Continuous Frame Research Articles

Discretizing Lp norms and frame theory

Given an N-dimensional subspace XN of Lp(Ω), we consider the problem of choosing M-sampling points which may be used to discretely approximate the Lp norm on the subspace. We are particularly interested in knowing when the number of sampling points M can be chosen on the order of the dimension N. For the case p=2 it is known that M may always be chosen on the order of N as long as the subspace XN satisfies a natural L∞ bound, and for the case p=∞ there are examples where M may not be chosen on the order of N. We show for all 1≤p<2 that there exist classes of subspaces of Lp([0,1]) which satisfy the L∞ bound, but where the number of sampling points M cannot be chosen on the order of N. We show as well that the problem of discretizing the Lp norm of subspaces is directly connected with frame theory. In particular, we prove that discretizing a continuous frame to obtain a discrete frame which does stable phase retrieval requires discretizing both the L2 norm and the L1 norm on the range of the analysis operator of the continuous frame.

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network.

Due to the amount of transmitted data and the security of personal or private information in wireless communication, there are cases where the information for a multimedia service should be directly transferred from the user's device to the cloud server without the captured original images. This paper proposes a new method to generate 3D (dimensional) keypoints based on a user's mobile device with a commercial RGB camera in a distributed computing environment such as a cloud server. The images are captured with a moving camera and 2D keypoints are extracted from them. After executing feature extraction between continuous frames, disparities are calculated between frames using the relationships between matched keypoints. The physical distance of the baseline is estimated by using the motion information of the camera, and the actual distance is calculated by using the calculated disparity and the estimated baseline. Finally, 3D keypoints are generated by adding the extracted 2D keypoints to the calculated distance. A keypoint-based scene change method is proposed as well. Due to the existing similarity between continuous frames captured from a camera, not all 3D keypoints are transferred and stored, only the new ones. Compared with the ground truth of the TUM dataset, the average error of the estimated 3D keypoints was measured as 5.98 mm, which shows that the proposed method has relatively good performance considering that it uses a commercial RGB camera on a mobile device. Furthermore, the transferred 3D keypoints were decreased to about 73.6%.

Pavement Marking Incorporated With Binary Code for Accurate Localization of Autonomous Vehicles

Accurate localization is a critically important issue for autonomous vehicles as it is closely related to the safety and efficiency of autonomous driving. However, current technologies for autonomous vehicle localization face many challenges. To provide accurate and robust localization services to autonomous vehicles, we propose a novel solution by employing a newly designed pavement marking. This marking operates on color contrast, temperature contrast, and binary code with some special features. We also trained and customized an object detector based on a deep learning model: YOLOv5, and integrated it with the decoding algorithm. The localization system is capable of running at a steady frame rate of more than 50 FPS. Road trials up to 80 km/h were conducted, and satisfactory results confirmed the feasibility and robustness of the localization system. Specifically, with a common onboard camera, more than four continuous frames can be detected and decoded correctly when the speed is slower than 30 km/h. At least one frame can be detected and decoded correctly at a higher speed (i.e., 30– 50 km/h). With a high-speed camera, more than 18 frames can be detected and decoded even at 80 km/h. The findings suggest that the specially designed road marking and associated algorithms can provide a viable and economical option for accurate localization of autonomous vehicles. The performance of the system has potentials for further improvement by using better hardware such as faster CPUs, GPUs, and thermal imaging techniques.

Tracking by Joint Local and Global Search: A Target-Aware Attention-Based Approach.

Tracking-by-detection is a very popular framework for single-object tracking that attempts to search the target object within a local search window for each frame. Although such a local search mechanism works well on simple videos, however, it makes the trackers sensitive to extremely challenging scenarios, such as heavy occlusion and fast motion. In this article, we propose a novel and general target-aware attention mechanism (termed TANet) and integrate it with a tracking-by-detection framework to conduct joint local and global search for robust tracking. Specifically, we extract the features of the target object patch and continuous video frames; then, we concatenate and feed them into a decoder network to generate target-aware global attention maps. More importantly, we resort to adversarial training for better attention prediction. The appearance and motion discriminator networks are designed to ensure its consistency in spatial and temporal views. In the tracking procedure, we integrate target-aware attention with multiple trackers by exploring candidate search regions for robust tracking. Extensive experiments on both short- and long-term tracking benchmark datasets all validated the effectiveness of our algorithm.

