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This paper investigates a hybrid coupled plasmonic gas sensor with stacked gold-SiO2 layers for air-quality monitoring. The gas absorption characteristics of hybrid-stacked layers sensors are studied and compared with single-layer sensors. Gases with different refractive indexes changing from 1 to 1.4 are studied. The total absorption radar cross section (ACS) has increased from 1.681 × 105 nm2 for single-layer sensors to 5.021 × 105 nm2 for hybrid stacked layers sensors. A graphene monolayer is used to enhance the total gas absorption. It acts as an insulator to the dipole sensor. The lumped-element equivalent circuit is developed using a particle swarm optimization technique (PSO). The sensitivity is 788 nm/RIU for the single-layer sensor and 910 nm/RIU for the hybrid-stacked layers sensor with a graphene monolayer placed as a cover for the plasmonic dipole. A polarization-insensitive sensor concerning the direction of the electric field (single layer/hybrid-stacked layers) is constructed from 45° quad-rotated dipole arms of sensors. The total ACS is enhanced to 2.31 × 105 nm2 for the polarization-insensitive single-layer sensor and 6.76 × 105 nm2 for the polarization-insensitive hybrid-stacked layers sensor. Planar arrays of 3 × 3, 4 × 4, and 5 × 5 elements of the last sensor are investigated for absorption and sensitivity enhancement. Ethanol, acetone, nitrogen dioxide, and toluene gases are tested with a total ACS peak value of 8.7 × 106 nm2. The sensitivity is 895.5 nm/RIU for 4 × 4 array elements. [ABSTRACT FROM AUTHOR]

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. [ABSTRACT FROM AUTHOR]

Rapid technological advancements have led to increased demands for sensors. Hence, high performance suitable for next-generation technology is required. As sensing technology has numerous applications, various materials and patterning methods are used for sensor fabrication. This affects the characteristics and performance of sensors, and research centered specifically on these patterns is necessary for high integration and high performance of these devices. In this paper, we review the patterning techniques used in recently reported sensors, specifically the most widely used capacitive sensors, and their impact on sensor performance. Moreover, we introduce a method for increasing sensor performance through three-dimensional (3D) structures. [ABSTRACT FROM AUTHOR]

Abstract Multi‐dimensional electronic devices for sensing applications have received immense attention in the fields of bio‐interface electronics and soft robotics to achieve structural manifolds in recent years. The structural diversities of sensors are tuned based on the environments in which they encounter multipurpose and complex form factors of electronics. 3D electronics are one of the emerging sensor platforms characterized by structural adaptability and compatibility on free target surfaces, resulting in unprecedented improvement of spatial resolution. Herein, the recent progress made in the fabrication and usage of 3D electronic sensors that are classified into two groups based on the methodological concepts used for fabrication in bio‐interfaced electronics and soft robotics applications, is reviewed. The latest advances in the design and development of 3D printable and 2D to 3D shape‐deformable electronic sensors are reviewed with a focus on printable materials and deformation mechanisms, respectively. 3D printing techniques enable the facile fabrication of arbitrary and seamless electronics without the use of conventional microfabrication technologies. Deformable electronics cover their self‐tune architectures and functions depending on mechanical deformation based on buckling, origami, and kirigami fabrication strategies. Finally, the printed and deformable 3D sensors show their potential for use in next‐generation sensing platforms.

The efficient computation of viewpoints while considering various system and process constraints is a common challenge that any robot vision system is confronted with when trying to execute a vision task. Although fundamental research has provided solid and sound solutions for tackling this problem, a holistic framework that poses its formal description, considers the heterogeneity of robot vision systems, and offers an integrated solution remains unaddressed. Hence, this publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework based on Feature-Based Constrained Spaces (C -spaces) as the backbone for solving it. A C -space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the constraint. The present study demonstrates that many viewpoint constraints can be efficiently formulated as C -spaces, providing geometric, deterministic, and closed solutions. The introduced C -spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems. The effectiveness and efficiency of the concepts introduced are verified on a simulation-based scenario and validated on a real robot vision system comprising two different sensors. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) surface imaging systems have gained popularity in monitoring the status and condition of separation processes by characterizing the internal and external structures of particles such as size, shape, density and composition. This review article mainly focuses on demonstrating the state of the art of 3D imaging systems in mineral processing based of the recent growth in 3D sensors. The structure of this manuscript comprises an overview of the two 3D imaging systems, including RhoVol and X-ray computed tomography, based on the basic principles. Their applications in mineral processing are then explained. By integrating with other imaging tools and the 3D printing technology, 3D surface imaging systems will play an important role in the automation and control of mineral processing in the future.

