scholarly journals AI Enabled IoRT Framework for Rodent Activity Monitoring in a False Ceiling Environment

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5326
Author(s):  
Balakrishnan Ramalingam ◽  
Thein Tun ◽  
Rajesh Elara Mohan ◽  
Braulio Félix Gómez ◽  
Ruoxi Cheng ◽  
...  
Keyword(s):  
Built Environment ◽  
Information Sharing ◽  
Real World ◽  
Confidence Level ◽  
Performance Metrics ◽  
Detection Algorithm ◽  
Activity Monitoring ◽  
Detection Accuracy ◽  
Activity Detection ◽  
3D Printed

Routine rodent inspection is essential to curbing rat-borne diseases and infrastructure damages within the built environment. Rodents find false ceilings to be a perfect spot to seek shelter and construct their habitats. However, a manual false ceiling inspection for rodents is laborious and risky. This work presents an AI-enabled IoRT framework for rodent activity monitoring inside a false ceiling using an in-house developed robot called "Falcon." The IoRT serves as a bridge between the users and the robots, through which seamless information sharing takes place. The shared images by the robots are inspected through a Faster RCNN ResNet 101 object detection algorithm, which is used to automatically detect the signs of rodent inside a false ceiling. The efficiency of the rodent activity detection algorithm was tested in a real-world false ceiling environment, and detection accuracy was evaluated with the standard performance metrics. The experimental results indicate that the algorithm detects rodent signs and 3D-printed rodents with a good confidence level.

Abstract P505: Real-world Data Validation Of A Novel P-wave Based Automatic Atrial Fibrillation Detection Algorithm

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Åke Olsson ◽  
Magnus Samulesson
Keyword(s):  
Real World ◽  
False Negative ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
The Novel ◽  
Real World Data ◽  
P Waves ◽  
World Data ◽  
Rr Variability

Background: Automatic ECG algorithms using only RR-variability in ECG to detect AF have shown high false positive rates. By including P-wave presence in the algorithm, research has shown that it can increase detection accuracy for AF. Methods: A novel RR- and P-wave based automatic detection algorithm implemented in the Coala Heart Monitor ("Coala", Coala Life AB, Sweden) was evaluated for detection accuracy by the comparison to blinded manual ECG interpretation based on real-world data. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where the algorithm had detected both irregular RR-rhythms and strong P-waves in either chest or thumb recording (non-AF episodes classified by algorithm as Category 12).The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown.The blinded recordings were each manually interpreted by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the detection algorithm to determine the number of additional false negative indications for AF as presented to the user. Results: The trained cardiologist manually interpreted 0 of the 100 recordings as AF. Manual interpretation showed that the novel automatic AF algorithm yielded 0 % False Negative error and 100 % Negative Predictive Value (NPV) for detection of AF. Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings). The 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves constituted 18% of all recordings with irregular RR-rhythms. Respiratory sinus arrhythmia was the single most prevalent condition and was found in 47% of irregular RR-rhythms with strong P-waves. Conclusion: The novel, P-wave based automatic ECG algorithm used in the Coala, showed a zero percent False Negative error rate for AF detection in ECG recordings with RR-variability but presence of P-waves, as compared to manual interpretation by a cardiologist.

P1456Evaluation of an enhanced, cloud-based AF-detection algorithm based on real-world arrhythmic recordings

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
A Olsson ◽  
M Samuelsson
Keyword(s):  
Positive Predictive Value ◽  
Predictive Value ◽  
Performance Metrics ◽  
Detection Algorithm ◽  
P Wave ◽  
Algorithm Analysis ◽  
Detection Accuracy ◽  
Ecg Signal ◽  
Single Individual ◽  
Data Set

