A Robust and Flexible Geometry calibration Method for Phase Measuring Deflectometry

This paper proposes a new shearing approach to eliminate the systematic errors in phase measuring deflectometry (PMD). Besides a normal measurement using PMD, another three measurements are taken when the test surfaces are translated and rotated with an amount of known magnitude in our proposed method. The four measurements give three sheared wavefronts, and then the difference Zernike polynomial fitting and a least squares algorithm are proposed to reconstruct the true test surface figure. The proposed method is demonstrated and verified through computer simulation and experimental results. The error propagation when the sheared wavefronts include random noise and the introduced errors due to the incorrect shearing values are both analyzed.

Underwater environment is complex and random. The images obtained from underwater by sonar always have uneven background gray distribution and fuzzy details of boundary. Hence the low-quality sonar images need to be enhanced before analysis. This paper presents a sonar image enhancement algorithm based on the approximation spaces of random sets. First of all, the knowledge representation of the underwater image is constructed by the approximation spaces of random sets. According to the background knowledge, the image is divided by the upper and lower approximation space of the random set. Then the optimal partition is obtained according to the approximate equivalence relation of the upper and lower approximation. Based on the optimal partition, an improved dark channel theory is presented to enhance each region of the image. After that, sonar images with different backgrounds are used to test the proposed method. The experimental results show that the gray distribution of the sonar image enhanced by this algorithm is more uniform and the boundary details are clearer. The proposed algorithm has the advantage of solving the optimal division for the set of pixels with approximate grayscale. Moreover, the proposed algorithm can get better image enhancement effect in the premise of maintaining the texture of the sonar images.

Image-based displacement measurement techniques are widely used for sensing the deformation of structures, and plays an increasing role in structural health monitoring owing to its benefit of non-contacting. In this study, a non-overlapping dual camera measurement model with the aid of global navigation satellite system (GNSS) is proposed to sense the three-dimensional (3D) displacements of high-rise structures. Each component of the dual camera system can measure a pair of displacement components of a target point in a 3D space, and its pose relative to the target can be obtained by combining a built-in inclinometer and a GNSS system. To eliminate the coupling of lateral and vertical displacements caused by the perspective projection, a homography-based transformation is introduced to correct the inclined image planes. In contrast to the stereo vision-based displacement measurement techniques, the proposed method does not require the overlapping of the field of views and the calibration of the vision geometry. Both simulation and experiment demonstrate the feasibility and correctness of the proposed method, heralding that it has a potential capacity in the field of remote health monitoring for high-rise buildings.

Abstract. Known scene geometry and camera calibration parameters give important information to video content analysis systems. In this paper, we propose a novel method for camera pose estimation based on people observation in the input video captured by static camera. As opposed to previous techniques, our method can deal with false positive detections and inaccurate localization results. Specifically, the proposed method does not make any assumption about the utilized object detector and takes it as a parameter. Moreover, we do not require a huge labeled dataset of real data and train on the synthetic data only. We apply the proposed technique for camera pose estimation based on head observations. Our experiments show that the algorithm trained on the synthetic dataset generalizes to real data and is robust to false positive detections.

Bistatic inverse synthetic aperture radar (ISAR) can help image the high-speed target for its advantage in tracking and more observation angle to provide more information than monostatic ISAR. However, the complex image geometry makes it difficult to achieve a clear image of the target with geometry distortion correction and calibration. Furthermore, high-speed motion will make the image blurred or defocussed. To address these problems, a bistatic ISAR (B-ISAR) imaging method for high-speed motion target with geometric distortion correction and calibration is proposed. According to the motion decomposition idea, we established the B-ISAR echo model of the high-speed motion target. Then, based on the range Doppler algorithm, we deduce the analytic formula of the geometric distortion factor and calibration factor, and transform the imaging problem into a parameter estimation problem. With the sparsity of the scattering points, the required parameters are solved using the expectation maximization algorithm based on the maximum a posteriori probability criterion. With the estimated parameters, a clear B-ISAR image of a high-speed motion target with geometric correction and calibration is obtained. The simulations show that the proposed method has better resolution and simultaneously attains geometric distortion correction and calibration of the image.

In this paper, the proposed system to control the robotic by using an adaptive controller and using the artificial neural network with optimized PID by using the PSO is used to regulate the motion of a robot. The problems of reverse kinematic is solved by using a proposed method which is important to determine the robotic arm joints angle values , when traced in a different path. The D-H approach Devavit – Hartenberg can be used to solve the Forward Kinematics problem. The dynamic model was computedusing the Lagrange model, which is a mathematical model. Computing a dynamic model was a crucial step in designing a robotics control. The proposed adaptive controller, which is based on a PSO-optimized artificial neural network, is utilized to increase system response. In this paper designing a GUI by using MATLAB to compute the inverse and forward kinematics and to compute the trajectory planning. Derived the inverse kinematic and the forward kinematic by traditional methods is complicated, by applying the proposed method is an easier and fast way. The main problem in the dynamic model was the non-linearity, so by using the proposed method, this method will selects optimal parameters of the PID controller that overcome the plant non-linearity. The performance of the system show good response when using the proposed method. Rom the simulation results the overshot approaches to the zero the raising time reduced to 0.11 for the first joint1, the best settling time is 0.24 in the first joint, in joint 3 the delay time was reduced to the 0.1. Based on these findings, the suggested method outperforms other standard methods such as PID controller in terms of system performance.

