First-trimester maternal screening is a widely used test for detecting fetal aneuploidies and neural tube defects for over two decades. Human chorionic gonadotropin hormone (beta-hCG) and pregnancy-associated plasma p...
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In the Internet of Vehicles (IoV) era, connected vehicles are leveraging intelligent sensors to share sensitive and real-time information to enhance road safety, driving comfort, and traffic efficiency. Despite these ...
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Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay...
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Complementary metal oxide semiconductor(CMOS)aging mechanisms including bias temperature instability(BTI)pose growing concerns about circuit *** results in threshold voltage increases on CMOS transistors,causing delay shifts and timing violations on logic *** amount of degradation is dependent on the circuit workload,which increases the challenge for accurate BTI aging prediction at the design *** this paper,a BTI prediction method for logic circuits based on statistical static timing analysis(SSTA)is proposed,especially considering the correlation between circuit workload and BTI *** consists of a training phase,to discover the relationship between circuit scale and the required workload samples,and a prediction phase,to present the degradations under different workloads in Gaussian probability *** method can predict the distribution of degradations with negligible errors,and identify 50%more BTI-critical paths in an affordable time,compared with conventional methods.
In modern edge computing, deploying multi-deep neural network (DNN) applications is essential for addressing complex tasks such as visual classification, object tracking, and navigation. The intricate nature of these ...
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ISBN:
(数字)9798350349597
ISBN:
(纸本)9798350349603
In modern edge computing, deploying multi-deep neural network (DNN) applications is essential for addressing complex tasks such as visual classification, object tracking, and navigation. The intricate nature of these Machine Learning (ML) applications, coupled with their soft and hard real-time performance requirements, underscores the necessity for automated optimisation of both scheduling and mapping configurations on computationally robust heterogeneous embedded systems. This paper introduces a novel approach for the automated mapping and scheduling of multiple DNNs onto heterogeneous hardware platforms. Our methodology leverages an integer linear program (ILP) scheduler formulation that accommodates soft and hard real-time constraints. This is complemented by a mapping generation process that employs (a) an advanced ILP formulation and (b) a Genetic Algorithm (GA) designed to identify optimised solutions for large-scale mappings. The GA is mainly utilised when the expansive design space renders the ILP formulation impractical in terms of computational solving time. We rigorously test and evaluate our framework using scaling input model configurations and a real-world mixed-model scenario. The results demonstrate that our hybrid optimisation solution, which integrates Prepositional Satisfiability Problem (SAT) decoding, the NSGA-II GA, and ILP, significantly enhances scalability. This improvement is vital for efficiently deploying complex systems, marking a substantial advancement in the embedded ML field.
We present TopoMortar, a brick wall dataset that is the first dataset specifically designed to evaluate topology-focused image segmentation methods, such as topology loss functions. TopoMortar enables to investigate i...
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Lung cancer must be detected as early as possible, but it may be difficult with existing methods since they often depend on human judgment and outdated image processing. These techniques take a lot of time and are pro...
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Activities of daily living such as drinking and eating can be severely impaired for patients suffering from neurodegenerative diseases. One promising solution are assistive devices that apply corrective forces while s...
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ISBN:
(数字)9798350386523
ISBN:
(纸本)9798350386530
Activities of daily living such as drinking and eating can be severely impaired for patients suffering from neurodegenerative diseases. One promising solution are assistive devices that apply corrective forces while still allowing the intended movements. However, real-time estimation of the required forces requires a detailed understanding of the limb's impedance characteristics. Here, we test and validate the stiffness response of a computationally efficient neuro-musculoskeletal arm model and its response to various force perturbations. We demonstrate that the arm model predicts stiffness characteristics that closely match experimental data recorded from humans and presents real-time applicability, allowing for implementation in practical scenarios and. Additionally, we predict the stiffness response for novel force levels and arm configurations. In the future, these predictions could be used to estimate corrective forces for assistive devices in real-time.
This paper develops a data-driven stabilization method for continuous-time linear time-invariant systems with theoretical guarantees and no need for signal derivatives. The framework, based on linear matrix inequaliti...
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With increasing numbers of mobile robots arriving in real-world applications, more robots coexist in the same space, interact, and possibly collaborate. Methods to provide such systems with system size scalability are...
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Cloud-orchestrated Internet of Things (IoT) facilitates proper utilization of network resources and placating user demands in smart communications. Multiple concurrent access (MCA) techniques designed for cloud-assist...
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