Processing Speed Efficiency Research Articles

Camera-based interactive wall display using hand gesture recognition

the recognition of Hand gestures has become a critical point as it is widely used in everyday applications. the challenge in this is to improve the recognition effect and develop a fast recognition method. Glove and led-based methods involve external devices in detecting and interpreting hand gestures, making human-computer interaction less natural. So, different approaches have been used previously that use purely hand gestures in many systems based on human-computer interaction. This system provides a more natural human-computer interaction; it must be made efficient processing speed of classifying the test data (images) from among the training data (database stored for gestures recognition). This speed makes gesture recognition more effective and reliable to use as compared to previously proposed methods. In this research paper, a proposed system based on a camera-based interactive wall display using bare hand gestures with efficient processing speed for controlling the speed of the mouse and other functions. This system has three modules: one uses Genetic Algorithm and Otsu thresholding to identify the query images as the right or wrong gesture and perform the correct action in case of the proper motion, another module controls functions outside of PowerPoint files or Word documents, e.g., to open folders and go through drives, and the third module uses the convexity hull method for finding the number of fingers open in the user's gesture and operates accordingly.

Innovative Personal Assistance: Speech Recognition and NLP-Driven Robot Prototype

This paper presents the development and evaluation of a personal assistant robot prototype with advanced speech recognition and natural language processing (NLP) capabilities. Powered by a Raspberry Pi microprocessor, it is the core component of the robot's hardware. It is designed to receive commands and promptly respond by performing the requested actions, utilizing integrated speech recognition and NLP technologies. The prototype aims to enhance meeting efficiency and productivity through audio-to-text conversion and high-quality image capture. Results show excellent performance, with accuracy rates of 100% in Indonesian and 99% in English. The efficient processing speed, averaging 9.07 seconds per minute in Indonesian and 15.3 seconds per minute in English, further enhances the robot's functionality. Additionally, integrating a high-resolution webcam enables high-quality image capture at 1280 x 720 pixels. Real-time integration with Google Drive ensures secure storage and seamless data management. The findings highlight the prototype's effectiveness in facilitating smooth interactions and effective communication, leveraging NLP for intelligent language understanding. Integrating NLP-based speech recognition, visual documentation, and data transfer provides a comprehensive platform for managing audio, text, and image data. The personal assistant robot prototype presented in this research represents a significant advancement in human-robot interaction, particularly in meeting and collaborative work settings. Further refinements in NLP can enhance efficiency and foster seamless human-robot interaction experiences.

Discriminating and elimination of damaged soybean seeds based on image characteristics

To identify and eliminate damaged soybean seeds, images of Kaiyu 857 soybean seeds including those with insect damage, mildew, and other defects were acquired with an intelligent camera. After splitting the kernels from the background through using the data fusion, morphological corrosion expansion and a series of image processing algorithms, we extracted eight shape features, three color features and three texture features as the input layer to set up a BP neural network classification model with an average recognition accuracy of 97.25%. The identifying and eliminating device was tested five times with a mixture of 1000 differently damaged soybeans of seeds. The average accuracy rates of identification and elimination for normal, mildewed, insect-damaged, skin-damaged, broken and partly defective kernels reached 99.24%, 98.2%, 96.4%, 85.6%, 92.4% and 85.2% respectively. The efficient processing speed of the device reached 125 grains per minute. The results are of significance for the development of precise selection systems for soybeans or other crop seeds.

CDKF approach for estimating a static parameter of carrier frequency offset based on nonlinear measurement equations in OFDM systems

“Central difference Kalman filtering (CDKF)” is proposed as a new state of the art approach for carrier frequency offset estimation in orthogonal frequency division multiplexing systems. The parameter of interest to be estimated in this problem is a static value rather than a dynamically varying parameter. Therefore, classical approaches (e.g., maximum likelihood method or best linear unbiased estimator) might be more pertinent than Bayesian approaches if it is assumed to be a deterministic value. Nonetheless, it is shown and justified that a recently developed extended Kalman variant, i.e., CDKF, outperforms previously proposed methods in terms of mean squared error with efficient processing speed. Particularly, it is shown that CDKF outperforms recently proposed Gaussian particle filter for this one-dimensional static parameter estimation problem.

