Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveragi...
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ISBN:
(数字)9798331508456
ISBN:
(纸本)9798331508463
Traditional methods of monitoring vital signs in vehicles often fall short when it comes to response time, especially in fast-moving situations. To tackle this, our paper introduces a groundbreaking solution: leveraging millimeter-wave radar technology to monitor vital signs in real time within the vehicle, providing timely alerts when necessary. This approach not only enhances the accuracy of readings but also ensures a swift response that is crucial during vehicle operation. By tapping into the unique abilities of millimeter-wave radar to detect subtle human signs, we propose a novel method that processes radar signals to monitor vital signs like heart rate and respiratory rate. This technique is highly effective in meeting the stringent demands of real-time accuracy and speed, especially while driving. Our system includes three main components: radar sensors, a signalprocessing unit, and an early-warning control module, working together seamlessly to ensure optimal monitoring. The radar sensor continuously transmits and receives millimeter-wave signals, allowing it to track vital signs without interruption. A specially designed feature extraction algorithm filters out noise and extracts critical information, including heart and respiratory rates. Then, advancedalgorithms analyze this data in real time, spotting any abnormalities and triggering alerts through the control module. What sets this system apart is its rigorous testing under various vehicle speeds and environmental conditions, confirming its stability, reliability, and fast response times. In real-world applications, the system responds to a high heart rate in 3.7 to 4.0 seconds and to a rapid breathing rate in 4.1 to 4.3 seconds. This millimeter-wave radar-based system offers a fresh approach to vehicle safety, combining practicality with promising future potential.
Efficient path planning is key for safe autonomous navigation over complex and unknown terrains. Lunar Zebro (LZ), a project of the Delft University of Technology, aims to deploy a compact rover, no larger than an A4 ...
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ISBN:
(数字)9798350367232
ISBN:
(纸本)9798350367249
Efficient path planning is key for safe autonomous navigation over complex and unknown terrains. Lunar Zebro (LZ), a project of the Delft University of Technology, aims to deploy a compact rover, no larger than an A4 sheet of paper and weighing not more than 3 kilograms. In this work, we introduce a Robust Artificial Potential Field (RAPF) algorithm, a new path-planning algorithm for reliable local navigation solution for lunar microrovers. RAPF leverages and improves state of the art Artificial Potential Field (APF)-based methods by incorporating the position of the robot in the generation of bacteria points and considering local minima as regions to avoid. We perform both simulations and on field experiments to validate the performance of RAPF, which outperforms state-of-the-art APF-based algorithms by over 15% in reachability within a similar or shorter planning time. The improvements resulted in a 200% higher success rate and 50% lower computing time compared to the conventional APF algorithm. Near-optimal paths are computed in real-time with limited available processing power. The bacterial approach of the RAPF algorithm proves faster to execute and smaller to store than path planning algorithms used in existing planetary rovers, showcasing its potential for reliable lunar exploration with computationally constrained and energy constrained robotic systems.
The majority of physical signals that are of interest to processing tasks are of non-stationary type, and since the Fourier transform (FT) cannot give enough details on Time-frequency domain, the short-time fourier tr...
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ISBN:
(数字)9798350349351
ISBN:
(纸本)9798350349368
The majority of physical signals that are of interest to processing tasks are of non-stationary type, and since the Fourier transform (FT) cannot give enough details on Time-frequency domain, the short-time fourier transform (STFT) was developed and has remained one of the most classic signal analysis method where many sophisticated algorithms have been created based on its modification. In this paper, HW/SW co-design technique is used to implement the STFT targeting Nexys Video FPGA board. The proposed architecture can work with a clock sampling rate up to 106 MHz to ensure a very less computation time with minimum power and resources consumption. To evaluate the effectiveness of the proposed system, the analysis of both real non-stationary signal generated by Analog Discovery 2 and acquired Electro-Cardio-Gram (ECG) signal using AD8232 module is validated. A comparative study has been performed with some similar related works. The proposed system represents an appropriate approach that enables us to achieve the best performances in terms of real-time, flexibility and rapidity of time-frequency analysis with the ability to intervene at all levels of the development process.
Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the...
