Learning Deconvolution Network for Semantic Segmentation

Navigation systems help users access unfamiliar environments. Current technological advancements enable users to encapsulate these systems in handheld devices, which effectively increases the popularity of navigation systems and the number of users. In indoor environments, lack of Global Positioning System (GPS) signals and line of sight with orbiting satellites makes navigation more challenging compared to outdoor environments. Radio frequency (RF) signals, computer vision, and sensor-based solutions are more suitable for tracking the users in indoor environments. This article provides a comprehensive summary of evolution in indoor navigation and indoor positioning technologies. In particular, the paper reviews different computer vision-based indoor navigation and positioning systems along with indoor scene recognition methods that can aid the indoor navigation. Navigation and positioning systems that utilize pedestrian dead reckoning (PDR) methods and various communication technologies, such as Wi-Fi, Radio Frequency Identification (RFID) visible light, Bluetooth and ultra-wide band (UWB), are detailed as well. Moreover, this article investigates and contrasts the different navigation systems in each category. Various evaluation criteria for indoor navigation systems are proposed in this work. The article concludes with a brief insight into future directions in indoor positioning and navigation systems.

/pdf/indoor-positioning-and-wayfinding-systems-a-survey-49mq36uclc.pdf

Indoor positioning and wayfinding systems: a survey

https://doi.org/10.1016/j.compbiomed.2022.105233

A review of deep learning-based detection methods for COVID-19

Security is the most important issue in Internet of Things (IoT)-based smart cities and blockchain (BC). So, the present paper aims to detect and organize the literature regarding security in the IoT-based smart cities and BC context. It also proposes an agenda for future research. Therefore, the authors did a statistical review of security in IoT and BC in smart cities. The present investigation aims to determine the principal challenges and disturbances in IoT because of the BC adoption, the central BC applications in IoT-based smart cities and the BC future in IoT-based smart cities.,IoT) has a notable influence on modernizing and transforming the society and industry for knowledge digitizing. Therefore, it may be perceived and operated in real time. The IoT is undergoing exponential development in industry and investigation. Still, it contains some security and privacy susceptibilities. Naturally, the research community pays attention to the security and privacy of the IoT. Also, the academic community has put a significant focus on BC as a new security project. In the present paper, the significant mechanisms and investigations in BC ground have been checked out systematically because of the significance of security in the IoT and BC in smart cities. Electronic databases were used to search for keywords. Totally, based on different filters, 131 papers have been gained, and 17 related articles have been obtained and analyzed. The security mechanisms of BC in IoT-based smart cities have been ranked into three main categories as follows, smart health care, smart home and smart agriculture.,The findings showed that BC’s distinctive technical aspects might impressively find a solution for privacy and security problems encountering the IoT-based smart cities development. They also supply distributed storage, transparency, trust and other IoT support to form a valid, impressive and secure distributed IoT network and provide a beneficial guarantee for IoT-based smart city users’ security and privacy.,The present investigation aims to be comprehensive, but some restrictions were also observed. Owing to the use of some filters for selecting the original papers, some complete works may be excluded. Besides, inspecting the total investigations on the security topic in BC and the IoT-based smart cities is infeasible. Albeit, the authors attempt to introduce a complete inspection of the security challenges in BC and the IoT-based smart cities. BC includes significant progress and innovation in the IoT-based smart cities’ security domain as new technology. Still, it contains some deficiencies as well. Investigators actively encounter the challenges and bring up persistent innovation and inspection of related technologies in the vision of the issues available in diverse application scenarios.,The use of BC technology in finding a solution for the security issues of the IoT-based smart cities is a research hotspot. There is numerable literature with data and theoretical support despite the suggestion of numerous relevant opinions. Therefore, this paper offers insights into how findings may guide practitioners and researchers in developing appropriate security systems dependent upon the features of IoT-based smart city systems and BC. This paper may also stimulate further investigation on the challenge of security in BC and IoT-based smart cities. The outcomes will be of great value for scholars and may supply sights into future investigation grounds in the present field.,As the authors state according to their knowledge, it is the first work using security challenges on BC and IoT-based smart cities. The literature review shows that few papers discuss how solving security issues in the IoT-based smart cities can benefit from the BC. The investigation suggests a literature review on the topic, recommending some thoughts on using security tools in the IoT-based smart cities. The present investigation helps organizations plan to integrate IoT and BC to detect the areas to focus. It also assists in better resource planning for the successful execution of smart technologies in their supply chains.

Systematic literature review on the security challenges of blockchain in IoT-based smart cities

The number of connected mobile devices and Internet of Things (IoT) is growing around us, rapidly. Since most of people's daily activities are relying on these connected things or devices. Specifically, this past year (with COVID-19) changed daily life in abroad and this is increased the use of IoT-enabled technologies in the health sector, work, and play. Further, the most common service via using these technologies is the localization/positioning service for different applications including: geo-tagging, billing, contact tracing, health-care system, point-of-interest recommendations, social networking, security, and more. Despite the availability of a large number of localization solutions in the literature, the precision of localization cannot meet the needs of consumers. For that reason, this paper provides an in-depth investigation of the existing technologies and techniques in the localization field, within the IoT era. Furthermore, the benefits and drawbacks of each technique with enabled technologies are illustrated and a comparison between the utilized technologies in the localization is made. The paper as a guideline is also going through all of the metrics that may be used to assess the localization solutions. Finally, the state-of-the-art solutions are examined, with challenges and perspectives regarding indoors/outdoors environments are demonstrated. 

