Energy harvesting applied to smart shoes

The appeal of energy harvesting systems lies in the possibility of capturing free energy that would be dissipated and is therefore obtainable without costs. Today, advanced techniques and devices exist for capturing from the environment, storing, and managing quotas of natural energy, which are made available in the form of electrical energy. At the same time, the most recent microprocessors grant an extremely high power efficiency, which permits their operation with minimal power consumption. As a consequence, low-consuming devices can be power supplied by using energy harvesting systems. If this concept is applied to wearable electronics, the most efficient choice is that of exploiting the energy released when the users walk, by developing systems that are embedded in the shoe sole. At each step, the force exerted on the device can be transformed into a relatively high amount of electrical energy, for example by using piezoelectric elements and electromagnetic induction systems. The paper describes the design of four different solutions for smart shoes that make use of energy harvesting apparatuses for the power supply of sensors and complex monitoring systems, for example aimed at GPS localization. An initial comparative assessment of the four architectures is reported, by weighing production costs, ease of manufacture and energy harvesting performance

Deep understanding of shopper behaviours and interactions using RGB-D vision

AbstractIn retail environments, understanding how shoppers move about in a store's spaces and interact with products is very valuable. While the retail environment has several favourable characteristics that support computer vision, such as reasonable lighting, the large number and diversity of products sold, as well as the potential ambiguity of shoppers' movements, mean that accurately measuring shopper behaviour is still challenging. Over the past years, machine-learning and feature-based tools for people counting as well as interactions analytic and re-identification were developed with the aim of learning shopper skills based on occlusion-free RGB-D cameras in a top-view configuration. However, after moving into the era of multimedia big data, machine-learning approaches evolved into deep learning approaches, which are a more powerful and efficient way of dealing with the complexities of human behaviour. In this paper, a novel VRAI deep learning application that uses three convolutional neural networks to count the number of people passing or stopping in the camera area, perform top-view re-identification and measure shopper–shelf interactions from a single RGB-D video flow with near real-time performances has been introduced. The framework is evaluated on the following three new datasets that are publicly available: TVHeads for people counting, HaDa for shopper–shelf interactions and TVPR2 for people re-identification. The experimental results show that the proposed methods significantly outperform all competitive state-of-the-art methods (accuracy of 99.5% on people counting, 92.6% on interaction classification and 74.5% on re-id), bringing to different and significative insights for implicit and extensive shopper behaviour analysis for marketing applications

Is There Daily Growth Hysteresis versus Vapor Pressure Deficit in Cherry Fruit?

The growth of cherry fruit is generally described using a double sigmoid model, divided into four growth stages. Abiotic factors are considered to be significant components in modifying fruit growth, and among these, the vapor pressure deficit (VPD) is deemed the most effective. In this study, we investigated sweet cherry fruit growth through the continuous, hourly monitoring of fruit transversal diameter over two consecutive years (2019 and 2020), from the beginning of the third stage to maturation (forth stage). Extensometers were used in the field and VPD was calculated from weather data. The fruit growth pattern up to the end of the third stage demonstrated three critical steps during non-rainy days: shrinkage, stabilization and expansion. In the third stage of fruit growth, a partial clockwise hysteresis curve of circadian growth, as a response to VPD, appeared on random days. The pattern of fruit growth during rainy days was not distinctive, but the amount and duration of rain caused a consequent decrease in the VPD and indirectly boosted fruit growth. At the beginning of the fourth stage, the circadian growth changed and the daily transversal diameter vs VPD formed fully clockwise hysteresis curves for most of this stage. Our findings indicate that hysteresis can be employed to evaluate the initial phenological phase of fruit maturation, as a fully clockwise hysteresis curve was observable only in the fourth stage of fruit growth. There are additional opportunities for its use in the management of fruit production, such as in precision fruit farming

Addressing Gaps in Small-Scale Fisheries: A Low-Cost Tracking System

none5During the last decade vessel-position-recording devices, such as the Vessel Monitoring System and the Automatic Identification System, have increasingly given accurate spatial and quantitative information of industrial fisheries. On the other hand, small-scale fisheries (vessels below 12 m) remain untracked and largely unregulated even though they play an important socio-economic and cultural role in European waters and coastal communities and account for most of the total EU fishing fleet. The typically low-technological capacity of these small-scale fishing boats—for which space and power onboard are often limited—as well their reduced operative range encourage the development of efficient, low-cost, and low-burden tracking solutions. In this context, we designed a cost-effective and scalable prototypic architecture to gather and process positional data from small-scale vessels, making use of a LoRaWAN/cellular network. Data collected by our first installation are presented, as well as its preliminary processing. The emergence of a such low-cost and open-source technology coupled to artificial intelligence could open new opportunities for equipping small-scale vessels, collecting their trajectory data, and estimating their fishing effort (information which has historically not been present). It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to inform coastal resource and fisheries management.openAnna Nora Tassetti, Alessandro Galdelli, Jacopo Pulcinella, Adriano Mancini, Luca BologniniNora Tassetti, Anna; Galdelli, Alessandro; Pulcinella, Jacopo; Mancini, Adriano; Bolognini, Luc