Abstract Background Cloud-based solutions offer the ability to centrally and continuously enhance detection algorithms for arrhythmias such as Atrial Fibrillation (AF) based on generated data. Methods The Coala Heart Monitor (Coala) system was evaluated by manual interpretation of 1,000 consecutive anonymous printouts of chest- and thumb-ECG waveforms, without any exclusion. The anonymized printouts were blinded from algorithm analysis, apart from gender and age within a 10-year span. The recordings were derived from actual Coala users in Sweden with no training, control or influence, under a defined time period. The prevalence of cardiac conditions in the user population was unknown. The blinded recordings were manually interpreted by a trained cardiologist. The interpretation was compared with the automatic analysis performed by an algorithm in the Coala Cloud to evaluate ECG signal performance and calculate performance metrics. An enhanced algorithm utilizing P-wave detection was then evaluated on the data set and compared with the performance metrics of the existing algorithm. Results Metric Results with current algorithm Results with enhanced algorithm Prevalence of AF in the recordings 14.4% (143 of 990 recordings) 14.4% (143 of 990 recordings) Sensitivity for detecting AF 0.972 (95% CI = 0.930–0.992) 0.951 Specificity for detecting AF 0.946 (95% CI = 0.928–0.960) 0.976 Negative predictive value for detecting AF 0.995 (95% CI = 0.987–0.999) 0.992 Positive predictive value for detecting AF 0.751 (95% CI = 0.683–0.812) 0.872 Accuracy 0.950 0.973 Conclusion The enhanced algorithm was found to improve the Positive Predictive Value for detecting AF as compared to the existing algorithm (0.872 vs 0.751). The reduced sensitivity for the enhanced algorithm was due to 3 consecutive recordings from a single individual who had misplaced the Coala with corresponding altered morphology of the ECG signal. The recordings were still reported as having an irregular rhythm by the algorithm. The evolution demonstrates that cloud-based systems offer an ability to enhance detection accuracy by using reference data to train algorithms.

Abstract P504: Quality Validation Of Different ECG Leads, And An Automatic P-wave Detection Algorithm For Af, Based On Real-world Data

Circulation ◽  
2020 ◽  
Vol 141 (Suppl_1) ◽  
Author(s):  
Magnus Samuelsson ◽  
Åke Olsson
Keyword(s):  
Respiratory Sinus Arrhythmia ◽  
Real World ◽  
Detection Algorithm ◽  
P Wave ◽  
Detection Accuracy ◽  
Real World Data ◽  
P Waves ◽  
Sinus Arrhythmia ◽  
Ecg Leads

Background: Single-lead ECG has shown in research to be affected by artifacts leading to lower diagnostic yield of Atrial Fibrillation (AF). Use of multiple ECG leads and algorithms for detection of AF has shown to increase detection accuracy and reduce false positives. Methods: A novel RR- and P-wave based automatic algorithm implemented in the 2-lead Coala Heart Monitor (Coala) was evaluated for detection accuracy and quality by the comparison to blinded manual ECG interpretation. Evaluation was conducted on 100 consecutive anonymous printouts of chest- and thumb-ECG waveforms, where both an irregular RR-rhythm and strong P-waves in either chest or thumb recording were detected.The recordings, without exclusions, were generated from 5,512 real-world data recordings from actual Coala users in Sweden (both OTC and Rx users) during the period of March 5 to March 22, 2019, with no control or influence by the researchers or any other organization or individual. The prevalence of cardiac conditions in the user population was unknown. The blinded recordings were each manually interpreted and assessed for quality by a trained cardiologist. The manual interpretation was compared with the automatic analysis performed by the cloud-based detection algorithm to determine the detection quality of the respective ECG leads. Results: Strong P-waves were detected more often in the chest ECG as compared to the thumb ECG (90 vs 32 recordings). The assessed quality of the ECG tracings was higher in the chest ECGs as compared to the thumb ECGs (4.61 vs 3.88). Irregular RR-rhythms were detected in 569 recordings (10 % of a total of 5,512 recordings), the 100 non-AF recordings containing both irregular RR-rhythms and strong P-waves thus constituted 18% of all recordings with irregular RR-rhythms. Non-pathological rhythm (normal) was present in 84% of the recordings although all of these recordings contained irregular rhythm disturbances (respiratory sinus arrhythmia, PAC/PVC etc). Respiratory sinus arrhythmia was the single most prevalent condition and found in 47% of the recordings with irregular RR-rhythms with strong detected P-waves. Conclusion: The combination of chest and thumb ECG for detection of AF by an automatic P-wave based algorithm is shown to be more than 300% superior to thumb ECG alone with the majority of automatically detected P-waves and highest assessed ECG quality in the chest recordings.

