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The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the "diameter calculation region" and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems. [ABSTRACT FROM AUTHOR]

The rebar installation quality significantly impacts the safety and durability of reinforced concrete (RC) structures. Traditional manual inspection is time-consuming, inefficient, and highly subjective. In order to solve this problem, this study uses a depth camera and aims to develop an intelligent inspection method for the rebar installation quality of an RC slab. The Random Sample Consensus (RANSAC) method is used to extract point cloud data for the bottom formwork, the upper and lower rebar lattices, and individual rebars. These data are utilized to measure the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing between rebars in the RC slab. This paper introduces the concept of the “diameter calculation region” and combines point cloud semantic information with rebar segmentation mask information through the relationship between pixel coordinates and camera coordinates to measure the nominal diameter of the rebar. The verification results indicate that the maximum deviations for the concrete cover thickness, the distance between the upper and lower rebar lattices, and the spacing of the double-layer bidirectional rebar in the RC slab are 0.41 mm, 1.32 mm, and 5 mm, respectively. The accuracy of the nominal rebar diameter measurement reaches 98.4%, demonstrating high precision and applicability for quality inspection during the actual construction stage. Overall, this study integrates computer vision into traditional civil engineering research, utilizing depth cameras to acquire point cloud data and color results. It replaces inefficient manual inspection methods with an intelligent and efficient approach, addressing the challenge of detecting double-layer reinforcement. This has significant implications for practical engineering applications and the development of intelligent engineering monitoring systems.

Real-time monitoring of glucose metabolism can not only provide information on cell growth status, but also contribute to the deep understanding of the mechanism of cell metabolism. Electrochemical sensors have become an important tool in real-time detection of glucose. However, most reported glucose sensors are based on two-dimensional electrodes that are hardly compatible with three-dimensional cell culture systems for accurate detection. Herein, a 3D glucose electrochemical sensor with high sensitivity and selectivity is prepared via immobilizing glucose oxidase (GOD) on the surface of a porous electrode which could catalyze H2O2 reduction. Then, collagen hydrogel with tumor cells (MCF-7) therein is filled into the pores of the 3D sensor, and thus an integrated platform of 3D cell culture for electrochemical detection is constructed. This platform allows real-time monitoring of the change in glucose concentration in culture medium when MCF-7 cells are cultured under different conditions, providing a new method for evaluating the cell metabolism activities. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) sensors usually require a calibration procedure. In some cases, scale factor errors depend on the signs of the projections of the vector input signal onto the sensitivity axes of the sensor. To eliminate the ambiguity of scale factor errors, the angular positions of the sensor can be restricted so that the corresponding projections have a definite sign. This paper presents an analytical solution of the optimal calibration problem for a 3D sensor under a constraint on its angular positions. [ABSTRACT FROM AUTHOR]

We propose a three-dimensional (3D) image inpainting system using the 3D encoder–decoder generative adversarial network (IISU3EDGAN) for providing accurate detection results in vision-based rotor dynamic balancing processes. The proposed IISU3EDGAN system integrates 3D sensors with a deep learning network to reconstruct corrupted rotor images, thereby optimizing the detection parameters. In rotor component detection processes, overexposed images caused by reflections of the metallic rotor shaft affect the accuracy and performance of vision-based inspection systems. Traditional image restoration technologies or inpainting methods are inadequate for solving this problem. By contrast, our proposed system can repair 3D overexposed images of rotors. Compared with traditional image processing methods, the proposed system can adequately manage the complexity of corrupted images. In addition, it can be used to process complex overexposed rotor images and maintain image details. The proposed system can be applied to a wider range of rotor types, and it can be used to optimize the parameters of vision-based rotor detection systems to improve the accuracy of rotor component detection. We conducted experiments and observed that by using 3D sensors and deep learning, the proposed system improved the success rate in the first round of rotor dynamic balancing, reduced the number of rounds required for balancing, and increased the rotor production output. These results thus indicate that the IISU3EDGAN system is applicable and robust and that it can be used to improve the overall efficiency of dynamic balancing on rotor production lines.

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.