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Neutron computed tomography (nCT) is a 3D char-acterization technique used to image the internal morphology or chemical composition of samples in biology and materials sciences. A typical workflow involves placing the sample in the path of a neutron beam, acquiring projection data at a predefined set of orientations, and processing the resulting data using an analytic reconstruction algorithm. Typical nCT scans require hours to days to complete and are then processed using conventional filtered back-projection (FBP), which performs poorly with sparse views or noisy data. Hence, the main methods in order to reduce overall acquisition time are the use of an improved sampling strategy combined with the use of advanced reconstruction methods such as model-based iterative reconstruction (MBIR). In this paper, we propose an adaptive orientation selection method in which an MBIR reconstruction on previously-acquired measurements is used to define an objective function on orientations that balances a data-fitting term promoting edge alignment and a regularization term promoting orientation diversity. Using simulated and experimental data, we demonstrate that our method produces high-quality reconstructions using significantly fewer total measurements than the conventional approach.
We demonstrate a photonics-aided THz-wireless transmission over 4.6 km free space by plano-convex lenses. The use of plano-convex lenses greatly extends the wireless transmission distance. advanced digital signal proc...
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ISBN:
(数字)9781957171159
ISBN:
(纸本)9781665475570
We demonstrate a photonics-aided THz-wireless transmission over 4.6 km free space by plano-convex lenses. The use of plano-convex lenses greatly extends the wireless transmission distance. advanced digital signalprocessing (DSP) algorithms improve the spectral efficiency of the system.
Emotion identification from speech signals has been a research issue in human machine interaction applications for many years. Emotions incredibly play a vital role in our mental functioning. SER is the process of ide...
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ISBN:
(数字)9798350381887
ISBN:
(纸本)9798350381894
Emotion identification from speech signals has been a research issue in human machine interaction applications for many years. Emotions incredibly play a vital role in our mental functioning. SER is the process of identifying an individual speaker’s emotional state through the speech stream. The purpose of emotion recognition using speech processing is to develop a system that cans precisely asses a person’s emotional state based on their speech signals. They would use machine learning algorithms to analyze features such as pitch, tone, rhythm, and intensity of speech, and then classify the emotions expressed in the speech as happy, sad, angry, fearful, or neutral. The main applications of emotion recognition using speech processing include human-computer interaction, psychotherapy, and customer service. Inhuman-computer interaction, emotion recognition systems can provide more personalized and empathetic responses, enhancing the user experience. By analyzing their patients’ facial expressions and body language, therapists using emotion recognition systems in psychotherapy can better meet the needs of their patients. In customer service, emotion recognition systems can help companies to improve their customer satisfaction by detecting and responding to negative emotions expressed by customers. Neutral, Anger, Being happy, & Sadness are some of the few universal emotions that can be taught to any intelligent system with limited processing resources and ability to recognize and synthesize. as required. Mel-frequency cepstral coefficients (MFCC), a chromogram, a Mel scale spectrogram are extracted together with Tonal Centroid and Spectral Contrast characteristics. Deep Neural Network is used to understand human emotions, advancedsignalprocessing techniques, and sophisticated machine learning algorithms.
This study examines the challenges of detecting and classifying offensive language in Arabic, with a focus on natural language processing (NLP) methods. Arabic's distinctive linguistic characteristics, such as its...
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ISBN:
(数字)9798350327472
ISBN:
(纸本)9798350327489
This study examines the challenges of detecting and classifying offensive language in Arabic, with a focus on natural language processing (NLP) methods. Arabic's distinctive linguistic characteristics, such as its extensive morphology, present substantial obstacles to accurately classifying offensive content. This paper evaluates the approaches currently in use in this field and conducts a critical analysis of publicly accessible datasets frequently used to detect offensive language in Arabic. In addition to highlighting areas for further study and growth in the detection of Arabic offensive language, the aim is to offer insights into the state-of-the-art methodologies being used. In the analysis of public datasets, this paper evaluates their suitability for offensive language detection tasks in Arabic, considering factors such as dataset size, annotation quality, and representation of diverse offensive language categories. Furthermore, this paper investigates the existing methods utilized for detecting and classifying offensive language in Arabic, examining a range of NLP approaches used in the context of detecting Arabic offensive language. This includes machine learning algorithms, deep learning architectures, and linguistic aspects, all tailored to address the specific challenges associated with offensive language detection in the Arabic vernacular. Our investigation emphasizes the need for customized algorithms that take into account Arabic's unique linguistic characteristics. Future research should focus on developing advanced models that incorporate domain-specific knowledge, handle dialectal variations, and effectively capture the intricacies of Arabic morphology.