IBMDA: Information based misbehavior detection algorithm for VANET

2020 ◽  
Vol 26 (3) ◽  
pp. 185-207
Author(s):  
Dinesh Singh ◽  
Ranvijay ◽  
Rama Shankar Yadav
Keyword(s):  
Information Sharing ◽  
High Speed ◽  
Cluster Head ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Misbehavior Detection ◽  
Incident Delay ◽  
Event Information ◽  
Safety Event ◽  
On The Road

The safety event information sharing among the vehicles in motion is the primary goal to design the vehicular ad hoc network (VANET). The shared safety event information assists vehicles to avoid road accidents and driving inconvenience. The advantages of safety event information sharing in VANET has become blunt due to the misbehavior of vehicles. The vehicle’s misbehavior like dissemination of false information, reply of bogus messages, etc., can create traffic hazards on the road and may result in the loss of property and human lives. In VANET, the detection of such misbehaving vehicles along with minimum time delay in flooding safety event information (i.e., incident delay) to others is challenging due to the high speed of vehicles. The formation of stable VANET topology is a feasible solution among many to improve the performance of misbehavior detection and reducing incident delay even with high speed of vehicles. In this paper, we propose an information based misbehavior detection algorithm (IBMDA) that effectively works in stable cluster based VANET. Our proposed IBMDA algorithm that runs on the selected cluster head vehicles is used to verify the content of received safety event messages. The identification of vehicles as malicious or non malicious depends on the result of verification at cluster heads. An illustrative example is given to explore our proposed algorithm easily and effectively. The highway scenario is considered to test the performance of our proposed IBMDA algorithm. The simulation is performed with a detailed comparative analysis using ns-3 simulator. It is observed that under the considered scenario, our proposed algorithm improves the misbehavior detection accuracy up to 6.46% and reduces average incident delay approximately up to 14.78% as compared to existing algorithms.

The Traffic Sign Detection Algorithm Based on Region of Interest Extraction and Double Filter

2019 ◽  
Vol 13 ◽  
Author(s):  
Dongxian Yu ◽  
Jiatao Kang ◽  
Zaihui Cao ◽  
Neha Jain
Keyword(s):  
Region Of Interest ◽  
Detection Algorithm ◽  
Light Filter ◽  
Support Vector ◽  
Detection Accuracy ◽  
Traffic Light ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Sign Detection ◽  
Traffic Sign Detection

In order to solve the current traffic sign detection technology due to the interference of various complex factors, it is difficult to effectively carry out the correct detection of traffic signs, and the robustness is weak, a traffic sign detection algorithm based on the region of interest extraction and double filter is designed.First, in order to reduce environmental interference, the input image is preprocessed to enhance the main color of each logo.Secondly, in order to improve the extraction ability Of Regions Of Interest, a Region Of Interest (ROI) detector based on Maximally Stable Extremal Regions (MSER) and Wave Equation (WE) was defined, and candidate Regions were selected through the ROI detector.Then, an effective HOG (Histogram of Oriented Gradient) descriptor is introduced as the detection feature of traffic signs, and SVM (Support Vector Machine) is used to classify them into traffic signs or background.Finally, the context-aware filter and the traffic light filter are used to further identify the false traffic signs and improve the detection accuracy.In the GTSDB database, three kinds of traffic signs, which are indicative, prohibited and dangerous, are tested, and the results show that the proposed algorithm has higher detection accuracy and robustness compared with the current traffic sign recognition technology.

scholarly journals High-Speed Lightweight Ship Detection Algorithm Based on YOLO-V4 for Three-Channels RGB SAR Image

2021 ◽  
Vol 13 (10) ◽  
pp. 1909
Author(s):  
Jiahuan Jiang ◽  
Xiongjun Fu ◽  
Rui Qin ◽  
Xiaoyan Wang ◽  
Zhifeng Ma
Keyword(s):  
Deep Learning ◽  
Gpu Computing ◽  
Hot Spot ◽  
Detection Algorithm ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Sar Image ◽  
Marine Monitoring ◽  
Ship Detection

