Sequential logic circuit gold codes for electronics and communication technologies.

The linear feedback shift register (LFSR) and Gold codes are used in telecommunications, Global Positioning System (GPS), satellite navigation, wireless systems, and code division multiple access (CDMA) dependent channel schemes for numerous radio communication technologies Gold codes are distinguished by their capacity to provide various orthogonal sequences.•The objective of the article is to focus on the design and simulation of the LFSR-based gold code generator chip in Xilinx ISE 14.7 software with the logic synthesis in Virtex-5 field programmable gate array (FPGA) and check the switching behavior with large frequency support applicable in fast-switching optical, and wireless electronics systems.•The methodology comprises design, functional simulation with different test inputs, and FPGA synthesis. The chip design is verified for the 10-bit seeding sequence of LFSRs to result in 1023-bit code with the frequency support of 310 MHz, and 9.285 ns delay.•The chip design is simulated based on seed data and different tap points of LFSR registers from which the bits are considered to generate the feedback. The design is scalable and has greater potential to extend to a larger extent. The behavior of the gold code depends on the maximum length sequence, absolute cross-correlation, and size of LFSR.

Landfill gas collection efficiency: Categorization of data from existing in-situ measurements.

Landfill methane emissions are commonly estimated using cover-type dependent default collection efficiency values, with a first-order decay model or measured gas collection. Current default collection efficiencies used in the United States were predominately derived from 4 studies conducted during or prior to 2007 that relied on flux chambers. Flux chambers are limited by small sample sizes, placement restrictions, and the inability to measure emissions from gas or leachate collection systems. Since 2007, over 14 new studies have been completed using more advanced technologies that allow for direct measurement of methane plumes from most or all of a landfill's surface. On average, these measurements are 2-3 times greater than emissions predicted by current models and collection efficiency defaults. In lieu of measuring emissions from all landfills, updating collection efficiency defaults can bring modeled emissions into better alignment with measurements. To this end, collection efficiency estimates derived from measured data were categorized into cover types and then adjusted to account for cases where whole plume measurement was an amalgamation of multiple cover types. The resultant adjusted default values were 41% for daily cover, 69% for intermediate cover, and 71% for final cover. Direct measurement of landfill methane emissions is preferrable to account for the full range of variables driving landfill emissions, including collection system design and operation. However, applying these updated defaults back into the landfill emission models eliminates underprediction of landfill emissions for the dataset reviewed, and would provide a more accurate estimate of landfill gas emissions where measurements are unavailable.

Correction: Kumar et al. Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System. Sensors 2022, 22, 5160.

In the original publication [...].

An enhanced self-learning-based clustering scheme for real-time traffic data distribution in wireless networks.

The process of examining the data flow over the internet to identify abnormalities in wireless network performance is known as network traffic analysis. When analyzing network traffic data, traffic classification becomes an important task. The traffic data classification is used to determine whether data in network traffic is in real-time or not. This analysis controls network traffic data in a network and allows for efficient network performance improvement. Real-time and non-real-time data are effectively classified from the given input data set using data mining clustering and classification algorithms. The proposed work focuses on the performance of traffic data classification with high clustering accuracy and low Classification Time (CT). This research work is carried out to fill the gap in the existing network traffic classification algorithms. However, the traffic data classification remained unaddressed for performing the network traffic analysis effectively. Then, we proposed an Enhanced Self-Learning-based Clustering Scheme (ESLCS) using an enhanced unsupervised algorithm and adaptive seeding approach to improve the classification accuracy while performing the real-time traffic data distribution in wireless networks. Test-bed results demonstrate that the proposed model enhances the clustering accuracy and True Positive Rate (TPR) effectively as well as reduces the CT time and Communication Overhead (CO) substantially to compare with the peer-existing routing techniques.

Networked control system with MANET communication and AODV routing.

The industries are presently exploring the use of wired and wireless systems for control, automation, and monitoring. The primary benefit of wireless technology is that it reduces the installation cost, in both money and labor terms, as companies already have a significant investment in wiring. The research article presents the work on the analysis of Mobile Ad Hoc Network (MANET) in a wireless real-time communication medium for a Networked Control System (NCS), and determining whether the simulated behavior is significant for a plant or not. The behavior of the MANET is analyzed for Ad-hoc on-demand distance vector routing (AODV) that maintenances communication among 150 nodes for NCS. The simulation is carried out in Network Simulator (NS2) software with different nodes cluster to estimate the network throughput, end-to-end delay, packet delivery ratio (PDR), and control overhead. The benefit of MANET is that it has a fixed topology, which permits flexibility since mobile devices may be used to construct ad-hoc networks anywhere, scalability because more nodes can be added to the network, and minimal operating expenses in that no original infrastructure needs to be developed. AODV routing is a flat routing system that does not require central routing nodes. As the network grows in size, the network can be scaled to meet the network design and configuration requirements. AODV is flexible to support different configurations and topological nodes in dynamic networks because of its versatility. The advantage of such network simulation and routing behavior provides the future direction for the researchers who are working towards the embedded hardware solutions for NCS, as the hardware complexity depends on the delay, throughput, and PDR.