Research on Autopilot Trajectory Planning Based on Deep Learning

Vehicle trajectory is usually used in driving behavior modeling and traffic safety. In order to obtain vehicle tracks in traffic video, an automatic acquisition method based on depth learning is proposed. In this paper, the YOLOV5 detector is used to detect vehicles in the region of interest in continuous frames, and the DEEPSORT tracker is used to track robust and fast vehicles. This tracker can effectively reduce the jump of target ID, improve tracking stability, obtain the real-world coordinates of vehicles through perspective transformation, and use a locally weighted regression algorithm to smooth the track. The experimental results show that the average multi-target tracking accuracy of the method in the expressway monitoring data set is 96.1%, and the average accuracy of trajectory acquisition is more than 93%. The smoothing algorithm can effectively remove the trajectory noise.

Parametric study of topographic effect on train-bridge interaction of a continuous rigid frame bridge during earthquakes

Parametric study of topographic effect on train-bridge interaction of a continuous rigid frame bridge during earthquakes

Shaking table test on seismic performance of continuous rigid frame bridge with cast-in-place and fabricated super high piers

The seismic performance of a continuous rigid frame bridge with cast-in-place or fabricated super high piers (CRFB-CSP or CRFB-FSP) is still in the exploratory stage. In this study, four 1/20 scaled models of a main CRFB-CSP, CRFB-FSP, and two adjacent bridges (AB) are designed and manufactured. By considering the longitudinal direction as an example, the seismic performance of CRFB-CSP and CRFB-FSP under the action of non-long-period (NLP), non-pulse-like long-period (NPLP), and pulse-like long-period (PLP) ground motions is studied through a shaking table test; in particular, the impact of pounding between the main bridge (MB) and AB is considered. The results show that both CRFB-CSP and CRFB-FSP reflect higher-order mode participation (HMP) characteristics with and without pounding conditions. The influence of HMP on the seismic response of the CRFB-CSP and CRFB-FSP increases as the ground motion intensity increases. The HMP of the CRFB-FSP is more significant than that of the CRFB-CSP. After considering the pounding, the influence of the HMP increases significantly under the PLP ground motion. Moreover, the peak pounding force (Pf) between the MB and AB is higher under PLP and NPLP ground motion excitations. Additionally, Pf is greatest when the velocity pulse period is close to the first-order vibration period of the MB. The Pf value between CRFB-FSP and AB is greater than that between CRFB-CSP and AB. After considering the pounding, the longitudinal peak displacements of the main beams of the CRFB-CSP and CRFB-FSP decrease. Pounding usually increases the horizontal and vertical acceleration responses of the main beam and pier top of the CRFB-CSP and CRFB-FSP, which may have a significant impact on the failure mode of the CRFB-FSP piers.

Characterization of a novel victorivirus from Nigrospora chinensis, a fungus isolated from tobacco.

Here, we characterized a new mycovirus from the fungus Nigrospora chinensis, which was named "Nigrospora chinensis victorivirus 1" (NcVV1). The NcVV1 genome is 5283 bp in length, containing two continuous open reading frames (ORFs), ORF1 and ORF2. ORF1 and ORF2 were predicted to encode a putative coat protein (CP) and an RNA-dependent RNA polymerase (RdRp), respectively. The stop codon of ORF1 overlaps with the start codon of ORF2 by the tetranucleotide sequence AUGA. Phylogenetic analysis based on amino acid sequences of RdRp and CP indicated that NcVV1 clustered with members of the genus Victorivirus in the family Totiviridae. To our knowledge, this was the first report of a mycovirus infecting N. chinensis.

Muti-Frame Point Cloud Feature Fusion Based on Attention Mechanisms for 3D Object Detection.

