Violent attacks have been one of the hot issues in recent years. In the presence of closed-circuit televisions (CCTVs) in smart cities, there is an emerging challenge in apprehending criminals, leading to a need for innovative solutions. In this paper, the propose a model aimed at enhancing real-time emergency response capabilities and swiftly identifying criminals. This initiative aims to foster a safer environment and better manage criminal activity within smart cities. The proposed architecture combines an image-to-image stable diffusion model with violence detection and pose estimation approaches. The diffusion model generates synthetic data while the object detection approach uses YOLO v7 to identify violent objects like baseball bats, knives, and pistols, complemented by MediaPipe for action detection. Further, a long short-term memory (LSTM) network classifies the action attacks involving violent objects. Subsequently, an ensemble consisting of an edge device and the entire proposed model is deployed onto the edge device for real-time data testing using a dash camera. Thus, this study can handle violent attacks and send alerts in emergencies. As a result, our proposed YOLO model achieves a mean average precision (MAP) of 89.5% for violent attack detection, and the LSTM classifier model achieves an accuracy of 88.33% for violent action classification. The results highlight the model’s enhanced capability to accurately detect violent objects, particularly in effectively identifying violence through the implemented artificial intelligence system.

In this paper, we proposed and experimentally demonstrated the association of a fiber Bragg Grating (FBG) sensing system with You Only Look Once V7 (YOLO V7) to identify the vibration signal of a faulty machine. In the experiment, the YOLO V7 network architecture consists of a backbone, three detection heads (Headx3), a path aggregation network (PAN), and a feature pyramid network (FPN). The proposed architecture has an FBG sensor and the FBG interrogator employed for collecting sensing vibration signals or vibration data when degradation or fault occurs. An FBG interrogator collects vibration data independently, and then the YOLO V7 object detection algorithm is the recognition architecture of the vibration pattern of the signal. Thus, the proposed vibration recognition or detection is an assurance for detecting vibration signals that can support monitoring the machine’s health. Moreover, this research is promising for ensuring a high accuracy detection of faulty signals rate in industrial equipment monitoring and offers a robust system, resulting in remarkable accuracy with an overall model accuracy of 99.7%. The result shows that the model can identify the faulty signal more accurately and effectively detect the faulty vibration signal using the detection algorithm.

This research investigates the integration of free-space optics (FSO) with fiber Bragg grating (FBG) sensors in self-healing ring architectures, aiming to improve reliability and signal-to-noise ratio in temperature sensing within sensor systems. The combination of FSO’s wireless connectivity and FBG sensors’ precision, known for their sensitivity and immunity to electromagnetic interference, is particularly advantageous in demanding environments such as aerospace and structural health monitoring. The self-healing architecture enhances system resilience, automatically compensating for failures to maintain consistent monitoring capabilities. This study emphasizes the use of intensity wavelength division multiplexing (IWDM) to manage the complexities of increasing the multiplexing number of FBG sensors. Challenges arise with the overlapping spectra of FBGs when multiplexing several sensors. To address this, a hybrid approach combining an unsupervised autoencoder (AE) with a convolutional neural network (CNN) is proposed, significantly enhancing the accuracy and efficiency of sensor signal detection. These advancements signify substantial progress in sensor technology, validating the effectiveness of the AE-CNN hybrid model in refining FBG sensor systems and underscoring its potential for robust and reliable applications in critical sectors.

In recent years, condition monitoring has become the leading method for diagnosing the health of motor-based machines, with motor vibration or strain being a critical parameter for assessing machine health and detecting abnormal vibrations in multiple running motors posing a significant challenge. This article presents a novel approach to condition monitoring of motors that ensures the prevention of motor wear, reduces high maintenance costs, and increases the durability of machines. The method utilizes a single fiber Bragg grating (FBG) sensor to sense the vibrations of three electrical motors, and you only look once version 7 (YOLO-v7) as the signal detection algorithm. The Adan optimization technique is used to enhance the YOLO-v7 performance. A maximum of eight possible cases of normal and abnormal dynamic strain can be generated by these three motors operating simultaneously. YOLO-v7 ensures and evaluates the normal and abnormal signals of each motor. The detection result demonstrates the model accuracy of 98.4%. The model performance shows that strains from the motor machine are accurately detected, indicating anomalies. Thus, our proposed experimental setup is flexible, cost-effective, robust, less computation, fast and improves the sensing quality in anomalies in machine conditioning monitoring.

In the modern world, robots have become increasingly essential across various industries. Activity monitoring has emerged as a key method for diagnosing environmental conditions and enhancing the intelligence of mechanical robots. Vibration or strain from different activities is a critical parameter for evaluating and detecting activities, which presents a significant challenge in accurately assessing robotic performance across diverse tasks. This paper demonstrates a novel method for activity monitoring of mechanical robot dog machines that prevents motor wear, reduces high maintenance costs, and increases the durability of machines. The method utilizes an optical fiber-based Fiber Bragg Grating (FBG) sensor system to detect dynamic strain resulting from vibration signals generated by robotic movements, ensuring precise monitoring of robotic dog activities and the You Only Look Once version 7 (YOLO-v7) algorithm for activity detection. Two model modules are implemented: the first experiment collects dynamic strain data for up to eight possible activities and the second experiment collects the five different weights dragged by the robot dog. YOLO-v7 ensures and evaluates robot activities. The detection results demonstrate the eight activities and different weight-carrying model accuracy of 95.31% and 98.81% respectively. The model performance shows that strains from the motor machine are accurately detected, signalling anomalies. Thus, our proposed experimental setup is flexible, cost-effective, robust, computationally efficient, fast, and improves the sensing quality of robot pose action monitoring.

Simultaneous monitoring of temperature and vibration of electromechanical systems, public buildings, and volcanic terrain is an important indicator of preventing accidents. This work showcases the simultaneous temperature and vibration measurement using a single-fiber Bragg grating (FBG) sensor. The concurrent interrogation of vibration and temperature by the FBG sensing system can be integrated with free space optics (FSO), which saves on the costs associated with fiber optic cables and overcomes terrain barriers. Furthermore, the real-time measured vibration frequencies and different temperature levels can be recognized by a deep neural network (DNN) sequential model. In addition to the FSO support, the sensing system can be arranged in multiple sensing locations to form a wide range of measurements, offering a highly cost-effective solution.