MIMO iterative receiver SNR estimation strategies for link to system interfacing

This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)‐based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise‐based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition‐based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation.     

SCM: Secure and accountable TLS certificate management

In classical public‐key infrastructure (PKI), the certificate authorities (CAs) are fully trusted, and the security of the PKI relies on the trustworthiness of the CAs. However, recent failures and compromises of CAs showed that if a CA is corrupted, fake certificates may be issued, and the security of clients will be at risk. As emerging solutions, blockchain‐ and log‐based PKI proposals potentially solved the shortcomings of the PKI, in particular, eliminating the weakest link security and providing a rapid remedy to CAs' problems. Nevertheless, log‐based PKIs are still exposed to split‐world attacks if the attacker is capable of presenting two distinct signed versions of the log to the targeted victim(s), while the blockchain‐based PKIs have scaling and high‐cost issues to be overcome. To address these problems, this paper presents a secure and accountable transport layer security (TLS) certificate management (SCM), which is a next‐generation PKI framework. It combines the two emerging architectures, introducing novel mechanisms, and makes CAs and log servers accountable to domain owners. In SCM, CA‐signed domain certificates are stored in log servers, while the management of CAs and log servers is handed over to a group of domain owners, which is conducted on the blockchain platform. Different from existing blockchain‐based PKI proposals, SCM decreases the storage cost of blockchain from several hundreds of GB to only hundreds of megabytes. Finally, we analyze the security and performance of SCM and compare SCM with previous blockchain‐ and log‐based PKI schemes.     

Fast and Ultraclean Approach for Measuring the Transport Properties of Carbon Nanotubes

In this work, a fast approach for the fabrication of hundreds of ultraclean field‐effect transistors (FETs) is introduced, using single‐walled carbon nanotubes (SWCNTs). The synthesis of the nanomaterial is performed by floating‐catalyst chemical vapor deposition, which is employed to fabricate high‐performance thin‐film transistors. Combined with palladium metal bottom contacts, the transport properties of individual SWCNTs are directly unveiled. The resulting SWCNT‐based FETs exhibit a mean field‐effect mobility, which is 3.3 times higher than that of high‐quality solution‐processed CNTs. This demonstrates that the hereby used SWCNTs are superior to comparable materials in terms of their transport properties. In particular, the on–off current ratios reach over 30 million. Thus, this method enables a fast, detailed, and reliable characterization of intrinsic properties of nanomaterials. The obtained ultraclean SWCNT‐based FETs shed light on further study of contamination‐free SWCNTs on various metal contacts and substrates.     

Five-category classification of pathological brain images based on deep stacked sparse autoencoder

Multimedia Tools and Applications - Magnetic resonance imaging (MRI) is employed in medical treatment broadly, due to the quick development of computer technology. It is beneficial to classify the...     

Social learning for adaptive delta management: Tidal River Management in the Bangladesh Delta

ABSTRACT

The article analyzes Tidal River Management in Bangladesh from a social learning perspective. Four cases were investigated using participatory assessment. Knowledge acquisition through transformations in the Tidal River Management process was explored as an intended learning outcome. The study finds that social learning occurred more prominently at the individual stakeholder level and less at the collective level. For Tidal River Management to be responsive and sustainable, especially in times of increased uncertainty and climate vulnerability, more attention needs to be paid to coordination and facilitation of multi-level learning that includes all stakeholders.     

Complexity and trends analysis of hydrometeorological time series for a river streamflow: A case study of Songhua River Basin,China

In China's national economic growth, an important role is being played by the Songhua River because of the river's abundant resources and natural conditions. Therefore, study of hydrometeorological time series is very important to understand the basin behaviour. This research uses the snow cover data derived from MODIS, streamflow, and meteorological records in the Songhua River Basin to evaluate similarity, complexity, and trends in the snow cover, temperature, precipitation, and streamflow. In this paper, we suggest a new method of ranking the statistics symbolic sequences to examine the degree of similarity (distance measurement) between meteorological stations and compare it with non‐parametric correlation methods and also investigate the deviations in the complexity of a hydrometeorological time series. Information‐based similarity index and multiscale entropy confirm that the hydrometeorological time series of different stations have self‐similarity and abundant complexity. Wavelet entropy is also used to investigate the basin behaviour by taking streamflow records and population. It is found that with the increase in population and urbanization, the complexity values are increased. The results also exhibit that due to increase in urbanization, it affects the hydrological process and nature of environment resulting in complex catchment behaviour. Furthermore, the streamflow trend results displayed significant decline (22.21 m³/s × year^?1) in the Songhua River. The results also indicated that the seasonal snow cover trend has no impact on changes of the streamflow. However, the decline of the streamflow may be influenced by the significant human activity upstream of the Songhua River.     

Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content

Emotion detection from the text is a challenging problem in the text analytics. The opinion mining experts are focusing on the development of emotion detection applications as they have received considerable attention of online community including users and business organization for collecting and interpreting public emotions. However, most of the existing works on emotion detection used less efficient machine learning classifiers with limited datasets, resulting in performance degradation. To overcome this issue, this work aims at the evaluation of the performance of different machine learning classifiers on a benchmark emotion dataset. The experimental results show the performance of different machine learning classifiers in terms of different evaluation metrics like precision, recall ad f-measure. Finally, a classifier with the best performance is recommended for the emotion classification.     

IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19

The prediction of human diseases, particularly COVID-19, is an extremely challenging task not only for medical experts but also for the technologists supporting them in diagnosis and treatment. To deal with the prediction and diagnosis of COVID-19, we propose an Internet of Medical Things-based Smart Monitoring Hierarchical Mamdani Fuzzy Inference System (IoMTSM-HMFIS). The proposed system determines the various factors like fever, cough, complete blood count, respiratory rate, Ct-chest, Erythrocyte sedimentation rate and C-reactive protein, family history, and antibody detection (lgG) that are directly involved in COVID-19. The expert system has two input variables in layer 1, and seven input variables in layer 2. In layer 1, the initial identification for COVID-19 is considered, whereas in layer 2, the different factors involved are studied. Finally, advanced lab tests are conducted to identify the actual current status of the disease. The major focus of this study is to build an IoMT-based smart monitoring system that can be used by anyone exposed to COVID-19; the system would evaluate the user’s health condition and inform them if they need consultation with a specialist for quarantining. MATLAB-2019a tool is used to conduct the simulation. The COVID-19 IoMTSM-HMFIS system has an overall accuracy of approximately 83%. Finally, to achieve improved performance, the analysis results of the system were shared with experts of the Lahore General Hospital, Lahore, Pakistan.     

Proportional Fairness-Based Power Allocation Algorithm for Downlink NOMA 5G Wireless Networks

Non-orthogonal multiple access (NOMA) is one of the key 5G technology which can improve spectrum efficiency and increase the number of user connections by utilizing the resources in a non-orthogonal manner. NOMA allows multiple terminals to share the same resource unit at the same time. The receiver usually needs to configure successive interference cancellation (SIC). The receiver eliminates co-channel interference (CCI) between users and it can significantly improve the system throughput. In order to meet the demands of users and improve fairness among them, this paper proposes a new power allocation scheme. The objective is to maximize user fairness by deploying the least fairness in multiplexed users. However, the objective function obtained is non-convex which is converted into convex form by utilizing the optimal Karush-Kuhn-Tucker (KKT) constraints. Simulation results show that the proposed power allocation scheme gives better performance than the existing schemes which indicates the effectiveness of the proposed scheme.