Stress is a condition that can suppress the person’s psychic state in achieving something. Stress, at some level, can harm human health since it can cause various diseases that humans often underestimate. These disea...
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ISBN:
(纸本)9798350320725
Stress is a condition that can suppress the person’s psychic state in achieving something. Stress, at some level, can harm human health since it can cause various diseases that humans often underestimate. These diseases include headaches, cramps, heart attacks, high blood pressure, and even strokes can occur. Stress levels are detected by manually filling out questionnaires or conducting self-assessment tests. However, it seems subjective because the results depend on honesty in answering the questionnaire. Therefore, in this study, we conduct a study to classify human stress levels by observing brain wave activity using an Electroencephalogram (EEG). Since EEG signals can directly reflect the brain’s electrical activity, they can be used as an objective measure for classifying stress levels. In this study, the method used in classification is K-Nearest Neighbour. The signalprocessing stages include pre-processing, feature extraction using Independent Component Analysis (ICA), and then classification using K-Nearest Neighbour. This research used 62 data as training and testing data. After testing the system, it was concluded that the K-Nearest Neighbour method could classify stress into normal and high levels with an accuracy of 75% with a k-value of 7.
Corner points are commonly defined as the intersection of two edges, and the Harris algorithm, which performs corner point detection based on the grey value variation between a patch and its neighborhood, is commonly ...
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ISBN:
(纸本)9781665454810
Corner points are commonly defined as the intersection of two edges, and the Harris algorithm, which performs corner point detection based on the grey value variation between a patch and its neighborhood, is commonly used in various computer vision tasks. For low-light images, Harris algorithm is affected because the details of the image become blurred by low contrast. This paper proposes an improved Harris algorithm, which is inspired by the Contrast Limited Adaptive Histogram Equalization (CLAHE). After extracting a patch of the target image, the gray value of the patch is adjusted based on the cumulative distribution function (CDF). As a result, the gray value of the patch becomes evenly distributed, and the variation of the gray value of the patch becomes sharper. The improved Harris algorithm has been compared with the original Harris algorithm on different images of low-light scenes. Experimental results show that the proposed algorithm can effectively detect corner points in low contrast regions, and the repeatability of corner points matching in the low-light regions is significantly improved.
The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome ...
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ISBN:
(纸本)9781728111797
The traditional marketing research tools (Personal Depth Interview, Surveys, FGD, etc.) are cost-prohibitive and often criticized for not extracting true consumer preferences. Neuromarketing tools promise to overcome such limitations. In this study, we proposed a framework, MarketBrain, to predict consumer preferences. In our experiment, we administered marketing stimuli (five products with endorsements), collected EEG signals by EMOTIV EPOC+, and used signalprocessing and classification algorithms to develop the prediction system. Wavelet Packet Transform was used to extract frequency bands (delta, theta, alpha, beta(1) ,beta(2), gamma) and then statistical features were extracted for classification. Among the classifiers, Support Vector Machine (SVM) achieved the best accuracy (96.01 +/- 0.71) using 5-fold crass-validation. Results also suggested that specific target consumers and endorser appearance affect the prediction of the preference. So, it is evident that EEG-based neuromarketing tools can help brands and businesses effectively predict future consumer preferences. Hence, it will lead to the development of an intelligent market driving system for neuromarketing applications.
Automotive radar systems are indispensable for advanced driver assistance systems and traffic safety. Besides existing monostatic radar techniques, bi-static radar sensing like passive coherent location offers additio...
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Automotive radar systems are indispensable for advanced driver assistance systems and traffic safety. Besides existing monostatic radar techniques, bi-static radar sensing like passive coherent location offers additional options to improve the radar visibility of vulnerable road users. Regarding the testing and evaluation of signalprocessingalgorithms including parameter estimation, it is essential to provide electromagnetically shielded and reproducible measurement conditions, in addition to field tests in real traffic scenarios. This study describes the possibility to emulate relevant performance parameters for bi-static radar scenarios in the frequency range from 1 GHz to 6 GHz in a metal-shielded semi-anechoic chamber. Of special interest are the bi-static angle between transmitter, target, and receiver, and the resulting bi-static Doppler frequencies of a realistic vehicular traffic scenario. According to the concept of cooperative passive coherent location, Doppler scattering measurements are presented and compared to electromagnetic simulations. The authors find promising agreement between measured and ground truth data in the delay-Doppler spectrum.
