Different Fields Of View Research Articles

A Multi-FoV Viewport-Based Visual Saliency Model Using Adaptive Weighting Losses for 360$^\circ$ Images

360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> media allows observers to explore the scene in all directions. The consequence is that the human visual attention is guided by not only the perceived area in the viewport but also the overall content in 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> . In this paper, we propose a method to estimate the 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> saliency map which extracts salient features from the entire 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> image in each viewport in three different Field of Views (FoVs). Our model is first pretrained with a large-scale 2D image dataset to enable the interpretation of semantic contents, then fine-tuned with a relative small 360 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> image dataset. A novel weighting loss function attached with stretch weighted maps is introduced to adaptively weight the losses of three evaluation metrics and attenuate the impact of stretched regions in equirectangular projection during training process. Experimental results demonstrate that our model achieves better performance with the integration of three FoVs and its diverse viewport images. Results also show that the adaptive weighting losses and stretch weighted maps effectively enhance the evaluation scores compared to the fixed weighting losses solutions. Comparing to other state of the art models, our method surpasses them on three different datasets and ranks the top using 5 performance evaluation metrics on the Salient360! benchmark set. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/FannyChao/MV-SalGAN360</uri> .

Accuracy of maxillofacial prototypes fabricated by different 3-dimensional printing technologies using multi-slice and cone-beam computed tomography.

PurposeThis study aimed to compare the accuracy of 3-dimensional (3D) printed models derived from multidetector computed tomography (MDCT) and cone-beam computed tomography (CBCT) systems with different fields of view (FOVs).Materials and MethodsFive human dry mandibles were used to assess the accuracy of reconstructions of anatomical landmarks, bone defects, and intra-socket dimensions by 3D printers. The measurements were made on dry mandibles using a digital caliper (gold standard). The mandibles then underwent MDCT imaging. In addition, CBCT images were obtained using Cranex 3D and NewTom 3G scanners with 2 different FOVs. The images were transferred to two 3D printers, and the digital light processing (DLP) and fused deposition modeling (FDM) techniques were used to fabricate the 3D models, respectively. The same measurements were also made on the fabricated prototypes. The values measured on the 3D models were compared with the actual values, and the differences were analyzed using the paired t-test.ResultsThe landmarks measured on prototypes fabricated using the FDM and DLP techniques based on all 4 imaging systems showed differences from the gold standard. No significant differences were noted between the FDM and DLP techniques.ConclusionThe 3D printers were reliable systems for maxillofacial reconstruction. In this study, scanners with smaller voxels had the highest precision, and the DLP printer showed higher accuracy in reconstructing the maxillofacial landmarks. It seemed that 3D reconstructions of the anterior region were overestimated, while the reconstructions of intra-socket dimensions and implant holes were slightly underestimated.

A Local-Global Dual-Stream Network for Building Extraction From Very-High-Resolution Remote Sensing Images.

Buildings constitute one of the most important landscapes in remote sensing (RS) images and have been broadly analyzed in a wide range of applications from urban planning to other socioeconomic studies. As very-high-resolution (VHR) RS imagery becomes more accessible, the current building extraction methods are confronted with the challenges of the diverse appearances, various scales, and complicated structures of buildings in complex scenes. With the development of context-aware deep learning methods, it has been proven by numerous works that capturing contextual information can offer spatial relation cues for robust recognition and detection of the objects. In this article, we propose a novel local-global dual-stream network (DS-Net) that adaptively captures local and long-range information for the accurate mapping of building rooftops in VHR RS images. The local branch and the global branch of DS-Net work in a complementary manner to each other with different fields of view on the input image. Through a well-defined dual-stream architecture, DS-Net learns hierarchical representations for both the local and global branches, and a deep feature sharing strategy is further developed to enforce more collaborative integration of the two branches. Extensive experiments were carried out to verify the effectiveness of our model on three widely used VHR RS data sets: the Massachusetts buildings data set, the Inria Aerial Image Labeling data set, and the DeepGlobe Building Detection Challenge data set. Empirically, the proposed DS-Net achieves competitive or superior performance compared with the current state-of-the-art methods in terms of quantitative measures and visual evaluations.

Intelligent Perception System of Robot Visual Servo for Complex Industrial Environment.

