Luis M. Ledesma-Carrillo

Reconfigurable FPGA-Based Unit for Singular Value Decomposition of Large m x n Matrices

Singular value decomposition (SVD) allows the factorization of real or complex matrices providing quantitative information with fewer dimensions along which data points exhibit more variation. These days SVD computation is being used in numerous applications, and because of its importance, different approaches for SVD hardware computation have been proposed, however, their application is limited by the inherent SVD calculation complexity making it possible to analyze up to 8 x 8 matrices until now, complying certain constrains like symmetry. This paper presents a generic and novel FPGA-based hardware architecture for SVD computation on large m × n matrices utilizing Hestenes approach and one-side Jacobi rotations. Four different study cases (2 x 2, 8 x 7, 16 x 32, and 32 x 127 matrices) validate the performance of the FPGA-based computation unit reaching a maximum estimation error of 3.3718 % in the SVD estimation of a large matrix.

Novel FPGA-based Methodology for Early Broken Rotor Bar Detection and Classification Through Homogeneity Estimation

Early detection of induction-motor faults has been an increasing matter of research in the last few years. The reliable identification of broken rotor bars (BRB) is still under investigation as it is one of the most common and difficult-to-detect faults in induction motors. Many methods have been proposed to deal with this issue. Recent approaches combine techniques looking for improving the performance of the diagnosis. Their major disadvantage is the high computational requirements, which restrains them from being used in online detection. The contribution in this paper is twofold. The first one is a novel methodology for induction motor BRB detection and the fault severity classification using homogeneity as index, which, to the best of our knowledge, has never been used as an indicator for fault diagnosis, analyzing one phase of the induction motor startup-transient current. Because of the low computational complexity in homogeneity calculation, the second contribution of this paper is a hardware-processing unit based on a field programmable gate array device for online detection and classification of BRB. Obtained results demonstrate the high efficiency of the proposed methodology as a deterministic technique for incipient BRB diagnosis in induction motors, which can detect and differentiate among half, one, or two BRBs with a certainty greater than 99.7%.

Gabor and the Wigner-Ville transforms for broken rotor bars detection in induction motors

Induction Motors are subjected to unavoidable stresses that create failures in their different parts and result in substantial cost penalties. An effective incipient fault detection technique can reduce the maintenance expenses by preventing high cost failures and unscheduled downtimes. Broken rotor bars (BRB) is the most common rotor-related failure, and the startup transient is suitable for their detection. Therefore, several time-frequency representations have been proposed for this aim. This paper presents a performance comparison between the Gabor transform and the Wigner-Ville with a Choi-Williams kernel (WVD-CW) transform for broken rotor bar detection in induction motors. Preliminary results show that WVD-CW has better time-frequency resolution than the Gabor transform ensuring the BRB fault detection with high certainty.

FPGA-based reconfigurable unit for real-time power quality index estimation

Power quality monitoring is an important subject for investigation and research. In this work a generic FPGA-based portable architecture for real-time power quality index (PQI) estimation is proposed. Different from off-the-shelf specialized equipment, the proposed hardware implementation offers higher exactitude for PQI estimation and representation of voltage and current signals. Unlike previous approaches, the proposed FPGA-based PQI computation unit estimates up to fourteen PQI, it is highly portable to different platforms, and it can be implemented on a single chip.

Detection and diagnosis of lubrication and faults in bearing on induction motors through STFT

The Induction motors are nowadays widely used in a variety of industrial applications due to their simple build and high reliability, On the other hand, adequate bearing lubrication is so important to guarantee an adequate operation during a long time. Thus, in this work, a method for detection and diagnosis of lubrication and mechanical faults in bearing used on induction motors through Short Time Fourier Transform (STFT) is proposed. Obtained results demonstrate the correct detection of lubrication and mechanical faults in bearing by the identification of different spectral components for each faulty case and the healthy condition.

Tunable field depth: hyperbolic optical masks

For controlling the depth of field, in an optical system working at full pupil apertures, we unveil the use of a pair of hyperbolic phase masks. For suitably framing our proposal, we link the Strehl ratio versus defocus with the area under the modulation transfer function (MTF). We show that by using hyperbolic phase masks, one can simultaneously reduce the impact of focus errors as well as increase the area under the MTF. We show that hyperbolic amplitude masks, with moderate absorption, can reduce the artifact noise caused by the use of phase masks. Finally, by exploiting the Lohmann–Alvarez technique, we describe the use of pairs of hyperbolic masks for governing field depth at fixed pupil apertures.

FPGA-based reconfigurable unit for image encryption using orthogonal functions

Encryption is an important tool in many areas of application and research. The advances in communications have encouraged researchers to find new techniques for providing data security, confidentiality, integrity and authentication. The techniques proposed until now for image encryption apply well-known image processing techniques, increasing their computational complexity and processing time, threatening their use on real-time applications. On the other hand, the already proposed hardware implementations for image encryption do not allow portability to distinct FPGA platforms, and they do not guarantee high speed and optimal resource utilization. In this work, a generic real-time, FPGA-based, reconfigurable architecture for online image encryption using orthogonal functions is proposed. The introduced architecture implements a novel highly-efficient algorithm for high speed image encryption using minimal resources, which is portable to different FPGA platforms from distinct vendors. Obtained results demonstrate the effectiveness of the proposed approach on different cases of study, reaching processing rates up to 39 frames per second.

Extended depth of field in images through complex amplitude pre-processing and optimized digital post-processing

Many applications require images with high resolution and an extended depth of field. Directly changing the depth of field in optical systems results in losing resolution and information from the captured scene. Different methods have been proposed for carrying out the task of extending the depth of field. Traditional techniques consist of optical-system manipulation by reducing the pupil aperture along with the image resolution. Other methods propose the use of optical arrays with computing-intensive digital post-processing for extending the depth of field. This work proposes a pre-processing optical system and a cost-effective post-processing digital treatment based on an optimized Kalman filter to extend the depth of field in images. Results demonstrate that the proposed pre-processing and post-processing techniques provide images with high resolution and extended depth of field for different focalization errors without requiring optical system calibration. In assessing the resulting image through the universal image quality index, this technique proves superior.

FPGA-based reconfigurable unit for image filtering in frequency domain

Digital filtering is a key step of image processing in many applications. Due to its importance in this work a general FPGA-based reconfigurable architecture for real-time, online image filtering in the frequency domain is presented. The proposed FPGA-based implementation is portable to distinct platforms from different vendors. Obtained results from different study cases show the high capability and performance of the proposed hardware implementation by applying any user designed filtering operation on an image, and outperforming by two orders of magnitude its software implementation counterpart.

Hadamard circular masks: high focal depth with high throughput

We present a class of binary masks that encode, in polar coordinates, the values of a Hadamard matrix of order N. For order N ≥ 2, the binary masks increase the Strehl ratio vs. focus error by the factor N, with the highest possible light throughput. Since a Strehl ratio with high tolerance to defocus does not guarantee a modulation transfer function (MTF) with low sensitivity to focus errors, then, we show that for N = 16 the binary mask reduces also the impact of focus error on the MTF. Equivalently, the discrete binary mask has Fisher information with low variations to defocus.