This work attempts to discuss the observability of linear time-invariant systems with event-triggered measurements. A new notion of observability, namely, ǫ-observability is defined with parameter ǫ, which relates to ...
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Since the Covid-19 pandemic, the growth of E-commerce in Indonesia has increased sharply. Due to the shift in human habits or activities from offline to online. However, the lack of facilities and infrastructure is a ...
Since the Covid-19 pandemic, the growth of E-commerce in Indonesia has increased sharply. Due to the shift in human habits or activities from offline to online. However, the lack of facilities and infrastructure is a problem fisher face when they want to distribute their catch. This study develops an online marketplace to provide convenience for fishers in marketing their catch. This study aims to create a Customer to Customer (C2C) marketing system model with Progressive Web Application (PWA) technology. PWAs can develop applications in a fast, reliable, and attractive user experience. The product display uses the closest distance calculation based on the region category. Transactions are limited to using the COD system to adjust to the background of knowledge, trust, and customer convenience. The development research results show that the system can work well following the analysis of user needs.
Quantum simulation of many-body systems in materials science and chemistry are promising application areas for quantum computers. However, the limited scale and coherence of near-term quantum processors pose a signifi...
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To anticipate the choice dependent on recorded information is conceivable through Predictive Analytics. Machine learning calculations are arranged into four classifications: supervised, unsupervised, semi-supervised, ...
To anticipate the choice dependent on recorded information is conceivable through Predictive Analytics. Machine learning calculations are arranged into four classifications: supervised, unsupervised, semi-supervised, and reinforcement learning. Be that, as it may, in this paper, we will consider supervised learning. The working of supervised learning algorithms is to prepare informational collection as information, which comprises features and class names for learning. Prior to learning, descriptive analytics is applied to authentic information to comprehend the highlights and their effect on preparing. When learning is finished, the classifier is applied on informational test collection or constant informational index, which comprises just features. The classifier needs to foresee the class name, which is obscure in the informational test index. Here we examine a portion of the famous supervised learning algorithms like SMO, Naïve Bayes, J48, Random Forest, k-NN and the performance of these algorithms on different datasets based on time and accuracy. ML algorithms can be applied on different applications, such as email messages as spam or non-spam, an estimate of client purchasing conduct dependent on recorded deals information, oddity location, misrepresentation disclosure, cancer detection, diabetes prediction, etc. Our outcomes anticipated that SMO is best at exactness and IBk is best at preparing rapidly.
The aim of this paper is to present an active and effective method for shaping the local three-dimensional defor-mation. It is based on a proposed device called a "segment controller"and it can reduce vibrat...
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Vision-based occupancy prediction, also known as 3D Semantic Scene Completion (SSC), presents a significant challenge in computer vision. Previous methods, confined to onboard processing, struggle with simultaneous ge...
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In the current technological world, the data is getting generated abundantly through various sources. The time dependent data generated through sensors is one among them. The most of the time dependent or time series ...
In the current technological world, the data is getting generated abundantly through various sources. The time dependent data generated through sensors is one among them. The most of the time dependent or time series data is streaming in real-time onfixed flow rates and sometimes, it will be on variation. When there is a change or no change in the flow of data, the existing state of the art prediction models provides low accuracy of prediction. Hence, it is proposed to have better accuracy of prediction on the streaming data with LSTM and Kafka Framework. The Kafka-LSTM model performed much better than the other conventional models. The models were evaluated using mean absolute error and root mean square error. The proposed method performs with better accuracy and reduced error rate than the other conventional models.
Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably sa...
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Every year there is improving in the connection speed of mobile communication technology. Due to market requirements the wireless communication improves in addition of speed the reliability and functionality improves ...
Every year there is improving in the connection speed of mobile communication technology. Due to market requirements the wireless communication improves in addition of speed the reliability and functionality improves too. For digital telecommunication systems QAM considered as a modulation scheme, such as in 802.11 Wi-Fi standards. Efficiencies with high spectral can be done with QAM with a size of constellation that are suitable for specific application, limitation occur only by linearity of the communications channel and the noise level. In this paper 64 QAM modulator design, implemented and waveform generated using MATLAB Simulink with binary and gray symbol mapping, PN9 and 15-bit source, with Normal Raised Cosine NRC and Root Raised Cosine RRC filter effect on both time and frequency domain.
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