Managing Safety and Security in the Smart City: Covid-19, Emergencies and Smart Surveillance

The chapter examines the role and potential inherent in surveillance systems in smart cities today. The Covid-19 pandemic and the resultant restrictions to mobility, on the one hand, and the need for strengthened enforcement measures highlighted the already existing weaknesses and contingencies besetting surveillance in smart cities. The chapter makes a case that the adoption of smart city surveillance and infrastructure management systems may contribute to the improvement of safety and security in the smart city as well as to an overall enhancement of the smart city's resilience. The discussion in this chapter focuses on the complex processes of data acquisition, data sharing, and data utilization to explain in which ways they all add to smart surveillance systems that-while aware of individual freedoms and privacy issues-contribute to the process of making a smart city resilient. To showcase the applicability of these findings, a wireless mesh network (WMN) surveillance system is presented. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An integrative systems biology view of host-pathogen interactions: The regulation of immunity and homeostasis is concomitant, flexible, and smart.

The systemic bio-organization of humans and other mammals is essentially "preprogrammed", and the basic interacting units, the cells, can be crudely mapped into discrete sets of developmental lineages and maturation states. Over several decades, however, and focusing on the immune system, we and others invoked evidence - now overwhelming - suggesting dynamic acquisition of cellular properties and functions, through tuning, re-networking, chromatin remodeling, and adaptive differentiation. The genetically encoded "algorithms" that govern the integration of signals and the computation of new states are not fully understood but are believed to be "smart", designed to enable the cells and the system to discriminate meaningful perturbations from each other and from "noise". Cellular sensory and response properties are shaped in part by recurring temporal patterns, or features, of the signaling environment. We compared this phenomenon to associative brain learning. We proposed that interactive cell learning is subject to selective pressures geared to performance, allowing the response of immune cells to injury or infection to be progressively coordinated with that of other cell types across tissues and organs. This in turn is comparable to supervised brain learning. Guided by feedback from both the tissue itself and the neural system, resident or recruited antigen-specific and innate immune cells can eradicate a pathogen while simultaneously sustaining functional homeostasis. As informative memories of immune responses are imprinted both systemically and within the targeted tissues, it is desirable to enhance tissue preparedness by incorporating attenuated-pathogen vaccines and informed choice of tissue-centered immunomodulators in vaccination schemes. Fortunately, much of the "training" that a living system requires to survive and function in the face of disturbances from outside or within is already incorporated into its design, so it does not need to deep-learn how to face a new challenge each time from scratch. Instead, the system learns from experience how to efficiently select a built-in strategy, or a combination of those, and can then use tuning to refine its organization and responses. Efforts to identify and therapeutically augment such strategies can take advantage of existing integrative modeling approaches. One recently explored strategy is boosting the flux of uninfected cells into and throughout an infected tissue to rinse and replace the infected cells.

Design of an AMR Using Image Processing and Deep Learning for Monitoring Safety Aspects in Warehouse

The latest spinoffs in the field of Autonomous Vehicles have paved way for a revolution in mobility and transportation;particularly in the warehousing and distribution sector. AMRs, Autonomous Mobile Robots, are being deployed to assist in warehousing activities as they present multiple advantages. In this paper, an AMR coupled with image processing and deep learning is introduced as a novel approach to solve a two-fold problem: surveillance and disinfection. Deep learning will make use of real-time data collected by the AMR's camera as a smart surveillance method for abnormal event detection. YOLOv4 is used to train a custom dataset for object detection on five different classes. The latter obtained a 74.40% accuracy. The vehicle will also be used to diffuse disinfecting agents as a mean to sanitize the stores and stocks against Covid-19. Moreover, autonomous navigation of the AMR will be based on image processing techniques for path track detection. © 2022 IST-Africa Institute and Authors.

Action recognition application using artificial intelligence for smart social surveillance system

Computer vision is an important area of artificial intelligence that aims to help it gain the ability to activate similar to humans. In the past, we often classify fruit by hand. Today, it is performed by the development of image-processing technology. When the quantity of fruits is huge, we need machine learning for classifying them. There-fore, we propose the fruit classification using the Tensorflow and Keras model (high-level framework of Tensorflow) in the paper. This is a simple problem of computer vision since it solves the basis problems such as object detection or face recognition. In the paper, we focus on modifying the network architecture of the Tensorflow model. As a result, the accuracy of the proposed model achieves 99% with only five epochs. © The Authors.

Smart Surveillance for COVID-19 Health Protocol Compliance in Vehicles

Nowadays, the Corona Virus outbreak in 2019 (COVID-19) has become a global pandemic. The public must implement health protocols to reduce the spread of COVID-19. Trends show that the number of COVID-19 is increasing over time. This study proposes and develops a smart model to detect COVID-19 Health protocol violators in vehicles. This model can detect violations of the use of masks and social distancing in vehicles. The proposed model is a combination of the YOLO object detection method and the Hourglass architecture. The experimental results of the proposed model can detect violations with a high success rate. Here, the standard YOLOv4 detection model as baseline yields an mAP of 0.87 for validation and 0.74 for test data. On the other hand, the proposed method produces an mAP of 0.92 on the validation data, 0.78 on the test data. From these results, this smart model is quite promising to help reduce the spread of COVID-19. © 2021 ACM.