In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size...
In the present paper classification of Brain Tumours through MRI imaging as Fine, Medium and Coarse textures is proposed. The other methods of classification of brain tumours that are frequently used are based on size, shape and spread of the tumour. The importance of classifying tumours based on texture is that there is correlation between malignancy of the tumour and the texture of the tumour. The other methods normally don't give this information. In this paper relation between texture of the tumour and it's malignancy has been explored. Four methods of feature extraction have been used, in order to obtain texture information of the tumour. They are, Gray Level Co-occurrence Matrix (GLCM), Gradient Matrix (GM), Laplacian Of Gaussian (LoG) and the Run-Length Matrix (RLM). Once the features are extracted, they are passed to Decision Tree classification algorithm. The classification algorithms classified the extracted features as having Fine, Medium or Coarse textures. The result obtained showed correlation between texture of the tumour and its malignancy. If the texture is predicted as Fine, then it is most likely a non-malignant, or benign tumour, and its diagnosis is most likely going to give a Complete response. A Medium texture would most likely give a Partial response, and is not very malignant. This is important, since knowing this information can benefit the patient and clinicians. This improves the diagnostic confidence of the clinicians and allows them to diagnose and report multiple patients that results in lower cost of the examination as well as access to diagnostics to people living in remote areas.
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake de...
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ISBN:
(数字)9798350353006
ISBN:
(纸本)9798350353013
Recently, the proliferation of highly realistic synthetic images, facilitated through a variety of GANs and Diffusions, has significantly heightened the susceptibility to misuse. While the primary focus of deepfake detection has traditionally centered on the design of detection algorithms, an investigative inquiry into the generator architectures has remained conspicuously absent in recent years. This paper contributes to this lacuna by rethinking the architectures of CNN-based generator, thereby establishing a generalized representation of synthetic artifacts. Our findings illuminate that the up-sampling operator can, beyond frequency-based artifacts, produce generalized forgery artifacts. In particular, the local interdependence among image pixels caused by upsampling operators is significantly demon-strated in synthetic images generated by GAN or diffusion. Building upon this observation, we introduce the concept of Neighboring Pixel Relationships(NPR) as a means to capture and characterize the generalized structural artifacts stemming from up-sampling operations. A comprehensive analysis is conducted on an open-world dataset, comprising samples generated by 28 distinct generative models. This analysis culminates in the establishment of a novel state-of-the-art performance, showcasing a remarkable 12.8% im-provement over existing methods. The code is available at https://***/chuangchuangtan/NPR-DeepfakeDetection.
It is widely expected that 6G networks will rely on Unmanned Aerial Vehicles (UAVs) acting as flying Base Stations (BSs) to provide a wide range of services that current networks cannot handle. One of the major trends...
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ISBN:
(数字)9789082797091
ISBN:
(纸本)9781665467995
It is widely expected that 6G networks will rely on Unmanned Aerial Vehicles (UAVs) acting as flying Base Stations (BSs) to provide a wide range of services that current networks cannot handle. One of the major trends deals with Vehicle-To-Everything (V2X) communications, where vehicles must be connected to the network to offer applications such as advanced driving and extended sensing. In this context, vehicles could deeply count on flying BS to increase the throughput or reduce the experienced latency, thus satisfying such services constraints. Consequently, path planning must be designed so that UAVs can keep stable links with moving vehicles. In this sense, Reinforcement Learning (RL) techniques are becoming the main enabler for solving such problem, since they offer the possibility to learn how to act in an environment with little prior information, given that full knowledge of the scenario is usually not available. In this paper, we present a RL-based approach to solve the path planning problem in a vehicular scenario, where UAVs, exploiting beamforming, are required to follow as long as possible moving cars. Different RL architectures, as well as a benchmark solution not using RL, are compared to select the best strategy maximising the sum throughput.
In recent years, traffic flow prediction has attracted increasing interest from both academia and industry, and existing data-driven learning models for traffic flow prediction have achieved excellent success. However...