A Comprehensive Review of Indoor/Outdoor Localization Solutions in IoT era: Research Challenges and Future Perspectives

Considerable number of indoor navigation systems has been proposed to augment people with visual impairments (VI) about their surroundings. These systems leverage several technologies, such as computer-vision, Bluetooth low energy (BLE), and other techniques to estimate the position of a user in indoor areas. Computer-vision based systems use several techniques including matching pictures, classifying captured images, recognizing visual objects or visual markers. BLE based system utilizes BLE beacons attached in the indoor areas as the source of the radio frequency signal to localize the position of the user. In this paper, we examine the performance and usability of two computer-vision based systems and BLE-based system. The first system is computer-vision based system, called CamNav that uses a trained deep learning model to recognize locations, and the second system, called QRNav, that utilizes visual markers (QR codes) to determine locations. A field test with 10 blindfolded users has been conducted while using the three navigation systems. The obtained results from navigation experiment and feedback from blindfolded users show that QRNav and CamNav system is more efficient than BLE based system in terms of accuracy and usability. The error occurred in BLE based application is more than 30% compared to computer vision based systems including CamNav and QRNav. The developed navigation systems are able to provide reliable assistance for the participants during real time experiments. Some of the participants took minimal external assistance while moving through the junctions in the corridor areas. Computer vision technology demonstrated its superiority over BLE technology in assistive systems for people with visual impairments.

/pdf/comparative-analysis-of-computer-vision-and-ble-technology-1jr9n9wh9i.pdf

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

People with blindness or low vision utilize mobile assistive tools for various applications such as object recognition, text recognition, etc. Most of the available applications are focused on recognizing generic objects. And they have not addressed the recognition of food dishes and fruit varieties. In this paper, we propose a smartphone-based system for recognizing the food dishes as well as fruits for children with visual impairments. The Smartphone application utilizes a trained deep CNN model for recognizing the food item from the real-time images. Furthermore, we develop a new deep convolutional neural network (CNN) model for food recognition using the fusion of two CNN architectures. The new deep CNN model is developed using the ensemble learning approach. The deep CNN food recognition model is trained on a customized food recognition dataset.The customized food recognition dataset consists of 29 varieties of food dishes and fruits. Moreover, we analyze the performance of multiple state of art deep CNN models for food recognition using the transfer learning approach. The ensemble model performed better than state of art CNN models and achieved a food recognition accuracy of 95.55 % in the customized food dataset. In addition to that, the proposed deep CNN model is evaluated in two publicly available food datasets to display its efficacy for food recognition tasks.

/pdf/smartphone-based-food-recognition-system-using-multiple-deep-1bs3gkwtew.pdf

Smartphone-based food recognition system using multiple deep CNN models

We present CamNav, a vision-based navigation system that provides users with indoor navigation services. CamNav captures images in real time while the user is walking to recognize their current location. It does not require any installation of indoor localization devices. In this paper, we describe the techniques of our system that improve the recognition accuracy of an existing system that uses oriented FAST and rotated BRIEF (ORB) as part of its location-matching procedure. We employ multiscale local binary pattern (MSLBP) features to recognize places. We implement CamNav and conduct required experiments to compare the obtained accuracy when using ORB, the scale-invariant feature transform (SIFT), MSLBP features, and the combination of both ORB and SIFT features with MSLBP. A dataset composed of 42 classes was constructed for assessment. Each class contains 100 pictures designed for training one location and 24 pictures dedicated for testing. The evaluation results demonstrate that the place recognition accuracy while using MSLBP features is better than the accuracy when using SIFT features. The accuracy when using SIFT, MSLBP, and ORB features is 88.19%, 91.27%, and 96.33%, respectively. The overall accuracy of recognizing places increased to 93.55% and 97.52% after integrating MSLBP with SIFT with ORB, respectively.

CamNav: a computer-vision indoor navigation system

Safe navigation for visually impaired is challenging without assistive technology. This paper proposes a pedestrian crossing detection approach to help visually impaired people. We introduce the use of multiple convolutional neural networks (CNNs) by utilizing wavelet transform subbands as inputs in which networks are trained to detect zebra crossing. In our method, the original image is decomposed into wavelet subbands, and the input images are constructed from image approximation based on the coefficients of three subbands. In the multiple networks approach, the segmentation results of the networks were integrated to create the final segmentation map. The results presented in this study prove that our method fully outperforms the SegNet networks and other state-of-the-art results using the Synthia database.

Jayakanth Kunhoth

Papers

Indoor positioning and wayfinding systems: a survey

Comparative analysis of computer-vision and BLE technology based indoor navigation systems for people with visual impairments

Smartphone-based food recognition system using multiple deep CNN models

CamNav: a computer-vision indoor navigation system

A vision-based zebra crossing detection method for people with visual impairments