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Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.
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We study the ability of neural networks to steer or control trajectories of dynamical systems on graphs, which we represent with neural ordinary differential equations (neural ODEs). To do so, we introduce a neural-ODE control (NODEC) framework and find that it can learn control signals that drive graph dynamical systems into desired target states. While we use loss functions that do not constrain the control energy, our results show that NODEC produces control signals that are highly correlated with optimal (or minimum energy) control signals.
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Here we introduce so-far the largest subject-rated database of its kind, namely, "Effect of Paddy vegetation on path-loss between CC2650 SoC 32-bit Arm Cortex-M3 based sensor nodes operating at 2.4 GHz Radio Frequency (RF)". This database contains received signal strength measurements collected through campaigns in the IEEE 802.15.4 standard precision agricultural monitoring infrastructure developed for Paddy rice crop monitoring from period 03/07/2019 to 18/11/2019.
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This dataset is used to develop an algorithm for evaluating machining quality. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To evaluate machining quality, the vibration signals can be segmented and extracted the corresponding features, in the time, frequency, and time-frequency domains. After serving with the features, a model can be developed to estimate the machining quality, such as the roughness of a workpiece.
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This dataset is used to develop an algorithm for automatic segmenting the collected signals. When machining a workpiece in a milling process, vibration signals can be recorded by a 3-axis accelerometer, which is attached on the spindle of a CNC milling machine. To segment the recorded signals, a moving window (0.5 sec) is applied to sample the vibration signals and manually labeled the corresponding modes, i.e. dry run or milling, of each window. To verify the algorithm, 3 types of operations are provided and recorded in csv format.
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This dataset is associated with an IEEE journal submission titled: "Prediction of larynx function using multichannel surface EMG classification" by the associated authors. The dataset consists of surface electromyography (sEMG) signals recorded from 10 study participants (5 control, 5 laryngectomees), each undertaking 3 recording sessions.
During each session the following were recorded:
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Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation.
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The dataset consists of echo data collected at the Matre research station (61°N) of the Institute of Marine Research (IMR), Norway. Six square sea cages (12 × 12 m and 15 m depth; approximately 2000 m^3) were used. The fish's vertical distribution and density were observed continuously by a PC-based echo integration system (CageEye MK IV, software version 1.1.1., CageEye AS, Steinkjer, Norway) connected to an upward facing transducer which multiplexes between 50 kHz (42° acoustic beam angle) and 200 kHz (14° beam angle).
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