A data-driven approach has been developed to classify indoor activities using only commonly available passive environmental sensors, such as CO2, temperature, humidity, and passive infrared (PIR). An integrated IoT sy...
ISBN:
(数字)9781837242177
A data-driven approach has been developed to classify indoor activities using only commonly available passive environmental sensors, such as CO2, temperature, humidity, and passive infrared (PIR). An integrated IoT system comprising of sensor nodes, edge node and an intelligent server is designed and developed to provide real-time activity classification. Spectral Clustering and bidirectional long short-term memory (BiLSTM) are employed to achieve automatic labeling and room state prediction. The results show that the overall classification accuracy ranges from 88% to 96% for five target states across three distinct environments using CO2 and PIR values as input variables. Additionally, incorporating more input variables has been evaluated to access the ability of real-time classification of proposed model. An innovative monitoring mode can provide a different approach for detecting activities and occupancy in the future.
The conception of a control strategy of Doubly-Fed Induction Generator (DFIG) for providing a high quality of energy, without harmonic accumulations, to the electric network is a real challenge because of the no-linea...
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This paper introduces a fluorometer that is both economically viable and optimized for sensitivity. The sensor is designed to detect low concentrations of fluorophores in the visible spectrum range, utilizing a deep u...
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Visualization techniques in design space exploration with high dimensional data are helpful in enhancing the decision making in the context of multiple objective optimization. Visualization of Pareto solutions obtaine...
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This paper proposes a channel estimation method for movable antenna (MA)-aided wideband communication systems to acquire complete channel state information (CSI), i.e., the channel frequency responses (CFRs) between a...
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ISBN:
(数字)9798350368369
ISBN:
(纸本)9798350368376
This paper proposes a channel estimation method for movable antenna (MA)-aided wideband communication systems to acquire complete channel state information (CSI), i.e., the channel frequency responses (CFRs) between any position pair within the transmit (Tx) region and the receive (Rx) region across all subcarriers. To start with, we express the CFRs as a combination of the field-response vectors (FRVs), delay-response vector (DRV), and path-response tensor (PRT), which exhibit sparse characteristics and can be recovered by using a limited number of channel measurements at several position pairs of Tx and Rx MAs over a few subcarriers. Specifically, we first formulate the recovery of the FRVs and DRV as a problem of multiple measurement vectors in compressed sensing (MMV-CS), which can be solved via a simultaneous orthogonal matching pursuit (SOMP) algorithm. Next, we estimate the PRT using the least-square (LS) method. Finally, simulation results demonstrate that the proposed SOMP-based channel estimation method can reconstruct the complete wideband CSI with a high accuracy.
With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier...
With the advancement of cloud technology, the storage and computing overhead in large-scale biometric authentication is mitigated by outsourcing data to the cloud. Since biometric features serve as a unique identifier bound to each individual, transmitting them directly to the cloud may bring about serious privacy disclosure risks. To guarantee users' biometric features, there are many solutions have been proposed. However, most of them neglect to protect identity security. In light of the challenges, this paper proposes a strong privacy-preserving fingerprint authentication via clustering. Besides safeguarding fingerprint features, the scheme also blurs the identities of users to enable anonymity of identity. Meanwhile, the authentication efficiency of the proposed scheme is improved by vector processing of fingerprints and fast retrieval of clustered identities. Furthermore, a dual-server matching architecture effectively reduces the communication overhead of the service provider. The security analysis and experimental results indicate that the proposed scheme provides strong privacy preservation while maintaining high efficiency.
In this research, a global path planning method based on recurrent neural networks by means of a new loss function is presented, which regardless of the complexity of the configuration space, generates the path in a r...
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Efficient Road sign recognition is key to improving road safety, navigation and to enhance driver assistance for an intelligent transportation system (ITS). However, achieving efficient road sign and traffic recogniti...
Efficient Road sign recognition is key to improving road safety, navigation and to enhance driver assistance for an intelligent transportation system (ITS). However, achieving efficient road sign and traffic recognition is accompanied by many challenges, in our last few years, several deep learning algorithms have been deployed for responding to these limitations. With the exponential increase in the number of vehicles in Morocco, the need for accurate and real-time detection and classification of road signs has become essential. In this paper, we propose an advanced approach for recognizing Moroccan road signs utilizing the YOLOv8 (You Only Look Once) model that very known for its efficiency and effectiveness in object detection tasks. the model is trained and evaluated under a comprehensive dataset that comprising diverse Moroccan road signs. The test of YOLOv8 model give an encouraging result in different performance metrics such as accuracy, recall, precision and mAP which have respectively an average of 0.95, 0.94, 0.96 and 0.97.
Power system cyber-physical uncertainties, including measurement ambiguities stemming from cyber attacks and data losses, along with system uncertainties introduced by massive renewables and complex dynamics, reduce t...
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