ABSTRACT Traditional multicast streams are typically sent using unicast routing plans, which follow a single path or, at best, multiple paths of the same cost. However, this method is not efficient for modern networks with various access points. To improve this, we propose a new approach that combines multipath and multicast streams with a load‐balancing model. This method aims to enhance multicast traffic transmission in a multipath environment. Our approach separates the Control Plan (CP) from the User Plan (UP). The CP uses Multipath TCP functions, while the UP handles IPTV (Internet Protocol Television) packets, which can be either TCP or UDP. This separation allows for better load distribution, bandwidth conservation, and balanced traffic across available paths. Real‐world tests show that all multicast substreams can be smoothly transferred across the network evenly divided and routed through suitable paths whether symmetrical or asymmetrical scénario. Thus, the results of tests on the main performance and reliability parameters such as bandwidth utilization, cross traffic and delay, prove the algorithm's proactivity and responsiveness to network conditions and validate the effectiveness of our approach. In summary, using multicast in multipath TCP networks improves the streaming experience for end users.

Summary The Multi‐Path Transmission Control Protocol is a new generation protocol for the transport layer which allows multihomed devices to exploit simultaneously their own interfaces. This feature would normally lead to some high‐performance achievement in regard to regular TCP. However, the heterogeneous paths might cause the occurrence of the 'out‐of‐order' problem which requires the intervention of a more efficient scheduler to reach a high level of global bandwidth utilization. Since scheduling plays a key role in performance improvements, several studies have been implemented exploiting this idea, namely those that adopt the reactive approach and those others adopting the proactive one. In this paper, our goal is to improve the multipath throughput. The testbed experimentation consists primarily in the evaluation and analysis of the behaviour of different recent scheduling approaches with an asymmetric paths topology. Then, we propose an alternating scheduling approach which shed the light on bandwidth utilization. The results show that this approach is a more efficient solution than taking the reactive and proactive ones separately as it is based on a preliminary study that we conducted on the main performance and reliability parameters and has been implemented in the asymmetric multipath networks. This technique has led to a smart scheduling that provides a high level of throughput aggregation.

This paper presents VCMalloc, a custom memory allocator based on a new dynamic memory management approach that keeps data allocated in a contiguous form in memory. It can preserve the virtual contiguity of data even after performing different memory operations like “Reallocation” and “Free” without consuming much system resources. A novel control plane that we call a “Memory Management System” is introduced with a set of algorithms for the physical-virtual memory mappings, assuring fast, efficient, and safe tracking of data and user pointers. The proposed allocator prioritizes virtual contiguity while it keeps the physical memory completely up to the operating system to manage. It was tested and compared with the current implementation of Malloc as well as a state-of-the-art memory allocator MIMalloc in the recent versions of the Microsoft Windows operating system. For the allocator itself, a considerable performance increase has been achieved in basic operations of up to 28% increase in allocation speed and 26% in reallocation speed. The data structures allocated by VCMalloc have shown remarkable performance increases of up to 31% faster for matrix multiplication. Furthermore, VCMalloc has also demonstrated its superiority in real-world SPEC CPU 2017 benchmark applications.

Deforestation has become a major problem consisting of a continuous regression of forested areas in the world, and for this purpose, an efficient detection of these changes has become more than necessary. In this work, a new method for deforestation change detection is proposed. This approach is based on a supervised fusion of local texture features extracted from SAR images. ALOS PALSAR (Advanced Land Observation Satellite Phased Array type L‐band Synthetic Aperture Radar) multi‐temporal data have been used in this work. Normalised radar cross‐section (NRCS) and polarimetric features extracted from HH and HV polarised data allowed recognising different categories of land covers termed as NRCS classification. Grey‐level co‐occurrence matrix (GLCM) texture features were extracted by using a different moving window sizes applied on local regions previously obtained by binarisation of the NRCS results. A total of 300 samples of regions and five GLCM characteristics have been used here. The detection of deforestation appears clearly in the resulted images with a very satisfactory precision of the reached regions, and the obtained results of the proposed supervised approach have indeed led to very good detection results of the deforestation change.