Continuous frames of point-cloud-based object detection is a new research direction. Currently, most research studies fuse multi-frame point clouds using concatenation-based methods. The method aligns different frames by using information on GPS, IMU, etc. However, this fusion method can only align static objects and not moving objects. In this paper, we proposed a non-local-based multi-scale feature fusion method, which can handle both moving and static objects without GPS- and IMU-based registrations. Considering that non-local methods are resource-consuming, we proposed a novel simplified non-local block based on the sparsity of the point cloud. By filtering out empty units, memory consumption decreased by 99.93%. In addition, triple attention is adopted to enhance the key information on the object and suppresses background noise, further benefiting non-local-based feature fusion methods. Finally, we verify the method based on PointPillars and CenterPoint. Experimental results show that the mAP of the proposed method improved by 3.9% and 4.1% in mAP compared with concatenation-based fusion modules, PointPillars-2 and CenterPoint-2, respectively. In addition, the proposed network outperforms powerful 3D-VID by 1.2% in mAP.

Structured Sparse Coding With the Group Log-regularizer for Key Frame Extraction

Key frame extraction based on sparse coding can reduce the redundancy of continuous frames and concisely express the entire video. However, how to develop a key frame extraction algorithm that can automatically extract a few frames with a low reconstruction error remains a challenge. In this paper, we propose a novel model of structured sparse-coding-based key frame extraction, wherein a nonconvex group log-regularizer is used with strong sparsity and a low reconstruction error. To automatically extract key frames, a decomposition scheme is designed to separate the sparse coefficient matrix by rows. The rows enforced by the nonconvex group log-regularizer become zero or nonzero, leading to the learning of the structured sparse coefficient matrix. To solve the nonconvex problems due to the log-regularizer, the difference of convex algorithm (DCA) is employed to decompose the log-regularizer into the difference of two convex functions related to the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$I_{1}$</tex> norm, which can be directly obtained through the proximal operator. Therefore, an efficient structured sparse coding algorithm with the group log-regularizer for key frame extraction is developed, which can automatically extract a few frames directly from the video to represent the entire video with a low reconstruction error. Experimental results demonstrate that the proposed algorithm can extract more accurate key frames from most SumMe videos compared to the state-of-the-art methods. Furthermore, the proposed algorithm can obtain a higher compression with a nearly 18% increase compared to sparse modeling representation selection (SMRS) and an 8% increase compared to SC-det on the VSUMM dataset.

Estimation formula of finished bridge pre-camber in continuous rigid-frame bridges.

Continuous rigid-frame bridges are widely used, but the large deflection in the mid-span during operation has always been their disease. This problem is generally solved by setting the finished bridge pre-camber. There are many calculation methods for pre-camber, and the effects are different. In this paper, based on a large number of design parameters of continuous rigid-frame bridges obtained from the investigation, 18 finite element analysis models of different span combinations were established, and 30 sets of valid data were obtained under the action of multi-factor. The results show that the shrinkage and creep of concrete is the most important factor for the mid-span deflection of continuous rigid frame bridges, and the deflection amount has an obvious functional relationship with the span. The effect of prestress loss on mid-span deflection is second, and stiffness reduction has little effect on mid-span long-term deflection. In this paper, the least-squares method is used to perform polynomial fitting, and the fitting formula for the mid-span finished bridge pre-camber is finally obtained. The applicability of the calculation formula is proved by comparing it with the specification solution, the empirical solution, and the measured value.

Pseudo-duals of continuous frames in Hilbert spaces

Pseudo-duals of continuous frames in Hilbert spaces

In-plane behavior and seismic performance of differently detailed, multi-story, perforated, partially grouted masonry walls

Partially grouted (PG) masonry structures are widely used in many regions worldwide, especially those regions with low and mid-intensity wind and/or seismic actions. Depending on the design code, the grouted/reinforced cells should be uniformly distributed along a shear wall rather than concentrated. Thus, this study focused on PG shear walls with grout and reinforcement placed at the ends aiming to evaluate their in-plane behavior and seismic performance. Also, the walls examined were three-story and perforated. Finite element models validated against previous experimental tests were used to perform the study. Besides two traditional bilinear idealizations for the actual wall response, a trilinear approach is presented with deduced equations for the seismic performance factors (SPFs). Results demonstrated that the reinforced masonry beam over the openings effectively coupled the wall piers yielding a frame-type action. Also, it appears that the walls performed as a continuous frame, with the grouted parts acting like columns and the ungrouted parts acting like confined masonry. The loss of ductility evidenced in the backbone curves and the decrease of the SPFs confirmed that a high vertical pre-compression led the walls to a brittle response while also increasing the lateral load capacity. Concentrating the grouting and reinforcement at the wall pier ends showed a similar detailing design efficiency compared to distributing them along the wall piers. The stiffness degradation was more intense when the walls were subjected to a lower pre-compression level. Furthermore, the stiffness degradation curves were best fitted with power and logarithmic functions for walls with the lower and higher axial load, respectively.

