Dr. Marco Aurelio Nuño-Maganda
Intelligent Systems Department, Universidad Politecnica de Victoria, Ciudad Victoria, Tamaulipas, Mexico
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Academic Career Length
23Years (2004-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications1 January 2005
Conference Paper
Marco Aurelio Nuño-MagandaMAM.O. Arias-Estrada
M.O. Arias-Estrada
One of the most extended algorithms for image scaling is bicubic interpolation. In this paper, a hardware architecture for bicubic interpolation (HABI) is proposed. The HABI proposed is integrated by three main blocks: the first one generates the interpolation coefficients, which implements the bicubic function to be used in HABI; the second one performs the interpolation process and the third one is a control unit that synchronizes the processing and the pipeline stages. The architecture work with monochromatic images, but it can be extended for working with RGB color images. Our design description is coded in Handel-C language and implemented on a Xilinx Virtex II Pro FPGA. The proposed system runs 10 times faster than an Intel Pentium 4-based PC at 2.4 GHz. Comparison with other related works are provided
1 February 2015
Conference Paper
CTCesar Torres-Huitzil
Cesar Torres-Huitzil
Marco Aurelio Nuño-MagandaMobile artifacts such as smartphones have made possible the development of wearable systems for user activity monitoring and recognition due to the synergy of communication, computation and sensing capabilities in battery-powered systems-on-chip. Due to user acceptability, smartphones are able to measure nonintrusively proprioceptive motion outside of a controlled environment for rather long periods of time using embedded inertial sensors. Though work has been done for accelerometer-based activity recognition, the portability of the smartphone to a single fixed tight position has been a major constraint to easy the interpretation of the collected data. In this paper, a human activity hierarchical recognition system based on time-domain features and neural networks without the need of the smartphone to be constrained to a single fixed body position is presented. Experimental results on Android-capable smartphones on four on-body locations show that the recognition system achieves high classification rates, above 92%, for five activities including static, walking, running, and up-down stairs walking, running continuously in near real-time with reduced power consumption.
1 August 2014
Conference Paper
RPRaul Humberto Pena-Gonzalez
Raul Humberto Pena-Gonzalez
Marco Aurelio Nuño-MagandaDetect, classify and keep track, in real-time, on different kinds of objects or vehicles that are moving on a road is crucial for traffic managements systems, among other research areas. In this paper, a vision based system to detect, track, count and classify moving vehicles, on any kind of road, is shown. The data acquisition system consists of a HD-RGB camera placed on the road, while the information processing is performed by clustering and classification algorithms. The system obtained an efficiency score over the 95 percent in test cases, as well, the correct classification of 85 percent of the test objects. Also, the system achieves 30 fps in image processing with a resolution of 1280×720.
Journal Article
Marco Aurelio Nuño-MagandaHHHIRAM HERRERA-RIVAS
HIRAM HERRERA-RIVASCTCesar Torres-Huitzil
Cesar Torres-HuitzilHMHeidy Marin-Castro
Heidy Marin-CastroYCYuriria Coronado-Pérez
Yuriria Coronado-PérezIndoor positioning is a recent technology that has gained interest in industry and academia thanks to the promising results of locating objects, people or robots accurately in indoor environments. One of the utilized technologies is based on algorithms that process the Received Signal Strength Indicator (RSSI) in order to infer location information without previous knowledge of the distribution of the Access Points (APs) in the area of interest. This paper presents the design and implementation of an indoor positioning mobile application, which allows users to capture and build their own RSSI maps by off-line training of a set of selected classifiers and using the models generated to obtain the current indoor location of the target device. In an early experimental and design stage, 59 classifiers were evaluated, using data from proposed indoor scenarios. Then, from the tested classifiers in the early stage, only the top-five classifiers were integrated with the proposed mobile indoor positioning, based on the accuracy obtained for the test scenarios. The proposed indoor application achieves high classification rates, above 89%, for at least 10 different locations in indoor environments, where each location has a minimum separation of 0.5 m.
