Flash-Based Platform for Teaching Stored Program-Controlled Switching Courses

Stored program controlled switching (SPCS) is crucial to communication, especially to the Public Service Telephone Network (PSTN). With the rapid development of telecommunication, networking and switching technology the SPCS course is showing its importance in Electrical/Communication Engineering (ECE) or other related engineering disciplines in communication. In this paper, a Macromedia Flash based platform is developed for undergraduate SPCS course. Because there are many complex processes in signaling and switching, such as calling process, operation of different switching mode and signaling system in SPCS course, it is too difficult for undergraduate students to understand these concepts. All those concepts in SPCS course are animated with Macromedia Flash, and related texts and sounds are followed by the animation. Comparing with the traditional methods, the platform showed better efficiency during the SPCS course teaching because of visualizing many theories in the course

Robust Visual Tracking by Motion Analyzing

In recent years, Video Object Segmentation (VOS) has emerged as a complementary method to Video Object Tracking (VOT). VOS focuses on classifying all the pixels around the target, allowing for precise shape labeling, while VOT primarily focuses on the approximate region where the target might be. However, traditional segmentation modules usually classify pixels frame by frame, disregarding information between adjacent frames. In this paper, we propose a new algorithm that addresses this limitation by analyzing the motion pattern using the inherent tensor structure. The tensor structure, obtained through Tucker2 tensor decomposition, proves to be effective in describing the target's motion. By incorporating this information, we achieved competitive results on Four benchmarks LaSOT\cite{fan2019lasot}, AVisT\cite{noman2022avist}, OTB100\cite{7001050}, and GOT-10k\cite{huang2019got} LaSOT\cite{fan2019lasot} with SOTA. Furthermore, the proposed tracker is capable of real-time operation, adding value to its practical application.Comment: found some key point that is missed,considering that it will take a lot of time to reproduce the results and revise our mistakes,we would like to withdraw the manuscript to avoid further mislea

Effects on accuracy of Uyghur handwritten signature recognition

In this paper, an approach for off-line Uyghur signature recognition is proposed. The signature images were preprocessed using improved techniques adapted to the Uyghur signature. The preprocessing are included noise reduction, binarization, normalization and thinning. Two types of preprocessing steps were conducted with and without thinning. The directional features, global baseline, upper and lower line features, local central features were extracted respectively after the two kinds of preprocessing. Experiments were performed selecting Euclidean distance and Chi-square distance based measure methods and using K nearest neighbor classifier for Uyghur signature samples from 50 different people with 1000 signatures. A correct recognition rate of 96.0% was achieved with thinning. The experimental results indicated that thinning has significant importance to the extracted features and its effects to the accuracy were related with the nature of extracted features

Multilingual Offline Signature Verification Based on Improved Inverse Discriminator Network

To further improve the accuracy of multilingual off-line handwritten signature verification, this paper studies the off-line handwritten signature verification of monolingual and multilingual mixture and proposes an improved verification network (IDN), which adopts user-independent (WI) handwritten signature verification, to determine the true signature or false signature. The IDN model contains four neural network streams with shared weights, of which two receiving the original signature images are the discriminative streams, and the other two streams are the reverse stream of the gray inversion image. The enhanced spatial attention models connect the discriminative streams and reverse flow to realize message propagation. The IDN model uses the channel attention mechanism (SE) and the improved spatial attention module (ESA) to propose the effective feature information of signature verification. Since there is no suitable multilingual signature data set, this paper collects two language data sets (Chinese and Uyghur), including 100,000 signatures of 200 people. Our method is tested on the self-built data set and the public data sets of Bengali (BHsig-B) and Hindi (BHsig-H). The method proposed in this paper has the highest discrimination rate of FRR of 10.5%, FAR of 2.06%, and ACC of 96.33% for the mixture of two languages

Connecting Text Classification with Image Classification: A New Preprocessing Method for Implicit Sentiment Text Classification