It is predicted that the number of Internet of Things (IoT) devices will be more than 75 billion by 2025, where a large portion of IoT devices will be long-range (LoRa) powered by batteries. The battery lifetime limitations and the spectrum shortage have been the main problems in realizing LoRa wide area network (LoRAWAN) for the devices in hard-toreach areas. The dynamic spectrum access technique has gained tremendous research interest as a promising paradigm due to its outstanding performance in improving spectrum efficiency. How to realize intelligent resource allocation (RA) to avoid collisions among IoT devices with low energy consumption is an important problem in LoRaWAN. However, either synchronization and prior information estimation, such as channel state information (CSI), are required, or the energy consumption of LoRa devices is not considered in related work, which may decrease the energy efficiency of the LoRa devices. In addition, the necessary prior information may be challenging to obtain in future networks. To address these issues, we propose a deep Q learning-based RA (DQLRA) method for LoRaWAN. In our proposed method, the gateway (GW) trains the deep neural network (DNN) only based on the transmission state, i.e., transmission failure or success, and the corresponding device number of each LoRa device. Then, each LoRa device can make decisions based on its device number and ACK or NACK information using the trained DNN. Synchronization and prior information estimation are not required in our proposed method, which may improve the energy efficiency of IoT devices. Simulation results show that the proposed method can achieve the optimal frame success rate (FSR) in most scenarios.

Hierarchical network partition of DC power systems containing a large number of switches

This paper aims to utilize the deep learning architecture to break through the limitations of camera perspective, image background, uneven crowd density distribution and pedestrian occlusion to estimate crowd density accurately. In this paper, we proposed a new neural network called Deep Scale-Adaptive Convolutional Neural Network (DSA-CNN), which can convert a single crowd image to density map for crowd counting directly. For a crowd image with any size and resolution, our algorithm can output the density map of the crowd image by end-to-end method and finally estimate the number of the crowd in the image. The proposed DSA-CNN consists of two parts: the seven layers CNN network structure and DSA modules. In order to ensure the proposed method is robust to camera perspective effect, DSA-CNN has adopted different sizes of filters in the network and combines them ingeniously. In order to reduce the depth of the data to increase the speed of training, the proposed method utilized 1 × 1 filter in DSA module. To validate the effectiveness of the proposed model, we conducted comparative experiments on four popular public datasets (ShanghiTech dataset, UCF_CC_50 dataset, WorldExpo’10 dataset and UCSD dataset). We compare the proposed method with other well-known algorithms on the MAE and MSE indicators, such as MCNN, Switching-CNN, CSRNet, CP-CNN and Cascaded-MTL. Experimental results show that the proposed method has excellent performance. In addition, we found that the proposed model is easily trained, which further increases the usability of the proposed model.

This study investigated correlations among ram semen quality parameters, testicular vascularity and echotexture. Measurements were performed biweekly for a year, before (BE) and after ejaculation (AE), on seven Chios breed rams [three rams (group A) and four rams (group B) of 2-6 and 9-13years old, respectively], considering breeding (1) and non-breeding period (2). Hemodynamics [Pulsatility Index (PI), Resistive Index (RI)] were evaluated in each testis. Images of testicular parenchyma were analysed to evaluate echogenicity [Mean Value (MV), Contrast (Con), Gray Value Distribution (GVD), Run Length Distribution (RunLD), Long Run Emphasis (LRunEm)]. Semen volume (V), concentration (C), kinetics (CASA), morphology (Sperm Blue), viability (Eosin-Nigrosin) and DNA fragmentation (Acridine Orange) were assessed. In group A/2, semen V was correlated to left testis PI and right testis RI, BE (r=0.4, p=0.013/r=0.6, p<0.001, respectively), and to right testis PI and RI, AE (r=0.5, p<0.001). In A/1/AE, semen C was correlated to left testis PI (r=-0.5, p=0.003). In group A/1/BE, RI of both testes was correlated to total and tail abnormalities (left: r=-0.5, p=0.008/ p=0.004, respectively - right: r=-0.4/r=-0.5, p=0.01/ p=0.008, respectively). In group B/1/BE, sperm tail and total abnormalities were correlated to left testis Con (r=0.5, p=0.02/ p<0.001, respectively), whereas in A/2/BE, they were correlated to left testis LRunEm (r=-0.8/r=-0.7, p<0.001, respectively). In group A/1/BE, DNA damage was correlated to right testis LRunEm (r=-0.6, p<0.001) and tail abnormalities to left testis PI (r=-0.5, p=0.03). In group B/1/AE, right testis LRunEm, GVD, RunLD, were correlated to sperm DNA integrity (r=-0.9, p<0.001). Immotile spermatozoa and viability were correlated to left testis Con in group B/2/AE (r=-0.6/r=0.6, p<0.001) and to right testis MV in group A/1/AE (r=-0.6/r=0.6, p<0.001). In conclusion, alterations in both ram testes blood flow and echotexture seem to be moderately correlated with sperm assessed parameters, depending on season and ram age.

