Mario Alberto Ibarra-Manzano

Stereo Vision Algorithm Implementation in FPGA Using Census Transform for Effective Resource Optimization

In this work, we present the implementation in a reconfigurable architecture of a dense stereo vision algo- rithm based on census transform. Analyzing census transform algorithm we found that size and access memory could be reduced, which consequently also reduced the latency time. Furthermore, architecture resources are optimized and efficient thanks to binary operations and integer arithmetic used by census transform directly compatible with the FPGA. Final architecture is able to construct 130 dense disparity maps per second for each corresponding pair of stereo images. A performance analysis, among other three disparity map implementations and our architecture, shows that at the end, we propose a better trade off among performance, latency, logic elements and memory size. The optimization and the resource saving rend our architecture an interesting option to solve the problem of stereo vision in real time, quite used in autonomous navigation.

A CPG system based on spiking neurons for hexapod robot locomotion

In this paper, we propose a locomotion system based on a central pattern generator (CPG) for a hexapod robot, suitable for embedded hardware implementation. The CPG system was built as a network of spiking neurons, which produce rhythmic signals for three different gaits (walk, jogging and run) in the hexapod robot. The spiking neuron model used in this work is a simplified form of the well-known generalized Integrate-and-Fire neuron model, which can be trained using the Simplex method. The use of spiking neurons makes the system highly suitable for digital hardware implementations that exploit the inherent parallelism to replicate the intrinsic, computationally efficient, distributed control mechanism of CPGs. The system has been implemented on a Spartan 6 FPGA board and fully validated on a hexapod robot. Experimental results show the effectiveness of the proposed approach, based on existing models and techniques, for hexapod rhythmic locomotion.

Design and implementation of a vehicular access control using RFID

This work presents the design and implementation of vehicular access control using radio frequency identification (RFID). This system controls the accesses of three different parking and also it checks the access of each driver. With this system is possible to monitor, administer and report all the accesses and departs in each parking, this information can be available on a Web site. To this end, a database is generated with the names of the authorized people for accessing at the parking. A code number is assigned to each person, which represents the transponder tag number.

Broken Rotor Bar Detection in VSD-fed Induction Motors at Startup by High-Resolution Spectral Analysis

The fault detection in an induction motor (IM) operated by a variable speed drive (VSD) is an actual industrial need as most of the line-fed machines are replaced by a VSD, due to their improved speed regulation and fast dynamic response. However, undesired harmonics are always present when the IM is fed through a VSD. Under this operating condition, most developed techniques are unable to detect faults in the IM. This paper presents a technique based on the multiple signal classification (MUSIC) method, and it is applied to a VSD-fed IM during the startup transient; in order to verify the capability of the method to identify one broken rotor bar. From the experimental results, the proposed method is proven to be sensitive enough to detect one broken rotor bar, enabling a reliable diagnosis under different fundamental supply frequencies and load conditions.

An efficient reconfigurable architecture to implement dense stereo vision algorithm using high-level synthesis

This article presents a reconfigurable architecture to calculate a dense disparity map of two stereo images based on census transform. This architecture is simplified and efficient as a result of binary operations and integer arithmetic used by census transform. Our architecture was prototyped using GAUT which is a practical tool to develop high-level synthesis. We take advantage of GAUT rapid prototyping to implement different architectures and to make a general comparison among them, that lets us to optimize the architecture, and at the same time, to improve the systems performance. The optimization and the resource saving rend our architecture an interesting option to solve the problem of stereo vision in real time, quite used in autonomous navigation.

Design and Optimization of Real-Time Texture Analysis Using Sum and Difference Histograms Implemented on an FPGA

This work presents the adequacy of a dense texture analysis algorithm based on sum and difference histograms (SDHs) into a reconfigurable architecture. A deep analysis of the SDHs algorithm helps found a significant reduction in operations, size and access memory which consequently optimize resources, memory and latency time during the implementation. Furthermore, integer arithmetic operations used by SDHs are directly compatible with the FPGA making faster and efficient the implementation. Final architecture constructs 30 dense texture feature images of 640 × 480 pixels per second. Also a performance comparison among three different texture analysis architectures highlights a better trade off in performance, latency, logic elements and memory size of our system. The optimization and the resource saving make the proposed architecture an interesting choice to solve the problem of texture analysis in real time, quite used in artificial vision, autonomous navigation and medical applications.

Real-time classification based on color and texture attributes on an FPGA-based architecture

The design and the implementation of algorithms on FPGA-based architectures, is a complex task, above all for image processing. Many vision applications (video monitoring, obstacle detection from a vehicle) require real time performance. This paper analyzes only a classical function involved in these applications: pixel characterization by an attribute vector, and pixel classification as belonging or not to an interest class. Typical attributes are color and texture. Color is described by the chrominance given by the a and b coordinates in the CIE-Lab color space. Texture is only computed from the L* coordinate, describing the local intensity variations in a neighborhood of every pixel. AdaBoost has been selected in order to learn how to classify every pixel from its attribute vector. From a learning data base, it is learnt off line how to select and combine a given number of weak classifiers; then, the classifier parameters are loaded on an FPGA-based kit. This paper proposes different architectures and presents some results obtained from images acquired from a robot, in order to classify a pixel as Ground or Obstacle.

Access Control System Using an Embedded System and Radio Frequency Identification Technology

The radio frequency identification technique has been known since decades ago, however due to some important advances in technology, the amount of micro devices built-in on a chip and the cost of manufacturing, this technology has been implemented in many applications nowadays. A radio frequency identification system used to security check in a University is presented in this work. In this system several autonomous embedded sub-systems are included, which are placed at different entrances at school. Every embedded sub-system has a link up to a master control that registers every event at the entrance. This master control has the command to close or open any entry at any time if a case of emergency is occurred.A detailed analysis about the elements that are included in this autonomous access control is presented. Some important aspects of this system are also presented. Conclusions and perspectives are presented at the end of this work.

An FPGA implementation for texture analysis considering the real-time requirements of vision-based systems

This article presents an architecture based on FPGA for the calculation of texture attributes using an adequacy of the technique of sum and differences of histograms. The attributes calculated by this architecture will be used in a process of classification for identification of objects during the navigation of an autonomous robot of service. Because of that, the constraint of real-time execution plays an essential role during the architecture design. So, the architecture is designed to calculate 30 dense images with 6 different attributes of texture for 10 different displacements. Exploiting the reuse of operations in parallel on the FPGA and taking into account the requisites in the time of calculation, it is possible to use the resources in an efficient and optimised way in order to obtain an architecture with the best trade off between resources and the time of calculation. Thanks to the high performance of this architecture, it can be used in applications like medical diagnosis or target detection.

Boosting for Image Interpretation by Using Natural Features

In this paper a research in classification of natural images by using Adaboost (adapting boosting) method is presented. This technique is used to identify the nature of the main regions in the image, that is, to identify if they are roads, trees, shades, sky, bushes or others interesting regions; image is previously segmented and each of its regions are represented by a R12 data vector (including features as color, texture and context), in at least 5 classes. The proposed methodology is presented for a multi-class classification problem and for validating our results, performances ratios between Adaboost and the support vector machines are discussed. This methodology is intent to be applied in medical imagery and in visual based navigation on natural environments; in robot navigation, very good results are obtained even in poorly color saturated images. Finally, the results are described and presented showing that Adaboost is a reliable classification technique giving slightly better performances than SVM for regions in natural images.