Robot control based on visual information perception is a hot topic in the industrial robot domain and makes robots capable of doing more things in a complex environment. However, complex visual background in an industrial environment brings great difficulties in recognizing the target image, especially when a target is small or far from the sensor. Therefore, target recognition is the first problem that should be addressed in a visual servo system. This paper considers common complex constraints in industrial environments and proposes a You Only Look Once Version 2 Region of Interest (YOLO-v2-ROI) neural network image processing algorithm based on machine learning. The proposed algorithm combines the advantages of YOLO (You Only Look Once) rapid detection with effective identification of ROI (Region of Interest) pooling structure, which can quickly locate and identify different objects in different fields of view. This method can also lead the robot vision system to recognize and classify a target object automatically, improve robot vision system efficiency, avoid blind movement, and reduce the calculation load. The proposed algorithm is verified by experiments. The experimental result shows that the learning algorithm constructed in this paper has real-time image-detection speed and demonstrates strong adaptability and recognition ability when processing images with complex backgrounds, such as different backgrounds, lighting, or perspectives. In addition, this algorithm can also effectively identify and locate visual targets, which improves the environmental adaptability of a visual servo system

THE EFFECT OF CORIANDER LEAVES EXTRACT ON THE DEGREE OF WISTAR RATS LIVER MICROSCOPIC DAMAGE WAS GIVEN MERCURIC CHLORIDE ORALLY

Background Mercury is a heavy metal that is widely used in various aspects of human life. However, mercury exposure can damage the liver. The high antioxidant found in coriander leaves is expected to be a solution to the problem in preventing liver damage due to mercury exposure. Aim Proving that administration of coriander leaf extract can reduce the degree of wistar rats liver microscopic damage due to mercury exposure. Methods Experimental study with post test only control group design for 14 days, used 24 male Wistar rats, divided randomly into 4 groups : C (-), C (+) (given mercury orally 10 mg / kgBW), X1 ( given mercury orally 10 mg / kgBW and coriander leaf extract 100 mg / kgBW) and X2 (given mercury orally 10 mg / kgBW and coriander leaf extract 200 mg / kgBW). On the 15th day, rats were terminated, the livers were harvested, microscopic preparations were made and stained with Hematoxylin-Eosin. Observed with a magnification of 400x in 5 different fields of view of each sample and were assessed using Manja Roenigk's score. Results Were obtained normal, mild damage, moderate damage, and severe damage respectively in the group: C (-): 80%, 20%, 0%, 0%; C (+): 0%, 3%, 67%, 30%; X1: 47%, 33%, 17%, 3%; X2: 77%, 23%, 0%, 0%. Spearman test found significant correlation (p = 0,000) with storng correlation ( r = 0.781) and the direction between the two variables is negative (-) . Kruskal Wallis test found significant differences (p = 0,000). Continued Mann Whitney test found significant differences between groups C (-) with C (+) (0,000), C (-) with X1 (0.004), C (+) with X1 (0,000), C (+) with X2 (0,000) ), and X1 with X2 (0.007) and the difference is not significant between C (-) and X2 (0.756). Conclusion Giving coriander leaves extract can reduce the degree of microscopic damage of Wistar rat liver due to mercury exposure.

Polarization Radiometric Calibration in Laboratory for a Channeled Spectropolarimeter

The process of radiometric calibration would be coupled with the polarization properties of an optical system for spectropolarimetry, which would have significant influences on reconstructed Stokes parameters. In this paper, we propose a novel polarization radiometric calibration model that decouples the radiometric calibration coefficient and polarization properties of an optical system. The alignment errors of the polarization module and the variation of the retardations at different fields of view are considered and calibrated independently. According to these calibration results, the input Stokes parameters at different fields of view can be reconstructed accurately through the proposed model. Simulations are performed for the presented calibration and reconstruction methods, which indicate that the measurement accuracy of polarization information is improved compared with the traditional undecoupled calibration method.

Open-source, cost-effective, portable, 3D-printed digital lensless holographic microscope.

In this work, the design, construction, and testing of the most cost-effective digital lensless holographic microscope to date are presented. The architecture of digital lensless holographic microscopy (DLHM) is built by means of a 3D-printed setup and utilizing off-the-shelf materials to produce a DLHM microscope costing US$52.82. For the processing of the recorded in-line holograms, an open-source software specifically developed to process this type of recordings is utilized. The presented DLHM setup has all the degrees of freedom needed to achieve different fields of view, levels of spatial resolution, and 2D scanning of the sample. The feasibility of the presented platform is tested by imaging non-bio and bio samples; the resolution test targets, a section of the head of a Drosophila melanogaster fly, red blood cells, and cheek cells are imaged on the built microscope.