Learning from the imbalanced data samples so as to achieve accurate classification is an important research content in data mining field. It is very difficult for classification algorithm to achieve a higher accuracy because the uneven distribution of data samples makes some categories have few samples. A imbalanced data classification algorithm of support vector machines (KE-SVM) is proposed in this article, this algorithm achieve the initial classification of data samples by training the maximum margin classification SVM model, and then obtaining a new kernel extension function. based on Chi square test and weight coefficient calculation, through training the samples again by the new vector machine with kernel function to improve the classification accuracy. Through the simulation experiments of real data sets of artificial data set, it shows that the proposed method has higher classification accuracy and faster convergence for the uneven distribution data.

This paper presents a new method for coefficient determination of feedback linearization control method for position control of switched reluctance motors. Feed back linearization method is unstable against uncertainties of rotor inertia and stator resistance, this disadvantage makes us to design a method that is robust against those mentioned uncertainties, in this paper by adding a torque term to the motor dynamical equation, we have made the controlling method, robust against uncertainties of rotor inertia and stator resistance. The main problem in adaptive feed back linearization method is determination of its feed back coefficients, multi-objective optimization based on genetic algorithm is proposed as an stochastic method to determine the coefficients. The major gain of our proposed method compared to those reported so far, is that, it needs less time to converge and also less speed rise time. Because of the significance of determining the coefficients precisely, the proposed method can be used in these applications, the simulation results have been compared with trial and error method and show the benefits of the proposed method.

Blood pressure (BP) is an important parameter for the early detection of heart disease because it is associated with symptoms of hypertension or hypotension. A single photoplethysmography (PPG) method for the classification of BP can automatically analyze BP symptoms. Users can immediately know the condition of their BP to ensure early detection. In recent years, deep learning methods have presented outstanding performance in classification applications. However, there are two main problems in deep learning classification methods: classification accuracy and time consumption during training. We attempt to address these limitations and propose a method for the classification of BP using the K-nearest neighbors (KNN) algorithm based on PPG. We collected data for 121 subjects from the PPG–BP figshare database. We divided the subjects into three classification levels, namely normotension, prehypertension, and hypertension, according to the BP levels of the Joint National Committee report. The F1 scores of these three classification trials were 100%, 100%, and 90.80%, respectively. Hence, it is validated that the proposed method can achieve improved classification accuracy without additional manual pre-processing of PPG. Our proposed method achieves higher accuracy than convolutional neural networks (deep learning), bagged tree, logistic regression, and AdaBoost tree.

A video record plays a crucial role in providing evidence for crime scenes or road accidents. However, the main problem with the video record is that it is often vulnerable to various video tampering attacks. Although visual evidence is required to conduct an integrity verification before investigations, it is still difficult for human vision to detect a forgery. In this paper, we propose a novel video integrity verification method (IVM) that takes advantage of a blockchain framework. The proposed method employs an effective blockchain model in centralized video data, by combining a hash-based message authentication code and elliptic curve cryptography to verify the integrity of a video. In our method, video content with a predetermined size (segments) is key-hashed in a real-time manner and stored in a chronologically chained fashion, thus establishing an irrefutable database. The verification process applies the same procedure to the video segment and generates a hash value that can be compared with the hash in the blockchain. The proposed IVM is implemented on a PC environment, as well as on an accident data recorder-embedded system for verification. The experimental results show that the proposed method has better detection capabilities and robustness toward various kinds of tampering, such as copy-move, insert, and delete, as compared to other state-of-the-art methods. An analysis based on execution time along with an increase in the number of blocks within the blockchain shows a minimal overhead in the proposed method.

Synthetic aperture radar (SAR) images have two main problems: Degradation by speckle that causes low-contrast images and misinterpretation, and the large amount of data that makes the computation time an important issue. In addition to denoising, it is necessary to recover the missing data due to errors in the onboard SAR equipment, downlink equipment, and ground data registration. In this paper, we propose a method using high-order total variation (High-TV) denoising in compressive sensing (CS) framework, based on maximum a posteriori (MAP) estimation to simultaneously denoise and recover the large-size, complex-valued SAR images. Experiments on RADARSAT-1 raw data showed that the proposed method, called MAP High-TV, can suppress the speckle effectively without exhibiting a staircase effect on the images, and perfectly recovers the missed data. The performance comparison among the proposed method, MAP-MIDAL (the pioneer CS-based method), and three state of the art filters (NSM, POTDF and FANS), explores the capability of MAP High-TV for both denoising and recovering the complex-valued SAR images.