Approximately 15% of the world’s population suffers from different types of disabilities. These people face many challenges when trying to interact with their home appliances. Various solutions are introduced to increase their quality of life, such as controlling their devices remotely through their voices. However, these solutions use command templates that fail to understand the unstructured or semi-structured command. Many authors have recently integrated intelligent personal assistant (IPA) systems, such as Google Assistant, Siri, and Alexa, with control circuits to exploit the advantages of the NLP of these IPAs to control traditional home appliances. However, this solution still struggles with understanding unstructured commands and requires the internet to be available for controlling the devices. This research proposes a new IPA system integrated with IoT, called IRON, for disabled people to use to control customizable devices with a structured and unstructured voice command. The proposed algorithm receives voice orders from the person in a structured or unstructured form and transforms them into text based on the Google Speech-to-Text API. The natural language processing technique splits the commands into tokens to determine the device name and the command type, whether it is a question about device status or a statement. Afterward, the logistic regression classifies the rest of the tokens as positive or negative to turn on or off the device, then sends the command to a Raspberry Pi to control the device. The proposed IRON system is implemented using logistic regression, naïve Bayes, and the support vector machine and is trained on a created dataset consisting of 3000 normal, negative, and unstructured commands. The simulation results show that the IRON system can determine 90% of the device’s names for all commands. Moreover, the IRON correctly classifies 100% of the commands as positive or negative within approximately 30 s.

Over the past several years, many children have died from suffocation due to being left inside a closed vehicle on a sunny day. Various vehicle manufacturers have proposed a variety of technologies to locate an unattended child in a vehicle, including pressure sensors, passive infrared motion sensors, temperature sensors, and microwave sensors. However, these methods have not yet reliably located forgotten children in the vehicle. Recently, visual-based methods have taken the attention of manufacturers after the emergence of deep learning technology. However, the existing methods focus only on the forgotten child and neglect a forgotten pet. Furthermore, their systems only detect the presence of a child in the car with or without their parents. Therefore, this research introduces a visual-based framework to reduce hyperthermia deaths in enclosed vehicles. This visual-based system detects objects inside a vehicle; if the child or pet are without an adult, a notification is sent to the parents. First, a dataset is constructed for vehicle interiors containing children, pets, and adults. The proposed dataset is collected from different online sources, considering varying illumination, skin color, pet type, clothes, and car brands for guaranteed model robustness. Second, blurring, sharpening, brightness, contrast, noise, perspective transform, and fog effect augmentation algorithms are applied to these images to increase the training data. The augmented images are annotated with three classes: child, pet, and adult. This research concentrates on fine-tuning different state-of-the-art real-time detection models to detect objects inside the vehicle: NanoDet, YOLOv6_1, YOLOv6_3, and YOLO7. The simulation results demonstrate that YOLOv6_1 presents significant values with 96% recall, 95% precision, and 95% F1.

Sparsity adaptive matching pursuit (SAMP) is a greedy reconstruction algorithm for compressive sensing signals. SAMP reconstructs signals without prior information of sparsity and presents better...

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.