Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Cost-effective visual odometry system for vehicle motion control in agricultural environments

In precision agriculture, innovative cost-effective technologies and new improved solutions, aimed at making operations and processes more reliable, robust and economically viable, are still needed. In this context, robotics and automation play a crucial role, with particular reference to unmanned vehicles for crop monitoring and site-specific operations. However, unstructured and irregular working environments, such as agricultural scenarios, require specific solutions regarding positioning and motion control of autonomous vehicles. In this paper, a reliable and cost-effective monocular visual odometry system, properly calibrated for the localisation and navigation of tracked vehicles on agricultural terrains, is presented. The main contribution of this work is the design and implementation of an enhanced image processing algorithm, based on the cross-correlation approach. It was specifically developed to use a simplified hardware and a low complexity mechanical system, without compromising performance. By providing sub-pixel results, the presented algorithm allows to exploit low-resolution images, thus obtaining high accuracy in motion estimation with short computing time. The results, in terms of odometry accuracy and processing time, achieved during the in-field experimentation campaign on several terrains proved the effectiveness of the proposed method and its fitness for automatic control solutions in precision agriculture applications

Surface Functionalization of Fabrics and Threads for Smart Textiles

The future of electronics is moving toward wearable devices and therefore requires a shift away from hard, inflexible materials towards fibers, threads, and fabrics that conform to the shape of the body. Therefore new methods for incorporating textiles as electronic components are needed to replace conventional processing techniques used with smooth, flat substrates like glass, silicon, and many polymers. Toward this end, this work investigates different methods that can be used to tune textile surfaces for electronic functionality, including weaving, solution grafting, and initiated chemical vapor deposition (iCVD). While all of these methods were used to make triboelectrically-active textiles, iCVD combined with simple solution chemistry was also used to synthesize ionically conductive thin films on textiles for solid-state electrolytes. In general these methods elucidate facile pathways towards smart clothing fabrication

Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.This research is partially supported by the Spanish Ministry of Education, Culture and Sports under FPU fellowship with identifier FPU13/03917

Small caliber arterial prosthesis: prototype and pre-industrialization

Cardiovascular diseases represent the leading cause of death in developed countries. Modern surgical methods show poor efficiency in the substitution of small-diameter arteries (< 6 mm). Due to the mismatch in mechanical properties between the native artery and the substitute, the behavior of the vessel wall remains a major cause of inefficient substitutions an unsolved complication in surgery since 1970. The purpose of this work was to obtain polylysine-enriched vascular substitutes, derived from decellularized porcine femoral and carotid arteries. Polylysine was selected as a matrix cross-linker, increasing the mechanical resistance of the scaffold with respect to decellularized vessels, without altering the native biocompatibility and haemocompatibility properties. The biological characterization showed excellent biological performances, while mechanical tests displayed that the Young’s modulus of the polylysine-enriched matrix was comparable to that of the native vessel. Concerning haemocompatibility, the performed analyses show that polylysine-enriched matrices increase coagulation time, with respect to commercial Dacron® vascular substitutes. Based on these findings, polylysine-enriched decellularized vessels resulted in a promising approach for vascular substitution. In order to consider the industrial phase, a business plan (BP) and the preengineering of the production reactor were developed in the present project. Is crucial to consider the scale-up process of the project from the initial stages to increase the chances of its success. In this way, the feasibility study will contemplate the viability of the project from an economic point of view thus, the BP is the most important document of the study. From an engineering point of view, the industrial production reactor is the heart of the plant, so designing it from the outset will allow considering costs and performing the necessary experiments for its final design

Space programs summary no. 37-59, volume 3, for the period 1 August to 30 September, 1969. Supporting research and advanced development

Technical research and development work for NASA space progra

Impact of Ear Occlusion on In-Ear Sounds Generated by Intra-oral Behaviors

We conducted a case study with one volunteer and a recording setup to detect sounds induced by the actions: jaw clenching, tooth grinding, reading, eating, and drinking. The setup consisted of two in-ear microphones, where the left ear was semi-occluded with a commercially available earpiece and the right ear was occluded with a mouldable silicon ear piece. Investigations in the time and frequency domains demonstrated that for behaviors such as eating, tooth grinding, and reading, sounds could be recorded with both sensors. For jaw clenching, however, occluding the ear with a mouldable piece was necessary to enable its detection. This can be attributed to the fact that the mouldable ear piece sealed the ear canal and isolated it from the environment, resulting in a detectable change in pressure. In conclusion, our work suggests that detecting behaviors such as eating, grinding, reading with a semi-occluded ear is possible, whereas, behaviors such as clenching require the complete occlusion of the ear if the activity should be easily detectable. Nevertheless, the latter approach may limit real-world applicability because it hinders the hearing capabilities.</p