HookNet: Multi-resolution convolutional neural networks for semantic segmentation in histopathology whole-slide images.

We propose HookNet, a semantic segmentation model for histopathology whole-slide images, which combines context and details via multiple branches of encoder-decoder convolutional neural networks. Concentric patches at multiple resolutions with different fields of view, feed different branches of HookNet, and intermediate representations are combined via a hooking mechanism. We describe a framework to design and train HookNet for achieving high-resolution semantic segmentation and introduce constraints to guarantee pixel-wise alignment in feature maps during hooking. We show the advantages of using HookNet in two histopathology image segmentation tasks where tissue type prediction accuracy strongly depends on contextual information, namely (1) multi-class tissue segmentation in breast cancer and, (2) segmentation of tertiary lymphoid structures and germinal centers in lung cancer. We show the superiority of HookNet when compared with single-resolution U-Net models working at different resolutions as well as with a recently published multi-resolution model for histopathology image segmentation. We have made HookNet publicly available by releasing the source code1 as well as in the form of web-based applications2,3 based on the grand-challenge.org platform.

Dynamic measurement of beam divergence angle of different fields of view of scanning lidar* *Project supported by the National Key Scientific Instrument and Equipment Development Projects of China (Grant No. 62027823), the Shenzhen Fundamental Research Program (Grant No. JCYJ2020109150808037), and the National Natural Science Foundation of China (Grant No. 61775048).

The laser beam divergence angle is one of the important parameters to evaluate the quality of the laser beam. It can not only accurately indicate the nature of the beam divergence when the laser beam is transmitted over a long distance, but also objectively evaluate the performance of the laser system. At present, lidar has received a lot of attention as a core component of environment awareness technology. Micro-electromechanical system (MEMS) micromirror has become the first choice for three-dimensional imaging lidar because of its small size and fast scanning speed. However, due to the small size of the MEMS micromirror, the lidar scanning system has a small field of view (FOV). In order to achieve a wide range of scanning imaging, collimating optical system and wide-angle optical system are generally added to the system. However, due to the inherent properties of the optical lens, it is impossible to perfect the imaging, so the effects of collimating and expanding the beam will be different at different angles. This article aims to propose a measurement system that dynamically measures the divergence angles of MEMS scanning lidar beams in different fields of view to objectively evaluate the performances of scanning lidar systems.

Automatic vertebrae recognition from arbitrary spine MRI images by a category-Consistent self-calibration detection framework.

Accurate vertebrae recognition is crucial in spinal disease localization and successive treatment planning. Although vertebrae detection has been studied for years, reliably recognizing vertebrae from arbitrary spine MRI images remains a challenge. The similar appearance of different vertebrae and the pathological deformations of the same vertebrae makes it difficult for classification in images with different fields of view (FOV). In this paper, we propose a Category-consistent Self-calibration Recognition System (Can-See) to accurately classify the labels and precisely predict the bounding boxes of all vertebrae with improved discriminative capabilities for vertebrae categories and self-awareness of false positive detections. Can-See is designed as a two-step detection framework: (1) A hierarchical proposal network (HPN) to perceive the existence of the vertebrae. HPN leverages the correspondence between hierarchical features and multi-scale anchors to detect objects. This correspondence tackles the image scale/resolution challenge. (2) A Category-consistent Self-calibration Recognition (CSRN) Network to classify each vertebra and refine their bounding boxes. CSRN leverages the dictionary learning principle to preserve the most representative features; it imposes a novel category-consistent constraint to force vertebrae with the same label to have similar features. CSRN then innovatively formulates message passing into the deep learning framework, which leverages the label compatibility principle to self-calibrate the wrong pre-recognitions. Can-See is trained and evaluated on a capacious and challenging dataset of 450 MRI scans. The results show that Can-See achieves high performance (testing accuracy reaches 0.955) and outperforms other state-of-the-art methods.

