating lobes.

Millimeter wave (mmWave) multiple-input multipleoutput (MIMO) communication with large antenna arrays has been proposed to enable gigabit per second communication for next generation cellular systems and local area networks. A key difference relative to lower frequency solutions is that in mmWave systems, precoding/combining can not be performed entirely at digital baseband, due to the high cost and power consumption of some components of the radio frequency (RF) chain. In this paper we develop a low complexity algorithm for finding hybrid precoders that split the precoding/combining process between the analog and digital domains. Our approach exploits sparsity in the received signal to formulate the design of the precoder/combiners as a compressed sensing optimization problem. We use the properties of the matrix containing the array response vectors to find first an orthonormal analog precoder, since sparse approximation algorithms applied to orthonormal sensing matrices are based on simple computations of correlations. Then, we propose to perform a local search to refine the analog precoder and compute the baseband precoder. We present numerical results demonstrate substantial improvements in complexity while maintaining good spectral efficiency.

Touchscreen-based mobile devices (TMDs) are one of the most popular and widespread kind of electronic device. Many manufacturers have published its own design principles as a guideline for developers. Each platform has specific constrains and recommendations for software development; specially in terms of user interface. Four sets of design principles from iOS, Windows Phone, Android and Tizen OS has been mapped against a set of usability heuristics for TMDs. The map shows that the TMDs usability heuristics cover almost every design pattern with the addition of two new dimensions: user experience and cognitive load. These new dimensions will be considered when updating the proposal of usability heuristics for TMDs.

Nowadays, communications media are growing drastically, but the language barrier still remains high. Among the available services to reduce this gap, we use Julius, a voice recognition system. The paper proposes a Spanish language model, not yet available in Julius. To this end, we used the development toolkit HTK (Hidden Markov Model Toolkit). After creating it, each model was partially trained and validated.

In this paper we present an efficient initialization strategy that improves the performance of overcomplete dictionary learning algorithms. The procedure exploits incoherent structures that can be manipulated and adapted to a given dataset relatively fast. The algorithm involves an iterative adaptation of the dictionary to the dataset with pruning of the less used atoms and constructions of new atoms that fit the data better. Experimental simulations show that the proposed method improves the performance of classical and new developments in dictionary learning algorithms.

The linear transformation of input (typically RGB) data into a color space is important in image compression. Most schemes adopt fixed transforms to decorrelate the color channels. Energy compaction transforms such as the Karhunen-Loève (KLT) do entail a complexity increase. Here, we propose a new data-dependent transform (aKLT), that achieves compression performance comparable to the KLT, at a fraction of the computational complexity. More important, we also consider an application-aware setting, in which a classifier analyzes reconstructed images at the receiver's end. In this context, KLT-based approaches may not be optimal and transforms that maximize post-compression classifier performance are more suited. Relaxing energy compactness constraints, we propose for the first time a transform which can be found offline optimizing the Fisher discrimination criterion in a supervised fashion. In lieu of channel decorrelation, we obtain spatial decorrelation using the same color transform as a rudimentary classifier to detect objects of interest in the input image without adding any computational cost. We achieve higher savings encoding these regions at a higher quality, when combined with region-of-interest capable encoders, such as JPEG 2000.

This paper aims to identify approaches that generate appropriate synthetic data (computer generated) for cardiac phase-resolved blood-oxygen-level-dependent (CP-BOLD) MRI. CP-BOLD MRI is a new contrast agent- and stress-free approach for examining changes in myocardial oxygenation in response to coronary artery disease. However, since signal intensity changes are subtle, rapid visualization is not possible with the naked eye. Quantifying and visualizing the extent of disease relies on myocardial segmentation and registration to isolate the myocardium and establish temporal correspondences and ischemia detection algorithms to identify temporal differences in BOLD signal intensity patterns. If transmurality of the defect is of interest pixel-level analysis is necessary and thus a higher precision in registration is required. Such precision is currently not available affecting the design and performance of the ischemia detection algorithms. In this work, to enable algorithmic developments of ischemia detection irrespective to registration accuracy, we propose an approach that generates synthetic pixel-level myocardial time series. We do this by 1) modeling the temporal changes in BOLD signal intensity based on sparse multi-component dictionary learning, whereby segmentally derived myocardial time series are extracted from canine experimental data to learn the model; and 2) demonstrating the resemblance between real and synthetic time series for validation purposes. We envision that the proposed approach has the capacity to accelerate development of tools for ischemia detection while markedly reducing experimental costs so that cardiac BOLD MRI can be rapidly translated into the clinical arena for the noninvasive assessment of ischemic heart disease.

