Maritime surveillance is of utmost priority for a nation's security, and hence it's economy. For maritime awareness, coastal surveillance, and maritime activities in the Region of Interest (ROI) should be moni...
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Maritime surveillance is of utmost priority for a nation's security, and hence it's economy. For maritime awareness, coastal surveillance, and maritime activities in the Region of Interest (ROI) should be monitored. One of the ways to keep this in check is to restrain unwanted infiltration. Monitoring unwanted infiltration is feasible through vessel trajectory forecasting and anomaly detection in real time. Most solutions for trajectory predictions are available but require a huge amount of historical data, and high-power computing resources. Here, the requirement is developing a decision support framework consisting of both lite weight approaches for short-term predictions and deep learning-based techniques for long-term forecasting. This paper aims to find the suitability of Linear Stationary Models (LSM) like the Auto-Regressive Integrated Moving Average Model (ARIMA) for predicting and forecasting the Vessel Trajectory as means of lite weight short-term predictions. For this purpose, the Automatic Identification System (AIS) dataset of the U.S. West Coast is used. The significant effort was for data pre-processing to create a robust dataset for model training. An appropriate model after the model-selection process is used for trajectory forecasting. The model's accuracy is validated using Root Mean Square Error (RMSE) performance indices for residual and forecast errors. A window generator model is integrated with the best-fitted ARIMA model for recursive real-time predictions, with varied sizes and visualization. The proposed time-series model provided a very high accuracy as the RMSE value for prediction and 48 hours forecast are 0.023 and 0.017, respectively.
In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guid...
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ISBN:
(数字)9798350340266
ISBN:
(纸本)9798350340273
In the past few decades, the demand for assistive robots in the field of healthcare has steadily increased. We conducted research on an autonomous robot system based on imitation learning to assist in liver scans guided by ultrasound. Traditional procedures for liver examination using ultrasound imaging typically require a physician to slide the ultrasound probe along the patient's lower ribs for multi-angle observation. To automate this process, we employed imitation learning to capture the physician's manipulation techniques. However, the imitation generalization system often struggles to accurately extract the starting and ending points due to the obstruction of fat and skin, and the individual variations in patient anatomy lead to inconsistencies in the positions of lower ribcage. To improve the scanning process, we introduced stiffness estimation to differentiate between the abdomen and the rib, allowing real-time adjustments for optimal alignment of lower ribcage. Furthermore, due to the need for occasional physician intervention during the detection process, we implemented mechanisms for pausing and resuming the trajectory learning system to ensure continued operation. Ultimately, the proposed approach was evaluated using the Franka Panda robot system and demonstrated autonomous liver ultrasound examination in scenarios involving multiple human subjects.
Collagen-based cryogels have shown promise as resilient biomaterials capable of sustaining large-scale deformation and cyclic loading under compressive strain. This study investigates the mechanical properties of coll...
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In the framework of impedance control, the torque trajectory of the joint is unpredictable due to nonlinear factors during the movement of the exoskeleton. To precisely predict command torque trajectory and compensate...
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Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile a...
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ISBN:
(数字)9798350330991
ISBN:
(纸本)9798350331004
Nonvolatile memories are widely used in emerging energy-harvesting Internet-of-Things (IoT) applications, and nonvolatile memories constructed from FeFET devices hold great promise. This paper presents a nonvolatile and single-event-upset (SEU)-recoverable latch based on FeFET and CMOS for energyharvesting devices. The latch uses n-type FeFET devices to provide nonvolatility without any additional control signals. Moreover, since the soft error problem has become increasingly severe, radiation hardening by design gains a great attention as a promising approach to mitigate the reliability issue. The latch uses feedback interlocked loops with n-type FeFETs and C-elements, enabling it to provide nonvolatility and SEU-recovery simultaneously. Simulation results with Candence Virtuoso verifies that the proposed latch design has correct functioning with excellent performance compared to the state-of-the-art designs.
Deep reinforcement learning (DRL) has achieved remarkable progress in online path planning tasks for multi-UAV systems. However, existing DRL-based methods often suffer from performance degradation when tackling unsee...
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The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory ...
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ISBN:
(数字)9798350358414
ISBN:
(纸本)9798350358421
The increasing complexity and memory demands of Deep Neural Networks (DNNs) for real-time systems pose new significant challenges, one of which is the GPU memory capacity bottleneck, where the limited physical memory inside GPUs impedes the deployment of sophisticated DNN models. This paper presents, to the best of our knowledge, the first study of addressing the GPU memory bottleneck issues, while simultaneously ensuring the timely inference of multiple DNN tasks. We propose RT-Swap, a real-time memory management framework, that enables transparent and efficient swap scheduling of memory objects, employing the relatively larger CPU memory to extend the available GPU memory capacity, without compromising timing guarantees. We have implemented RT-Swap on top of representative machine-learning frameworks, demonstrating its effectiveness in making significantly more DNN task sets schedulable at least 72% over existing approaches even when the task sets demand up to 96.2% more memory than the GPU's physical capacity.
Convolutional Neural Networks (CNNs) represent a revolutionary breakthrough in improving crop productivity and sustainability when integrated into real-time monitoring systems for soil health in precision agriculture....
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Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great co...
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ISBN:
(数字)9798350361513
ISBN:
(纸本)9798350372304
Water resources used by human activities ranges typically from personal and household, agricultural, industrial, recreational to environmental pursuits. The effects of these water utilizations are actually of great concerns by many due to various threats created by human functions and the nature itself, for instance, climate change, pollution, scarcity, and even conflicts. To mitigate these threats, implementation of water quality management based on recognized standards and guidelines not only will they provide solid framework and benchmark used in relation to the assessment of the water quality but will also enable the identification of corresponding classification indicated by the water quality index (WQI) pertinent and relevant to the surface water dataset. This paper aims at applying selected predictive modeling techniques that are highly optimized for use in semi-automating the work of the water quality classification (WQC) and the water quality index (WQI) that subsequently can be used in assisting the planning, problem-solving and/or decision-making processes. The preliminary results obtained are quite satisfactory as follow: predicting WQI using neural network model (NN) outperforms both the Multiple Linear Regression (MLR) and the Support Vector Machine (SVM) based on a mean absolute error (MAE) lower than the two models and predicting WQC using SVM, and ANN models based on accuracy score with SVM returns a favorable accuracy score higher than two others.
Brain-inspired navigation technologies combine environmental perception,spatial cognition,and target navigation to create a comprehensive navigation research *** have used various sensors to gather environmental data ...
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Brain-inspired navigation technologies combine environmental perception,spatial cognition,and target navigation to create a comprehensive navigation research *** have used various sensors to gather environmental data and enhance environmental perception using multimodal information *** spatial cognition,a neural network model is used to simulate the navigation mechanism of the animal brain and to construct an environmental cognition ***,existing models face challenges in achieving high navigation success rate and *** addition,the limited incorporation of navigation mechanisms borrowed from animal brains necessitates further *** the basis of the braininspired navigation process,this paper launched a systematic study on brain-inspired environment perception,brain-inspired spatial cognition,and goal-based navigation in brain-inspired navigation,which provides a new classification of brain-inspired cognition and navigation techniques and a theoretical basis for subsequent experimental *** the future,brain-inspired navigation technology should learn from more perfect brain-inspired mechanisms to improve its generalization ability and be simultaneously applied to large-scale distributed intelligent body cluster *** multidisciplinary nature of braininspired navigation technology presents challenges,and multidisciplinary scholars must cooperate to promote the development of this technology.
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