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig's back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig's back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming. [ABSTRACT FROM AUTHOR]

Background: To explore the digitisation of jaw movement trajectories through devices and discuss the physiological factors and device-dependent variables with their subsequent effects on the jaw movement analyses. Methods: Based on predefined eligibility criteria, the search was conducted following PRISMA-P 2015 guidelines on MEDLINE, EBSCO Host, Scopus, PubMed, and Web of Science databases in 2022 by 2 reviewers. Articles then underwent Cochrane GRADE approach and JBI critical appraisal for certainty of evidence and bias evaluation. Results: Thirty articles were included following eligibility screening. Both in vitro experiments (20%) and in vivo (80%) devices ranging from electronic axiography, electromyography, optoelectronic and ultrasonic, oral or extra-oral tracking, photogrammetry, sirognathography, digital pressure sensors, electrognathography, and computerised medical-image tracing were documented. 53.53% of the studies were rated below "moderate" certainty of evidence. Critical appraisal showed 80% case–control investigations failed to address confounding variables while 90% of the included non-randomised experimental studies failed to establish control reference. Conclusion: Mandibular and condylar growth, kinematic dysfunction of the neuromuscular system, shortened dental arches, previous orthodontic treatment, variations in habitual head posture, temporomandibular joint disorders, fricative phonetics, and to a limited extent parafunctional habits and unbalanced occlusal contact were identified confounding variables that shaped jaw movement trajectories but were not highly dependent on age, gender, or diet. Realistic variations in device accuracy were found between 50 and 330 µm across the digital systems with very low interrater reliability for motion tracing from photographs. Forensic and in vitro simulation devices could not accurately recreate variations in jaw motion and muscle contractions. [ABSTRACT FROM AUTHOR]

Point cloud data generated by LiDAR sensors play a critical role in 3D sensing systems, with applications encompassing object classification, part segmentation, and point cloud recognition. Leveraging the global learning capacity of dot product attention, transformers have recently exhibited outstanding performance in point cloud learning tasks. Nevertheless, existing transformer models inadequately address the challenges posed by uncertainty features in point clouds, which can introduce errors in the dot product attention mechanism. In response to this, our study introduces a novel global guidance approach to tolerate uncertainty and provide a more reliable guidance. We redefine the granulation and lower-approximation operators based on neighborhood rough set theory. Furthermore, we introduce a rough set-based attention mechanism tailored for point cloud data and present the rough set transformer (RST) network. Our approach utilizes granulation concepts derived from token clusters, enabling us to explore relationships between concepts from an approximation perspective, rather than relying on specific dot product functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method. Our method establishes concepts based on granulation generated from clusters of tokens. Subsequently, relationships between concepts can be explored from an approximation perspective, instead of relying on specific dot product or addition functions. Empirically, our work represents the pioneering fusion of rough set theory and transformer networks for point cloud learning. Our experimental results, including point cloud classification and segmentation tasks, demonstrate the superior performance of our method.