Influence of hydrodynamic pressure on fragility of high-pier continuous rigid frame bridge subjected to ground motion

To investigate the seismic fragility of deep-water and high-pier continuous rigid frame bridge subjected to the combined action of hydrodynamic pressure and ground motion, a representative deep-water and high-pier continuous rigid frame bridge is examined to study the influence of hydrodynamic pressure and ground motion on structural fragility. The corresponding damage indicators and conditional exceedance probabilities are defined and quantified. The probabilistic seismic demand model (e.g., pier and bearing) is analyzed using incremental dynamic analysis (IDA) in the nonlinear finite element software OpenSEES. A comparative analysis is conducted to identify the effect of hydrodynamic pressure on the dynamic response of the bridge. Hydrodynamic pressure plays an important role in influencing the conditional failure probability of piers and bearings. Damage probability increases with increasing water depth, particularly for the pier top in the longitudinal direction.

Marcinkiewicz Averages of Smooth Orthogonal Projections on Sphere

We construct a single smooth orthogonal projection with desired localization whose average under a group action yields the decomposition of the identity operator. For any full rank lattice Gamma subset mathbb {R}^d, a smooth projection is localized in a neighborhood of an arbitrary precompact fundamental domain mathbb {R}^d/Gamma . We also show the existence of a highly localized smooth orthogonal projection, whose Marcinkiewicz average under the action of SO(d), is a multiple of the identity on L^2(mathbb {S}^{d-1}). As an application we construct highly localized continuous Parseval frames on the sphere.

Experimental Studies on the Influence of Different Support Conditions and Structural Systems for the Lateral Torsional Buckling Behaviour of Steel Structures

AbstractThe use of slender steel members promotes the competitiveness of steel structures in structural engineering. However, it requires an efficient design, especially for the stability behaviour including lateral torsional buckling which is usually verified by the simplified design method with reduction factors. This method is based on the model of a simply supported member with ideal fork boundary conditions at the ends. However, commonly used joints in engineering practice, such as typical main and secondary beam joints, deviate from the idealisations of these ideal fork restraints. And at system level, there are only a few simply supported members, more common are structural systems such as continuous girders or frames. So far, there is a lack of knowledge on the influence of common joints used in engineering practice and structural system effects on the load‐bearing capacity. This paper deals with the results of experimental studies carried out within a German national (FOSTA) research project. The lateral torsional buckling behaviour of different support and boundary conditions as well as structural systems is investigated for hot‐rolled steel members with doubly and mono‐symmetric sections. In total, 14 tests on support conditions are carried out at the University of Stuttgart and 20 tests on two‐span members at the Ruhr‐Universität Bochum. The aim is to develop easy‐to‐use design rules that consider the effects of realistic conditions and structural system effects on the lateral torsional buckling resistance.

Surgical Phase Recognition: From Public Datasets to Real-World Data

Automated recognition of surgical phases is a prerequisite for computer-assisted analysis of surgeries. The research on phase recognition has been mostly driven by publicly available datasets of laparoscopic cholecystectomy (Lap Chole) videos. Yet, videos observed in real-world settings might contain challenges, such as additional phases and longer videos, which may be missing in curated public datasets. In this work, we study (i) the possible data distribution discrepancy between videos observed in a given medical center and videos from existing public datasets, and (ii) the potential impact of this distribution difference on model development. To this end, we gathered a large, private dataset of 384 Lap Chole videos. Our dataset contained all videos, including emergency surgeries and teaching cases, recorded in a continuous time frame of five years. We observed strong differences between our dataset and the most commonly used public dataset for surgical phase recognition, Cholec80. For instance, our videos were much longer, included additional phases, and had more complex transitions between phases. We further trained and compared several state-of-the-art phase recognition models on our dataset. The models’ performances greatly varied across surgical phases and videos. In particular, our results highlighted the challenge of recognizing extremely under-represented phases (usually missing in public datasets); the major phases were recognized with at least 76 percent recall. Overall, our results highlighted the need to better understand the distribution of the video data phase recognition models are trained on.

