Vehicular Ad hoc Networks (VANETs) can help reduce traffic-related accidents by broadcasting Emergency Messages (EMs) in advance between vehicles. Due to the high-speed mobility of VANETs and attenuation of the wireless signal, reliable and fast transmission of EMs is a challenging task. Such as the chosen next-hop vehicle may have driven away from the neighborhood of the sender before receiving the EM, and rerouting may increase the delay when the EMs encounter transmission failure. To this end, we propose a Speed and Position aware Dynamic Routing (SPDR) for EM dissemination in VANETs. First, we introduce a speed metric dynamic greedy routing to provide a dynamic hop-by-hop rebroadcast of the EM. SPDR dynamically shrinks the Routing Decision Area (RDA) range based on the velocity variance of candidate neighbors and prioritizes the farthest vehicle in the shrunk RDA as the optimal next-hop, enhancing the reliable transmission of EMs. Then, we present a collaborative forwarding strategy to enable candidate neighbors to collaborate in communication. In case of transmission failure, SPDR elects the candidate vehicle close to the destination as the forwarder to reduce rerouting. Simulations in a practical motorway scenario using NS-2 and VanetMobiSim show that SPDR outperforms the existing protocols in terms of message delivery ratio, network throughput, and average dissemination delay.

Bioacoustic event detection is a demanding endeavor involving recognizing and classifying the sounds animals make in their natural habitats. Traditional supervised learning requires a large amount of labeled data, which are hard to come by in bioacoustics. This paper presents a few-shot learning (FSL) method incorporating transductive inference and data augmentation to address the issues of too few labeled events and small volumes of recordings. Here, transductive inference iteratively alters class prototypes and feature extractors to seize essential patterns, whereas data augmentation applies SpecAugment on Mel spectrogram features to augment training data. The proposed approach is evaluated by using the Detecting and Classifying Acoustic Scenes and Events (DCASE) 2022 and 2021 datasets. Extensive experimental results demonstrate that all components of the proposed method achieve significant F-score improvements of 27% and 10%, for the DCASE-2022 and DCASE-2021 datasets, respectively, compared to recent advanced approaches. Moreover, our method is helpful in FSL tasks because it effectively adapts to sounds from various animal species, recordings, and durations.

Few-shot (FS) sound event detection (SED) is the process of identifying and recognizing specific sounds or events within an audio recording, specifically in the field of bioacoustics. This task is especially challenging due to the substantial unpredictability, complexity of the sounds, and the limited amount of labeled training data available. We recommend a transudative learning technique with data augmentation in response to the recent success of transudative inference (TI) in the field of computer vision. It aims to make predictions about new, unseen data by maximizing the mutual information between features of the labeled training data and the features of the unlabeled test data. We use the detection and classification of acoustic scenes and events (DCASE)-2021 and DCASE-2022 official datasets from the DCASE to do FS bioacoustics event detection experiments. Research findings illustrate that the recommended approach using TI along with spectrogram augmentation improves the performance of the FS bioacoustics event detection system. The proposed method improves the F-score by 0.10 and 0.16 on DCASE-2021 and DCASE-2022, respectively, compared with the average baseline and state-of-the-art methods.

This paper is about the development and design of semi-autonomous sentry robot using aurdino controller. There is a global issue of that everyone wants to secure themselves from different sorts of danger and to navigate their surroundings. This robot called Sentry Gun, because it consists of a Gun mounted on a stand and an operator can also operate it from a secure location. Sentry Gun is based on latest research, it uses high resolution camera to scan the target area and movement can be detected by using software i.e processing .org As compare to humans it can perform far better while operating in autonomous mode in red zones, it will bring a significant fall in the death toll, if installed outside every check post, law enforcement agencies and borders, to stop illegal intrusions.