Social Network Visualization Method using Inherence Relationship of User Based on Cloud

Most of social network visualization methods have been only focusing on presentation of social network relationship. However, the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social network. This paper proposes a cloud based on visualization method to visualize an inherence relationship of user on social network. The proposed method can intuitionally understand the user's social relationship since the method uses correlation matrix to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses Hadoop based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

The ageing anaesthetist

The ageing anaesthetist

Fast Background Subtraction Based on a Multilayer Codebook Model for Moving Object Detection

Moving object detection is an important and fundamental step for intelligent video surveillance systems because it provides a focus of attention for post-processing. A multilayer codebook-based background subtraction (MCBS) model is proposed for video sequences to detect moving objects. Combining the multilayer block-based strategy and the adaptive feature extraction from blocks of various sizes, the proposed method can remove most of the nonstationary (dynamic) background and significantly increase the processing efficiency. Moreover, the pixel-based classification is adopted for refining the results from the block-based background subtraction, which can further classify pixels as foreground, shadows, and highlights. As a result, the proposed scheme can provide a high precision and efficient processing speed to meet the requirements of real-time moving object detection.

퍼지를 이용한 클라우드 기반의 소셜 네트워크 서비스 계층적 시각화

Recently, the visualization method of social network service have been only focusing on presentation of visualizing network data, which the methods do not consider an efficient processing speed and computational complexity for increasing at the ratio of arithmetical of a big data regarding social networks. This paper proposes a cloud based on visualization method to visualize a user focused hierarchy relationship between user`s nodes on social network. The proposed method can intuitionally understand the user`s social relationship since the method uses fuzzy to represent a hierarchical relationship of user nodes of social network. It also can easily identify a key role relationship of users on social network. In addition, the method uses hadoop and hive based on cloud for distributed parallel processing of visualization algorithm, which it can expedite the big data of social network.

A novel segmentation method for breast ultrasound images based on neutrosophic l‐means clustering

Fully automatic and accurate breast lesion segmentation is an essential and challenging task. In this paper, the authors develop a novel, effective, and fully automatic method for breast ultrasound (BUS) image segmentation. The segmentation method utilizes a novel phase feature to improve the image quality, and a novel neutrosophic clustering approach to detect the accurate lesion boundary. First, a region of interest is generated to cut off complex background. After speckle reduction, an enhancement algorithm based on phase in max-energy orientation (PMO) is developed to further improve the image quality. The PMO is a newly proposed 2D phase feature obtained by filtering the image in the frequency domain and calculating the phase accumulation in the orientation with maximum energy. Finally, the authors propose a novel clustering approach called neutrosophic l-means (NLM) to detect the lesion boundary. NLM is a generalized clustering method that can be used to solve other clustering problems as well. In this paper, NLM is used to segment images with vague boundaries, and to deal with uncertainty better. To evaluate the performance of the proposed method, the authors compare it with the traditional fuzzy c-means clustering, active contour, level set, and watershed-based segmentation methods, using a common database. Radiologist's manual delineations are used as the golden standards. Five assessment metrics are utilized to evaluate the performance from different aspects. Both accuracy and efficiency are analyzed. Sensitivity analysis is also conducted to test the robustness of the proposed method. Compared with the other methods, the proposed method generates the most similar boundaries to the radiologist's manual delineations (TP rate is 92.4%, FP rate is 7.2%, and similarity rate is 86.3%; Hausdorff distance is 22.5 pixels and mean absolute distance is 4.8 pixels), with efficient processing speed (averagely 9.8 s per image). Sensitivity analysis shows the robustness of the proposed method as well. The proposed method is a fully automatic segmentation method for BUS images that can generate accurate lesion boundaries even for complicated cases. The fast processing speed, robustness, and accuracy of the proposed method suggest its potential applications in clinics.

Hierarchical Method for Foreground Detection Using Codebook Model

This paper presents a hierarchical scheme with block-based and pixel-based codebooks for foreground detection. The codebook is mainly used to compress information to achieve a high efficient processing speed. In the block-based stage, 12 intensity values are employed to represent a block. The algorithm extends the concept of the block truncation coding, and thus it can further improve the processing efficiency by enjoying its low complexity advantage. In detail, the block-based stage can remove most of the backgrounds without reducing the true positive rate, yet it has low precision. To overcome this problem, the pixel-based stage is adopted to enhance the precision, which also can reduce the false positive rate. Moreover, the short-term information is employed to improve background updating for adaptive environments. As documented in the experimental results, the proposed algorithm can provide superior performance to that of the former related approaches.