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. [ABSTRACT FROM AUTHOR]

Novel collider experiments demand an increased performance of the silicon detectors used, such as withstanding up to 1 × 1 0 17 n eq / cm 2 in unprecedented pile-up conditions and providing time resolution around 10 ps. Currently, Low Gain Avalanche Diodes (LGADs) are the standard, achieving resolutions below 30 ps. However, their limited radiation hardness is an area of ongoing research. As an alternative to LGADs, 3D sensors are interesting due to their proven radiation hardness. In 3D sensors, where the columns are etched into the sensor from the top (junction columns) and the back (ohmic columns), the drift distances can be very short, the depletion voltage is low and the electric field can be high, resulting in fast and short signals. In this study, the time resolution of different 3D pixel and strip sensors is investigated with signals generated by electrons as well as an infra-red laser. TCT Timing measurements allow to study the position dependence of the time resolution, which is interesting for 3D sensors due to their complex electric field structure. Position-timing maps prove the direct correlation between time resolution and electric field. The time resolution of 3D sensors before and after irradiation is presented, showing that 3D sensors can reach the time resolution of standard 50 μ m LGADs, which is limited by Landau fluctuations. In addition, the results demonstrate that the radiation-induced performance degradation in 3Ds is less severe than in LGADs. Furthermore, initial results from a production run of dedicated fast 3D sensors which have recently been produced at CNM as a common RD50 project will be presented. Finally, the option of using fast 3D sensors as timing detectors in future collider experiments will be discussed. [ABSTRACT FROM AUTHOR]

This article presents a new adaptive vision-based method (AVBM) of performing automatic detection for rotor balancing, and the online implementation proved that the method achieved rapid real-time optimization of system balancing configuration for a rotor dynamic balance machine. The proposed AVBM integrated 3D sensors and dynamic balancing platform using 3D computer vision technique and dynamic balance algorithm to improve the efficiency of rotor dynamic balancing. AVBM applied 3D ToF sensors on active rotor dynamic balance machines to grab 3D point cloud of rotor shaft and balance sprues. By 3D depth data, the background noise can be removed to detect the positions of shaft center, key phasor and balance sprues of rotor automatically. After combining with unbalance vector from dynamic balancing machine, the AVBM system calculated the optimal balance configuration by the vector analysis algorithm. Compares to conventional methods, conventional rotor dynamic balancing process relies on technicians to mount washers on particular balance sprues based on their experience, therefore uncertainty causes productivity decline. Experiments in industrial examples showed that the proposed AVBM required fewer rounds to achieve acceptance, whereas the conventional industrial rotor balancing method performed by operators required more than three rounds in average. Consider the overall dynamic balancing process for the motor, the processing time required for each motor without AVBM was 348.9 seconds, and the daily rotor balancing count of each dynamic balancing machine was 83. The processing time with AVBM was shortened from 348.9 to 283.9 seconds, the daily rotor balancing count had increased from 83 to 101, and the production improvement had reached 22 %. That is, the proposed AVBM can accurately analyze the dynamic balance and greatly reduce the redundant dynamic balance operations. The main advantage of the proposed AVBM over conventional methods is its efficiency, effectiveness and robustness in online optimization of rotor dynamic balance.

In this paper, a large screen interactive 3D sensor technology with clustering filtering method and unscented Kalman filter has been addressed. The surface and interactive area with the screen is mapped together using image processing and machine vision processing of 3D sensor. Then, the predictable and accurate multi-point interaction can be obtained. Also some operations can be achieved such as zooming, panning, selecting and moving image components, etc. Vision method, infrared induction, dynamic capture, image processing and other mixed technologies have been simultaneously utilized to recognize human gestures and simulate mouse event which can give participants a fully interactive experience. Experimental results show that this large-screen interactive technology with 3D sensor can achieve accurate multi-point interaction and gesture control, and have good human-machine interaction effect.

The continuously adaptive mean shift algorithm suffers from the tracking offset phenomenon while tracking targets with colors similar to that of the background. In this paper, to improve the performance of this algorithm, the depth information is combined with the back-projection color image and the information from the moving prediction algorithm. The target search window is predicted based on switching filtering algorithm with the Extended Kalman Filter (EKF) method. The moving tracking synthesis algorithm which used 3D sensors and combines color, depth and prediction information is used to solve the problems that the continuously adaptive mean shift algorithm encounters, namely disturbance and the tendency to enlarge the tracking window. The experimental results show that the proposed algorithm can accurately track a moving target in the presence of a complex background, and greatly improves the interference resistance and robustness of the system.

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.