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ISBN:
(纸本)9781665450867
In recent years, traffic flow prediction has attracted increasing interest from both academia and industry, and existing data-driven learning models for traffic flow prediction have achieved excellent success. However, this requires a large number of datasets for efficient model training, while it is difficult to acquire all the data from one agent, and thus data collaboration among different agents becomes an attracting trend. Moreover, with the increase in the number of agents, how to perform accurate multi-agent traffic forecasting while protecting privacy is an important issue. To address this challenge, we introduce a privacy-preserving federated learning framework. In this paper, we propose a novel Dynamic Spatio-Temporal traffic flow prediction model based on graph convolutional network (DST-GCN), which incorporates both dynamic spatial and temporal dependence of intersection traffic. In addition, we provide an improved federated learning framework with opportunistic client selection (FLoS). In the proposed FLoS protocol, we employ a FedAVG algorithm for secure parameter aggregation and design an optimal client selection algorithm to reduce the communication overhead during the transfer of model updates. Experiments based on real-world datasets demonstrate that our proposed DST-GCN traffic prediction model outperforms state-of-the-art baseline models. And our proposed FLoS can achieve superior results while reducing communication consumption.
The fusion of edge computing and artificial intelligence, known as Edge AI, represents a paradigm shift that facilitates the direct execution of AI algorithms on edge devices. As these devices become increasingly powe...
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ISBN:
(数字)9798350360868
ISBN:
(纸本)9798350360875
The fusion of edge computing and artificial intelligence, known as Edge AI, represents a paradigm shift that facilitates the direct execution of AI algorithms on edge devices. As these devices become increasingly powerful, their role in developing and deploying AI systems becomes more significant. By eliminating the need to transmit and analyze data at remote machines, Edge AI applications can significantly reduce latency and enhance efficiency by processing data closer to the source. In this study, we thoroughly investigate the performance of our object classification model deployed in a vision inspection system on four types of edge devices (Jetson AGX Orin, Jetson Orin Nano, NUC, and Raspberry Pi). Our object classification models are trained using proprietary industrial datasets provided by industry partners. These models, in FP32, are converted into lower precision processing, being INT8, to evaluate the accuracy variation between FP32 and INT8 precision, and inference speed for different edge devices. In our experiments, we identified that the average accuracy deviation for INT8 models is −2.78%, with some models exhibiting variations exceeding −10.95%. Most devices have an average inference speed less than 100 ms per image (as requested by industrial partners), except the Raspberry Pi, which records more than 2 seconds of inferencing an image. Intel NUC consumes 107 W, which is averagely comparable with a server PC, while AGX Orin, Orin Nano, and Raspberry Pi consume less than 20 W of power. The outcomes of our evaluations offer valuable insights for selecting appropriate devices for specific scenarios. These detailed observations on the strengths and limitations of different edge devices can guide future research and advancements in Edge AI technology.
Vehicle-to-Everything (V2X) communication can considerably improve the efficiency and safety of autonomous driving and advanced driver-assistance systems (ADASs). However, V2X communication can be considerably degrade...
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ISBN:
(数字)9798350387414
ISBN:
(纸本)9798350387421
Vehicle-to-Everything (V2X) communication can considerably improve the efficiency and safety of autonomous driving and advanced driver-assistance systems (ADASs). However, V2X communication can be considerably degraded in the presence of cyberattacks, such as radio jamming. Traditionally, beamforming techniques can be applied to increase the signal-to-interference plus noise ratio (SINR). This paper evaluates broadband beamforming in the mmWave spectrum against radio jamming in V2X communication. The exploitation of the mm Wave spectrum in 5G-V2X communication has a natural advantage against radio jamming. First, attenuation is stronger in the mmWave spectrum in the range of 40 GHz or higher than in the traditional 5.9 GHz. Second, to generate broadband radio jamming, the radio jammer requires much more complex hardware and energy consumption. Third, by using broadband beamforming, broadband radio jamming can be considerably attenuated, limiting the degradation of the spectrum by the radio jamming. According to our numerical results, gains of close to 30 dB SINR can be achieved. We propose a broad-band beamforming technique based on the canonical polyadic decomposition via generalized eigenvalue decomposition (CPD-GEVD). The CPD-GEVD broadband beamforming outperforms state-of-the-art beamforming algorithms in most V2X scenarios presented in this paper.
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