Journal Article
Marco Aurelio Nuño-MagandaIDIsmael Antonio Dávila-Rodríguez
Ismael Antonio Dávila-RodríguezYHYahir Hernández-Mier
Yahir Hernández-Mier
Jose Hugo Barron-ZambranoJEJuan Elizondo-Leal
Juan Elizondo-LealSPSaid Polanco-Martagón
Said Polanco-Martagón +1
Citrus fruits are the second most important crop worldwide. One of the most important tasks is sorting, which involves manually separating the fruit based on its degree of maturity, and in many cases, involves a task carried out manually by human operators. A machine vision-based citrus sorting system can replace labor work for the inspection of fruit sorting. This article proposes a vision system for citrus fruit sorting implemented on a dedicated and efficient Field Programmable Gate Array (FPGA) hardware architecture coupled with a mechanical sorting machine, where the FPGA performs fruit segmentation and color and size classification. We trained a decision tree (DT) using a balanced dataset of reference images to perform pixel classification. We evaluate the segmentation task using a pixel accuracy metric, defined as the ratio between correctly segmented pixels produced by a DT and the total pixels in the reference image segmented offline using Otsu’s thresholding algorithm. The balance between correctly classified images by color or size and their corresponding labels of that color and size evaluates the color and size classification algorithms. Considering these metrics, the system reaches an accuracy of 97% for fruit segmentation, 94% for color classification, and 90% for size classification, running at 60 fps.
Journal Article
JEJuan Elizondo-Leal
Juan Elizondo-LealJRJosé Gabriel Ramírez-Torres
José Gabriel Ramírez-TorresJose Hugo Barron-ZambranoADAlan Diaz-Manríquez
Alan Diaz-ManríquezMarco Aurelio Nuño-Maganda
Vicente Paul Saldivar-AlonsoDistance transform (DT) and Voronoi diagrams (VDs) have found many applications in image analysis. Euclidean distance transform (EDT) can generate forms that do not vary with the rotation, because it is radially symmetrical, which is a desirable characteristic in distance transform applications. Recently, parallel architectures have been very accessible and, particularly, GPU-based architectures are very promising due to their high performance, low power consumption and affordable prices. In this paper, a new parallel algorithm is proposed for the computation of a Euclidean distance map and Voronoi diagram of a binary image that mixes CUDA multi-thread parallel image processing with a raster propagation of distance information over small fragments of the image. The basic idea is to exploit the throughput and the latency in each level of memory in the NVIDIA GPU; the image is set in the global memory, and can be accessed via texture memory, and we divide the problem into blocks of threads. For each block we copy a portion of the image and each thread applies a raster scan-based algorithm to a tile of pixels. Experiment results exhibit that our proposed GPU algorithm can improve the efficiency of the Euclidean distance transform in most cases, obtaining speedup factors that even reach 3.193.
About Me
I am a Professor in the Department of Information Technology at the Polytechnic University of Victoria, Mexico, where I have served since 2009. I earned a B.S. in Computer Engineering from the Universidad Nacional Autónoma de México (UNAM) in 2001, and an M.S. and Ph.D. in Computer Science from the National Institute of Astrophysics, Optics and Electronics (INAOE) in 2003 and 2009, respectively.
My research interests focus on designing and developing intelligent computational systems, with an emphasis on mobile computing, computer vision, machine learning, and embedded systems. My current research projects include real-time mobile computer vision, machine-learning-based visual analysis, and developing Augmented Reality (AR) and Virtual Reality (VR) applications for education, scientific visualization, and interactive environments. I am particularly interested in integrating emerging computing technologies to develop efficient and innovative solutions for practical applications in science, engineering, and education. Other research interests include FPGA-based embedded systems and high-performance implementations of computer vision and machine-learning algorithms. I am especially interested in how algorithms, software architectures, and specialized hardware interact to develop computationally efficient solutions for real-world applications.