A recently developed sparse representation algorithm, has been proved to be useful for multi‐object tracking and this study is a proposal for developing its parallelisation. An online dictionary learning is used for object recognition. After detection, each moving object is represented by a descriptor containing its appearance features and its position feature. Any detected object is classified and indexed according to the sparse solution obtained by an orthogonal matching pursuit (OMP) algorithm. For a real‐time tracking, the visual information needs to be processed very fast without reducing the results accuracy. However, both the large size of the descriptor and the growth of the dictionary after each detection, slow down the system process. In this work, a novel accelerating OMP algorithm implementation on a graphics processing unit is proposed. Experimental results demonstrate the efficiency of the parallel implementation of the used algorithm by significantly reducing the computation time.

This paper presents an analysis and comparison of attitude control systems on-board Low Earth Orbit (LEO) microsatellites based on different reaction wheel configurations. Two configurations are presented, four reaction wheels configuration named tetrahedral configuration and classical three reaction wheels configuration. Three control algorithms, Proportional Derivative, Sliding Mode and Feedback Linearization using Euler angle formulation with different reaction wheel configurations are compared in terms of the settling time and power efficiency. Numerical analysis clearly indicates that the tetrahedral configuration with PD controller is a more logical choice for optimal 3-axis attitude maneuver for LEO microsatellite than the classical three reaction wheels. Also, the results prove that the controllers show the capability of providing accuracy better than 0.015 deg in attitude and 0.015 milli-deg/sec in rate.

In IKONOS imagery, both multispectral (MS) and panchromatic data are provided, with different spatial resolutions. IKONOS images can be fused or pan-sharpened for the visual interpretation of large-area-scale applications. To date, many image fusion techniques have been developed. In particular, the intensity-hue-saturation (IHS)-based methods are well known in quick image pan-sharpening. However, most of these processes produce color distortion due to the unnatural spectral response of IKONOS sensors, particularly in vegetated areas. Hence, many recent methods make use of vegetation enhancement in order to correct the unnatural color appearance. Generally, this enhancement is accomplished using vegetation indexes (VIs). A new fusion approach that produces images with natural colors is proposed. Moreover, in this technique, a high-resolution normalized difference VI is also proposed and used in delineating the vegetation. The procedure is performed in two steps: MS fusion using the IHS technique and vegetation enhancement. Vegetation enhancement is a correction step, and it depends on the considered application. The new approach provides very good results in terms of objective quality measures. In addition, visual analysis proves that the concept of the proposed approach is promising, and it improves well the fusion quality by enhancing the vegetated zones.

In this paper, we present a global optimisation method based on a multi-objective Genetic Algorithm (GA) for the design of filter banks in a lossy image coding scheme. To be effective, the filter banks should satisfy a number of desirable criteria related to such scheme. We formulate the optimization problem as multi-objective and we use the Non-dominated Sorting Genetic Algorithm approach (NSGAII) to solve this problem by searching solutions that achieve the best compromise between the different objectives criteria, these solutions are known as Pareto Optimal Solutions. Flexibility in the design is introduced by relaxing Perfect Reconstruction (PR) condition and defining a PR violation measure as an objective criterion to maintain near perfect reconstruction (N-PR) solutions. Furthermore, the optimized filter banks are near-orthogonal. This can only be made possible by minimizing the deviation from the orthogonality in the optimization process. Our designed filter banks lead to a significant improvement in performance of coding with respect to the 9/7 filter bank of JPEG2000 at high compression ratios and offer a slight improvement at low compression ratios.

This paper deals with a major problem encountered in the area of remote sensing consisting of the registration of multitemporal and/or multisensor images. In general, such images have different gray-level characteristics, and simple techniques such as those based on correlation cannot be applied directly. In this work, a new automatic satellite image registration approach is proposed. This technique exploits the invariant relations between regions of a reference and a sensed image, respectively. It involves an edge-based selection of the most distinctive control points (CPs) in the reference image. The search for the corresponding CPs in the sensed image is based on local similarity detection by means of template matching according to a combined invariants-based similarity measure. The final warping of the images according to the selected CPs is performed by using the thin-plate spline interpolation. The procedure is fully automatic and computationally efficient. The proposed algorithm for this technique has been successfully applied to register multitemporal SPOT and synthetic aperture radar images from urban and agricultural areas. The experimental results demonstrate the robustness, efficiency and accuracy of the algorithm.