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The measurement of pig weight holds significant importance for producers as it plays a crucial role in managing pig growth, health, and marketing, thereby facilitating informed decisions regarding scientific feeding practices. On one hand, the conventional manual weighing approach is characterized by inefficiency and time consumption. On the other hand, it has the potential to induce heightened stress levels in pigs. This research introduces a hybrid 3D point cloud denoising approach for precise pig weight estimation. By integrating statistical filtering and DBSCAN clustering techniques, we mitigate weight estimation bias and overcome limitations in feature extraction. The convex hull technique refines the dataset to the pig's back, while voxel down-sampling enhances real-time efficiency. Our model integrates pig back parameters with a convolutional neural network (CNN) for accurate weight estimation. Experimental analysis indicates that the mean absolute error (MAE), mean absolute percent error (MAPE), and root mean square error (RMSE) of the weight estimation model proposed in this research are 12.45 kg, 5.36%, and 12.91 kg, respectively. In contrast to the currently available weight estimation methods based on 2D and 3D techniques, the suggested approach offers the advantages of simplified equipment configuration and reduced data processing complexity. These benefits are achieved without compromising the accuracy of weight estimation. Consequently, the proposed method presents an effective monitoring solution for precise pig feeding management, leading to reduced human resource losses and improved welfare in pig breeding.

A large and growing number of applications benefit from innovative and powerful 3D image sensors. Graphene photodetectors can achieve 3D sensing functionalities by intrinsic optoelectronic frequency mixing due to the nonlinear output characteristics of the sensor. In first proof of principle distance measurement demonstrations, we achieve modulation frequencies of 3.1 MHz, signal-to-noise ratios of ∼40 dB, distance detection up to at least 1 m, and a mean accuracy of 25.6 mm. The scalable More than Moore detector approach enables geometrical fill factors close to 100% and can easily complement powerful functionalities by simple back-end integration on top of CMOS electronics.

Multifunctional wearable sensors and systems are of growing interest over the past decades because of real-time health monitoring and disease diagnosis capability. Owing to the tremendous efforts of scientists, wearable sensors and systems with attractive advantages such as flexibility, comfort, and long-term stability have been developed, which are widely used in temperature monitoring, pulse wave detection, gait pattern analysis, etc. Due to the complexity of human physiological signals, it is necessary to measure multiple physiological information simultaneously to evaluate human health comprehensively. This review summarizes the recent advances in multifunctional wearable sensors, including single sensors with various functions, planar integrated sensors, three-dimensional assembled sensors, and stacked integrated sensors. The design strategy, manufacturing method, and potential application of each type of sensor are discussed. Finally, we offer an outlook on future developments and provide perspectives on the remaining challenges and opportunities of wearable multifunctional sensing technology.

One of the most relevant problems related to Unmanned Aerial Vehicle's (UAV) autonomous navigation for industrial inspection is localization or pose estimation relative to significant elements of the environment. This paper analyzes two different approaches in this regard, focusing on its application to unstructured scenarios where objects of considerable size are present, such as a truck, a wind tower, an airplane, a building, etc. The presented methods require a previously developed Computer-Aided Design (CAD) model of the main object to be inspected. The first approach is based on an occupancy map built from a horizontal projection of this CAD model and the Adaptive Monte Carlo Localization (AMCL) algorithm to reach convergence by considering the likelihood field observation model between the 2D projection of 3D sensor data and the created map. The second approach uses a point cloud prior map of the 3D CAD model and a scan-matching algorithm based on the Iterative Closest Point Algorithm (ICP) and the Unscented Kalman Filter (UKF). The presented approaches have been extensively evaluated using simulated as well as previously recorded real flight data. We focus on aircraft inspection as a test example, but our results and conclusions can be directly extended to other applications. To support this assertion, a truck inspection has been performed. Our tests reflected that creating a 2D or 3D map from a standard CAD model and using a 3D laser scan on the created maps can optimize the processing time, resources and improve robustness. The techniques used to segment unexpected objects in 2D maps improved the performance of AMCL. In addition, we showed that moving around locations with relevant geometry after take-off when running AMCL enabled faster convergence and high accuracy. Hence, it could be used as an initial position estimation method for other localization algorithms. The ICP-NL method works well in environments with elements other than the object to inspect, but it can provide better results if some techniques to segment the new objects are applied. Furthermore, the proposed ICP-NL scan-matching method together with UKF performed faster, in a more robust manner, than NDT. Moreover, it is not affected by flight height. However, ICP-NL error may still be too high for applications requiring increased accuracy.

