Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Sinha, Rashmi Sharan; Hwang, Seung-Hoon

doi:10.3390/electronics8090989

Cited by 49 publications

(35 citation statements)

References 40 publications

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“…Also, it has to be noted that this hybrid system model was deployed in a tire manufacturing unit, and it produced efficient results in automatically diagnosing the bubble-defects in treads and sidewalls of tires. In the future work, more advanced CNN enabled approaches can be implemented for automated detection of defects [26][27][28][29][30], thus ensuring and realizing a sustainable tire manufacturing process.…”

Section: Discussionmentioning

confidence: 99%

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

et al. 2019

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In recent times, the application of enabling technologies such as digital shearography combined with deep learning approaches in the smart quality assessment of tires, which leads to intelligent tire manufacturing practices with automated defects detection. Digital shearography is a prominent approach that can be employed for identifying the defects in tires, usually not visible to human eyes. In this research, the bubble defects in tire shearography images are detected using a unique ensemble hybrid amalgamation of the convolutional neural networks/ConvNets with high-performance Faster Region-based convolutional neural networks. It can be noticed that the routine of region-proposal generation along with object detection is accomplished using the ConvNets. Primarily, the sliding window based ConvNets are utilized in the proposed model for dividing the input shearography images into regions, in order to identify the bubble defects. Subsequently, this is followed by implementing the Faster Region-based ConvNets for identifying the bubble defects in the tire shearography images and further, it also helps to minimize the false-positive ratio (sometimes referred to as the false alarm ratio). Moreover, it is evident from the experimental results that the proposed hybrid model offers a cent percent detection of bubble defects in the tire shearography images. Also, it can be witnessed that the false-positive ratio gets minimized to 18 percent.Electronics 2020, 9, 45 2 of 13 be observed that enabling technologies for smart tire quality assessment for realizing intelligent tire manufacturing has been gaining prominence to address challenges such as automated tire defects detection.Generally, it can be witnessed that in the modern-day scenario, there is a humongous and swift growth in the gadgets, equipment, and devices connected to the internet. These devices, gadgets, and equipment have profound computing characteristics, and at the same time, they are exceedingly performance-oriented [2]. Due to all these facts, the concept of deep learning has evolved into a newer dimension, and it plays a significant part in processing and recognizing images, speech, and video, and so on. Furthermore, the implementation of a deep learning paradigm for automation in conventional manufacturing units significantly minimizes the usage of physical labor, and it also enhances the overall competence and efficacy of these units. Digital shearography is a laser-based measuring approach that relies on the processing of digital data, interferometry, and phase-shifting paradigm [3-5].A shearography system was developed by applying a spatial light modulator for controlling the amount of shearing and the direction of the phase light automatically and accurately. The system eliminates the nonlinear random error and enhances the efficiency of testing [6]. A system was developed using shearography to examine the exterior heatproof covering of a cylinder, and defect detection was done using an artificial intelligence-based recognition algorithm for deep...

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Section: Discussionmentioning

confidence: 99%

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

et al. 2019

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“…However, despite having been used in several works [ 31 , 32 ], VLC suffers from issues such as interference with other ambient lights, signal shadowing and usually requires the receiver to be in Line-Of-Sight (LOS), which can affect the accuracy of the location estimation. A detailed comparison of deep learning and other machine learning algorithms used in localization for IoT environment is covered in [ 33 , 34 ].…”

Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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“…In this subsection, we relax the problem (12) to an SDP problem. The semidefinite programs can be solved almost as easily as linear programs with interior-point methods [64] and several advanced SDP solvers are readily available [65].…”

Section: Semidefinite Relaxationmentioning

confidence: 99%

“…In view of (14) and (15), if the constraints (12b) and (12c) in (12) are relaxed to drop the (nonconvex) rank-one constraints, then we obtain the following (convex) SDR problem.…”

Section: Semidefinite Relaxationmentioning

confidence: 99%

“…During the deployment of powerful location-based applications such as enhanced 911 services, asset management, and workflow automation, wireless location is a classical problem and has attracted intensive research recently [1][2][3][4][5][6]. Technically, wireless location can be based on a variety of different kinds of measurements, such as range [7], angle [8], energy [9], visible light [10,11], or fingerprinting [12][13][14]. The common range-based measurements include time of arrivals (TOAs) [15,16] and time difference of arrivals (TDOAs) [17,18].…”

Section: Introductionmentioning

confidence: 99%

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A Semidefinite Relaxation Method for Elliptical Location

2020

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Wireless location is a supporting technology in many application scenarios of wireless communication systems. Recently, an increasing number of studies have been conducted on range-based elliptical location in a variety of backgrounds. Specifically, the design and implementation of position estimators are of great significance. The difficulties arising from implementing a maximum likelihood estimator for elliptical location come from the nonconvexity of the negative log-likelihood functions. The need for computational efficiency further enhances the difficulties. Traditional algorithms suffer from the problems of high computational cost and low initialization justifiability. On the other hand, existing closed-form solutions are sensitive to the measurement noise levels. We recognize that the root of these drawbacks lies in an oversimplified linear approximation of the nonconvex model, and accordingly design a maximum likelihood estimator through semidefinite relaxation for elliptical location. We relax the elliptical location problems to semidefinite programs, which can be solved efficiently with interior-point methods. Additionally, we theoretically analyze the complexity of the proposed algorithm. Finally, we design and carry out a series of simulation experiments, showing that the proposed algorithm outperforms several widely used closed-form solutions at a wide range of noise levels. Extensive results under extreme noise conditions verify the deployability of the algorithm.

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Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Cited by 49 publications

References 40 publications

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Quality Assessment of Tire Shearography Images via Ensemble Hybrid Faster Region-Based ConvNets

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

A Semidefinite Relaxation Method for Elliptical Location

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