Constituting the agriculture solid substance manufacture, the post-harvest sector processing schema is direct to preventing reduce the losses in intelligent agriculture. Many processing schemata will be preventing post-harvest losses on the agriculture solid substance manufacture, especially sekai-ichi apple is the regularly used fruit also used to make active in human-related activities of the sensory and control function consisting of an agricultural industry. Sekai-ichi apple is being a definite number of diseases induce, but it is to the highest degree of wastage involving in the Post-Harvest process. Especially sekai-ichi apple count loss is an unsafe many time because it not critically post-harvest. Regardless of consideration, the existing hierarchical model specified post-harvest losses prevention research has deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. This paper suggests a "Hierarchical Model within Ontology Enabled IoT" for distinguishable healthy separation of sekai-ichi apple by using Boosted Continuous Non-spatial whole Attribute Extraction (BCNAE). Sekai-ichi apple count loss is always safe on critically post-harvest. Proposed Post-Harvest hierarchical model specified post-harvest losses prevention and deficiencies to precise and quick detection of wastage for ensuring healthy separation of agriculture surroundings. In these suggestions, the separation cognitive operation takes the three levels of processing schemes such as lower level, middle level, and higher level. Firstly, the lower level is express agreements with the dynamic functioning for maintaining the definite number of manual induces. This lower level showing an absorption with the activity of manual separation by the human reliability determination. Secondly, the middle level is an express arrangement with the dynamic functioning for reducing the overfitting and accommodate to fitting the right shape deliberation. Middle level is establishing being generalized by concentrating the time-varying features in the occurrence of a change for the worse identification. Finally, the upper level is express for features refining with the help of the function of sekai-ichi apple image segmentation connection. This interpretability process helps to make the proven position of a prominent classification in a particular fruit on the agriculture solid substance. These three processing flow constructs the ontology structure with manually collected sekai-ichi apple images from a 3D sensor. The observational consequences express that the proposed BCNAE framework recognizes a detection performance carrying out with an optimized—separation ratio for time-variant of the separation process. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach that acquires camera parameters and generates an integrated 3D joint using an RGB-D camera network distributed in an arbitrary location in space. The proposed technique consists of three steps. In the first step, camera parameters are calculated using partial joints as feature points. The internal and external parameters between cameras are not calculated using a specially manufactured calibration plate. In the second step, a 3D joint set is estimated by integrating the joints obtained from each camera using the calculated camera parameters. At the same time, a 3D volumetric model in the form of a point cloud is reconstructed. The third step consists of a joint correction algorithm. The resultant 3D joint with high reliability is estimated by correcting the position of the joint using the 3D point cloud reconstructed previously. The generated 3D joint can accurately express the shape and movement of 3D human. The estimated 3D joint was compared with the joint measured using a motion capture device to evaluate its performance. The temporal standard deviation between two measurements shows very low value from 1.966 mm to 7.99 mm. [Display omitted] • Performing camera calibration using incomplete or partial joints obtained from images output from cameras installed in irregular positions. • Temporal improvement of the precision of transformation parameters obtained through camera calibration. • 3D joint generation while minimizing the error of pose estimation algorithm. • Correction of 3d joint using 3d point cloud registration. • Simultaneous 3D reconstruction of 3D indoor space and humans. [ABSTRACT FROM AUTHOR]

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.

Pedestrian detection is vitally important in many computer vision tasks but still suffers from some problems, such as illumination and occlusion if only the RGB image is exploited, especially in outdoor and long-range scenes. Combining RGB with depth information acquired by 3D sensors may effectively alleviate these problems. Therefore, how to utilize depth information and how to fuse RGB and depth features are the focus of the task of RGB-D pedestrian detection. This paper first improves the most commonly used HHA method for depth encoding by optimizing the gravity direction extraction and depth values mapping, which can generate a pseudo-color image from the depth information. Then, a two-branch feature fusion extraction module (TFFEM) is proposed to obtain the local and global features of both modalities. Based on TFFEM, an RGB-D pedestrian detection network is designed to locate the people. In experiments, the improved HHA encoding method is twice as fast and achieves more accurate gravity-direction extraction on four publicly-available datasets. The pedestrian detection performance of the proposed network is validated on KITTI and EPFL datasets and achieves state-of-the-art performance. Moreover, the proposed method achieved third ranking among all published works on the KITTI leaderboard. In general, the proposed method effectively fuses RGB and depth features and overcomes the effects of illumination and occlusion problems in pedestrian detection.

