Measurement Sensors最新文献

The healthcare industry has blossomed into one of the most pivotal and technologically advanced sectors in the past decade. Individuals grapple with the peril of untimely demise from diverse ailments as patients suffer from delayed treatment. The paramount objective is to forge a dependable patient care system utilizing the Internet of Things (IoT), enabling physicians to monitor patients' well-being within medical facilities or even the confines of their homes. The system aids in tracking the patient's SpO2 level, body temperature, pulse rate (beats per minute), room temperature, and humidity, then trains the data with machine learning algorithms for the patient and finally monitors it through the Blynk IoT system. The cloud-stored data can be harnessed to ascertain and supervise one's health and predict forthcoming perils. This study unveils an efficacious decision-making model custom-tailored for Internet of Things (IoT) ventures, and the proposed trained algorithm satiates these requirements, offering efficiency and precision, rendering it appropriate for numerous IoT applications. Finally, the Shapley Additive Explanations (SHAP) is used here for finding out the most influential parameters, and Explainable AI (XAI) is utilized with the help of SHAP values for enhanced information on affecting parameters. The SVC model's hyperparameter is properly adjusted, yielding a testing accuracy of 98.83 % and a training accuracy of 98.71 %. On cross-validation, the lightweight Sklearn model achieved a mean accuracy of almost 99 %. And with a SHAP weightage magnitude of 1.38, 0.91, and 0.44 for Class ‘Good’, Class ‘Poor’, and Class ‘Bad’, respectively, patients SpO2 level is the most significant feature.

In today's computer world, a lot of real-time applications require rapid processing units. Arithmetic Logic Units (ALU) and Multiply-Accumulate (MAC) are the fundamental parts of these circuits and are necessary for their effective and rapid operation. The most significant prevalent part of digital signal processing devices is multipliers. The multiplier, adder, and registers need to be changed in order to maintain accuracy and increase execution speed, which will improve the performance of the ALU and MAC. The development of greater multipliers is being given priority for application in processors because of the increasing constraints on latency. To accelerate multiplication, it is essential to develop quicker multipliers. Vedic multipliers are preferred over different current expansions due to their low power consumption, fast operation, and efficient use of space. Vedic mathematics-based algorithms are often utilized to build quick, low-power multipliers. In addition to simulation results, this section covers the four sutras of Vedic mathematics: Urdhva Tiryakbhyam, Ekadhikena Purvena, Ekanyunena Purvena, and Nikhilam. Vedic multipliers are also compared to a variety of modern multipliers, including booth, Wallace, and array multipliers. All of the sutras are evaluated according to area, speed, power, propagation delay, and mean relative error (MRE) in the current research. The results of the study will be applied in the biomedical field.

Getting around in modern cities has become a daily puzzle for both residents and travelers. As cities increasingly evolve into complex hubs of innovation and development, the demand for efficient transportation solutions has never been higher. In the multifaceted field of urban transportation, cost-effective and efficient mobility remains a high priority. This paper looks at how cities are planning better ways for people to travel and move within the growing scope of multimodal transportation, a paradigm shift beyond traditional single-mode transit systems. Our approach to improving urban transport revolves around a few key principles: integration, innovation, and collaboration. Integration means bringing together different modes of transportation – like buses, trains, bikes, and even new technologies like ride-sharing services to make it easier for people to switch between them. This transformative approach not only aims to reduce congestion and reduce environmental footprints but also prioritizes user experience while ensuring a harmonious blend of convenience, sustainability, and accessibility. We use new technology and ideas to ensure that travel is easy, quick, and good for the environment. Looking at new trends and examples from around the world, this overview shows how cities are shaping a better future for everyone's daily commute. In this paper, we use Lingo software to solve a numerical example related to multi-modal transportation, demonstrating practical solutions for real-world implementation. Through innovation, we can find creative solutions to urban transportation challenges. Additionally, public transportation enhancements, such as the introduction of synchronized bus routes and electric vehicle charging stations, underline the commitment to sustainability and inclusivity. In the dynamic urban transportation landscape, cities will revolutionize our approach to providing cost-effective and efficient mobility solutions.