This paper focuses on the spatial registration algorithm under the earth-center earth-fixed (ECEF) coordinate system for multiple mobile platforms. The sensor measurement biases are discussed with the attitude information of the platform into consideration. First, the biased measurement model is constructed. Besides, the maximum likelihood registration (MLR) algorithm is discussed to simultaneously estimate the measurement biases and the target state. Finally, an improved online MLR (IMLR) algorithm is proposed through a sliding window of adaptive size. Simulation results demonstrate that the proposed IMLR algorithm effectively improves the realtime ability of the system and can approach similar estimation accuracy to the conventional MLR algorithm. [ABSTRACT FROM AUTHOR]

The efficient computation of viewpoints while considering various system and process constraints is a common challenge that any robot vision system is confronted with when trying to execute a vision task. Although fundamental research has provided solid and sound solutions for tackling this problem, a holistic framework that poses its formal description, considers the heterogeneity of robot vision systems, and offers an integrated solution remains unaddressed. Hence, this publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework based on Feature-Based Constrained Spaces (C-spaces) as the backbone for solving it. A C-space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the constraint. The present study demonstrates that many viewpoint constraints can be efficiently formulated as C-spaces, providing geometric, deterministic, and closed solutions. The introduced C-spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems. The effectiveness and efficiency of the concepts introduced are verified on a simulation-based scenario and validated on a real robot vision system comprising two different sensors.

A design modification to an existing 3D-trenched pixel detector is proposed, aimed at an improved fabrication yield. The device concept is studied and its performance is evaluated by TCAD simulations, in comparison to the existing one. Although the modified design features a less uniform electric field distribution, it is still expected to yield a very good timing performance and high radiation tolerance, also exploiting charge multiplication effects.

The individual identification of pigs is the basis for precision livestock farming (PLF), which can provide prerequisites for personalized feeding, disease monitoring, growth condition monitoring and behavior identification. Pig face recognition has the problem that pig face samples are difficult to collect and images are easily affected by the environment and body dirt. Due to this problem, we proposed a method for individual pig identification using three-dimension (3D) point clouds of the pig’s back surface. Firstly, a point cloud segmentation model based on the PointNet++ algorithm is established to segment the pig’s back point clouds from the complex background and use it as the input for individual recognition. Then, an individual pig recognition model based on the improved PointNet++LGG algorithm was constructed by increasing the adaptive global sampling radius, deepening the network structure and increasing the number of features to extract higher-dimensional features for accurate recognition of different individuals with similar body sizes. In total, 10,574 3D point cloud images of ten pigs were collected to construct the dataset. The experimental results showed that the accuracy of the individual pig identification model based on the PointNet++LGG algorithm reached 95.26%, which was 2.18%, 16.76% and 17.19% higher compared with the PointNet model, PointNet++SSG model and MSG model, respectively. Individual pig identification based on 3D point clouds of the back surface is effective. This approach is easy to integrate with functions such as body condition assessment and behavior recognition, and is conducive to the development of precision livestock farming.

The intelligence of modern technologies relies on perceptual systems based on microscale sensors. However, because of the traditional top‐down fabrication approaches performed on planar silicon wafers, a large proportion of existing microscale sensors have 2D structures, which severely restricts their sensing capabilities. To overcome these restrictions, over the past few decades, increasing efforts have been devoted to developing new fabrication methods for microscale sensors with 3D engineered structures, from bulk chemical etching and 3D printing to molding and stress‐induced assembly. Herein, the authors systematically review these fabrication methods based on the applications of the resulting 3D sensors and discuss their advantages compared to their 2D counterparts. This is followed by a perspective on the remaining challenges and possible opportunities. [ABSTRACT FROM AUTHOR]

In the past few years, there has been an increasing interest toward 3D silicon radiation detectors. Owing to their unique architecture, 3D detectors provide a remarkable radiation hardness at relatively low bias voltage (hence low power dissipation), that makes them the most appealing solution for use in the innermost layers of tracking detectors in High Energy Physics (HEP) experiments. Besides this primary application, the use of 3D sensor technology has been extended also to other fields, like thermal neutron detection and microdosimetry for proton and ion therapy. In this paper, we will review the state of the art and on going efforts in 3D detectors, covering the main design and technological issues, as well as selected results from the experimental characterization and TCAD simulation.