Each individual describes unique patterns during their gait cycles. This information can be extracted from the live video stream and used for subject identification. In appearance based recognition methods, this is done by tracking silhouettes of persons across gait cycles. In recent years, there has been a profusion of sensors that in addition to RGB video images also provide depth data in real-time. When such sensors are used for gait recognition, existing RGB appearance based methods can be extended to get a substantial gain in recognition accuracy. In this paper, this is accomplished using information fusion techniques that combine features from extracted silhouettes, used in traditional appearance based methods, and the height feature that can now be estimated using depth data. The latter is estimated during the silhouette extraction step with minimal additional computational cost. Two approaches are proposed that can be implemented easily as an extension to existing appearance based methods. An extensive experimental evaluation was performed to provide insights into how much the recognition accuracy can be improved. The results are presented and discussed considering different types of subjects and populations of different height distributions. [ABSTRACT FROM AUTHOR]

A computer keyboard is an important human–machine interaction device. A key-cap and a keyboard are flexibly connected, and its assembly quality, especially flatness, is crucial. In manufacturing quality control, the requirement for assembly quality of keyboards is quite strict, where the flatness measurement of key-caps is a key link. In this paper, an apparatus based on multi-line structured light imaging, and machine vision is designed for online rapid detection of key-cap flatness, is proposed. First, a novel 3D measurement scheme is presented to replace the complete 3D surface reconstruction of conventional methods. Then, an array of cameras with a perspective projection transformation matrix calibration approach is adopted to expand the scanning range of the 3D sensor for large-scale and high-resolution target imaging. In addition, a multi-step image processing is applied to accurately extract the centerline of the projected fringes on a key-cap with printed character, which indirectly helps to improve the measurement accuracy. Finally, a sparse sampling flatness estimation algorithm is proposed to improve the detection efficiency. According to experiments in real production lines, our apparatus can achieve a detection accuracy of 99.74% for various computer keyboards. [ABSTRACT FROM AUTHOR]

For human gesture recognition applications, 3D-sensor-based approaches have received considerable attention and are crucial for future applications of an advanced body sensor network (BSN). A practical gesture recognition system is to apply active gesture patterns of a gesture-making user for body action classifications. 3D-sensor-based gesture recognition, categorized as biometric recognition in BSN applications, is greatly lack of the extensible cognition ability due to substandard recognition accuracy on the gesture-making user identity. To overcome this problem, this paper proposes an active gesture-based user identity recognition approach using a robust feature design, called user adaptation (UA) features, derived from a UA process. Two different UA features, namely Eigen Centroid-UA and Eigen Transform-UA features, were developed in this paper to accurately represent the adaptive learning tendency of gesture recognition for a specific gesture-making user. Compared with traditional 3D sensor gesture-based identity recognition approaches that employ only the feature of fixed body skeleton information without any UA designs, the presented UA-feature can exhibit fine adaptation learning continuously to the specific action user; therefore, superior identity recognition accuracy will be constantly ensured. To demonstrate the efficiency and effectiveness of the developed robust UA features in this paper, experiments on gesture-making user identification by Gaussian mixture model applying the proposed Eigen Centroid-UA feature and verification by support vector machine applying the proposed Eigen Transform-UA feature were conducted. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the hardware cost of the interactive interface of the large screen interactive system is high and the noise disturbance can lead to the inaccurate positioning of the interactive target. This paper proposes an any-wall touch system with the switching filter based on 3-D sensor. The proposed any-wall touch system can be achieved by connecting only one computer and the human-computer interaction effect can be well obtained. The main contributions of this paper are as follows: 1) introduce the switching system method into the filter for the first time and propose a switching filter based on the average dwell time switching rule to realize the balance between accuracy and real-time and 2) this presented method can realize touching technology on the wall, which does not need liquid crystal display screen or LED large screen and other expensive display side of the hardware. Using the projector projection wall as a display side and the independent development of interactive software, it can achieve any wall touch function. Many experiments show that the proposed technology has a multi-functional, simple and portable, high precision, which can be widely extended to the classroom, corporate conference room and display platform and other interactive sites. [ABSTRACT FROM PUBLISHER]

Summary: A novel time‐of‐flight pixel designed in 0.18‐μm standard CMOS technology with an indirect pulsed technique is presented. This design is capable of operating in ambients with high levels of background light, since a novel in‐pixel background suppression circuit makes it robust up to 20 klux of ambient light. In addition to this, the pixel incorporates an adaptive number of accumulations circuit that selects the optimum number of accumulations for each situation at pixel level, avoiding saturation even under high illumination conditions. [ABSTRACT FROM AUTHOR]

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)63+ 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.

This paper presents a human-robot interaction method for controlling an autonomous mobile robot with a referential pointing gesture. A human user points to a specific location, robot detects the pointing gesture, computes its intersection with surrounding planar surface and moves to the destination. A depth camera mounted on the chassis is used. The user does not need to wear any extra clothing or markers. The design includes necessary mathematical concepts such as transformations between coordinate systems and vector abstraction of features needed for simple navigation, which other current research works misses. We provide experimental evaluation with derived probability models. We term this approach 'Linear HRI' and define 3 laws of Linear HRI. [ABSTRACT FROM AUTHOR]