Location of Latent Forensic Traces Using Multispectral Bands.

In this paper, a conventional camera modified to capture multispectral images, has been used to locate latent forensic traces with a smart combination of wavelength filters, capturing angle, and illumination sources. There are commercial multispectral capture devices adapted to the specific tasks of the police, but due to their high cost and operation not well adapted to the field work in a crime scene, they are not currently used by forensic units. In our work, we have used a digital SLR camera modified to obtain a nominal sensitivity beyond the visible spectrum. The goal is to obtain forensic evidences from a crime scene using the multispectral camera by an expert in the field knowing which wavelength filters and correct illumination sources should be used, making visible latent evidences hidden from the human-eye. In this paper, we show a procedure to retrieve from latent forensic traces, showing the validity of the system in different real cases (blood stains, hidden/erased tattoos, unlocking patterns on mobile devices). This work opens the possibility of applying multispectral inspections in the forensic field specially for operational units for the location of latent through non-invasive optical procedures.

Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm.

The Internet of Things (IoT) and Demand Response (DR) combined have transformed the way Information and Communication Technologies (ICT) contribute to saving energy and reducing costs, while also giving consumers more control over their energy footprint. Unlike current price and incentive based DR strategies, we propose a DR model that promotes consumers reaching coordinated behaviour towards more sustainable (and green) communities. A cooperative DR system is designed not only to bolster energy efficiency management at both home and district levels, but also to integrate the renewable energy resource information into the community's energy management. Initially conceived in a centralised way, a data collector called the "aggregator" will handle the operation scheduling requirements given the consumers' time preferences and the available electricity supply from renewables. Evaluation on the algorithm implementation shows feasible computational cost (CC) in different scenarios of households, communities and consumer behaviour. Number of appliances and timeframe flexibility have the greatest impact on the reallocation cost. A discussion on the communication, security and hardware platforms is included prior to future pilot deployment.

Event-Based Sensing and Control for Remote Robot Guidance: An Experimental Case.

This paper describes the theoretical and practical foundations for remote control of a mobile robot for nonlinear trajectory tracking using an external localisation sensor. It constitutes a classical networked control system, whereby event-based techniques for both control and state estimation contribute to efficient use of communications and reduce sensor activity. Measurement requests are dictated by an event-based state estimator by setting an upper bound to the estimation error covariance matrix. The rest of the time, state prediction is carried out with the Unscented transformation. This prediction method makes it possible to select the appropriate instants at which to perform actuations on the robot so that guidance performance does not degrade below a certain threshold. Ultimately, we obtained a combined event-based control and estimation solution that drastically reduces communication accesses. The magnitude of this reduction is set according to the tracking error margin of a P3-DX robot following a nonlinear trajectory, remotely controlled with a mini PC and whose pose is detected by a camera sensor.

Optimization of the Coverage and Accuracy of an Indoor Positioning System with a Variable Number of Sensors.

This paper focuses on optimal sensor deployment for indoor localization with a multi-objective evolutionary algorithm. Our goal is to obtain an algorithm to deploy sensors taking the number of sensors, accuracy and coverage into account. Contrary to most works in the literature, we consider the presence of obstacles in the region of interest (ROI) that can cause occlusions between the target and some sensors. In addition, we aim to obtain all of the Pareto optimal solutions regarding the number of sensors, coverage and accuracy. To deal with a variable number of sensors, we add speciation and structural mutations to the well-known non-dominated sorting genetic algorithm (NSGA-II). Speciation allows one to keep the evolution of sensor sets under control and to apply genetic operators to them so that they compete with other sets of the same size. We show some case studies of the sensor placement of an infrared range-difference indoor positioning system with a fairly complex model of the error of the measurements. The results obtained by our algorithm are compared to sensor placement patterns obtained with random deployment to highlight the relevance of using such a deployment algorithm.

On-Board Event-Based State Estimation for Trajectory Approaching and Tracking of a Vehicle.

For the problem of pose estimation of an autonomous vehicle using networked external sensors, the processing capacity and battery consumption of these sensors, as well as the communication channel load should be optimized. Here, we report an event-based state estimator (EBSE) consisting of an unscented Kalman filter that uses a triggering mechanism based on the estimation error covariance matrix to request measurements from the external sensors. This EBSE generates the events of the estimator module on-board the vehicle and, thus, allows the sensors to remain in stand-by mode until an event is generated. The proposed algorithm requests a measurement every time the estimation distance root mean squared error (DRMS) value, obtained from the estimator's covariance matrix, exceeds a threshold value. This triggering threshold can be adapted to the vehicle's working conditions rendering the estimator even more efficient. An example of the use of the proposed EBSE is given, where the autonomous vehicle must approach and follow a reference trajectory. By making the threshold a function of the distance to the reference location, the estimator can halve the use of the sensors with a negligible deterioration in the performance of the approaching maneuver.

Power measurement methods for energy efficient applications.

Energy consumption constraints on computing systems are more important than ever. Maintenance costs for high performance systems are limiting the applicability of processing devices with large dissipation power. New solutions are needed to increase both the computation capability and the power efficiency. Moreover, energy efficient applications should balance performance vs. consumption. Therefore power data of components are important. This work presents the most remarkable alternatives to measure the power consumption of different types of computing systems, describing the advantages and limitations of available power measurement systems. Finally, a methodology is proposed to select the right power consumption measurement system taking into account precision of the measure, scalability and controllability of the acquisition system.

Computational burden resulting from image recognition of high resolution radar sensors.

This paper presents a methodology for high resolution radar image generation and automatic target recognition emphasizing the computational cost involved in the process. In order to obtain focused inverse synthetic aperture radar (ISAR) images certain signal processing algorithms must be applied to the information sensed by the radar. From actual data collected by radar the stages and algorithms needed to obtain ISAR images are revised, including high resolution range profile generation, motion compensation and ISAR formation. Target recognition is achieved by comparing the generated set of actual ISAR images with a database of ISAR images generated by electromagnetic software. High resolution radar image generation and target recognition processes are burdensome and time consuming, so to determine the most suitable implementation platform the analysis of the computational complexity is of great interest. To this end and since target identification must be completed in real time, computational burden of both processes the generation and comparison with a database is explained separately. Conclusions are drawn about implementation platforms and calculation efficiency in order to reduce time consumption in a possible future implementation.

Parametric dense stereovision implementation on a system-on chip (SoC).

This paper proposes a novel hardware implementation of a dense recovery of stereovision 3D measurements. Traditionally 3D stereo systems have imposed the maximum number of stereo correspondences, introducing a large restriction on artificial vision algorithms. The proposed system-on-chip (SoC) provides great performance and efficiency, with a scalable architecture available for many different situations, addressing real time processing of stereo image flow. Using double buffering techniques properly combined with pipelined processing, the use of reconfigurable hardware achieves a parametrisable SoC which gives the designer the opportunity to decide its right dimension and features. The proposed architecture does not need any external memory because the processing is done as image flow arrives. Our SoC provides 3D data directly without the storage of whole stereo images. Our goal is to obtain high processing speed while maintaining the accuracy of 3D data using minimum resources. Configurable parameters may be controlled by later/parallel stages of the vision algorithm executed on an embedded processor. Considering hardware FPGA clock of 100 MHz, image flows up to 50 frames per second (fps) of dense stereo maps of more than 30,000 depth points could be obtained considering 2 Mpix images, with a minimum initial latency. The implementation of computer vision algorithms on reconfigurable hardware, explicitly low level processing, opens up the prospect of its use in autonomous systems, and they can act as a coprocessor to reconstruct 3D images with high density information in real time.

Differential binary encoding method for calibrating image sensors based on IOFBs.

Image transmission using incoherent optical fiber bundles (IOFBs) requires prior calibration to obtain the spatial in-out fiber correspondence necessary to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table called the Reconstruction Table (RT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a very fast method based on image-scanning using spaces encoded by a weighted binary code to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and the image reconstruction quality is very good compared to previous techniques based on spot or line scanning, for example.

Efficient smart CMOS camera based on FPGAs oriented to embedded image processing.

This article describes an image processing system based on an intelligent ad-hoc camera, whose two principle elements are a high speed 1.2 megapixel Complementary Metal Oxide Semiconductor (CMOS) sensor and a Field Programmable Gate Array (FPGA). The latter is used to control the various sensor parameter configurations and, where desired, to receive and process the images captured by the CMOS sensor. The flexibility and versatility offered by the new FPGA families makes it possible to incorporate microprocessors into these reconfigurable devices, and these are normally used for highly sequential tasks unsuitable for parallelization in hardware. For the present study, we used a Xilinx XC4VFX12 FPGA, which contains an internal Power PC (PPC) microprocessor. In turn, this contains a standalone system which manages the FPGA image processing hardware and endows the system with multiple software options for processing the images captured by the CMOS sensor. The system also incorporates an Ethernet channel for sending processed and unprocessed images from the FPGA to a remote node. Consequently, it is possible to visualize and configure system operation and captured and/or processed images remotely.

A focusing method in the calibration process of image sensors based on IOFBs.

A focusing procedure in the calibration process of image sensors based on Incoherent Optical Fiber Bundles (IOFBs) is described using the information extracted from fibers. These procedures differ from any other currently known focusing method due to the non spatial in-out correspondence between fibers, which produces a natural codification of the image to transmit. Focus measuring is essential prior to carrying out calibration in order to guarantee accurate processing and decoding. Four algorithms have been developed to estimate the focus measure; two methods based on mean grey level, and the other two based on variance. In this paper, a few simple focus measures are defined and compared. Some experimental results referred to the focus measure and the accuracy of the developed methods are discussed in order to demonstrate its effectiveness.

An intelligent architecture based on Field Programmable Gate Arrays designed to detect moving objects by using Principal Component Analysis.

This paper presents a complete implementation of the Principal Component Analysis (PCA) algorithm in Field Programmable Gate Array (FPGA) devices applied to high rate background segmentation of images. The classical sequential execution of different parts of the PCA algorithm has been parallelized. This parallelization has led to the specific development and implementation in hardware of the different stages of PCA, such as computation of the correlation matrix, matrix diagonalization using the Jacobi method and subspace projections of images. On the application side, the paper presents a motion detection algorithm, also entirely implemented on the FPGA, and based on the developed PCA core. This consists of dynamically thresholding the differences between the input image and the one obtained by expressing the input image using the PCA linear subspace previously obtained as a background model. The proposal achieves a high ratio of processed images (up to 120 frames per second) and high quality segmentation results, with a completely embedded and reliable hardware architecture based on commercial CMOS sensors and FPGA devices.

Sensor Calibration Based on Incoherent Optical Fiber Bundles (IOFB) Used For Remote Image Transmission.

Image transmission using incoherent optical fiber bundles (IOFB) requires prior calibration to obtain the spatial in-out fiber correspondence in order to reconstruct the image captured by the pseudo-sensor. This information is recorded in a Look-Up Table (LUT), used later for reordering the fiber positions and reconstructing the original image. This paper presents a method based on line-scan to obtain the in-out correspondence. The results demonstrate that this technique yields a remarkable reduction in processing time and increased image quality by introducing a fiber detection algorithm, an intensity compensation process and finally, a single interpolation algorithm.

Analysis of the optimal sampling rate for state estimation in sensor networks with delays.

When addressing the problem of state estimation in sensor networks, the effects of communications on estimator performance are often neglected. High accuracy requires a high sampling rate, but this leads to higher channel load and longer delays, which in turn worsens estimation performance. This paper studies the problem of determining the optimal sampling rate for state estimation in sensor networks from a theoretical perspective that takes into account traffic generation, a model of network behaviour and the effect of delays. Some theoretical results about Riccati and Lyapunov equations applied to sampled systems are derived, and a solution was obtained for the ideal case of perfect sensor information. This result is also interesting for non-ideal sensors, as in some cases it works as an upper bound of the optimisation solution.

Discriminant Context Information Analysis for Post-Ranking Person Re-Identification.

Existing approaches for person re-identification are mainly based on creating distinctive representations or on learning optimal metrics. The achieved results are then provided in the form of a list of ranked matching persons. It often happens that the true match is not ranked first but it is in the first positions. This is mostly due to the visual ambiguities shared between the true match and other "similar" persons. At the current state, there is a lack of a study of such visual ambiguities which limit the re-identification performance within the first ranks. We believe that an analysis of the similar appearances of the first ranks can be helpful in detecting, hence removing, such visual ambiguities. We propose to achieve such a goal by introducing an unsupervised post-ranking framework. Once the initial ranking is available, content and context sets are extracted. Then, these are exploited to remove the visual ambiguities and to obtain the discriminant feature space which is finally exploited to compute the new ranking. An in-depth analysis of the performance achieved on three public benchmark data sets support our believes. For every data set, the proposed method remarkably improves the first ranks results and outperforms the state-of-the-art approaches.