In the high-performance computing domain, Field Programmable Gate Array (FPGA) is a novel accelerator that exhibits high flexibility and performance characteristics distinct from other accelerators such as the Graphic...
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This paper proposes a measurement technique for an integrated complex filter. The proposed method is based on two measurement methods with integrated circuitry for calibration. It is accomplished by applying square wa...
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In real world applications of multiclass classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (e.g., no parking). Thus, it is crucial...
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In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customiz...
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ISBN:
(数字)9798350383454
ISBN:
(纸本)9798350383461
In recent years, Field-Programmable Gate Arrays (FPGAs) are gaining attention as computational acceleration devices in the field of high-performance computing. By implementing specialized circuits that can be customized to specific problems, FPGAs can achieve efficient parallelization with low latency even for complex tasks.
This paper describes the implementation and evaluation of an RC polyphase filter (RCPF) and circuitry for measuring its frequency characteristics. The integrated circuit is fabricated on a 0.6 µm CMOS process and...
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The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimization performance. Moreover, it has been...
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Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classifica...
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Task oriented chatbots are a sub-topic related to chatbots, where chatbots will perform certain tasks with specific goals. One part of creating a task-oriented chatbot is doing intent classification. Intent classification is a task of text classification. As in general text classification, the required dataset requires a label to carry out the classification process. To speed up and help the utterance analysis process, there is already a method, namely clustering, and Density-based clustering is a part of clustering that can determine cluster patterns based on arbitrary data, with DBScan as one of its algorithms. This research used 10000 client utterance data of awhatsapp based e-commerce conversation. SentenceBert also used as a state of art sentence embedding. This research yield silhouette score of 0.327 as the best result from eps of 0.1 and MinPts of 95. However, based on the cluster result, sentences labelled as noise can be further clustered. Text Preprocessing, text augmentation and sentence embedding techniques can be explored to increase the cluster performance.
Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of H...
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ISBN:
(数字)9798331532093
ISBN:
(纸本)9798331532109
Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.
Spatially aligning two computed tomography (CT) scans of the lung using automated image registration techniques is a challenging task due to the deformable nature of the lung. However, existing deep-learning-based lun...
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ISBN:
(数字)9798350313338
ISBN:
(纸本)9798350313345
Spatially aligning two computed tomography (CT) scans of the lung using automated image registration techniques is a challenging task due to the deformable nature of the lung. However, existing deep-learning-based lung CT registration models are not trained with explicit anatomical knowledge. We propose the deformable anatomy-aware registration toolkit (DART), a masked autoencoder (MAE)-based approach, to improve the keypoint-supervised registration of lung CTs. Our method incorporates features from multiple decoders of networks trained to segment anatomical structures, including the lung, ribs, vertebrae, lobes, vessels, and airways, to ensure that the MAE learns relevant features corresponding to the anatomy of the lung. The pretrained weights of the transformer encoder and patch embeddings are then used as the initialization for the training of downstream registration. We compare DART to existing state-of-the-art registration models. Our experiments show that DART outperforms the baseline models (Voxelmorph, ViT-V-Net, and MAE-TransRNet) in terms of target registration error of both corrField-generated keypoints with 17%, 13%, and 9% relative improvement, respectively, and bounding box centers of nodules with 27%, 10%, and 4% relative improvement, respectively. Our implementation is available at https://***/yunzhengzhu/DART.
Children with Autism Spectrum Disorder (ASD) often experience delays in motor skills, which can substantially affect their future motor function. The Movement Assessment Battery for Children - Second Edition (MABC-2) ...
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ISBN:
(数字)9798331540906
ISBN:
(纸本)9798331540913
Children with Autism Spectrum Disorder (ASD) often experience delays in motor skills, which can substantially affect their future motor function. The Movement Assessment Battery for Children - Second Edition (MABC-2) is a widely used tool in evaluating children’s motor skills across different age bands, specifically assessing children ages 3 to 16. It includes eight unique tasks per age band that measure fine and gross motor skills and balance, which explains a child’s motor abilities and classifies them by groups. This study extends previous research by exploring the potential of Virtual Reality (VR) to make the MABC-2 tasks more engaging and interactive for children, which could lead to better outcomes. The previous research created the balance and gross motor tasks of the MABC-2 in VR. We refined those tasks and completed the remaining ones. The VR tool was tested on seven individuals aged 19 to 21. Pre- and post-VR MABC-2 scores were collected and analyzed. Most tasks were accurately replicated in VR; however, significant statistical differences were found in the Threading Lace (p= 0.003) and Catching Ball (p = 0.007) tasks, indicating further refinement. Machine learning analysis was also conducted on data from 268 previous MABC-2 scores of children diagnosed with autism to classify them into motor skill proficiency zones based on their scores and to determine the most influential features for accurate prediction. The analysis revealed that balance scores were particularly influential in determining motor proficiency. This indicates the importance of balance in interventions to improve overall motor proficiency.
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