In this paper, a covert semantic communication framework is proposed for image transmission over wireless networks. In the proposed framework, devices extract and selectively transmit semantic information of image dat...
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In this paper, a covert semantic communication framework is proposed for image transmission over wireless networks. In the proposed framework, devices extract and selectively transmit semantic information of image data to a base station (BS). The semantic information consists of the objects in the image and a set of attributes of each object. A warden selects a device to detect and eavesdrops the semantic information. To ensure the security of semantic communications, a jammer, acts as the defender, requires to find a vulnerable device and transmits jamming signals to the vulnerable device. The metric to measure the performance of the covert semantic communications is defined as the difference in the average accuracy of the BS and the warden answering a set of questions for each image. To maximize the performance of covert semantic communications, each device and the jammer must jointly optimize their transmit power, determine the vulnerable device to be protected, and determine the partial semantic information that each device needs to transmit. To solve this problem, we propose a multi-agent policy gradient (MAPG) algorithm. The proposed algorithm enables each device and the jammer to cooperatively discover the vulnerable devices as well as find the semantic information transmission and power control policies that maximize the performance of the covert semantic communication system. Simulation results show that the proposed algorithm can improve the communication performance by up to 14.5% compared to the independent reinforcement learning.
In today’s digital era, ensuring the security of bank payments is imperative to maintain trust and integrity in financial transactions. With the proliferation of online banking and electronic payments, the risk of fr...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
In today’s digital era, ensuring the security of bank payments is imperative to maintain trust and integrity in financial transactions. With the proliferation of online banking and electronic payments, the risk of fraudulent activities has escalated, posing a constant challenge to financial institutions and their clients. To counteract this threat, advanced techniques utilizing machine learning algorithms have emerged as indispensable tools for detecting and preventing fraudulent transactions effectively. This study proposes a novel strategy for enhancing bank payment security through the utilization of Gradient Boosting Machines (GBM) for fraud detection. GBM, a powerful ensemble learning technique, has demonstrated exceptional performance across various classification tasks, rendering it well-suited for identifying fraudulent activities within bank payments. By harnessing GBM, our objective is to elevate the accuracy and efficiency of fraud detection systems, consequently curtailing financial losses attributable to fraudulent transactions while minimizing false positives that could inconvenience legitimate clients. Emphasis is placed on feature selection and engineering to extract pertinent information from transactional data, enabling the model to effectively discern. To validate the efficacy of our approach, we conduct experiments utilizing authentic bank payment datasets, assessing the GBM-based fraud detection system’s accuracy, precision, recall, and F1-score. Our findings reveal significant enhancements in fraud detection performance compared to conventional methods, underscoring the effectiveness of GBM in mitigating the risks associated with fraudulent activities in banking transactions. In essence, this research contributes to the ongoing endeavours aimed at fortifying bank payment security by leveraging advanced machine learning techniques. By harnessing the prowess of GBM for fraud detection, financial institutions can bolster their defences against evo
Regular inspection of railway tracks is critical for providing safety and efficiency in railway transportation systems to minimize accidents, maintenance costs and improve reliability by early detection. In this paper...
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ISBN:
(数字)9798331505776
ISBN:
(纸本)9798331505783
Regular inspection of railway tracks is critical for providing safety and efficiency in railway transportation systems to minimize accidents, maintenance costs and improve reliability by early detection. In this paper, a systematic review for damage detection in railway tracks using Acoustic signals (sound generated at the wheel and track interaction point) during the movement of the train is presented. Distributed Acoustic Sensing (DAS) is a valuable method for quick, long-distance monitoring of railway maintenance and technological development and crucial for accident avoidance. In regions prone to frequent train accidents, free automation of inspection processes is essential to prevent disasters and to provide safe and reliable operation of train vehicles. A wide range of Structural Health Monitoring (SHM) systems sensor data is noisy, making Machine Learning (ML) algorithms paramount for detecting abnormal railroad conditions among many others and achieving higher accuracy in maintenance and inspection. Several advancedsignalprocessing techniques are used to recognize crack or misalignment patterns by employing Fourier transform and wavelet analysis. Further, the signals are classified through machine learning algorithms to classify between normal and damaged conditions, presenting a non-invasive, real-time and economic monitoring system that validates the system’s capability to detect several types of damage and could enhance rail infrastructure safety. Integration of these acoustic sensing with a set of trains in a predictive analytics framework makes the study of the field a scalable tool for efficient railway management.
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