The sheep sector has not suffered the technification that other livestock sectors have. The lack of technological knowledge of the farmers and the economic limitations of the sector have made this technification difficult. One of the most widely used technologies is Precision Livestock Farming (PLF). PLF has already been used in other livestock sectors to improve farming efficiency. In the light of the problem that sheep farmers have in weighing lambs and their low precision, this paper proposes a system for estimating weight by means of 3D image capture. Thus, zenithal images of 272 lambs have been recorded. They have been processed using the capture of the upper area and the average depth of the pixels of the lamb. This estimates the weight of the animal with an error of less than 6%. This technology has a low economic cost and is easy to operate, helping farmers to be more willing to use it. This method manages to reduce the duration of the process, the stress of the animal and to improve the overall accuracy in weight estimation. Thus, it will help to have a greater control of the weight of the animal and to improve the economic profitability that the farmer obtains for the lambs., (Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

Ambient measurement systems (AMSs) can enable continuous assessment of functional performance at home, increasing the availability of data for monitoring of neuromuscular disease. An AMS passively measures movement whenever someone is in range of the sensor, without the need for any wearable sensors. The current study evaluates the performance of an AMS for three metrics associated with functional assessments in Duchenne muscular dystrophy (DMD): ambulation speed, rise-to-stand speed and arm-raise speed. Healthy paediatric subjects performed a series of functional tasks and were graded by both a human rater and an AMS. Linear mixed-effect models were fit to calculate agreement between the two measurement methods. For all activities, the AMS and human rater supplied similar measurements of average speed, with correlation coefficients of 0.76-0.92 and systematic differences ranging in magnitude from 0 to 0.48 m per second. The largest systematic difference was for the 10-m run, which was likely due to human rater reaction time. Systematic differences in arm-raise measurements were due to incomplete execution of movements by test participants. These results are consistent with previous studies comparing automated and manual measurements of movement. This study demonstrates that an AMS device is able to measure ambulation speed, rise-to-stand speed and arm-raise speed in a paediatric population in a controlled setting without the need for complicated installation, calibration or worn sensors.

An electrochemical sensing chip with an 8 × 8 array of titanium nitride three-dimensional nano-electrodes (TiN 3D-NEA) was designed and fabricated via a standard integrated complementary metal oxide semiconductor process. Each nano-electrode in 3D-NEA exhibited a pole-like structure with a radius of 100 nm and a height of 35 nm. The numeric simulation showed that the nano-electrode with a radius of around 100 nm exhibited a more uniformly distributed electric field and a much higher electric field magnitude compared to that of the microelectrode. Cyclic voltammetry study with Ru(NH₃)₆ 3+ also revealed that the TiN 3D-NEA exhibited a much higher current density than that obtained from the microelectrode by two orders of magnitude. Further studies showed that the electrocatalytical reduction of hydrogen peroxide (H₂O₂) could occur on a TiN 3D-NEA-based sensing chip with a high sensitivity of 667.2 mA⋅mM -1 ⋅cm -2 . The linear detection range for H₂O₂ was between 0.1 μM and 5 mM with a lowest detection limit of 0.1 μM. These results indicated that the fabricated TiN 3D-NEA exhibited high catalytic activity and sensitivity to H₂O₂ and could be a promising sensor for H₂O₂ measurement.

A common countermeasure to detect threatening drones is the electro-optical infrared (EO/IR) system. However, its performance is drastically reduced in conditions of complex background, saturation and light reflection. 3D laser sensor LiDAR is used to overcome the problems of 2D sensors like EO/IR, but it is not enough to detect small drones at a very long distance because of low laser energy and resolution. To solve this problem, A 3D LADAR sensor is under development. In this work, we study the detection methodology adequate to the LADAR sensor which can detect small drones at up to 2 km. First, a data augmentation method is proposed to generate a virtual target considering the laser beam and scanning characteristics, and to augment it with the actual LADAR sensor data for various kinds of tests before full hardware system developed. Second, a detection algorithm is proposed to detect drones using voxel-based background subtraction and variable radially bounded nearest neighbor (V-RBNN) method. The results show that 0.2 m L2 distance and 60% expected average overlap (EAO) indexes are satisfied for the required specification to detect 0.3 m size of small drones.

Electrochemical bio-sensing using paper-based detection systems is the main focus of this review. The different existing designs of 2-dimensional and 3-dimensional sensors, and fabrication techniques are discussed. This review highlights the effect of adopting different sensor designs, distinct fabrication techniques, as well as different modification methods, in order to produce reliable and reproducible reading. The use of various nanomaterials have been demonstrated in order to modify the surface of electrodes during fabrication to further enhance the signal for subsequent analysis. The reviewed sensors were classified into categories based on their applications, such as diagnostics, environmental and food testing. One of the major advantages of using paper-based electrochemical sensors is the potential for miniaturization, which only requires relatively small amount of samples, and the low cost for the purpose of mass production. Additionally, most of the devices reviewed were made to be portable, making them well-suited for on-site detection. Finally, paper-based detection is an ideal platform to fabricate cost-effective, user-friendly and sensitive electrochemical biosensors, with large capacity for customization depending on functional needs.