Home security and surveillance systems are rapidly evolving, with Artificial Intelligence (AI) playing a transformative role in enhancing safety and threat detection. While several AI methods and datasets for intruder-related risk assessment exist, they predominantly focus on face detection and recognition, leaving a significant gap in addressing high-risk scenarios involving malicious intent, such as theft or harm. The lack of dedicated datasets for recognizing complex intruder activities, such as carrying weapons or engaging in destructive actions like kicking doors or breaking locks, limits the development of robust solutions. This work bridges this gap by introducing the Intruder Activity Recognition Dataset (IARD), a video dataset specifically designed to recognize four critical intruder activities: Armed Intruder, Door Kick, Intruder Inside and Lock Breaking. Leveraging IARD, we thoroughly benchmark various state-of-the-art methods, among which a Vision Transformer is found to achieve an impressive 93.3% accuracy in recognizing intruder actions. Our contribution highlights the potential of IARD in advancing AI-driven surveillance systems, providing a foundational dataset and benchmark for recognizing complex intruder activities.

Image super-resolution (SR) focuses on reconstructing high-resolution images from their low-resolution counter-parts, often affected by sensor limitations or environmental factors. Convolutional Neural Networks (CNNs) are state-of-the-art for SR tasks but computationally heavy. This paper introduces a novel CRMN (Convolutional Recurrent Mixer Network), a hybrid deep learning-based SR technique designed to address the complexity of CNNs, which is validated in the context of meteorological radar images. Experiments on public benchmark datasets (Berkley432 and T291) and our newly manually collected precipitation dataset from the Meteorological Research Institute (IPMET) show that our CRMN model provides competitive results compared to leading SR methods with significantly fewer parameters, making it a promising and practical solution for SR applications, particularly radar meteorology.

Multisensor image fusion aims to generate a high-quality composite image by integrating information from diverse sources. While deep learning-based approaches enhance fusion quality, they often suffer from high computational costs and information loss due to single-step feature integration. We propose ConvFuse, a lightweight DL framework for infrared and visible image fusion. Our context-aware convformer block effectively preserves local-global and contextual details without relying on attention mechanisms. A progressive intermodality fusion strategy enhances modality-specific features, while a multiscale decoder ensures seamless feature integration across different scales. Experimental results on benchmark datasets show that ConvFuse surpasses transformer-based and SOTA DL-based methods in fusion quality and efficiency.

Detecting hazardous activities is essential for ensuring safety. However, existing datasets often lack coverage of the nuanced and diverse hazards present in indoor environments, which hinders the development of a specialized model. To address this, we introduce the Real-World Hazardous Activities Dataset (RHAD), a novel and diverse video dataset specifically curated for recognizing hazardous activities in real-world indoor settings. Leveraging RHAD, we introduce HazardNet, a hybrid deep-learning architecture designed for hazardous activity recognition. HazardNet integrates local and global spatial-temporal representation modules to effectively capture complex patterns, enabling a robust understanding of the activity. We perform comprehensive evaluations by benchmarking against a range of state-of-the-art activity recognition models. Experimental results show that our proposed model performs significantly better, surpassing the latest model, VideoMamba, with a 9.2% accuracy gain. Moreover, by providing the dataset and an effective recognition model, our work lays the foundation for further research, paving the way for enhanced safety measures and preventive interventions. The dataset and code are available at https://github.com/ShehzadCS18/RHAD.

With the continuous and fast-changing land-scape in communication networks and artificial intelligence (AI), the researchers are interested in expedited standardization and realization of 6G networks. Federated learning (FL) is one of the paradigms that allows the 6G networks to support a diverse range of devices. Very few studies address the problem of flexibility and heterogeneity for AI network architectures in FL paradigm, that could be a potential key changer for standardization and realization of 6G networks. However, they either consider width-only or depth-only to provide flexibility support. Furthermore, the existing studies do not address the problem of weight scale variation while performing the global model aggregation at the server side. In this regard, we propose flexible federated learning (FLEXFL) for the support of heterogeneous AI network architectures in 6G communication systems. The proposed network not only considers the width but also the depth of the network architecture to make it compliant with the global model aggregation. We also address weight scale variation (WSV) while updating the global model with weight normalization, which is one of the problems associated with existing studies. We perform experimental analysis on two publicly available datasets and a few network architectures to show the efficacy of the proposed approach. The results reveal that the FLEXFL outperforms existing state-of-the-art works in both the IID and non-IID settings, accordingly.

Facial emotion recognition (FER) plays a critical role in understanding human behavior, especially for individuals suffering from neurological disorders (NDs) like Parkinson’s disease (PD), Multiple Sclerosis (MS), and Stroke. Early and accurate detection of emotions is crucial for both the diagnosis of associated mood disorders and continuous monitoring. However, traditional methods often fall short in providing noninvasive, real-time solutions and lack the clinical expertise necessary to identify the specific emotion types associated with each ND category. In response, this research conducted under the ALAMEDA consortium presents an Internet of Things-based FER AI Toolkit designed to enhance early diagnosis and treatment for brain diseases. The toolkit is in line with the consortium’s clinical guidelines and provides a personalized, patient-focused solution that supports the goals of Industry 5.0 in healthcare. In line with Industry 5.0 principles, the FER AI Toolkit uses edge devices to collect real-time facial data while deep learning models running on cloud servers process this data. The recognized emotions are uploaded to the Semantic Knowledge Graph (SemKG) server. This allows healthcare professionals to make informed decisions based on real-time data. Additionally, the toolkit integrates seamlessly with key components of the ALAMEDA, including the Identity Authentication Manager (IAM) for secure access and the ALAMEDA Innovation Hub (AIH) for efficient resource management. By offering continuous and personalized healthcare insights, the FER AI Toolkit helps bridge the gap between diagnosis and patient well-being, ultimately advancing healthcare systems. Training materials and video demonstrations are available athttps://drive.google.com/drive/folders/1-iUz7FE2IrKHt5nMl3oGjsrCtk7Ps2bM?usp=sharingfor further learning.