In this study, a non-intrusive load monitoring (NILM) framework is developed for next generation shipboard power systems (SPS) based on a discrete wavelet transform signal processing and a convolutional neural network (CNN). We have applied the developed NILM method to a four-zone medium voltage direct current (MVDC) SPS to evaluate the effectiveness of the proposed method. Each zone of the MVDC SPS consists of multiple components, such as propulsion load, pulsed load, high ramp rate load, cooling load, and hotel load. The current signals from the main generators are the main inputs to the NILM model. The current signals are first processed through a discrete wavelet transform to create a coefficient vector that reflects the status of all the components in each zone. Then, a multi-class classification problem is formulated and solved using a CNN architecture model to monitor the load statuses in real time. The results of case studies show that the developed NILM model in comparison with benchmark methods can (i) accurately monitor the status of all components with a total accuracy of over 98%, (ii) identify unique pulsed loads with a total accuracy of over 99%, and (iii) sustain the functionality of load monitoring under extreme events such as cyber/physical attacks, load uncertainty, and noisy inputs.

As the security of computer networks in enterprises worldwide is dependent on the proper functioning of intrusion detection systems (IDSs) and intrusion prevention systems (IPSs), this effectiveness of both of them is of utmost priority. Leveraging diverse techniques, these network security systems are created to keep the reliability, the availability, and the integrity of the organizational networks safe. One plus point of using ML in intrusion detection system (IDS) is that it has successfully weeded out all the IDS attacks with a high degree of accuracy. In contrast, such systems may be believed to operate to their least competent levels when supersized data spaces have to be dealt with. In the process to solve this, application of feature selection techniques will play the crucial role to ignore non-relevant features which do not impact the issue of classification much. One more thing to keep in mind is that the ML-based IDSs often have problems with high false alarms and percentage accuracy because of the imbalanced training sets. The undertaking of this paper involves a through the analysis of the UNSW-NB15 intrusion detection data set as upon which our models will be tested and trained. We utilize two feature selection approaches: the PCA method, which is denoted as PCA, and the SVD method, called SVD. Furthermore, we categorize the datasets using these methods— Ridge Regression (RR), Stochastic Gradient Descent, and Convolutional Neural Network (CNN)-- on the transformed feature space. What is the most widely used for, is that it deals with both, binary and multiclass classification. The result measure that PCA and SVD are succeeded in getting better performance of IDS than others with enhancing the accuracy of classification models. More specifically, the RR classifier's precise was outstanding for the binary classification problem experiencing a rise in the accuracy from 98.13 % to 99.85 %. This shows the critical role of feature selection approaches and is also demonstrates the modeling capabilities of RR, SGD, and CNN classifiers and stands out as a solution to intrusion detection.

The rapid expansion of urban areas and the increasing number of vehicles on the road have resulted in accidents, traffic congestion, economic repercussions, environmental deterioration, and excessive fuel consumption. A dependable traffic management system is necessary to anticipate and regulate urban traffic patterns. Traffic forecast aids in the prevention of traffic issues. Urban traffic predictions often utilise historical and current traffic flow data to forecast road conditions. This article presents a traffic flow prediction system that utilises the Internet of Things (IoT), machine learning, and feature selection. Internet of Things (IoT) devices located on highways or within cars gather sensor data in real-time. The input data set comprises both real-time Internet of Things (IoT) data and historical traffic statistics. The input data is stored in a centralized cloud. The data is subjected to preprocessing in order to eliminate any unwanted interference and identify any exceptional values. The accuracy and root mean square error are contingent upon the process of feature selection. Particle swarm optimization identifies and extracts crucial features from input data. The classification model is constructed using K Nearest Neighbor, Multi layer Perceptron, and Bayes network approaches. The UCI traffic data is used for conducting experiments. The dataset has 47 attributes and 2102 occurrences. The accuracy of traffic flow prediction using PSO KNN is 96 %. The PSO KNN algorithm achieved a Mean Square Error (MSE) of 0.00289 and a Root Mean Square Error (RMSE) of 0.0595.