The identification of traffic signs is a major challenge for intelligent automobiles. Recognition of traffic signs gives useful data, such as alerts and directions, for cooperative intelligent transport systems (CITS)...
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The use of the probability density distribution of random processes leads to certain difficulties in the implementation of signalprocessingalgorithms, especially for processing non-Gaussian processes. One of the adv...
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ISBN:
(纸本)9798350332636
The use of the probability density distribution of random processes leads to certain difficulties in the implementation of signalprocessingalgorithms, especially for processing non-Gaussian processes. One of the advanced approaches that allows describe non-Gaussian random processes is to use the moment and cumulant description of random variables. Based on this approach, two new methods of joint signal discrimination and parameter estimation are proposed. Nonlinear signalprocessing and taking into account the parameters of non-Gaussian processes can significantly improve the quality of signalprocessing compared to known classical methods.
In the realm of next-generation vehicle safety, this project introduces a cutting-edge approach to auto-stop functionality in modern vehicles. Leveraging the state-of the-art YOLO (You Only Look Once) algorithm, the s...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
In the realm of next-generation vehicle safety, this project introduces a cutting-edge approach to auto-stop functionality in modern vehicles. Leveraging the state-of the-art YOLO (You Only Look Once) algorithm, the system focuses on enhancing driver safety through two critical aspects: facial identification and drowsiness monitoring. The YOLO algorithm efficiently detects and analyses facial features, providing real-time identification of the driver. Simultaneously, the system employs advanced drowsiness monitoring techniques, utilizing facial cues to gauge the driver's alertness levels. This futuristic safety initiative aims to mitigate potential accidents caused by driver fatigue or distraction by implementing an automatic auto-stop mechanism. The YOLO algorithm's speed and accuracy play a pivotal role in enabling swift and reliable detection of facial attributes, ensuring a seamless integration with the vehicle's safety system. By seamlessly integrating facial identification and drowsiness monitoring, the project presents a comprehensive solution to enhance road safety. The innovation lies in the project's ability to interpret facial expressions, track eye movements, and discern signs of drowsiness, enabling the vehicle to proactively intervene when a compromised driver state is detected. With an emphasis on real-time responsiveness, the system strives to revolutionize auto-stop technology, creating a safer driving environment for both the driver and other road users. This abstract encapsulates a groundbreaking fusion of advanced computer vision, machine learning, and automotive safety, ushering in a new era of proactive accident prevention in next-gen vehicles.
In today's globalized world, English communication skills are essential for career advancement and cross-cultural collaboration, enhancing access to information and opportunities. Automatic speech recognition, or ...
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ISBN:
(数字)9798350350654
ISBN:
(纸本)9798350350661
In today's globalized world, English communication skills are essential for career advancement and cross-cultural collaboration, enhancing access to information and opportunities. Automatic speech recognition, or ASR, is a separate machine-driven method for transcription and decoding spoken language. An ASR system typically uses a microphone to capture a speaker's audio input, analyze it using a model, algorithm, or pattern, and output the results, which are often text messages (Lai, Karat, Yankelovich, 2008). This paper provides a thorough method for utilizing Python-based tools and modules to extract and analyze linguistic characteristics from audio data. The suggested approach turns spoken language into text using voice recognition technology, and then it uses natural language processing (NLP) methods to extract different linguisticmetrics. Word count, sentence count, vocabulary size, average sentence length, average word length, sentiment score, speech pace, frequency of pauses, and average length of pauses are some of these measures. The technique also determines the speaker's speaking style. Keywords: audio analysis, linguistic features, natural language processing, pause detection, speech recognition, sentiment analysis, visualization.
The purpose of this study is to estimate and predict onion wholesale price volatility using statistical and machine learning algorithms. Traditional models like ARIMA and GARCH were compared against advanced machine l...
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ISBN:
(数字)9798331506452
ISBN:
(纸本)9798331506469
The purpose of this study is to estimate and predict onion wholesale price volatility using statistical and machine learning algorithms. Traditional models like ARIMA and GARCH were compared against advanced machine learning techniques like SVM, LSTM, and CNN, as well as hybrid approaches like ACF-LSTM, RF-LSTM, ACF-CNN, and RF-CNN. The performance of these models was assessed using RMSE, MAPE, and MAE. The results show that hybrid models, specifically RF-LSTM and RF-CNN, outperformed classic statistical models. Specifically, RF-LSTM and RF-CNN outperformed the statistical models by 62% in RMSE, 76% in MAPE, and 71 % in MSE for the training set, and 76% in RMSE, 78% in MAPE, and 74% in MSE for the testing set. These results highlight the superiority of hybrid models in describing the intricate non-linear patterns in the volatility of onion prices, providing agricultural commodities markets with more reliable and precise forecasting tools. The findings are expected to inform better decision-making processes for stakeholders across the onion supply chain, from farmers to policymakers.
