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.

Nowadays, underwater activities are becoming more and more important. As the number of underwater sensing devices grows rapidly, the amount of bandwidth needed also increases very quickly. Apart from underwater communication, direct communication across the water–air interface is also highly desirable. Air-to-water wireless transmission is crucial for sending control information or instructions from unmanned aerial vehicles (UAVs) or ground stations above the sea surface to autonomous underwater vehicles (AUVs). On the other hand, water-to-air wireless transmission is also required to transmit real-time information from AUVs or underwater sensor nodes to UAVs above the water surface. Previously, we successfully demonstrated a water-to-air optical camera-based OWC system, which is also known as optical camera communication (OCC). However, the reverse transmission (i.e., air-to-water) using OCC has not been analyzed. It is worth noting that in the water-to-air OCC system, since the camera is located in the air, the image of the light source is magnified due to diffraction. Hence, the pixel-per-symbol (PPS) decoding of the OCC pattern is easier. In the proposed air-to-water OCC system reported here, since the camera is located in the water, the image of the light source in the air will be diminished in size due to diffraction. Hence, the PPS decoding of the OCC pattern becomes more difficult. In this work, we propose and experimentally demonstrate a wide field-of-view (FOV) air-to-water OCC system using CUDA Deep-Neural-Network Long-Short-Term-Memory (CuDNNLSTM). Due to water turbulence and air turbulence affecting the AUV and UAV, a precise line-of-sight (LOS) between the AUV and the UAV is difficult to achieve. OCC can provide wide FOV without the need for precise optical alignment. Results revealed that the proposed air-to-water OCC system can support a transmission rate of 7.2 kbit/s through a still water surface, and 6.6 kbit/s through a wavy water surface; this satisfies the hard-decision forward error correction (HD-FEC) bit-error-rate (BER).

Inexpensive, lightweight, sensitive, compact, easy-to-install, and mass-producible vibration sensors are the future wave. This paper validates the use of a 3D-printed doublet bracket that turns out to be a scissor-like X-structure with scissor-like kinematics when vibrating. The main concept is simple: an intact standard single-mode fiber (SMF) is folded into a droplet-bending shape and embedded in an arch bridge-type assembly connected to an X-shaped structure. When regular vibrations are transmitted to the X-structure, the continuous scissor motion of the X-structure drives the arch bridge-type assembly to regularly extend and shrink, thereby changing the size of the fiber bending, creating variations in the optical loss, and allowing for the measurement of the vibration. In addition, the sensitivity of the vibration measurement can be adjusted by altering the torque by varying the intersection angle of the doublet bracket in the X-structure or the fiber loss by varying the number of turns of fiber bending. In this way, the sensitivity can be adjusted according to the magnitude of the vibration amplitude of the object being measured in various measurement scenarios. For instance, if the vibration amplitude is weak, a high-sensitivity frame will be utilized for the measurement, and vice versa. Furthermore, this 3D-printed-based X-structure and fiber-bending-based vibration sensor can be integrated with free-space optics (FSO) for more versatile measurements in various applications. In summary, this scheme showcases an affordable, lightweight, variable-sensitivity, simple, and easily mass-producible fiber-optic vibration sensing technology.