58 research outputs found
MPCNN: A Novel Matrix Profile Approach for CNN-based Sleep Apnea Classification
Sleep apnea (SA) is a significant respiratory condition that poses a major
global health challenge. Previous studies have investigated several machine and
deep learning models for electrocardiogram (ECG)-based SA diagnoses. Despite
these advancements, conventional feature extractions derived from ECG signals,
such as R-peaks and RR intervals, may fail to capture crucial information
encompassed within the complete PQRST segments. In this study, we propose an
innovative approach to address this diagnostic gap by delving deeper into the
comprehensive segments of the ECG signal. The proposed methodology draws
inspiration from Matrix Profile algorithms, which generate an Euclidean
distance profile from fixed-length signal subsequences. From this, we derived
the Min Distance Profile (MinDP), Max Distance Profile (MaxDP), and Mean
Distance Profile (MeanDP) based on the minimum, maximum, and mean of the
profile distances, respectively. To validate the effectiveness of our approach,
we use the modified LeNet-5 architecture as the primary CNN model, along with
two existing lightweight models, BAFNet and SE-MSCNN, for ECG classification
tasks. Our extensive experimental results on the PhysioNet Apnea-ECG dataset
revealed that with the new feature extraction method, we achieved a per-segment
accuracy up to 92.11 \% and a per-recording accuracy of 100\%. Moreover, it
yielded the highest correlation compared to state-of-the-art methods, with a
correlation coefficient of 0.989. By introducing a new feature extraction
method based on distance relationships, we enhanced the performance of certain
lightweight models, showing potential for home sleep apnea test (HSAT) and SA
detection in IoT devices. The source code for this work is made publicly
available in GitHub: https://github.com/vinuni-vishc/MPCNN-Sleep-Apnea
Inverse kinematic control algorithm for a welding robot - positioner system to trace a 3D complex curve
The welding robots equipped with rotary positioners have been widely used in several manufacturing industries. However, for welding a 3D complex weld seam, a great deal of points should be created to ensure the weld path smooth. This is a boring job and is a great challenge - rotary positioner system since the robot and the positioner must move simultaneously at the same time. Therefore, in this article, a new inverse kinematics solution is proposed to generate the movement codes for a six DOFs welding robot incorporated with a rotary positioner. In the algorithm, the kinematic error is minimized, and the actual welding error is controlled so that it is always less than an allowable limit. It has shown that the proposed algorithm is useful in developing an offline CAD-based programming tool for robots when welding complex 3D paths. The use of the algorithm increases the accuracy of the end-effector positioning and orientation, and reduces the time for teaching a welding robot - positioner system. Simulation scenarios demonstrate the potency of the suggested method
Participatory agro-climate information services: A key component in climate resilient agriculture
The brief promotes participatory agro-climate information services as a key component in achieving climate-smart agriculture. The brief emphasizes that actionable agro-climate information starts with—and responds to—gender-based needs of farmers, integrated at all stages of the value chain. Timely forecasts and accurate agroclimate advisories have been proven to provide farmers with production, adaptation, and mitigation benefits
UAV Relay-Assisted Emergency Communications in IoT Networks: Resource Allocation and Trajectory Optimization
In this paper, a UAV is deployed as a flying base station to collect data
from time-constrained IoT devices and then transfer the data to a ground
gateway (GW). In general, the latency constraint at IoT users and the limited
storage capacity of UAV highly hinder practical applications of UAV-assisted
IoT networks. In this paper, full-duplex (FD) technique is adopted at the UAV
to overcome these challenges. In addition, half-duplex (HD) scheme for
UAV-based relaying is also considered to provide a comparative study between
two modes. In this context, we aim at maximizing the number of served IoT
devices by jointly optimizing bandwidth and power allocation, as well as the
UAV trajectory, while satisfying the requested timeout (RT) requirement of each
device and the UAV's limited storage capacity. The formulated optimization
problem is troublesome to solve due to its non-convexity and combinatorial
nature. Toward appealing applications, we first relax binary variables into
continuous values and transform the original problem into a more
computationally tractable form. By leveraging inner approximation framework, we
derive newly approximated functions for non-convex parts and then develop a
simple yet efficient iterative algorithm for its solutions. Next, we attempt to
maximize the total throughput subject to the number of served IoT devices.
Finally, numerical results show that the proposed algorithms significantly
outperform benchmark approaches in terms of the number of served IoT devices
and the amount of collected data.Comment: 30 pages, 11 figure
Adaptive Compression and Joint Detection for Fronthaul Uplinks in Cloud Radio Access Networks
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