Face Spoofing, Age, Gender and Facial Expression Recognition Using Advance Neural Network Architecture-Based Biometric System.

Nowadays, the demand for soft-biometric-based devices is increasing rapidly because of the huge use of electronics items such as mobiles, laptops and electronic gadgets in daily life. Recently, the healthcare department also emerged with soft-biometric technology, i.e., face biometrics, because the entire data, i.e., (gender, age, face expression and spoofing) of patients, doctors and other staff in hospitals is managed and forwarded through digital systems to reduce paperwork. This concept makes the relation friendlier between the patient and doctors and makes access to medical reports and treatments easier, anywhere and at any moment of life. In this paper, we proposed a new soft-biometric-based methodology for a secure biometric system because medical information plays an essential role in our life. In the proposed model, 5-layer U-Net-based architecture is used for face detection and Alex-Net-based architecture is used for classification of facial information i.e., age, gender, facial expression and face spoofing, etc. The proposed model outperforms the other state of art methodologies. The proposed methodology is evaluated and verified on six benchmark datasets i.e., NUAA Photograph Imposter Database, CASIA, Adience, The Images of Groups Dataset (IOG), The Extended Cohn-Kanade Dataset CK+ and The Japanese Female Facial Expression (JAFFE) Dataset. The proposed model achieved an accuracy of 94.17% for spoofing, 83.26% for age, 95.31% for gender and 96.9% for facial expression. Overall, the modification made in the proposed model has given better results and it will go a long way in the future to support soft-biometric based applications.

Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning.

The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today's intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (p < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (p > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient's survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient's age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (χ2 (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient's survival in the situation.

Prediction of students' awareness level towards ICT and mobile technology in Indian and Hungarian University for the real-time: preliminary results.

An experimental study was conducted to predict the student's awareness of Information and Communication Technology (ICT) and Mobile Technology (MT) in Indian and Hungarian university's students. A primary dataset was gathered from two popular universities located in India and Hungary in the academic year 2017-2018. This paper focuses on the prediction of two major parameters from dataset such as usability and educational benefits using four machine learning classifiers multilayer perceptron (ANN), Support vector machine (SVM), K-nearest neighbor (KNN) and Discriminant (DISC). The multi-classification problem was solved with test, train and validated datasets using machine learning classifiers. One hand, feature aggregation with the train-test-validation technique improved the ANN's prediction accuracy of educational benefits for both countries. Another hand, ANN's accuracy decreases significantly in the prediction of usability. Further, SVM and ANN outperformed the KNN and the DISC in the prediction of awareness level towards ICT and MT in India and Hungary. Also, this paper reveals that the future awareness level for the educational benefits will be Very High or Moderate in both countries. Also, the awareness level is predicted as High and Moderate for usability parameter in both countries. Further, ANN and SVM accuracy and prediction time is compared with T-test at 0.05 significance level which distinguished CPU training time is taken by ANN and SVM using K-fold and Hold out method. Also, K-fold enhanced the significant prediction accuracy of SVM and ANN. the authors also used a STAC web platform to compare the accuracy datasets using T-test and ANOVA test at 0.05 significant level and we found ANN and SVM classifier has no significant difference in prediction accuracy in each dataset. Also, the authors recommend presented predictive models to be deployed as a real-time module of the institute's website for the real-time prediction of ICT & MT awareness level.

Effect of salts on the binding of some environmental pollutants to corn protein zein studied by HPLC.

The strength and the character of the binding of 14 organic solvents to the corn protein zein in distilled water and in various salt solutions were determined by preparing zein-coated carbon stationary phase and by measuring the retention characteristics of solvents on a high-performance liquid chromatographic column filled with this stationary phase. The relationship between the physicochemical parameters and binding characteristics of solvents was elucidated by principal component analysis. It was established that various interactive forces are involved in the binding of solvent to the protein, suggesting a mixed binding mechanism. Binding characteristics are equally influenced by the molecular hydrophobicity and by the polarity parameters of the solvent. Coordination numbers, ionization, and lattice energies of the monovalent cations significantly influenced the various aspects of the binding of organic solvents to zein.