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Conservationists are searching for cutting-edge technical solutions in response to the growing problem of poaching and its catastrophic effects on animal populations. Drones and other names for unmanned aerial vehicles, have shown promise as a tool for wildlife monitoring and anti-poaching operations in recent years. Drone data can be analyzed to provide important insights into animal behavior, migration patterns, and habitat conditions, assisting in the development of more informed conservation strategies. Since the device does not hurt the species being attacked but rather causes discomfort that results in spontaneous pull it is technologically more sophisticated. In order to ensure that the volume patterns of successive frames remain coherent across time, the graph regularized is applied to them. Equipped with various navigation systems such as the GPS and optical flow, they are able to practically navigate itself thanks to today's fly-by-wire technique. Among the many possible uses for drones in tandem with other technology include soil inspections and satellite surveillance. Additionally, the integration of artificial intelligence and machine learning algorithms improves the drone's ability to identify and differentiate between poachers and legitimate visitors or researchers. [ABSTRACT FROM AUTHOR]

Conventional speech emotion identification often uses sentence units as analysis length generally. However, human emotions frequently change their emotions instantaneously when they hear a specific word or keyword that affects each speaker’s emotion, and it is important to capture more detailed emotional expressions for recognition of the emotion. We propose an emotion identification by using acoustic features that analyze speech at each frame, which are shorter than conventional units such as sentences and phrases for capturing and expressing actual emotion. Therefore, we propose a real-time emotion identification system that uses frames as the unit of analysis for acoustic features to the emotion in units of words and morphemes, which are shorter than conventional linguistic units.

In an era where Unmanned Aerial Vehicles (UAVs) have become crucial in military surveillance and operations, the need for real-time and accurate UAV recognition is increasingly critical. The widespread use of UAVs presents various security threats, requiring systems that can differentiate between UAVs and benign objects, such as birds. This study conducts a comparative analysis of advanced machine learning models to address the challenge of aerial classification in diverse environmental conditions without system redesign. Large datasets were used to train and validate models, including Neural Networks, Support Vector Machines, ensemble methods, and Random Forest Gradient Boosting Machines. These models were evaluated based on accuracy and computational efficiency, key factors for real-time application. The results indicate that Neural Networks provide the best performance, demonstrating high accuracy in distinguishing UAVs from birds. The findings emphasize that Neural Networks have significant potential to enhance operational security and improve the allocation of defense resources. Overall, this research highlights the effectiveness of machine learning in real time UAV recognition and advocates for the integration of Neural Networks into military defense systems to strengthen decision-making and security operations. Regular updates to these models are recommended to keep pace with advancements in UAV technology, including more agile and stealthier designs. [ABSTRACT FROM AUTHOR]

The tiny machine learning (TinyML) has been considered to apply on the edge devices where the resource-constrained micro-controller units (MCUs) were used. Finding a good platform to deploy the TinyML effectively is very crucial. The paper aims to propose a multiple micro-controller hardware platform for productively running the TinyML model. The proposed hardware consists of two dual-core MCUs. The first MCU is utilized for acquiring and processing input data, while the second one is responsible for executing the trained TinyML network. Two MCUs communicate with each other using the universal asynchronous receiver-transmitter (UART) protocol. The multitasking programming technique is mainly applied on the first MCU to optimize the pre-processing new data. A three-phase motors faults classification TinyML model was deployed on the proposed system to evaluate the effectiveness. The experimental results prove that our proposed hardware platform was improved 34.8% of the total inference time including pre-processing data of the proposed TinyML model in comparing with single micro-controller hardware platform. [ABSTRACT FROM AUTHOR]

This article compares a wide set of data classification methods used for creating brain-computer interfaces based on the recognition of EEG patterns during motor imagery of the hand. The GBM (gradient boosting models) classifier was found to work better than other classifiers using the dataset provided. [ABSTRACT FROM AUTHOR]

The development of anticipatory user interfaces is a key issue in human-centred computing. Building systems that allow humans to communicate with a machine in the same natural and intuitive way as they would with each other requires detection and interpretation of the user's affective and social signals. These are expressed in various and often complementary ways, including gestures, speech, mimics etc. Implementing fast and robust recognition engines is not only a necessary, but also challenging task. In this article, we introduce our Social Signal Interpretation (SSI) tool, a framework dedicated to support the development of such online recognition systems. The paper at hand discusses the processing of four modalities, namely audio, video, gesture and biosignals, with focus on affect recognition, and explains various approaches to fuse the extracted information to a final decision. [ABSTRACT FROM AUTHOR]