On the use of 3D camera to accurately measure volume and weight of dairy cow feed

The paper discusses the challenges facing the dairy industry due to increased farm sizes and reduced staff-to-animal ratios, which are impacting animal welfare. The development of precision livestock farming (PLF) technologies has gained momentum to address these challenges. PLF technologies can assess animal welfare and health status by monitoring animal behavior and biological changes, and alerting farmers of any issues. However, the applicability of PLF tools in other productive phases of the dairy cattle is still limited. The article focuses on the challenges of managing unweaned dairy calves, particularly the variability in relation to when calves start consuming solid feed, and how PLF technologies can be used to monitor individual calf intake and manage weaning at the individual level. The attention is mainly focused on the advantages of using automated feeders for unweaned dairy calves, including labor savings, greater precision in measurement and control of individual intake of liquid and solid feed, and higher preweaning growth rates. In particular, a method is proposed, involving a 3D depth camera and a proper algorithm to measure the volume and weight of eaten feed. The method is preliminarily assessed in tests conducted in laboratory, which highlight a remarkable concurrence (differences as low as 2 %) with respect to nominal values

Rerepresenting Autonomated Vehicles in a Macroscopic Transportation Model

The main goal of this article is to determine a comprehensive and well applicable model architecture, which is adequate to estimate the system level advantages with regard to automated transportation and which is appropriate to determine possible costs and losses with regard to the approach of such transport modes. In the study the Budapest Transportation Model is applied. Taking autonomous vehicle penetration into account as an external variable, in the analysis a constant growth is assumed in the penetration of automated vehicles. This article has taken the most relevant factors of transportation network into account with regard to automated cars. It is also important to mention that the paper presents the most important modelling phases, where automated cars can be taken into account during the macroscopic modelling process. In the first step of the process during the network definition phase it is possible to consider the effect of automated vehicles on the transport system (e.g. separated routes). The next phase where the effect of automated vehicles should be taken into consideration is the mode choice step (e.g. different demand segments). And finally traffic assignment step, where the effect of automated vehicles can be represented. The easiest way for this is the modification of passenger car units through the parameter of assigned traffic per capacity ratio

The importance of physiological data variability in wearable devices for digital health applications

This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable. This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes

Global Assessment of PA variability through concurrent Physics-based X-parameter and Electro-Magnetic simulations

The novel technique introduced in [1] is exploited to address a full variability analysis of a GaAs MMIC X-band power amplifier, including the statistical variations of several technological parameters, both in the active and passive components. The active device is modelled by means of X-parameters, directly extracted from physics-based analysis. A non-50 O X-Par model is used to take into account the input port mismatch with respect to the conventional 50 O reference. The scattering parameters of the passive structures are extracted from accurate electromagnetic simulations and then imported into the circuit simulator through data intercharge files (e.g. MDIF or CITIfile) as a function of the most important MMIC fabrication parameters, e.g. the thickness of the MIM capacitor dielectric layer. The analysis shows that more than 10% of output power variations can be ascribed to the concurrent MIM and doping variations in conventional GaAs MMIC technology

Linking X Parameters to Physical Simulations for Design-Oriented Large-Signal Device Variability Modeling

We propose various techniques extending X parameters to include the effect of active microwave device variability by exploiting TCAD simulations. We discuss two possible implementations into Agilent ADS. Both approaches are validated against full microwave amplifier TCAD simulations

Are inductive current transformers performance really affected by actual distorted network conditions? An experimental case study

The aim of this work is to assess whether actual distorted conditions of the network are really affecting the accuracy of inductive current transformers. The study started from the need to evaluate the accuracy performance of inductive current transformers in off-nominal conditions, and to improve the related standards. In fact, standards do not provide a uniform set of distorted waveforms to be applied on inductive or low-power instrument transformers. Moreover, there is no agreement yet, among the experts, about how to evaluate the uncertainty of the instrument transformer when the operating conditions are different from the rated ones. To this purpose, the authors collected currents from the power network and injected them into two off-the-shelf current transformers. Then, their accuracy performances have been evaluated by means of the well-known composite error index and an approximated version of it. The obtained results show that under realistic non-rated conditions of the network, the tested transformers show a very good behavior considering their nonlinear nature, arising the question in the title. A secondary result is that the use of the composite error should be more and more supported by the standards, considering its effectiveness in the accuracy evaluation of instrument transformers for measuring purposes

A Custom, High-Channel-Count Data Acquisition System for Chemical Species Tomography of Aero-Jet Engine Exhaust Plumes

The fiber-laser imaging of gas turbine exhaust species project aims to provide a video-rate imaging (100 frames/s) diagnostic tool for application to the exhaust plumes of the largest civil aero-jet engines. This remit, enabled by chemical species tomography (CST) currently targeting carbon dioxide (CO 2 ), requires system design that facilitates expansion of multiple parameters. Scalability is needed in order to increase imaging speeds and spatial resolutions and extends the system toward other pertinent gases such as the oxides of nitrogen and sulfur and unburnt hydrocarbons. This paper presents a fully scalable, noninvasive instrument for installation in a commercial engine testing facility, technical challenges having been tackled iteratively through bespoke optical and mechanical design, and it specifically presents the high-speed data acquisition (DAQ) system required. Measurement of gas species concentration is implemented by tunable diode laser absorption with wavelength modulation spectroscopy (TDLAS-WMS) using a custom, high-speed 10-40-MS/s/channel 14-bit DAQ. For CO 2 tomography, the system uses six angular projections of 21 beams each. However, the presented DAQ has capacity for 192 fully parallel 10-Hz-3-MHz differential inputs, achieving a best-case signal-to-noise ratio (SNR) of 56.5 dB prior to filtering. A 12 Ethernet-connected digitization nodes based on field-programmable gate array technology with software control are distributed around a 7-m-diameter mounting “ring.” Hence, the high data rates of 8.96-Gb/s per printed circuit board and 107.52 Gb/s for the whole system can be reduced by using local digital lock-in amplifiers. We believe that this DAQ system is unique in both the TDLAS and CST literatures

Low-cost technologies used in corrosion monitoring

Globally, corrosion is the costliest cause of the deterioration of metallic and concrete structures, leading to significant financial losses and unexpected loss of life. Therefore, corrosion monitoring is vital to the assessment of structures’ residual performance and for the identification of pathologies in early stages for the predictive maintenance of facilities. However, the high price tag on available corrosion monitoring systems leads to their exclusive use for structural health monitoring applications, especially for atmospheric corrosion detection in civil structures. In this paper a systematic literature review is provided on the state-of-the-art electrochemical methods and physical methods used so far for corrosion monitoring compatible with low-cost sensors and data acquisition devices for metallic and concrete structures. In addition, special attention is paid to the use of these devices for corrosion monitoring and detection for in situ applications in different industries. This analysis demonstrates the possible applications of low-cost sensors in the corrosion monitoring sector. In addition, this study provides scholars with preferred techniques and the most common microcontrollers, such as Arduino, to overcome the corrosion monitoring difficulties in the construction industry.The authors are indebted to the projects PID2021‐126405OB‐C31 and PID2021‐126405OB‐C32 funded by FEDER funds—A Way to Make Europe and Spanish Ministry of Economy and Com‐petitiveness MICIN/AEI/10.13039/501100011033/, project PID2019‐106555RB‐I00 and project IDEAS 2.14 from Ports 4.0. It should also be noted that funding for this research was provided for Seyed‐milad Komarizadehasl by the European Social Fund and the Spanish Agencia Estatal de Investi‐gación del Ministerio de Ciencia Innovación y Universidades, grant (PRE2018‐083238).Peer ReviewedPostprint (published version