With the rising trend of digital technologies, such as augmented and virtual reality, Metaverse has gained a notable popularity. The applications that will eventually benefit from Metaverse is the telemedicine and e-health fields. However, the data and techniques used for realizing the medical side of Metaverse is vulnerable to data and class leakage attacks. Most of the existing studies focus on either of the problems through encryption techniques or addition of noise. In addition, the use of encryption techniques affects the overall performance of the medical services, which hinders its realization. In this regard, we propose Generative adversarial networks and spike learning based convolutional neural network (GASCNN) for medical images that is resilient to both the data and class leakage attacks. We first propose the GANs for generating synthetic medical images from residual networks feature maps. We then perform a transformation paradigm to convert ResNet to spike neural networks (SNN) and use spike learning technique to encrypt model weights by representing the spatial domain data into temporal axis, thus making it difficult to be reconstructed. We conduct extensive experiments on publicly available MRI dataset and show that the proposed work is resilient to various data and class leakage attacks in comparison to existing state-of-the-art works (1.75x increase in FID score) with the exception of slightly decreased performance (less than 3%) from its ResNet counterpart. while achieving 52x energy efficiency gain with respect to standard ResNet architecture.

As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.

The use of aerial base stations, AI cloud, and satellite storage can help manage location, traffic, and specific application-based services for vehicular social networks. However, sharing of such data makes the vehicular network vulnerable to data and privacy leakage. In this regard, this article proposes an efficient and secure data sharing scheme using community segmentation and a blockchain-based framework for vehicular social networks. The proposed work considers similarity matrices that employ the dynamics of structural similarity, modularity matrix, and data compatibility. These similarity matrices are then passed through stacked autoencoders that are trained to extract encoded embedding. A density-based clustering approach is then employed to find the community segments from the information distances between the encoded embeddings. A blockchain network based on the Hyperledger Fabric platform is also adopted to ensure data sharing security. Extensive experiments have been carried out to evaluate the proposed data-sharing framework in terms of the sum of squared error, sharing degree, time cost, computational complexity, throughput, and CPU utilization for proving its efficacy and applicability. The results show that the CSB framework achieves a higher degree of SD, lower computational complexity, and higher throughput.

In this letter, we propose an effective registration method for cloudy satellite images based on global and local thresholds. First, cloud candidates are determined by using optimal threshold and κ-means clustering. Then, using the local threshold, the cloud candidates are further classified into three categories: thick clouds, thin clouds, and ground. Finally, accurate registration is performed by eliminating features relating to cloudy areas. The experiments show that the proposed method provides segmentation accuracy of 93.29%. In addition, registration accuracy is improved by 24.83%, as compared with conventional methods.

Eye alignment to the optical system is very critical in many modern devices, such as for biometrics, gaze tracking, head mounted displays, and health. We show alignment in the context of the most difficult challenge: retinal imaging. Alignment in retinal imaging, even conducted by a physician, is very challenging due to precise alignment requirements and lack of direct user eye gaze control. Self-imaging of the retina is nearly impossible. We frame this problem as a user-interface (UI) challenge. We can create a better UI by controlling the eye box of a projected cue. Our key concept is to exploit the reciprocity, "If you see me, I see you", to develop near eye alignment displays. Two technical aspects are critical: a) tightness of the eye box and (b) the eye box discovery comfort. We demonstrate that previous pupil forming display architectures are not adequate to address alignment in depth. We then analyze two ray-based designs to determine efficacious fixation patterns. These ray based displays and a sequence of user steps allow lateral (x, y) and depth (z) wise alignment to deal with image centering and focus. We show a highly portable prototype and demonstrate the effectiveness through a user study.

Image registration is a challenging task, with applications in surveillance, motion estimation, and fusion systems. Due to the diversity of sensors, local distortions and large image size, satellite images are often difficult to accurately register. In the literature, local descriptor-based processing techniques, such as scale-invariant feature transforms (SIAdaptive Block ProcessingFTs), have been applied to register satellite images, which provide robust features. However, these techniques suffer from a high-computational cost, lack of features, and low-distribution quality, which affect the registration accuracy. In this paper, we develop an algorithm to register satellite images based on adaptive block processing to increase the number of features and to improve the distribution quality. In addition, outlier removal using statistical masks are associated with classical random sample consensus (RANSAC); a subsequent comparative analysis demonstrates the accuracy of the proposed method. Typically, a classical SIFT prevents its wide application in recent remote sensing, although this is no longer the case with the proposed adaptive block processing method.