The emergence of mobile and battery operated multimedia systems and the diversity of supported applications mount new challenges in terms of design efficiency of these systems which must provide a maximum application quality of service (QoS) in the presence of a dynamically varying environment. These optimization problems cannot be entirely solved at design time and some efficiency gains can be obtained at run-time by means of self-adaptivity. In this paper, we propose a new cross-layer hardware (HW)/software (SW) adaptation solution for embedded mobile systems. It supports application QoS under real-time and lifetime constraints via coordinated adaptation in the hardware, operating system (OS), and application layers. Our method relies on an original middleware solution used on both global and local managers. The global manager (GM) handles large, long-term variations whereas the local manager (LM) is used to guarantee real-time constraints. The GM acts in three layers whereas the LM acts in application and OS layers only. The main role of GM is to select the best configuration for each application to meet the constraints of the system and respect the preferences of the user. The proposed approach has been applied to a 3D graphics application and successfully implemented on an Altera FPGA.

Currently, we are witnessing a wide range of Wireless Sensor Networks (WSNs) in various fields of application. In most cases, these networks contain numerous battery-powered sensor nodes. Therefore, the lifetime of WSNs is intrinsically dependent. For this purpose, different studies have been conducted to reduce the power consumption. We propose a new technique for reducing the power consumption of clustered WSNs. We considered clusters in which sensors transmitted data to a Cluster Head (CH) by cooperative communication based on the Bit Error Probability (BEP) criterion for the multi-relay technique. Therefore, the proposed technique uses a multi-relay to reduce the transmission power to communicate between the source and destination which respects the predefined reliability level. The proof of the described technique was performed using a multiprocessor architecture. Second, we used an efficient software/hardware (Sw/Hw) architecture that utilized accelerator modules. The experimental results demonstrate significant gains in terms of energy consumption and workload compared to the SW implementation.

Augmented reality (AR) systems emerged and become a very gifted 3D embedded multimedia application. AR consists on adding specific 3D's animations on a video flow. The design and the implementation of such systems on FPGA are complex and difficult. To reduce computational resources that must be carefully used to execute complex application. This execution can be often done in unpredictable environments: it is the major problem to solve. The system architecture must be efficient and flexible enough to adapt system resources to the application requirements and the environment architectures and mobile's constraints. In this paper, we describe our concept of flexible architecture: we have developed IPs to obtain self-adaptive augmented reality systems to implement on FPGA.

The increasing popularity of nomad multimedia systems put new challenges for their design: increasing functionalities, limited energy and computation resources. These systems must provide a maximum application quality of service (QoS) in the presence of a dynamically varying environment (e.g. video streaming and multimedia conferencing) and multiple resources constraints. To respond to the changing resource availability and application demands, a new class of adaptation method is emerged. It combines the adaptation simultaneously upon the different layers related to the target system: hardware, application and OS. This paper presents an overview of a multilayer. The global manager (GM) is uses to handle large and long-term variations whereas the local manager (LM) is used to guarantee the real time constraint. This paper focuses on the LM that intervenes only in the application layer and OS layer.

The internal quality assessment of potato tubers is a crucial task in agro-laboratory processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in laboratory. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 95% and precision near 97.2% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.

The internal quality assessment of potato tubers is a crucial task in agro-industrial processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in industrial sorting lines. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 90\% and precision near 100\% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware.