Selective SWIFT-R. A Flexible Software-Based Technique for Soft Error Mitigation in Low-Cost Embedded Systems

Commercial off-the-shelf microprocessors are the core of low-cost embedded systems due to their programmability and cost-effectiveness. Recent advances in electronic technologies have allowed remarkable improvements in their performance. However, they have also made microprocessors more susceptible to transient faults induced by radiation. These non-destructive events (soft errors), may cause a microprocessor to produce a wrong computation result or lose control of a system with catastrophic consequences. Therefore, soft error mitigation has become a compulsory requirement for an increasing number of applications, which operate from the space to the ground level. In this context, this paper uses the concept of selective hardening, which is aimed to design reduced-overhead and flexible mitigation techniques. Following this concept, a novel flexible version of the software-based fault recovery technique known as SWIFT-R is proposed. Our approach makes possible to select different registers subsets from the microprocessor register file to be protected on software. Thus, design space is enriched with a wide spectrum of new partially protected versions, which offer more flexibility to designers. This permits to find the best trade-offs between performance, code size, and fault coverage. Three case studies have been developed to show the applicability and flexibility of the proposal.

Effectiveness of Low-Cost Planting Techniques for Improving Water Availability to Olea europaea Seedlings in Degraded Drylands

Reforestation projects in semiarid lands often yield poor results. Water scarcity, poor soil fertility, and structure strongly limit the survival and growth of planted seedlings in these areas. At two experimental semiarid sites, we evaluated a variety of low-cost planting techniques in order to increase water availability to plants. Treatments included various combinations of traditional planting holes; water-harvesting microcatchments; stone or plastic mulches; small waterproof sheets to increase water harvesting; dry wells; buried clay pots; and deep irrigation. Some of these treatments were also combined with addition of composted biosolids. Waterproof sheets significantly enhanced water harvesting (43%) and soil moisture in the planting hole (40%), especially for low-intensity rainfall events. Treatment effects on the survival and growth of Olea europaea seedlings varied between experimental sites. At the most water-limited site, clay pots, and dry wells improved seedling survival, while no treatment enhanced seedling growth. At the least water-stressed site, the application of composted sludge significantly improved seedling growth. We conclude that nutrient-mediated stress is subordinate to water stress in arid and semiarid environments, and we suggest modifications on the microsite scale to address these limiting conditions in Mediterranean drylands.

The impact of the internet on the pricing strategies of the European low cost airlines

This study seeks to analyse the price determination of low cost airlines in Europe and the effect that Internet has on this strategy. The outcomes obtained reveal that both users and companies benefit from the use of ICTs in the purchase and sale of airline tickets: the Internet allows consumers to increase their bargaining power comparing different airlines and choosing the most competitive flight, while companies can easily check the behaviour of users to adapt their pricing strategies using internal information. More than 2500 flights of the largest European low cost airlines have been used to carry out the study. The study revealed that the most significant variables for understanding pricing strategies were the number of rivals, the behaviour of the demand and the associated costs. The results indicated that consumers should buy their tickets before 25 days prior to departure.

A low cost system for the prediagnosis of the human tremor

Human tremor can be defined as a somewhat rhythmic and quick movement of one or more body parts. In some people, it is a symptom of a neurological disorder. From the mathematical point of view, human tremor can be defined as a weighted contribution of different sinusoidal signals which causes oscillations of some parts of the body. This sinusoidal is repeated over time, but its amplitude and frequency change slowly. This is why amplitude and frequency are considered important factors in the tremor characterization, and thus for its diagnosis. In this paper, a tool for the prediagnosis of the human tremor is presented. This tool uses a low cost device (<$40) and allows to compute the main factors of the human tremor accurately. Real cases have been tested using the algorithms developed in this investigation. The patients suffered from different tremor severities, and the components of amplitude and frequency were computed using a series of tests. These additional measures will help the experts to make better diagnoses allowing them to focus on specific stages of the test or get an overview of these tests. From the experimental, we stated that not all tests are valid for every patient to give a diagnosis. Guided by years of experience, the expert will decide which test or set of tests are the most appropriate for a patient.

Towards a real-time 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

In this project, we propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: the system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The range of algorithms and applications to be implemented and integrated will be quite broad, ranging from the acquisition, outlier removal or filtering of the input data and the segmentation or characterization of regions of interest in the scene to the very object recognition and pose estimation. Furthermore, in order to validate the proposed system, we will create a 3D object dataset. It will be composed by a set of 3D models, reconstructed from common household objects, as well as a handful of test scenes in which those objects appear. The scenes will be characterized by different levels of occlusion, diverse distances from the elements to the sensor and variations on the pose of the target objects. The creation of this dataset implies the additional development of 3D data acquisition and 3D object reconstruction applications. The resulting system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human-computer interaction (HCI) systems based on visual information.

Convergence analysis and validation of low cost distance metrics for computational cost reduction of the Iterative Closest Point algorithm

The Iterative Closest Point algorithm (ICP) is commonly used in engineering applications to solve the rigid registration problem of partially overlapped point sets which are pre-aligned with a coarse estimate of their relative positions. This iterative algorithm is applied in many areas such as the medicine for volumetric reconstruction of tomography data, in robotics to reconstruct surfaces or scenes using range sensor information, in industrial systems for quality control of manufactured objects or even in biology to study the structure and folding of proteins. One of the algorithm’s main problems is its high computational complexity (quadratic in the number of points with the non-optimized original variant) in a context where high density point sets, acquired by high resolution scanners, are processed. Many variants have been proposed in the literature whose goal is the performance improvement either by reducing the number of points or the required iterations or even enhancing the complexity of the most expensive phase: the closest neighbor search. In spite of decreasing its complexity, some of the variants tend to have a negative impact on the final registration precision or the convergence domain thus limiting the possible application scenarios. The goal of this work is the improvement of the algorithm’s computational cost so that a wider range of computationally demanding problems from among the ones described before can be addressed. For that purpose, an experimental and mathematical convergence analysis and validation of point-to-point distance metrics has been performed taking into account those distances with lower computational cost than the Euclidean one, which is used as the de facto standard for the algorithm’s implementations in the literature. In that analysis, the functioning of the algorithm in diverse topological spaces, characterized by different metrics, has been studied to check the convergence, efficacy and cost of the method in order to determine the one which offers the best results. Given that the distance calculation represents a significant part of the whole set of computations performed by the algorithm, it is expected that any reduction of that operation affects significantly and positively the overall performance of the method. As a result, a performance improvement has been achieved by the application of those reduced cost metrics whose quality in terms of convergence and error has been analyzed and validated experimentally as comparable with respect to the Euclidean distance using a heterogeneous set of objects, scenarios and initial situations.

Control and Guidance of Low-Cost Robots via Gesture Perception for Monitoring Activities in the Home

This paper describes the development of a low-cost mini-robot that is controlled by visual gestures. The prototype allows a person with disabilities to perform visual inspections indoors and in domestic spaces. Such a device could be used as the operator's eyes obviating the need for him to move about. The robot is equipped with a motorised webcam that is also controlled by visual gestures. This camera is used to monitor tasks in the home using the mini-robot while the operator remains quiet and motionless. The prototype was evaluated through several experiments testing the ability to use the mini-robot’s kinematics and communication systems to make it follow certain paths. The mini-robot can be programmed with specific orders and can be tele-operated by means of 3D hand gestures to enable the operator to perform movements and monitor tasks from a distance.

Nanoparticles of tungsten as low-cost monometallic catalyst for selective hydrogenation of 3-hexyne

Low-cost tungsten monometallic catalysts containing variable amounts of metal (4.5, 7.1 and 8.5%W) were prepared by impregnating alumina with ammonium metatungstate as an inexpensive precursor. The catalysts were characterized using ICP, XPS, XRD, TPR and hydrogen chemisorption. These techniques revealed mainly WO3-Al2O3 (W6+) species on the surface. The effects of the content of W nanoparticles and reaction temperature on activity and selectivity for the partial hydrogenation of 3-hexyne, a non-terminal alkyne, were assessed under moderate conditions of temperature and pressure. The monometallic catalysts prepared were found to be active and stereoselective for the production of (Z )-3-hexene, had the following order: 7.1WN/A > 8.5 WN/A ≥ 4.5 WN/A. Additionally, the performance of the synthesized xWN/A catalysts exhibited high sensitivity to temperature variation. In all cases, the maximum 3-hexyne total conversion and selectivity was achieved at 323 K. The performance of the catalysts was considered to be a consequence of two phenomena: a) the electronic effects, related to the high charge of W (+6), causing an intensive dipole moment in the hydrogen molecule (van der Waals forces) and leading to heterolytic bond rupture; the H+ and H- species generated approach a 3-hexyne adsorbate molecule and cause heterolytic rupture of the C≡C bond into C- = C+; and b) steric effects related to the high concentration of WO3 on 8.5WN/A that block the Al2O3 support. Catalyst deactivation was detected, starting at about 50 min of reaction time. Electrodeficient W6+ species are responsible for the formation of green oil at the surface level, blocking pores and active sites of the catalyst, particularly at low reaction temperatures (293 and 303 K). The resulting best catalyst, 7.1WN/A, has low fabrication cost and high selectivity for (Z )-3-hexene (94%) at 323 K. This selectivity is comparable to that of the classical and more expensive industrial Lindlar catalyst (5 wt% Pd). The alumina supported tungsten catalysts are low-cost potential replacements for the Lindlar industrial catalyst. These catalysts could also be used for preparing bimetallic W-Pd catalysts for selective hydrogenation of terminal and non-terminal alkynes.

Development and programming of Geophonino: A low cost Arduino-based seismic recorder for vertical geophones

The commercial data acquisition systems used for seismic exploration are usually expensive equipment. In this work, a low cost data acquisition system (Geophonino) has been developed for recording seismic signals from a vertical geophone. The signal goes first through an instrumentation amplifier, INA155, which is suitable for low amplitude signals like the seismic noise, and an anti-aliasing filter based on the MAX7404 switched-capacitor filter. After that, the amplified and filtered signal is digitized and processed by Arduino Due and registered in an SD memory card. Geophonino is configured for continuous registering, where the sampling frequency, the amplitude gain and the registering time are user-defined. The complete prototype is an open source and open hardware system. It has been tested by comparing the registered signals with the ones obtained through different commercial data recording systems and different kind of geophones. The obtained results show good correlation between the tested measurements, presenting Geophonino as a low-cost alternative system for seismic data recording.

Augmented and Virtual Reality techniques for footwear

The use of 3D imaging techniques has been early adopted in the footwear industry. In particular, 3D imaging could be used to aid commerce and improve the quality and sales of shoes. Footwear customization is an added value aimed not only to improve product quality, but also consumer comfort. Moreover, customisation implies a new business model that avoids the competition of mass production coming from new manufacturers settled mainly in Asian countries. However, footwear customisation implies a significant effort at different levels. In manufacturing, rapid and virtual prototyping is required; indeed the prototype is intended to become the final product. The whole design procedure must be validated using exclusively virtual techniques to ensure the feasibility of this process, since physical prototypes should be avoided. With regard to commerce, it would be desirable for the consumer to choose any model of shoes from a large 3D database and be able to try them on looking at a magic mirror. This would probably reduce costs and increase sales, since shops would not require storing every shoe model and the process of trying several models on would be easier and faster for the consumer. In this paper, new advances in 3D techniques coming from experience in cinema, TV and games are successfully applied to footwear. Firstly, the characteristics of a high-quality stereoscopic vision system for footwear are presented. Secondly, a system for the interaction with virtual footwear models based on 3D gloves is detailed. Finally, an augmented reality system (magic mirror) is presented, which is implemented with low-cost computational elements that allow a hypothetical customer to check in real time the goodness of a given virtual footwear model from an aesthetical point of view.

A relaxation technique enhances psychological well-being and immune parameters in elderly people from a nursing home: A randomized controlled study

Background: The aging process involves a decline in immune functioning that renders elderly people more vulnerable to disease. In residential programs for the aged, it is vital to diminish their risk of disease, promote their independence, and augment their psychological well-being and quality of life. Methods: We performed a randomized controlled study, evaluating the ability of a relaxation technique based on Benson’s relaxation response to enhance psychological well-being and modulate the immune parameters of elderly people living in a geriatric residence when compared to a waitlist control group. The study included a 2-week intervention period and a 3-month follow-up period. The main outcome variables were psychological well-being and quality of life, biomedical variables, immune changes from the pre-treatment to post-treatment and follow-up periods. Results: Our findings reveal significant differences between the experimental and control groups in CD19, CD71, CD97, CD134, and CD137 lymphocyte subpopulations at the end of treatment. Furthermore, there was a decrease in negative affect, psychological discomfort, and symptom perception in the treatment group, which increased participants’ quality of life scores at the three-month follow-up. Conclusions: This study represents a first approach to the application of a passive relaxation technique in residential programs for the elderly. The method appears to be effective in enhancing psychological well-being and modulating immune activity in a group of elderly people. This relaxation technique could be considered an option for achieving health benefits with a low cost for residential programs, but further studies using this technique in larger samples of older people are needed to confirm the trends observed in the present study. Trial registration: International Standard Randomised Controlled Trial Number Register ISRCTN85410212.

Learning Probabilistic Features for Robotic Navigation Using Laser Sensors

SLAM is a popular task used by robots and autonomous vehicles to build a map of an unknown environment and, at the same time, to determine their location within the map. This paper describes a SLAM-based, probabilistic robotic system able to learn the essential features of different parts of its environment. Some previous SLAM implementations had computational complexities ranging from O(Nlog(N)) to O(N2), where N is the number of map features. Unlike these methods, our approach reduces the computational complexity to O(N) by using a model to fuse the information from the sensors after applying the Bayesian paradigm. Once the training process is completed, the robot identifies and locates those areas that potentially match the sections that have been previously learned. After the training, the robot navigates and extracts a three-dimensional map of the environment using a single laser sensor. Thus, it perceives different sections of its world. In addition, in order to make our system able to be used in a low-cost robot, low-complexity algorithms that can be easily implemented on embedded processors or microcontrollers are used.

Algerian natural montmorillonites for arsenic(III) removal in aqueous solution

The adsorption of As(III) from aqueous solutions using naturally occurring and modified Algerian montmorillonites has been investigated as a function of contact time, pH, and temperature. Kinetic studies reveal that uptake of As(III) ions is rapid within the first 3 h, and it slows down thereafter. Equilibrium studies show that As(III) shows the highest affinity toward acidic montmorillonite even at very low concentration of arsenic. The kinetics of As(III) adsorption on all montmorillonites used is well described by a pseudo-second-order chemical reaction model, which indicates that the adsorption process of these species is likely to be chemisorption. Adsorption isotherms of As(III) fitted the Langmuir and Freundlich isotherm models well. The adsorption of As(III) is pH-dependent obtaining an optimal adsorption at pH 5. From the thermodynamic parameters, it is concluded that the process is exothermic, spontaneous, and favorable. The results suggest that M1, M2, and acidic-M2 could be used as low-cost and effective filtering materials for removal of arsenic from water.

3D model reconstruction using neural gas accelerated on GPU

In this work, we propose the use of the neural gas (NG), a neural network that uses an unsupervised Competitive Hebbian Learning (CHL) rule, to develop a reverse engineering process. This is a simple and accurate method to reconstruct objects from point clouds obtained from multiple overlapping views using low-cost sensors. In contrast to other methods that may need several stages that include downsampling, noise filtering and many other tasks, the NG automatically obtains the 3D model of the scanned objects. To demonstrate the validity of our proposal we tested our method with several models and performed a study of the neural network parameterization computing the quality of representation and also comparing results with other neural methods like growing neural gas and Kohonen maps or classical methods like Voxel Grid. We also reconstructed models acquired by low cost sensors that can be used in virtual and augmented reality environments for redesign or manipulation purposes. Since the NG algorithm has a strong computational cost we propose its acceleration. We have redesigned and implemented the NG learning algorithm to fit it onto Graphics Processing Units using CUDA. A speed-up of 180× faster is obtained compared to the sequential CPU version.