Sandwich-type electrochemical immunosensor based on nitrogen-doped porous carbon and nanoporous trimetallic nanozyme (PdAgCu) for determination of prostate specific antigen.

Asandwich-type electrochemical immunosensor was designed for the ultrasensitive detection of prostate-specific antigen (PSA), using Au nanoparticles (Au NPs) modified nitrogen-doped porous carbon (NPC) as sensor platform and trimetallic PdAgCu mesoporous nanospheres (PdAgCu MNSs) as enzyme-mimicking labels. NPC was prepared by a facile one-step pyrolysis strategy of biomimetic phylloid zeolite imidazole framework (ZIF-L) nanosheets. Through this strategy, the graphitization of the microcrystalline structure enhanced the electrical conductivity, while its enlarged specific surface area and abundant pore volume can enrich H2O2 to improve the catalytic efficiency. Moreover, Au NPs were used to modify NPC without cross-linking agents to further optimize electron transport while capturing primary antibodies, improving stability and sensitivity of theimmunosensor. PdAgCu MNSs with uniform size, cylindrical open mesoporous channels, and continuous crystal frame structure were self-assembling synthesized by electrostatic adsorption and ascorbic acid (AA) co-reduction with amphiphilic dioctadecyldimethylammonium chloride (DODAC) as surfactant-cum-micelle, whose unique structure maximizes the use of polyatoms to expose catalytic sites, exhibiting good biocompatibility and electrocatalytic ability. Under the optimal conditions, the immunosensor showed superior sensitivity, a wide dynamic detection range (10fgmL-1 ~ 100ngmL-1) and a low limit of detection (LOD, 3.29fgmL-1). This work provides a convenient strategy for the clinical detection of PSA.

A New Framework for Isolating Sensor Failures and Structural Damage in Noisy Environments Based on Stacked Gated Recurrent Unit Neural Networks

To address the problem of sensor faults and measurement noise being misinterpreted as structural damage in structural health monitoring (SHM), this paper proposes a new framework for distinguishing sensor faults and structural damage based on stacked gated recurrent neural networks (S-GRU NN) that considers measurement noise. In this framework, the sensor signal reconstruction model was constructed by learning and training the S-GRU NN. The sensor fault threshold was determined based on a statistical analysis of the response reconstruction error between the true and reconstruction values. The sensor fault and structural damage are then distinguished by the fact that the sensor fault is independent and the structural damage is global. The framework is compared with other isolation frameworks based on traditional deep learning models through numerical simulations of a three-span continuous beam and laboratory steel frame experiments. The results show that the S-GRU NN has better reconstruction effect and isolation performance of sensor faults and structural damage in noisy environment.

Arch-type feed spacer with wide passage node design for spiral-wound membrane filtration with reduced energy cost

A novel feed spacer for spiral-wound membrane modules is proposed. Based upon a continuous arch-type streamline frame two feed spacers with different node designs were constructed. In the first, Arch-Hole, two elliptic cylinders form a node zone with a passage. The other, named Arch, has a solid node. Their hydrodynamics, fouling and filtration performance were compared with a commercial design. This was a classic net-type spacer with nodes of relatively substantial size. All three were manufactured using 3D printing. Computational fluid dynamics simulation showed that the Arch-Hole design gives a more uniform velocity distribution with fewer dead zones. Subsequent evaluation of this design indicated less foulant accumulation as confirmed by optical coherence tomography and the evolution of channel pressure drop. Measured adenosine triphosphate and total organic carbon values for the module with this spacer were substantially lower than the corresponding values associated with the commercial design. Crucially with the Arch-Hole design, flux values remained higher than for the other two designs. Thus use of this spacer would minimize both capital and operational costs. Compared to the commercial one, use of the Arch-Hole design would reduce the energy cost of filtration significantly beyond the 69 % found for the application considered herein.