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.

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.

Fiber Bragg grating (FBG) sensing systems face significant challenges in resolving overlapping spectral signatures when multiple sensors operate within limited wavelength ranges, severely limiting sensor density and network scalability. This study introduces a novel Transformer-based neural network architecture that effectively resolves spectral overlap in both uniform and mixed-linewidth FBG sensor arrays, operating under bidirectional drift. The system uniquely combines dual-linewidth configurations with reflection and transmission mode fusion to enhance demodulation accuracy and sensing capacity. By integrating cloud computing, the model enables scalable deployment and near-real-time inference even in large-scale monitoring environments. The proposed approach supports self-healing functionality through dynamic switching between spectral modes during fiber breaks and enhances resilience against spectral congestion. Comprehensive evaluation across twelve drift scenarios demonstrates exceptional demodulation performance under severe spectral overlap conditions that challenge conventional peak-finding algorithms. This breakthrough establishes a new paradigm for high-density, distributed FBG sensing networks applicable to land monitoring, soil stability assessment, groundwater detection, maritime surveillance, and smart agriculture.

We present a novel FBG sensor system that employs both reflective and transmission wavelengths to significantly boost capacity within a single network. By integrating Free Space Optics (FSO) for remote communication, this architecture operates beyond physical constraints where laying fiber is not feasible, thereby enhancing deployment flexibility. To address the overlap of Bragg wavelengths commonly encountered in dense FBG networks, we utilize a Transformer Convolutional Neural Network (CNN) that leverages positional encoding to more effectively learn data patterns. Preliminary results demonstrate a significant enhancement in sensor capacity and detection accuracy. Moreover, the proposed Transformer CNN reduces both training and inference times by approximately 2.24× and 1.43×, respectively, compared to traditional Transformer models. By leveraging both reflective and transmission FBG readouts within a fixed spectral window, the system supports nearly double the number of sensors, offering a scalable and efficient solution well-suited for high-density, real-time industrial sensing applications.