Performance of Indoor VLC System Under Random Placement of LEDs With Nonimaging and Imaging Receiver

This article analyzes the performance of multiple-input-multiple-output indoor visible light communication (VLC) system by randomly deploying the light-emitting-diodes (LEDs) using Matern hardcore point process (MHCP). Furthermore, photodetectors (PDs) with two different field of view (FOVs) have been utilized for an imaging as well as nonimaging receiver structures. It is a widely known fact that in a conventional VLC system, signal-to-noise ratio (SNR) profile inside the room varies with respect to the LED placement and the PDs position. Consequently, the proposed work attempts to achieve uniform SNR across the room, by utilizing MHCP based LED placement at the transmitter, and a nonimaging receiver with four PDs using 1-FOV, 2-FOV, and imaging receiver configurations. Simulation results show that random deployment of LED using MHCP configuration results in a more uniform SNR profile inside the room as compared to regular LED deployment schemes. Furthermore, three different power allocation schemes for LEDs namely equal power, distance-based power, and an optimal power allocation are proposed. For the different power allocation schemes, the average SNR and the variance of the received optical power (OP) inside the room are derived and compared. In addition, the closed-form expression for the bit-error-rate (BER) probability is derived for the proposed MHCP configuration using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on</small> - <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">off</small> keying as a modulation scheme. Results show improvement in BER performance with the OP allocation in comparison to other power allocation schemes both for imaging and nonimaging receiver configuration.

Multiple Heterogeneous Sensor Tasking Optimization in the Absence of Measurement Feedback

Observations of resident space objects generated by sensors are the primary method of maintaining knowledge of the object states. With the increasing number of objects, efficient sensor allocation is becoming integral. This requires the coordination of multiple sensors with different capabilities in an optimized manner. While the optimization is greatly simplified if instantaneous communication between the sensors can be assumed and immediate processing is available, this is not a realistic setup. Information exchange and processing induces time delays that are longer than the time available to plan and start the sensor tasking step, without unnecessarily idling the sensor. In this paper, a method is introduced to form efficient sensor tasking in a multi-sensor system, without immediate communication between the sensors and observation processing, in the following called feedback. The coordination is exemplified using two sensors, with different fields of view in a follow-up scenario of objects in the geosynchronous region. The efficiency of the method is evaluated using the two line element catalog.

Design of Photoelectric Detection Sensor Incorporated With Meso-Lens Array and Its Detection Screen Performance Analysis

Traditional photoelectric detection target systems have the property of unclear target imaging at different detection distances, which leads to their low detection sensitivity. Furthermore, their use of photoelector array with detection blind area reduces the performance of detection. To improve the detection capability of traditional photoelectric detection target, a new design method using photoelectric detection sensor incorporated with meso-lens array and double-slit diaphragm has been proposed and the reasons for insufficient detection performance caused by imaging at different detection distances based on traditional photoelectric detection target principle have been analyzed. An optical path detection system with a primary lens, a meso-lens array and a double slit diaphragm has been designed; which combines the system of a photoelector with a main slit diaphragm and a secondary slit diaphragm to connect with the detection screen. Further, the elimination photoelector array blind area method is analyzed by using new photoelectric detection sensor incorporated with meso-lens array, and the target signal output function of the new photoelectric detection sensor is established. By conducting comparative experiments at different fields of view of detection screen, the detection performance of the meso-lens array incorporated photoelectric detection sensor shows better performance than the traditional photoelectric detection target.

Misalignment Calculation on Off-Axis Telescope System via Fully Connected Neural Network

On the process of alignment for pupil off-axis telescope system, the determination of decentering and tilt misalignments is a key step. In this paper, we proposed a new method which adopts the fully connected neural network (FCNN) as a fitting tool to establish the nonlinear mapping relation between misalignments of different fields of view (FOVs) and Zernike coefficients. Firstly, we establish a pupil off-axis reflection telescope model, then decentering and tilt misalignments are introduced to acquire corresponding aberrations that represented by Zernike coefficients. We use aberrations as the inputs of FCNN; misalignments and FOV as outputs. FCNN is trained by the combination of inputs and outputs as a dataset, and we use a new dataset to test the accuracy and effectiveness of the trained FCNN. The results show that the mean absolute error (MAE) of the X-axis decentering error, V-axis tilt error and angle of view are 0.0506 mm, 0.0204° and 0.0124°, respectively. These results demonstrate that the proposed method is effective and feasible to calculate the misalignments.