Synthetic Aperture Radar (SAR) has become one of the important technical means of marine monitoring in the field of remote sensing due to its all-day, all-weather advantage. National territorial waters to achieve ship monitoring is conducive to national maritime law enforcement, implementation of maritime traffic control, and maintenance of national maritime security, so ship detection has been a hot spot and focus of research. After the development from traditional detection methods to deep learning combined methods, most of the research always based on the evolving Graphics Processing Unit (GPU) computing power to propose more complex and computationally intensive strategies, while in the process of transplanting optical image detection ignored the low signal-to-noise ratio, low resolution, single-channel and other characteristics brought by the SAR image imaging principle. Constantly pursuing detection accuracy while ignoring the detection speed and the ultimate application of the algorithm, almost all algorithms rely on powerful clustered desktop GPUs, which cannot be implemented on the frontline of marine monitoring to cope with the changing realities. To address these issues, this paper proposes a multi-channel fusion SAR image processing method that makes full use of image information and the network’s ability to extract features; it is also based on the latest You Only Look Once version 4 (YOLO-V4) deep learning framework for modeling architecture and training models. The YOLO-V4-light network was tailored for real-time and implementation, significantly reducing the model size, detection time, number of computational parameters, and memory consumption, and refining the network for three-channel images to compensate for the loss of accuracy due to light-weighting. The test experiments were completed entirely on a portable computer and achieved an Average Precision (AP) of 90.37% on the SAR Ship Detection Dataset (SSDD), simplifying the model while ensuring a lead over most existing methods. The YOLO-V4-lightship detection algorithm proposed in this paper has great practical application in maritime safety monitoring and emergency rescue.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

scholarly journals A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI

2021 ◽  
Vol 11 (2) ◽  
pp. 674
Author(s):  
Marianna Koctúrová ◽  
Jozef Juhár
Keyword(s):  
Visual Stimuli ◽  
Computer Interface ◽  
Single Subject ◽  
Detection Accuracy ◽  
The Novel ◽  
Activity Detection ◽  
Speech Activity ◽  
Novel Approach ◽  
Eeg Data ◽  
Speech Activity Detection

With the ever-progressing development in the field of computational and analytical science the last decade has seen a big improvement in the accuracy of electroencephalography (EEG) technology. Studies try to examine possibilities to use high dimensional EEG data as a source for Brain to Computer Interface. Applications of EEG Brain to computer interface vary from emotion recognition, simple computer/device control, speech recognition up to Intelligent Prosthesis. Our research presented in this paper was focused on the study of the problematic speech activity detection using EEG data. The novel approach used in this research involved the use visual stimuli, such as reading and colour naming, and signals of speech activity detectable by EEG technology. Our proposed solution is based on a shallow Feed-Forward Artificial Neural Network with only 100 hidden neurons. Standard features such as signal energy, standard deviation, RMS, skewness, kurtosis were calculated from the original signal from 16 EEG electrodes. The novel approach in the field of Brain to computer interface applications was utilised to calculated additional set of features from the minimum phase signal. Our experimental results demonstrated F1 score of 86.80% and 83.69% speech detection accuracy based on the analysis of EEG signal from single subject and cross-subject models respectively. The importance of these results lies in the novel utilisation of the mobile device to record the nerve signals which can serve as the stepping stone for the transfer of Brain to computer interface technology from technology from a controlled environment to the real-life conditions.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals A Low-Cost Jamming Detection Approach Using Performance Metrics in Cluster-Based Wireless Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1179
Author(s):  
Carolina Del-Valle-Soto ◽  
Carlos Mex-Perera ◽  
Juan Arturo Nolazco-Flores ◽  
Alma Rodríguez ◽  
Julio C. Rosas-Caro ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Performance Metrics ◽  
Low Cost ◽  
Detection Algorithm ◽  
Anomalous Behavior ◽  
Wireless Sensor ◽  
Jamming Detection ◽  
Minimal Energy Expenditure

Wireless Sensor Networks constitute an important part of the Internet of Things, and in a similar way to other wireless technologies, seek competitiveness concerning savings in energy consumption and information availability. These devices (sensors) are typically battery operated and distributed throughout a scenario of particular interest. However, they are prone to interference attacks which we know as jamming. The detection of anomalous behavior in the network is a subject of study where the routing protocol and the nodes increase power consumption, which is detrimental to the network’s performance. In this work, a simple jamming detection algorithm is proposed based on an exhaustive study of performance metrics related to the routing protocol and a significant impact on node energy. With this approach, the proposed algorithm detects areas of affected nodes with minimal energy expenditure. Detection is evaluated for four known cluster-based protocols: PEGASIS, TEEN, LEACH, and HPAR. The experiments analyze the protocols’ performance through the metrics chosen for a jamming detection algorithm. Finally, we conducted real experimentation with the best performing wireless protocols currently used, such as Zigbee and LoRa.

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