The growing demand for miniaturized radiation-tolerant detection systems with fast responses and high-power budgets has increased the necessity for smart and efficient cooling solutions. Several groups have been successfully implementing silicon microfabrication to process superficial microchannels to circulate coolants, in particular, in high-energy physics experiments, where the combination of low material budget to reduce noise generated by multiple scattering events and high radiation fluences is required. In this study, we report tests performed on an 885-µm–thick vertically integrated system. The system consists of a layer of microfabricated silicon channels for temperature management integrated to radiation-tolerant microfabricated 3D sensors, with electrodes penetrating perpendicularly to the silicon bulk, bump-bonded to an ATLAS FE-I4 pixel readout chip of 100 µm thickness, 2 × 2 cm2, and 26,880 pixels (each measuring 250 × 50 μm2). The system’s electrical and temperature characterization under CO2 cooling as well as the response to minimum ionizing particles from radioactive sources and particle beams before and after 2.8 ×1015 neq cm−2 proton irradiation will be discussed.

Paper has been extensively accepted as an ideal platform for fabricating low‐cost, flexible, and environmentally friendly electronics. However, current paper‐based pressure sensors have not fully demonstrated the unique merits of paper, such as cost efficiency, disposability, cuttability, and foldability, mainly because of the limitation of the applied metal electrodes and device configuration. Here, an all‐organic, all‐paper, cuttable, and foldable pressure sensor is demonstrated for the first time, with the high‐conductivity polypyrrole (PPy) printing paper as electrode and the low‐conductivity PPy tissue paper as active layer. Hollow and 3D all‐paper sensors are realized through fabricating the kirigami or origami structure since the device can be freely cut and folded, presenting a universal strategy promising for flexible device design. The cuttability and foldability of device create a unique 3D perception capability. The devices show high sensitivity (4.8 kPa−1 at < 5.5 kPa, 1.7 kPa−1 at 5.5–40 kPa) and wide detection range (up to 40 kPa), providing a capability for full‐range detection of human physiological and motion signals. These results open a feasible route to realize hollow and 3D sensors and fully demonstrate the potential of paper sensors for diverse applications to new‐generation green electronics and portable electronics. [ABSTRACT FROM AUTHOR]

Gradual development of modern trends with more emphasis on visualization and measurement accuracy have resulted in the continuous improvement of measuring instruments, which are very closely linked to Personal Computers and/or Programmable Logic Controllers to the displaying unit and leads to greater utilization of 3D technology. 3D technology is used in security, biometrics, and in many other fields. This contribution is focused on understanding the issues of scanning and its advantages and disadvantages. It serves as a complete overview of the structure of the 3D sensors and their use.

Abstract 3D type of pixel sensors is a promising option for the innermost pixel layer at the High-Luminosity LHC. However, the required very high hit-rate capabilities, finer pixel granularity, extreme radiation hardness and reduced material budget call for a downscale of the pixel size as compared to existing 3D sensors, involving smaller pitch (e.g. 50 × 50 or 25 × 100 μ m 2), shorter inter-electrode spacing (∼ 30 μ m), narrower electrodes (∼ 6 μ m diameter), and reduced active thickness (∼ 100 – 150 μ m). Within a joint R&D effort with INFN, FBK has produced a new generation of 3D pixel sensors with these challenging features. In this talk preliminary results from the electrical and functional characterisation of the first prototypes are reported, included their behaviour after large radiation fluence, close to the ones expected in the High Luminosity LHC environment. Prospects for the next prototypes are also presented. [ABSTRACT FROM AUTHOR]

Abstract This document discusses the silicon-based detectors planned for the High Luminosity LHC. The special aspects to cope with the new environment and its challenges, e.g. very high radiation levels and very high instantaneous luminosity thus high pile-up, high occupancy and high data rates, are addressed. The different design choices of the detectors are put into perspective. Exciting topics like trackers, high granularity silicon-based calorimetry with novel 8 inch processing, fast timing and new triggers are described. [ABSTRACT FROM AUTHOR]