Design and validation of a shipborne multiple-field-of-view lidar for upper ocean remote sensing

The oceanic lidar, which has the advantage of profiling the properties of the upper ocean, is a promising active remote sensing tool for complementing the passive ocean color remote sensing in the three-dimensional observation of ocean. This paper introduces the design and validation of a shipborne multiple-field-of-view (MFOV) lidar. With the input of simultaneous in situ data, the lidar signals and the retrieved lidar attenuation coefficient α are validated quantitatively using simulation results. The simulation methods include quasi-single-scattering lidar equation, Monte Carlo (MC) method, and analytical model. The experimental lidar signals are consistent with the simulated lidar signals with coefficient of determination R2>0.91, and α retrieved from experimental lidar signals corresponds well with the simulated values with root mean square (RMS) of relative error δ<0.18. The results at different field of view (FOV) indicate the relationship between the beam attenuation coefficient c, diffuse attenuation coefficient Kd, and α. This work is beneficial to the research on the mechanism, retrieval and application of oceanic lidar.

Polygon Based Topology Formation and Information Gathering in Satellite Based Wireless Sensor Network

The satellite network is one of the major source of information and these days small satellites are gaining lot of focus. The group of small satellites form a distributed network that work collaboratively to accomplish the mission task. These networks are very similar to the terrestrial wireless sensor network in terms of restricted resources and constrained capabilities. Sometimes, network of small satellites is also called as space based wireless sensor network (SBWSN). In any distributed network, topology formation and its control plays a significant role. This is true for SBWSN also. The topology in SBWSN decides the area of coverage (field of view), time of coverage, data gathered and data transmitted to ground station. The proposed topology is a distributed network of small satellites formed by trivalent, toroidal and spherical polyhedron graph forming a fullerene, which is called as polygon based network topology (PBNT). It comprises of both pentagonal (Fp) and hexagonal (Fh) faces with K regular graphs such that K ≥ 3 with genus equal to 1. It also satisfies Eulers formula with n vertices. The fullerene comprises of simple rings and each of this ring forms the cluster. Each cluster is further represented as a triangular grid, that is linearly convex or non-linear, with K-connected graph along with Hamiltonian extendible cycle. The nodes/satellites on the triangular grid represent sensing nodes (low capability nodes/satellites), while the vertices of the ring are sink nodes (higher capability nodes/satellites). In this work, the topology is formed by small satellites (pico or nano satellites). In the proposed topology formation, the network is considered as virtual network with logical neighbours forming the cluster. Each node in the cluster covers a particular swath for a particular time interval based on the mission payload and on the p3 tiling. In the simulation, we consider n small satellites being placed in low earth orbit (LEO), (where n ranges from 3 to 150). The performance enhancements are seen during simulation in the following parameters, (1) Coverage Area: The coverage area increases as multiple satellites have different field of view at different times. (2) Reduces Gaps:The proposed distributed network also minimises uncovered areas as multiple satellites cover the target location at different time stamp which is not possible by a single large satellite. (3) Increase in Data Throughput: Each satellite in the network transmits data, when it is at perigee. The data throughput of the network increases, as data is transmitted by multiple satellites. Therefore, the throughput is increased by n-fold. (4) Continuous Connectivity: The data captured by one satellite in the network is made available to other using multi-hop communication. Thus the proposed topology also increases the continuous connectivity between satellites and also with the ground station. (5) Increases lifetime and Network Reliability: The SBWSN accomplishes its mission task even when one/more satellites encounters functional failure. The satellites in the network can reconfigure themselves and continue the mission task. Thus SBWSN also reduces the risk of mission failure and ensures mission reliability. Due to reconfiguration the lifetime of the network is also increased. The proposed topology is used for small satellites (specially nano and pico satellites) network, which permit the single board satellite weighing less than 10 kg (Pico satellite less than 1 kg and nano satellites less than 10 kg). The advantage of these small satellites network over single large satellite is low cost and reduced development time, as it uses commercially of the shelf (COTS) components. In this paper, we propose network architecture formed by the spherically embedded clusters formed by polyhedron. The vertices of polyhedron have both pentagonal (Fp) and hexagonal (Fh) faces with K regular graphs such that K ≥ 3, with genus equal to 1. The vertices of these polyhedron form the sink nodes and the other nodes are sensing nodes. Here satellites and nodes are interchangeably used. Sensing nodes are used for data gathering (pico/nano), while sink nodes are higher capability nodes which perform computational extensive operations in the network (nano or macro satellites). The sensing and sink nodes transmit data to the ground station when they are at perigee. The main objective of the proposed work is, technology demonstration of low cost, distributed small satellites network for earth observations replacing single huge satellite.

