This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytica...
详细信息
ISBN:
(数字)9789464593617
ISBN:
(纸本)9798331519773
This study considers the Block-Toeplitz structural properties inherent in traditional multichannel forward model matrices, using Full Matrix Capture (FMC) in ultrasonic testing as a case study. We propose an analytical convolutional forward model that transforms reflectivity maps into FMC data. Our findings demonstrate that the convolutional model excels over its matrix-based counterpart in terms of computational efficiency and storage requirements. This accelerated forward modeling approach holds significant potential for various inverse problems, notably enhancing Sparse Signal Recovery (SSR) within the context LASSO regression, which facilitates efficient Convolutional Sparse Coding (CSC) algorithms. Additionally, we explore the integration of Convolutional Neural Networks (CNNs) for the forward model, employing deep unfolding to implement the Learned Block Convolutional ISTA (BC-LISTA).
We show that the minimum number of vertices of a simplicial complex with fundamental group (Formula Presented)is at most O(n) and at least (Formula Presented). For the upper bound, we use a result on orthogonal 1-fact...
详细信息
We study the relationship between metric thickenings and simplicial complexes associated to coverings of metric spaces. Let U be a cover of a separable metric space X by open sets with a uniform diameter bound. The Vi...
详细信息
We provide a new model for texture synthesis based on a multiscale, multilayer feature extractor. Within the model, textures are represented by a set of statistics computed from ReLU wavelet coefficients at different ...
详细信息
With the growth of online shopping, organizations should update their portals and websites in response to changing conditions and competitors' actions. To attract and retain online customers, various tools such as...
With the growth of online shopping, organizations should update their portals and websites in response to changing conditions and competitors' actions. To attract and retain online customers, various tools such as reputation building, trust en-hancement, and gamification are utilized and reinforced. This study has investigated 243 users of DigiKala company through paper and electronic questionnaires. Due to the non-normality of the data, non-parametric statistical methods using SPSS and Smart-PLS3.0 software were employed to analyze structural relationships. Additionally, machine learning tools were utilized to optimize the analysis of customer behavior and identify patterns for effective customer retention strategies. The results show that contrary to the research background, the use of gamification on DigiKala's website has not had a positive and significant impact on customers using this company's website and their repurchase intention. Thus, it seems that DigiKala should focus on context and infrastructures, while simultaneously strengthening and revamping gamification dimensions.
Today’s information society relies on cryptography to achieve security goals such as confidentiality, integrity, authentication, and non-repudiation for digital communications. Here, public-key cryptosystems play a p...
详细信息
The distributions of the k-th largest level at the soft edge scaling limit of Gaussian ensembles are some of the most important distributions in random matrix theory, and their numerical evaluation is a subject of gre...
详细信息
We investigate the existence of Boolean degree d functions on the Grassmann graph of k-spaces in the vector space Fnq. For d = 1 several non-existence and classification results are known, and no non-trivial examples ...
详细信息
Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving ob...
详细信息
ISBN:
(纸本)9781665480468
Automated guided vehicles (AGV) allow for the automation of operations in warehouse environments. From an application point of view, vehicles can use sensors to move and perform a variety of tasks, including moving objects. In this paper, we focus on the analysis of the environment and the preparation of data for vehicle navigation. The proposed solution is based on two paths of action. In the first, the image of the room is processed by heuristics to locate the robot’s target - the palette. The found pattern allows one to locate the destination as well as create a mask. The mask can be used to train the U-Net network. When a network is trained, the use of heuristics for pallet location can be omitted. Locating the target allows the image to be processed to obtain an AGV navigation map. The proposed solution based on heuristics and U-Net networks has been described and tested in simulations to indicate the potential of the proposed approach.
The volume of multimedia data produced by various smart devices has increased dramatically with the advent of new digital technologies, including in the Internet of Vehicles (IoV). It has become more difficult to extr...
The volume of multimedia data produced by various smart devices has increased dramatically with the advent of new digital technologies, including in the Internet of Vehicles (IoV). It has become more difficult to extract valuable insights from multimedia data due to several challenges during data analysis. The main problem is the need to quickly and precisely identify abnormalities in multimedia data. This research presents an unusual occurrence of the audio forensics database named UOAFDB and a practical method for identifying and categorizing unusual occurrences in audio files. To study the detection of abnormal audio and the classification of rare sound (e.g., car crash—machine gun, explosion) events for audio forensics, we construct a large audio dataset containing ten rare special events (anomalies) with 15 different background environmental settings (e.g., beach, restaurant, and train). The suggested method determines the optimal amount of features using the best feature extraction methodology available by extracting Mel-frequency cepstral coefficients (MFCCs) features from the audio signals of the newly formed dataset. Modern deep learning algorithms use these features as input to assess performance. Additionally, we apply deep learning methods to the most recent and best available dataset and obtain promising outcomes. The experimental findings demonstrate promising results on the UOAFDB dataset.
暂无评论