• The four-point calibration algorithm is done through the self-developed human-computer interaction touch platform. • The single-touch and multi-touch performance can be accurately achieve through the proposed 3D interactive software system. • The proposed calibration algorithm can achieve precise positioning and real-time interactive control. In the depth image interaction system, there is a deviation between the detection range of the Kinect sensor and the projection interaction surface. Aiming at this problem, this paper proposes a trapezoidal correction algorithm based on the three-dimensional sensor Kinect, and performs the calibration on the interaction space to achieve precise interactive control. The performance of the algorithm is verified by the human-computer interaction control platform, which is developed independently. The algorithm proposed in this paper was applied to the 3D interactive software system, which can achieve accurate single-touch and multi-touch performance. Compared with the traditional optical trapezoidal calibration algorithm, the algorithm tracking accuracy rate and the tracking time have been greatly improved, and a good interactive effect can be well obtained. The presented method can be widely applied to various interactive systems such as image interaction systems, human-computer interaction systems, and multi-touch systems, etc. [ABSTRACT FROM AUTHOR]

This paper describes a non-invasive, automatic, and robust method for calibrating a scalable RGB-D sensor network based on retroreflective ArUco markers and the iterative closest point (ICP) scheme. We demonstrate the system by calibrating a sensor network comprised of six sensor nodes positioned in a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m . Here, the automatic calibration achieved an average Euclidean error of 3 c m at distances up to 9.45 m . To achieve robustness, we apply several innovative techniques: Firstly, we mitigate the ambiguity problem that occurs when detecting a marker at long range or low resolution by comparing the camera projection with depth data. Secondly, we use retroreflective fiducial markers in the RGB-D calibration for improved accuracy and detectability. Finally, the repeating ICP refinement uses an exact region of interest such that we employ the precise depth measurements of the retroreflective surfaces only. The complete calibration software and a recorded dataset are publically available and open source.

This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm.

This paper presents an analytical study of the depth estimation error of a stereo vision-based pedestrian detection sensor for automotive applications such as pedestrian collision avoidance and/or mitigation. The sensor comprises two synchronized and calibrated low-cost cameras. Pedestrians are detected by combining a 3D clustering method with Support Vector Machine-based (SVM) classification. The influence of the sensor parameters in the stereo quantization errors is analyzed in detail providing a point of reference for choosing the sensor setup according to the application requirements. The sensor is then validated in real experiments. Collision avoidance maneuvers by steering are carried out by manual driving. A real time kinematic differential global positioning system (RTK-DGPS) is used to provide ground truth data corresponding to both the pedestrian and the host vehicle locations. The performed field test provided encouraging results and proved the validity of the proposed sensor for being used in the automotive sector towards applications such as autonomous pedestrian collision avoidance.

3D imaging sensors for the acquisition of three dimensional (3D) shapes have created, in recent years, a considerable degree of interest for a number of applications. The miniaturization and integration of the optical and electronic components used to build them have played a crucial role in the achievement of compactness, robustness and flexibility of the sensors. Today, several 3D sensors are available on the market, even in combination with other sensors in a “sensor fusion†approach. An importance equal to that of physical miniaturization has the portability of the measurements, via suitable interfaces, into software environments designed for their elaboration, e.g., CAD-CAM systems, virtual renders, and rapid prototyping tools. In this paper, following an overview of the state-of-art of 3D imaging sensors, a number of significant examples of their use are presented, with particular reference to industry, heritage, medicine, and criminal investigation applications.

With the planned upgrade of the LHC to High-Luminosity-LHC [1] , the general purpose experiments ATLAS and CMS are planning to upgrade their innermost tracking layers with more radiation tolerant technologies. Chemical Vapor Deposition CVD diamond is one such technology. CVD diamond sensors are an established technology as beam condition monitors in the highest radiation areas of all LHC experiments. The RD42-collaboration at CERN is leading the effort to use CVD diamond as a material for tracking detectors operating in extreme radiation environments. An overview of the latest developments from RD42 is presented including the present status of diamond sensor production, a study of pulse height dependencies on incident particle flux and the development of 3D diamond sensors. [ABSTRACT FROM AUTHOR]