Effective doses of dental cone beam computed tomography: effect of 360-degree versus 180-degree rotation angles

The aims of this study were to compare radiation absorbed dose (AD) and effective dose (ED) to tissues from cone beam computed tomography (CBCT) scans with 360-degree versus 180-degree rotations with use of different fields of view (FOV), to compare EDs calculated from measured ADs versus dose area product (DAP) values, and to compare doses to the lens of the eye (LOE) from different scan parameters. ADs for each protocol were measured in tissues, including the LOE, by using an anthropometric phantom. EDs were calculated on the basis of dosimetry (EDm) and DAP values (EDd). Dose differences were determined with analysis of variance (ANOVA). ADs and EDs were substantially lower for 180-degree rotation scans compared with 360-degree rotation scans (P < .01). Remainder tissues had the greatest effect on effective dose for most FOVs. Doses were generally lower with small FOVs compared with large FOVs. Most EDm values were lower than EDd values in large FOVs but higher in small FOVs. Differences in EDm and EDd were variable and unpredictable. LOE doses were smaller with the 180-degree scans and smaller FOVs. Radiation doses were generally lower with 180-degree rotation scans and smaller FOVs. These parameters should be used for CBCT acquisitions, whenever possible, and should be made available in all units.

The Effects of Propofol on Neural Responses in the Mouse Primary Auditory Cortex.

Two-photon laser-scanning microscopy allows for the monitoring of all brain neurons with single-cell and single-action potential accuracy. This study aimed to investigate the neural responses of the primary auditory cortex to sound stimuli in awake and propofol-anesthetized mice using 2-photon laser-scanning microscopy. Twelve healthy adult male C57BL/6 mice were used in the present study. In each mouse, the scalp was removed over the entire dorsal skull, and the right primary auditory cortex (A1) located. The test stimulus, used in the awake and propofol-induced anesthetic state, was a group of tones with a random combination of 3 sound intensities and 8 sound frequencies. The calcium indicator GCaMP6s was virally expressed in cortical neurons and neuronal activity was recorded using 2-photon imaging. Calcium responses to sound stimuli in two thirds of the neuronal population of the A1 layer were significantly inhibited by propofol anesthesia. In a single neuron, the calcium responses were also inhibited by propofol anesthesia. In the waking state, △F/F (where F is the time series of fluorescence intensity) of all single neurons was significantly higher than that in the propofol-induced anesthetic state (n=669, P<0.001). Finally, in one example session and averaged across different fields of views (n=6 sessions), the response events to sound stimuli were also inhibited by propofol anesthesia. Anesthetic doses of propofol inhibited calcium transients and neuronal activity in the primary auditory cortex of mice.

5G mmWave Cooperative Positioning and Mapping Using Multi-Model PHD Filter and Map Fusion

5G millimeter wave (mmWave) signals can enable accurate positioning in vehicular networks when the base station and vehicles are equipped with large antenna arrays. However, radio-based positioning suffers from multipath signals generated by different types of objects in the physical environment. Multipath can be turned into a benefit, by building up a radio map (comprising the number of objects, object type, and object state) and using this map to exploit all available signal paths for positioning. We propose a new method for cooperative vehicle positioning and mapping of the radio environment, comprising a multiple-model probability hypothesis density filter and a map fusion routine, which is able to consider different types of objects and different fields of views. Simulation results demonstrate the performance of the proposed method.

Collaborative Tracking Method in Multi-Camera System

Visual tracking has been a popular task in computer vision in recent years, especially for long-term tracking. A novel object tracking framework is proposed in this paper. For surveillance cameras with overlapping areas, the target area is divided into several regions corresponding to each camera, and a simple re-matching method is used by matching the colors according to the segmented parts. For surveillance cameras without overlapping areas, a time estimation model is employed for continuously tracking objects in different fields of view (FoVs). A demonstration system for collaborative tracking in real time situation is realized finally. The experimental results show that compared with current popular algorithms, the proposed approach has good effect in accuracy and computation time for the application of continuously tracking the pedestrians.