Many Function Point (FP) technique adaptations have been proposed to estimate object-oriented software development projects. However, most research works propose rules to identify FP according previous versions of the FP Counting Practices Manual or they do not include some important UML specifications such as the composition relationship between classes. In this paper, we present the rules to identify logic files using analysis class diagrams that are included in the UML2FP technique that we have defined, and a software tool named Tupux that support UML2FP application. These rules were defined in accordance with the recommendations included in the FP Counting Practices Manual 4.3.1. We also present the results obtained by applying our rules to software size estimation in a case study performed with undergraduate and graduate students. These results have proved that a person can make mistakes when applying the technique which could be avoided by using a software tool.

This paper describes a new procedure for the design of incoherent frames used in the field of sparse representations. We present an efficient algorithm for the design of incoherent frames that works well even when applied to the construction of relatively large frames. The main advantage of the proposed method is that it uses a convex optimization formulation that operates directly on the frame, and not on its Gram matrix. Solving a sequence of convex optimization problems allows for the introduction of constraints on the frame that were previously considered impossible or very hard to include, such as non-negativity. Numerous experimental results validate the approach.

The problem of training a dictionary for sparse representations from a given dataset is receiving a lot of attention mainly due to its applications in the fields of coding, classification and pattern recognition. One of the open questions is how to choose the number of atoms in the dictionary: if the dictionary is too small then the representation errors are big and if the dictionary is too big then using it becomes computationally expensive. In this letter, we solve the problem of computing efficient dictionaries of reduced size by a new design method, called Stagewise K-SVD, which is an adaptation of the popular K-SVD algorithm. Since K-SVD performs very well in practice, we use K-SVD steps to gradually build dictionaries that fulfill an imposed error constraint. The conceptual simplicity of the method makes it easy to apply, while the numerical experiments highlight its efficiency for different overcomplete dictionaries.

Most of the usability evaluation methods may be used in order to evaluate transactional web applications. The problem arises when deciding which usability evaluation methods bring more information. A study has been done in order to develop a methodology for the usability evaluation of transactional web applications. The methodology was developed and validate trough a number of case studies.

Interactive television (iTV) is the convergence of television with digital media technologies. iTV must be treated as a unique medium with its own set of constraints and opportunities. A set of principles to follow, when designing iTV applications, is presented. Heuristics to be applied when evaluating iTV applicationspsila usability is described.

Some authors argue that it can be complicated to build systems with both usability and security, but the reality is that there is no real conflict between these two properties. Certainly, it takes more work to build systems that have the properties of usability and security, but in many cases it is a matter of doing just a good job not miracles. The purpose of this paper is to establish patterns that allow to align both usability and security aspects.

The importance of Human-Computer Interaction (HCI) education for software professionals should be evident and well understood, when designing computing programs, at all levels. Unfortunately there is a lack of HCI courses in Peruvian computing programs. Changing curricula is often difficult and involves a long bureaucratic process. However, making small changes in courses' approach and emphasis only requires good will from the professors.The current paper presents a proposal to introduce HCI topics and/or courses in the Informatics Engineering curricula at Pontificia Universidad Católica del Perú, based on the Chilean educational experience at Pontificia Universidad Católica de Valparaíso.

The importance of Human-Computer Interaction (HCI) education for software professionals should be evident and well understood, when designing Computer Science (CS) programs, at all levels. Unfortunately there is a lack of HCI courses in Chilean CS programs. It is difficult and usually highly bureaucratic to change curricula. It is only a matter of good will to make small changes in the courses' approach and emphasis. An appealing way to introduce HCI at all computer science curricula levels is by systematically including usability practices, especially usability evaluations.

The goal of this work is to show how the argument principle together with discrete Fourier transform (DFT) can be used to determine some properties of finite-length complex-valued sequences, precisely the localization of zeros with respect to unit circle of their related Z-transform. An upper bound of the DFT (in one step) length to exactly calculate the increase of the argument is also provided.