Ego-motion estimation and localization in large environments are key components in any assistive technology for real-time user orientation and navigation. We consider the case where a large known environment is explored without a priori assumptions on the initial location. In particular we propose a framework that uses a single portable 3D sensor to solve the place recognition problem and continuously tracks its position even when leaving the known area or when significant changes occur in the observed environment. We cast the place recognition step as a classification problem and propose an efficient search space reduction considering only navigable areas where the user can be localized. Classification hypotheses are then discarded exploiting temporal consistency w.r.t. a relative tracker that exploits only the sensor input data. The solution uses a compact classifier whose representation scales well with the map size. After being localized, the user is continuously tracked exploiting the known environment using an efficient data structure that provides constant access time for nearest neighbor searches and that can be streamed to keep only the local region close to the last known position in memory. Robust results are achieved by performing a geometrically stable selection of points, efficiently filtering outliers and integrating the relative tracker based on previous observations. We experimentally show that such a framework provides good localization results and that it scales well with the environment map size yielding real-time performance for both place recognition and tracking. [ABSTRACT FROM AUTHOR]

Silicon sensors are widely used as tracking detectors in high energy physics experiments. This results in several specific requirements like radiation hardness and granularity. Therefore research for highly performing silicon detectors is required. The RD50 Collaboration is a CERN R&D collaboration dedicated to the development of radiation hard silicon devices for application in high luminosity collider experiments. Extensive research is ongoing in different fields since 2001. The collaboration investigates both defect and material characterization, detector characterization, the development of new structures and full detector systems. The report gives selected results of the collaboration and places an emphasis on the development of new structures, namely 3D devices, CMOS sensors in HV technology and low gain avalanche detectors. [ABSTRACT FROM AUTHOR]

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In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works. [ABSTRACT FROM AUTHOR]

The sensing performance of chemical sensors can be achieved not only by modification or hybridization of sensing materials but also through new design in device geometry. The performance of a chemical sensing device can be enhenced from a simple three-dimensional (3D) chemiresistor-based gas sensor platform with an increased surface area by forming networked, self-assembled reduced graphene oxide (R-GO) nanosheets on 3D SU8 micro-pillar arrays. The 3D R-GO sensor is highly responsive to low concentration of ammonia (NH3) and nitrogen dioxide (NO2) diluted in dry air at room temperature. Compared to the two-dimensional planar R-GO sensor structure, as the result of the increase in sensing area and interaction cross-section of R-GO on the same device area, the 3D R-GO gas sensors show improved sensing performance with faster response (about 2%/s exposure), higher sensitivity, and even a possibly lower limit of detection towards NH3 at room temperature. [ABSTRACT FROM AUTHOR]

In the factory of the future, most of the operations will be done by autonomous robots that need visual feedback to move around the working space avoiding obstacles, to work collaboratively with humans, to identify and locate the working parts, to complete the information provided by other sensors to improve their positioning accuracy, etc. Different vision techniques, such as photogrammetry, stereo vision, structured light, time of flight and laser triangulation, among others, are widely used for inspection and quality control processes in the industry and now for robot guidance. Choosing which type of vision system to use is highly dependent on the parts that need to be located or measured. Thus, in this paper a comparative review of different machine vision techniques for robot guidance is presented. This work analyzes accuracy, range and weight of the sensors, safety, processing time and environmental influences. Researchers and developers can take it as a background information for their future works.

This paper provides an overview of state-of-the-art 3-D endoscopic imaging technologies. Physical objects in the world are 3-D, yet traditional endoscopes can only acquire 2-D images that lack depth information. This fundamental restriction greatly limits our ability to perceive and understand the complexity of real world objects. Lack of 3-D information also hinders our ability to quantitatively measure 3-D objects. In both medical imaging and industrial inspection applications, 3-D surface imaging capability would add one more dimension, literally and figuratively, to the existing imaging technologies. Over the past decades, tremendous new technologies and methods emerged in the 3-D surface imaging field. In this paper, we first provide a classification of these technologies. We then describe each category in detail, with representative designs and examples. This overview would be useful to researchers in the field since it provides a snapshot of the current state-of-the-art, from which subsequent research in meaningful directions is encouraged. This overview also contributes to the efficiency of research by preventing unnecessary duplication of already performed research. [ABSTRACT FROM PUBLISHER]