Leukaemia causes blood cell abnormalities quickly. Leukocytes injure bone marrow or blood and are followed by an increase in growing lymphocytes. Gelatinous tissue produces most of the body's blood. Thus, early an...
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ISBN:
(数字)9798350361186
ISBN:
(纸本)9798350361193
Leukaemia causes blood cell abnormalities quickly. Leukocytes injure bone marrow or blood and are followed by an increase in growing lymphocytes. Gelatinous tissue produces most of the body's blood. Thus, early and accurate cancer detection is essential for effective treatment that improves survival. Manually studying blood samples from microscopic pictures to identify this illness is slow, inaccurate, and timeconsuming. This sickness is currently diagnosed manually by studying blood samples from microscopic images, which is arduous, slow, and imprecise. Furthermore, leukemic cells appear and act similarly to normal cells under a microscope, making identification more challenging. Though DL has developed advancedalgorithms for detecting difficulties in recent decades, there is always room for improvement in performance, learning process, and efficacy. A hybrid approach using parallel swarm optimisation is more successful for identifying WBC leukaemia. To obtain high accuracy in picture classification, convolutional network hyperparameter tweaking is critical, but it involves significant computing effort. An automated leukaemia monitoring system for small blood samples was created utilising metaheuristics and parallel cat swarm optimisation (PCSO) to determine optimum convolutional neural network settings. Two public leukaemia blood sample databases, ALL IDB1 and ALL IDB2, are utilised. The method's resilience and effectiveness are shown by experimental findings and comparison with other cutting-edge algorithms under similar conditions.
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a prio...
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ISBN:
(纸本)9783031200762;9783031200779
Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a priori. However, in practical, either the real degradation or optimal up-sampling ratio rate is unknown or differs from assumption, leading to a deteriorating performance for both the pre-processing module and the consequent high-level task such as object detection. Here, we propose a novel self-supervised framework to detect objects in degraded low resolution images. We utilizes the downsampling degradation as a kind of transformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions. The Auto Encoding Resolution in Self-supervision (AERIS) framework could further take the advantage of advanced SR architectures with an arbitrary resolution restoring decoder to reconstruct the original correspondence from the degraded input image. Both the representation learning and object detection are optimized jointly in an end-to-end training fashion. The generic AERIS framework could be implemented on various mainstream object detection architectures with different backbones. The extensive experiments show that our methods has achieved superior performance compared with existing methods when facing variant degradation situations. Code is available at this link https://github. com/cuiziteng/ECCV AERIS.
We propose a toolkit for developing and comparing reinforcement learning (RL)based traffic signal controllers. The toolkit includes implementation of state-of-the-art deep-RL algorithms for signal control along with b...
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The entire design structure is being impacted by advancements in electronic technology, which is posing a number of challenges for digital systems. In the fields of communications, image processing, and multimedia, VL...
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This research proposes the development of an innovative AI-assisted Pronunciation Correction Tool designed to enhance the English language learning experience for nonnative speakers. The tool combines advanced speech ...
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ISBN:
(数字)9798350353068
ISBN:
(纸本)9798350353075
This research proposes the development of an innovative AI-assisted Pronunciation Correction Tool designed to enhance the English language learning experience for nonnative speakers. The tool combines advanced speech recognition technology with detailed phonetic analysis to provide personalized feedback on pronunciation errors. The system employs state-of-the-art automatic speech recognition (ASR) algorithms to accurately transcribe spoken language input. This transcription is then compared with the target pronunciation using sophisticated phonetic analysis techniques. The tool leverages a comprehensive phonetic database to identify specific phonemes, intonation patterns, and stress points that contribute to pronunciation challenges for English Language Learners (ELL). To ensure adaptability and effectiveness, the tool incorporates machine learning models that continuously evolve based on user interactions. The AI model learns from user-specific pronunciation patterns, adapting feedback to address individual strengths and weaknesses. Users receive instant visual and auditory cues highlighting areas of improvement, along with detailed suggestions for correction. The tool also offers interactive exercises and practice modules to reinforce learning and encourage consistent improvement. The proposed AI-assisted Pronunciation Correction Tool has the potential to revolutionize language learning. The robust 88% accuracy in evaluating intonation and stress patterns is crucial for achieving the research goal of providing comprehensive feedback on speech dynamics.
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