Use of an energy harvesting smart floor for indoor localization of people

The development of \u201cintelligent\u201d floors is a growing interest, but often the ensuing solutions involve high production costs as well as complicated installation and management. Aim of this paper is to propose a novel smart floor that makes use of an energy harvesting system in order to allow people localization and to track their movements in an indoor environment. The contribution starts from reviewing the state of the art of smart floor solutions, which are categorized according to the different applications they are addressed to. The system developed in this research is based on capacitive sensors that are mounted on a polymeric support and embedded between a bulk wooden base and floating parquet flooring. The paper outlines the detailed architecture of the proposed apparatus and reports the results of the preliminary test phase. The proposed solution is part of HDOMO, an Ambient Assisted Living (AAL) project aiming at the development of smart solutions for active aging

An ethical framework for trustworthy Neural Rendering applied in cultural heritage and creative industries

Artificial Intelligence (AI) has revolutionized various sectors, including Cultural Heritage (CH) and Creative Industries (CI), defining novel opportunities and challenges in preserving tangible and intangible human productions. In such a context, Neural Rendering (NR) paradigms play the pivotal role of 3D reconstructing objects or scenes by optimizing images depicting them. However, there is a lack of work examining the ethical concerns associated with its usage. Those are particularly relevant in scenarios where NR is applied to items protected by intellectual property rights, UNESCO-recognized heritage sites, or items critical for data-driven decisions. For this, we here outline the main ethical findings in this area and place them in a novel framework to guide stakeholders and developers through principles and risks associated with the use of NR in CH and CI. Such a framework examines AI's ethical principles, connected to NR, CH, and CI, supporting the definition of novel ethical guidelines

Influence of Sport Practice and Body Weight on Physical Fitness in Schoolchildren Living in the Campania Region

Background: Physical fitness (PF) levels correlate with health hallmarks at all ages. In this study, w aimed to determine the PF level of schoolchildren from the Campania Region (Italy) through health-related PF (HRPF) components, taking into account body weight and sport practice (SP). Methods: PF level was determined in 565 schoolchildren aged 10-13 (11.7 +/- 1.0 yrs; m: 353, f: 212) using some of the Eurofit battery tests. Results: 77% children practiced sport, boys more than girls (86% vs. 63%, respectively; p < 0.05). Boys performed better than girls (p < 0.05) in the Plate Tapping, Standing Broad Jump, Bent-Arm Hang, and 10 x 5 m Shuttle Run tests; girls performed better in the Sit-and-Reach Test (p < 0.05). Conclusion: Overweight/obese status negatively affects the muscular strength of lower limbs, even if it progressively improves during growth. SP was revealed to be a determinant in performance only in some PF tests, likely due to the heterogeneous training level among boys and girls who practice sport

Omnidirectional vision for robot localization in urban environments

Abstract. This paper addresses the problem of long term mobile robot localization in large urban environments. Typically, GPS is the preferred sensor for outdoor operation. However, using GPS-only localization methods leads to significant performance degradation in urban areas where tall nearby structures obstruct the view of the satellites. In our work, we use omnidirectional visionbased techniques to supplement GPS and odometry and provide accurate localization. We also present some novel Monte Carlo Localization optimizations and we introduce the concept of on line knowledge acquisition and integration presenting a framework able to perform long term robot localization in real environments. The vision system identifies prominent features in the scene and matches them with a database of geo-referenced features already known or integrated during the localization process. Results of robot localization in the old town of Fermo are presented. Results show good performance and the whole architecture behaves well also in long term experiments, showing a suitable and good system for real life robot applications

Integrating elevation data and multispectral high-resolution images for an improved hybrid Land Use/Land Cover mapping

ABSTRACTThe combination of elevation data together with multispectral high-resolution images is a new methodology for obtaining land use/land cover classification. It represents a step forward for both the accuracy and automation of LULC applications and allows users to setup thematic assignments through rules based on feature attributes and human expert interpretation of land usage. The synergy between different types of information means that LiDAR can give new hints at both the segmentation and hybrid classification steps, leading to a joint use of multispectral, spatial and elevation data. The output is a thematic map characterized by a custom-designed legend that is able to discriminate between land cover classes with similar spectral characteristics (level 3 of the CLC legend). Experimental results from a hilly farmland area with some urban structures (Musone river basin, Ancona, Italy) are used to highlight how the proposed methodology enhances land cover classification in heterogeneous environments

A Novel Remote Visual Inspection System for Bridge Predictive Maintenance

Predictive maintenance on infrastructures is currently a hot topic. Its importance is proportional to the damages resulting from the collapse of the infrastructure. Bridges, dams and tunnels are placed on top on the scale of severity of potential damages due to the fact that they can cause loss of lives. Traditional inspection methods are not objective, tied to the inspector’s experience and require human presence on site. To overpass the limits of the current technologies and methods, the authors of this paper developed a unique new concept: a remote visual inspection system to perform predictive maintenance on infrastructures such as bridges. This is based on the fusion between advanced robotic technologies and the Automated Visual Inspection that guarantees objective results, high-level of safety and low processing time of the results