This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants’ favorite music, and Interstellar’s main t...
详细信息
ISBN:
(数字)9798331527396
ISBN:
(纸本)9798331527402
This study aims to classify brainwave patterns using electroencephalogram (EEG) signals in response to various auditory stimuli, specifically Quran recitation, participants’ favorite music, and Interstellar’s main theme. By analyzing the unique EEG responses elicited by these stimuli, the study explores cognitive and emotional processing differences. Two prominent classifiers, Support Vector Machine (SVM) and Convolutional Neural Networks (CNN), were utilized. SVM, combined with Power Spectral Density (PSD) features, achieved the highest accuracy of 95% when distinguishing Quran recitation from rest. CNN, particularly with Kernel Density Estimation (KDE) features, exhibited strong performance in distinguishing between different auditory stimuli, with an average accuracy exceeding 90%. These results highlight the critical role of feature extraction techniques, such as PSD and KDE, in improving classification performance and deepening our understanding of EEG-based emotional and cognitive responses to auditory stimuli.
This paper introduces a novel advanced framework for the classification of Electroencephalography (EEG) signals through the integration of Riemannian geometry and a bespoke contrastive learning framework. Initially, t...
详细信息
ISBN:
(数字)9798331527396
ISBN:
(纸本)9798331527402
This paper introduces a novel advanced framework for the classification of Electroencephalography (EEG) signals through the integration of Riemannian geometry and a bespoke contrastive learning framework. Initially, the EEG signals are segmented and converted into covariance matrices, which are regularized to ensure positive definiteness, thus enabling their interpretation as Symmetric Positive Definite (SPD) matrices. These SPD matrices are then projected onto the Riemannian manifold, facilitating the extraction of discriminative features by utilizing Euclidean geometric operations within the tangent space. A specialized neural network architecture, TangentSpaceNet, is devised to map these features into a lower-dimensional space. In this space, a customized contrastive loss function, predicated on Euclidean distance, is implemented to enhance class separability. This approach substantially improves the robustness and accuracy of EEG signal classification. Empirical assessments highlight the effectiveness of the suggested approach, showcasing significant improvements in classifying performance 93% accuracy, 94%precision, 94% recall, and 94% F1-score. The novel fusion of Riemannian geometry and contrastive learning presents a sturdy and adaptable structure for analyzing EEG signals, presenting considerable prospects for utilization in brain-computer interfaces, cognitive neuroscience, and the identification of neurological conditions.
Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inver...
详细信息
Thanks to the ubiquitous and easily accessible nature of wireless signals, wireless sensing is regarded as one of the promising techniques in the next-generation Internet of Things. In this paper, we propose the inverse semantic communications as a new paradigm to achieve lightweight wireless sensing using the reconfigurable intelligent surface (RIS). Instead of extracting semantic information from messages, we aim to encode the task-related source messages into a hyper-source message. Specifically, we first develop a novel RIS hardware for encoding several signal spectrums into one MetaSpectrum. We then propose a self-supervised learning method for decoding the MetaSpectrums to obtain the original signal spectrums. Using the sensing data collected from the real world, we show that our framework can reduce the data volume by 90% compared to that before encoding, without affecting the execution of various sensing tasks. Experiment results also demonstrate that the amplitude response matrix of the RIS enables the encryption of the sensing data.
Employing massive Mobile AI-Generated Content (AIGC) Service Providers (MASPs) with powerful models, high-quality AIGC services can become accessible for resource-constrained end users. However, this advancement, refe...
详细信息
In this paper, a new quasi-resonant DC-DC converter topology is presented, which is the result of combining simpler configurations of single input and single output. The proposed topology is a combination of the Zeta ...
详细信息
In view of the complexity of the flux density harmonic components of bilateral-excitation flux modulation (BFM) machines, it is not easy to analyze the role of air-gap flux density harmonics on the electromagnetic tor...
详细信息
ISBN:
(数字)9798350362213
ISBN:
(纸本)9798350362220
In view of the complexity of the flux density harmonic components of bilateral-excitation flux modulation (BFM) machines, it is not easy to analyze the role of air-gap flux density harmonics on the electromagnetic torque of BFM machines. In this digest, Taking the novel asymmetric stator tooth (AST) BFM machine as the research object, the flux modulation phenomena of the PM magneto motive-force (MMF) and armature winding MMF are analyzed in detail. Meanwhile, based on the air-gap field modulation theory and Maxwell stress tensor equation, the contribution of air-gap flux density harmonics to the electromagnetic torque of the BFM machine is obtained.
We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based ...
详细信息
This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e....
详细信息
ISBN:
(数字)9798350367942
ISBN:
(纸本)9798350367959
This work focuses on advancing the security field in the hardware design space by formally defining the problem of Hardware Trojan (HT) detection. The goal is to model HT detection more closely to the real world, i.e., describing the problem as "The Seeker’s Dilemma" (an extension of Hide&Seek on a graph), where a detecting agent is unaware of whether HTs infect circuits or not. Using this problem formulation, we create a benchmark that consists of a mixture of HT-free and HT-infected restructured circuits while preserving their original functionalities. The restructured circuits are randomly infected by HTs, causing a situation where the defender is uncertain if a circuit is infected. Our innovative dataset will help the community better judge the detection quality of different methods by comparing their success rates in circuit classification. We use our benchmark to evaluate three state-of-the-art HT detection tools to show baseline results for this approach. We use Principal Component Analysis to assess the strength of our benchmark, where we observe that some restructured HT-infected circuits are mapped closely to HT-free circuits, leading to significant label misclassification by detectors.
An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. Th...
An intelligent livestock monitoring system has become increasingly popular for monitoring and managing animals in smart farms. Various technologies have been developed to localize the animals with wearable sensors. This paper addresses ranging from Received Signal Strength Indicator (RSSI). ESP32C3-Mini chips are used to construct the localization system, where both ear tags and base stations are built on these chips. Based on the distances captured by multiple base stations, triangulation is employed to estimate the position of the concerned tags. To improve the positioning accuracy, a weighted triangulation method is used. The advantage and distances of the proposed RSSI-based ranging method are discussed using real data.
We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based ...
We present a multisensor fusion framework for the onboard real-time navigation of a quadrotor in an indoor environment. The framework integrates sensor readings from an Inertial Measurement Unit (IMU), a camera-based object detection algorithm, and an Ultra-WideBand (UWB) localisation system. Often the sensor readings are not always readily available, leading to inaccurate pose estimation and hence poor navigation performance. To effectively handle and fuse sensor readings, and accurately estimate the pose of the quadrotor for tracking a predefined trajectory, we design a Maximum Correntropy Criterion Kalman Filter (MCC-KF) that can manage intermittent observations. The MCC-KF is designed to improve the performance of the estimation process when is done with a Kalman Filter (KF), since KFs are likely to degrade dramatically in practical scenarios in which noise is non-Gaussian (especially when the noise is heavy-tailed). To evaluate the performance of the MCC-KF, we compare it with a previously designed Kalman filter by the authors. Through this comparison, we aim to demonstrate the effectiveness of the MCC-KF in handling indoor navigation missions. The simulation results show that our presented framework offers low positioning errors, while effectively handling intermittent sensor measurements.
暂无评论