As a research hotspot in the field of natural language processing (NLP), sentiment analysis can be roughly divided into explicit sentiment analysis and implicit sentiment analysis. However, due to the lack of obvious emotion words in the implicit sentiment analysis task and because the sentiment polarity contained in implicit sentiment words is not easily accurately identified by existing text-processing methods, the implicit sentiment analysis task is one of the most difficult tasks in sentiment analysis. This paper proposes a new preprocessing method for implicit sentiment text classification; this method is named Text To Picture (TTP) in this paper. TTP highlights the sentiment differences between different sentiment polarities in Chinese implicit sentiment text with the help of deep learning by converting original text data into word frequency maps. The differences between sentiment polarities are used as sentiment clues to improve the performance of the Chinese implicit sentiment text classification task. It does this by transforming the original text data into a word frequency map in order to highlight the differences between the sentiment polarities expressed in the implicit sentiment text. We conducted experimental tests on two common datasets (SMP2019, EWECT), and the results show that the accuracy of our method is significantly improved compared with that of the competitor’s. On the SMP2019 dataset, the accuracy-improvement range was 4.55–7.06%. On the EWECT dataset, the accuracy was improved by 1.81–3.95%. In conclusion, the new preprocessing method for implicit sentiment text classification proposed in this paper can achieve better classification results

Learning the Relative Dynamic Features for Word-Level Lipreading

Lipreading is a technique for analyzing sequences of lip movements and then recognizing the speech content of a speaker. Limited by the structure of our vocal organs, the number of pronunciations we could make is finite, leading to problems with homophones when speaking. On the other hand, different speakers will have various lip movements for the same word. For these problems, we focused on the spatial–temporal feature extraction in word-level lipreading in this paper, and an efficient two-stream model was proposed to learn the relative dynamic information of lip motion. In this model, two different channel capacity CNN streams are used to extract static features in a single frame and dynamic information between multi-frame sequences, respectively. We explored a more effective convolution structure for each component in the front-end model and improved by about 8%. Then, according to the characteristics of the word-level lipreading dataset, we further studied the impact of the two sampling methods on the fast and slow channels. Furthermore, we discussed the influence of the fusion methods of the front-end and back-end models under the two-stream network structure. Finally, we evaluated the proposed model on two large-scale lipreading datasets and achieved a new state-of-the-art

Scene Uyghur Text Detection Based on Fine-Grained Feature Representation

Scene text detection task aims to precisely localize text in natural environments. At present, the application scenarios of text detection topics have gradually shifted from plain document text to more complex natural scenarios. Objects with similar texture and text morphology in the complex background noise of natural scene images are prone to false recall and difficult to detect multi-scale texts, a multi-directional scene Uyghur text detection model based on fine-grained feature representation and spatial feature fusion is proposed, and feature extraction and feature fusion are improved to enhance the network’s ability to represent multi-scale features. In this method, the multiple groups of 3 × 3 convolutional feature groups that are connected like the hierarchical residual to build a residual network for feature extraction, which captures the feature details and increases the receptive field of the network to adapt to multi-scale text and long glued dimensional font detection and suppress false positives of text-like objects. Secondly, an adaptive multi-level feature map fusion strategy is adopted to overcome the inconsistency of information in multi-scale feature map fusion. The proposed model achieves 93.94% and 84.92% F-measure on the self-built Uyghur dataset and the ICDAR2015 dataset, respectively, which improves the accuracy of Uyghur text detection and suppresses false positives

FAS-Res2net: An Improved Res2net-Based Script Identification Method for Natural Scenes

Problems such as complex image backgrounds, low image quality, diverse text forms, and similar or common character layouts in different script categories in natural scenes pose great challenges to scene script identification. This paper proposes a new Res2Net-based improved script identification method, namely FAS-Res2Net. In the feature extraction part, the feature pyramid network (FPN) module is introduced, which is beneficial to aggregate the geometric feature information extracted by the shallow network and the semantic feature information extracted by the deep network. Integrating the Adaptive Spatial Feature Fusion (ASFF) module is beneficial to obtain local feature information for optimal weight fusion. In addition, the global feature information of the image is extracted by introducing the swin transformer coding block, which makes the extracted feature information more abundant. In the classification part, the convolutional classifier is used to replace the traditional Linear classification, and the classification confidence of each category is output, which improves the identification efficiency. The improved algorithm achieved identification rates of 94.7% and 96.0% on public script identification datasets SIW-13 and CVSI-2015, respectively, which verified the superiority of the method