There has been a meteoric rise in the use of online payment systems recently. Multiple e-payment systems exist, each designed to provide the highest degree of security while maintaining the greatest possible convenien...
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Antennas are essential components in communication systems, serving as both transmitters and receivers. Their efficient operation depends on precise alignment with the strongest signal source, facilitated by tracker a...
Antennas are essential components in communication systems, serving as both transmitters and receivers. Their efficient operation depends on precise alignment with the strongest signal source, facilitated by tracker antennas. A recent research study focused on enhancing the responsiveness of these tracker antennas. By manually inputting PID (Proportional-Integral-Derivative) values for direction control, the study achieved notable *** results demonstrated that by fine-tuning PID values, particularly setting Kp=5, Ki=0.21812, and Kd=12, and targeting an angle of 90°, the response time was reduced to 1.2 seconds with an overshoot of 28.8% and 1.6 seconds of oscillation. This optimized control mechanism significantly improved antenna direction accuracy, aligning it with the desired setpoint. Furthermore, the importance of antenna alignment and polarization matching in communication systems was emphasized. Ensuring alignment between the receiving antenna's polarization and the incoming wave polarization is crucial for maintaining a strong signal reception power level. This alignment enables accurate tracking of antenna positions, supporting real-time GPS data communication. Additionally, the study involving horizontal azimuth angle measurements with tracker antennas demonstrated their capability to transmit real-time data from multiple positions, achieving 360° horizontal azimuth angles, along with varying elevations. These precise measurements play a vital role in object monitoring and effective communication.
In this work, we study the performance of an integrated sensing and communications (ISAC) system utilizing both a simultaneous transmitting and receiving (STAR) reconfigurable intelligent surface (RIS) and a solely re...
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Advancements in sparse signal recovery (SSR) theory have significantly enhanced direction-of-arrival (DOA) estimation performance, a critical aspect of array signal processing with a wide range of applications. This p...
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Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network...
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ISBN:
(数字)9798350387889
ISBN:
(纸本)9798350387896
Network traffic matrix prediction is used to estimate the patterns of future network flows before they are initiated. Traffic matrix prediction is needed by a wide range of network management functions such as network monitoring and it relies on historical data. In this paper, we address the task of multi-time step traffic matrix prediction using Recurrent Neural Networks (RNN). Our objective is to conduct a comparative study of different types of RNNs and to evaluate their ability to predict multi-time step Origin-Destination traffic matrices. Experiments show that RNNs are capable of predicting multiple steps of traffic matrices, however, the RMSE of the predictions increases as we move further away from the last true value. Our primary finding is that the RNN-GRU show has the best prediction ability in the very beginning steps with an RMSE of 0.048, while RNN-LSTM demonstrated higher capability with the last steps, having an RMSE value of 0.058.
High-tech improvements with reference for health advance to develop numerous medical tools have been build to support doctors and nurses performances. This analysis research applied web-based system of infusion monito...
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The large amount of unstructured educational data is an important data source of educational big data analysis. However, there is a lack of efficient and simple distributed solutions to process these unstructured docu...
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Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile a...
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ISBN:
(数字)9798350330991
ISBN:
(纸本)9798350331004
Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile and single-event-upset (SEU)-recoverable latch based on FeFET and CMOS for energyharvesting devices. The latch uses n-type FeFET devices to provide nonvolatility without any additional control signals. Moreover, since the soft error problem has become increasingly severe, radiation hardening by design gains a great attention as a promising approach to mitigate the reliability issue. The latch uses feedback interlocked loops with n-type FeFETs and C-elements, enabling it to provide nonvolatility and SEU-recovery simultaneously. Simulation results with Candence Virtuoso verifies that the proposed latch design has correct functioning with excellent performance compared to the state-of-the-art designs.
Mapless navigation has emerged as a promising approach for enablingautonomous robots to navigate in environments where pre-existing maps may beinaccurate, outdated, or unavailable. In this work, we propose an image-ba...
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Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation prob...
Affine correspondences have traditionally been used to improve feature matching over wide baselines. While recent work has successfully used affine correspondences to solve various relative camera pose estimation problems, less attention has been given to their use in absolute pose estimation. We introduce the first general solution to the problem of estimating the pose of a calibrated camera given a single observation of an oriented point and an affine correspondence. The advantage of our approach (P1AC) is that it requires only a single correspondence, in comparison to the traditional point-based approach (P3P), significantly reducing the combinatorics in robust estimation. P1AC provides a general solution that removes restrictive assumptions made in prior work and is applicable to large-scale image-based localization. We propose a minimal solution to the P1AC problem and evaluate our novel solver on synthetic data, showing its numerical stability and performance under various types of noise. On standard image-based localization benchmarks we show that P1AC achieves more accurate results than the widely used P3P algorithm. Code for our method is available at https://***/jonathanventura/P1AC/.
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