Antenna Theory And Design

A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems

One of the most common and deadly diseases in the world is lung cancer. Only early identification of lung cancer can increase a patient’s probability of survival. A frequently used modality for the screening and diagnosis of lung cancer is computed tomography (CT) imaging, which provides a detailed scan of the lung. In line with the advancement of computer-assisted systems, deep learning techniques have been extensively explored to help in interpreting the CT images for lung cancer identification. Hence, the goal of this review is to provide a detailed review of the deep learning techniques that were developed for screening and diagnosing lung cancer. This review covers an overview of deep learning (DL) techniques, the suggested DL techniques for lung cancer applications, and the novelties of the reviewed methods. This review focuses on two main methodologies of deep learning in screening and diagnosing lung cancer, which are classification and segmentation methodologies. The advantages and shortcomings of current deep learning models will also be discussed. The resultant analysis demonstrates that there is a significant potential for deep learning methods to provide precise and effective computer-assisted lung cancer screening and diagnosis using CT scans. At the end of this review, a list of potential future works regarding improving the application of deep learning is provided to spearhead the advancement of computer-assisted lung cancer diagnosis systems. 

A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images

Polypropylene (PP) is one of the raw materials to produce automotive interior parts. Under the high-temperature condition, the volatile gas of PP will have a pungent odor. Therefore, an effective gas detection method should be proposed. In this work, a lightweight interleaved residual dense network (LIRD) is proposed and coupled with an electronic nose (e-nose) to classify the volatile gas of industrial PP used in the automotive interior. The LIRD combines the lightweight interleaved group convolution (LIGC) and residual dense network (RDN). The LIGC module is proposed to effectively reduce the number of parameters in the convolution calculation and realize the information exchange among channels. To avoid the feature degradation, RDN is introduced to fuse the shallow and deep features, and the parameters are trained adaptively to enhance the classification stability. By means of the self-developed e-nose system, the volatile gas of two PP materials (2240 S and 1120 K) is detected under different temperature gradients. Compared with other deep learning methods, the LIRD has a better classification result of 99.20% and 99.00%, the best F1-score is 0.9920 and 0.9900, and the best Kappa coefficient is 0.9900 and 0.9875 in two PP materials. The results show that the combination of LIRD and e-nose is demonstrated as an effective analytical technique for quality monitoring in the industrial production process of automotive interior parts.

Lightweight Interleaved Residual Dense Network for Gas Identification of Industrial Polypropylene Coupled With an Electronic Nose

The aim of this study is to identify types and grades of Chinese liquors. For this, a hand-held electronic nose (e-nose) system is designed and a triangular difference-based binary coding (TDBC) recognition method is proposed. For a test sample of liquors, features extracted from five gas sensors of the e-nose are converted into binary codes (0 and 1) for each liquor category. Specifically, for each liquor category, if a feature value of a test sample is within the feature value range of all training samples, we mark it as 1, otherwise 0. Subsequently, for each liquor category, the sum of binary codes of the test sample is calculated, and the category corresponding to the maximum sum value is determined as the predicted label of the test sample. Using the e-nose-based TDBC method, average recognition accuracies of 97.5% and 99.0% for liquor-type identification and grade evaluation were achieved, which were considerably higher than those obtained using four traditional recognition methods. These results indicate that as a novel approach, the e-nose-based TDBC method allows the recognition of Chinese liquors accurately and quickly, which is of great significance for liquor detection and industrial quality assurance methods.

A Hand-Held Electronic Nose System for Rapid Identification of Chinese Liquors

This article presents a study on lung cancer detection based on electronic nose technology. The pattern recognition algorithm is extremely crucial for an electronic nose system, but the customary learning algorithms usually prefer the majority class for class imbalance learning due to the assumption of equal misclassification costs. To address this challenge, in this article, we propose a novel classification method named weighted discriminative extreme learning machine (WDELM) for lung cancer diagnosis. First, the WDELM assigns a different weight to each particular sample by using a flexible weighting strategy, which enables it to perform classification tasks with the unbalanced class distribution. Then, an alternating iterative algorithm is proposed to solve the convex objective function with a theoretical analysis presented. Finally, the effectiveness of the proposed method has been evaluated on lung cancer datasets and public datasets by comparing it with conventional methods. Experimental results have confirmed that the WDELM surpasses conventional methods and is competent for lung cancer diagnosis.

A Weighted Discriminative Extreme Learning Machine Design for Lung Cancer Detection by an Electronic Nose System

In the present paper, we proposed and analyzed a novel algorithmic framework for the joint design of the spectral coexistence of radar and communications system. The objects of interest in system design are the radar transmit code and the communication system code-book, whereby we state the design problem as a constrained maximization of the communication mutual information (MI) subject to a number of constraints guaranteeing both the radar performance, through a similarity constraint with some standard waveform and the effective power of radar interference. An iterative procedure, whose convergence is analytically proved, based on iterative alternating optimization of the suitably designed subproblems, is proposed and analyzed. Finally, we assess the effectiveness of the proposed approaches by numerical results.

Joint Optimization of the Spectral Coexistence of Radar and Communications System

This paper is concerned with a new robust design algorithm for signal mismatch, the modified steering vector and covariance matrix are investigated. Under the correlation coefficient and norm constraints, the mismatched desired signal steering vector is estimated via output power maximization. The original design is nonconvex, and usually Np-hard. Then the relaxed semidefinite programming (SDP) is employed to solve the problem, and the relaxation is tight via analytically proven. Subsequently, the sample covariance matrix is further restructured with the shrinkage method and the subspace theorem based on the corrected steering vector in order to remove the desired signal. Finally, we provide some numerical results to show that the proposed method gives satisfactory results under different scenarios.

Robust Design for signal mismatch with Steering Vector and Covariance Matrix Constraints

The invention discloses a multi-channel high-temperature sealing novel electronic nose thermal desorption system and a control method. When the concentration of a detected substance is relatively low,low-concentration gas can be adsorbed through the adsorption pipe. Then, the adsorption pipe is heated through a heating device, and required gas is obtained through thermal desorption. The adsorbedsubstances are blown out by using a small amount of carrier gas, so that the concentration of the detected substances is improved. Due to the specially designed high-temperature thermal desorption structure of the system, the problem that in a traditional sealing method, gas components are prone to being disturbed at the high temperature is solved, and through occlusion sealing between metal, theelectronic nose thermal desorption system with multiple channels, high sealing performance and high enrichment and concentration efficiency under the high-temperature condition is achieved.

Multi-channel high-temperature sealing novel electronic nose thermal desorption system and control method

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Papers

A Weighted Discriminative Extreme Learning Machine Design for Lung Cancer Detection by an Electronic Nose System

Joint Optimization of the Spectral Coexistence of Radar and Communications System

Robust Design for signal mismatch with Steering Vector and Covariance Matrix Constraints

Multi-channel high-temperature sealing novel electronic nose thermal desorption system and control method