Browsing by Author "Maiseli, Baraka"

Now showing 1 - 8 of 8
  • Thumbnail Image
    Item
    Collaborative Development and Utilization of iLabs in East Africa
    (2011) Mwikirize, Cosmas; Tumusiime, Arthur A.; Musasizi, Paul I.; Tickodri-Togboa, Sandy S.; Jiwaji, Adnaan; Nombo, Josiah; Maiseli, Baraka; Sapula, Teyana; Mwambela, Alfred
    Since 2005, Makerere University and the University of Dar es Salaaam have taken definitive steps to-ward the development and utilization of iLabs. This chapter presents the iLabs experiences of the two East African universities. The experiences presented here are characterized by: institutionalization of developer teams, development of ELVIS-based iLabs, staff & student exchanges, and utilization of iLabs to support curricula. The two universities have also undertaken to setup iLabs communities at peer universities and other higher institutions of learning in East Africa.
  • Thumbnail Image
    Item
    Diffusion-steered super-resolution method based on the Papoulis-Gerchberg algorithm
    (2016-05) Maiseli, Baraka
    Papoulis-Gerchberg (PG) algorithm, a technique to extrapolate signals, has attracted many researchers for its lower complexity, higher computational efficiency, and effectiveness. One field that receives these merits is super-resolution, which fuses multiple band-limited scenes to generate a high-resolution image. Most super-resolution methods based on the PG algorithm, however, underperform when input images are seriously degraded by blur, noise, and sampling. The current study addresses the challenges by embedding the PG algorithm into a super-resolution minimization problem. The proposed method is iterative and incorporates a diffusion-driven smoothness prior that updates its regularisation process according to the local image features. This well-crafted prior, which attempts to overcome the super-resolution ill-posedness, provides an automatic interplay between flat and contour regions, and ensures necessary levels of regularisations to generate sharper and detailed images. Results show that the current method outperforms some state-of-the-art super-resolution approaches including those based on total variation. Even more importantly, the authors' method contains a robust noise suppressor that treats comfortably noisy scenes.
  • Thumbnail Image
    Item
    In-Body Sensor Communication: Trends and Challenges
    (IEEE, 2021-07-07) Mohamed, Marshed; Maiseli, Baraka; Ai, Yun; Mkocha, Khadija; Al-Saman, Ahmed
    Wireless body area networks (WBANs) consist of interconnected devices that monitor the human body functions and the surrounding environment. Of these sensors, implants encounter multiple challenges due to their invasive nature. In addition, the transmission channel of the implants involves living tissues that pose practical challenges in channel modeling. Despite several promising applications of implants in the healthcare industry, there have been insufficient comprehensive reviews that extensively describe trends and challenges of this technology. This work reviews in-body WBANs and presents critical challenges that hinder advancement and application of the technology. We also discuss possible solutions that may be useful to realize in-body WBANs practically.
  • Thumbnail Image
    Item
    A multi-frame super-resolution method based on the variable-exponent nonlinear diffusion regularizer
    (2015-07) Maiseli, Baraka; Ogada, Elisha A.; Gao, Huijun
    In this work, the authors have proposed a multi-frame super-resolution method that is based on the diffusion-driven regularization functional. The new regularizer contains a variable exponent that adaptively regulates its diffusion mechanism depending upon the local image features. In smooth regions, the method favors linear isotropic diffusion, which removes noise more effectively and avoids unwanted artifacts (blocking and staircasing). Near edges and contours, diffusion adaptively and significantly diminishes, and since noise is hardly visible in these regions, an image becomes sharper and resolute—with noise being largely reduced in flat regions. Empirical results from both simulated and real experiments demonstrate that our method outperforms some of the state-of-the-art classical methods based on the total variation framework
  • Thumbnail Image
    Item
    Nonlinear anisotropic diffusion methods for image denoising problems: Challenges and future research opportunities
    (Elsevier, 2023-03-01) Maiseli, Baraka
    Nonlinear anisotropic diffusion has attracted a great deal of attention for its ability to simultaneously remove noise and preserve semantic image features. This ability favors several image processing and computer vision applications, including noise removal in medical and scientific images that contain critical features (textures, edges, and contours). Despite their promising performance, methods based on nonlinear anisotropic diffusion suffer from practical limitations that have been lightly discussed in the literature. Our work surfaces these limitations as an attempt to create future research opportunities. In addition, we have proposed a diffusion-driven method that generates superior results compared with classical methods, including the popular Perona–Malik formulation. The proposed method embeds a kernel that properly guides the diffusion process across image regions. Experimental results show that our kernel encourages effective noise removal and ensures preservation of significant image features. We have provided potential research problems to further expand the current results.
  • Thumbnail Image
    Item
    Perona–Malik model with self-adjusting shape-defining constant
    (Information Processing Letters, 2018-09-01) Maiseli, Baraka; Msuya, Hubert; Kessy, Suzan; Kisangiri, Michael
    For decades, the Perona–Malik (PM) diffusion model has been receiving a considerable attention of scholars for its ability to restore detailed scenes. The model, despite its promising results, demands manual tuning of the shape-defining constant—a process that consumes time, prompts for human intervention, and limits flexibility of the model in real-time systems. Most works have tried to address other weaknesses of the PM model (non-convexity and non-monotonicity, which produce chances for instability and multiple solutions), but automating PM remains an open-ended question. In this work, we have introduced a new implementation approach that fully automates the PM model. In particular, the tuning parameters have been conditioned to ensure that the model guarantees convergence and is entirely convex over the scale-space domain. Experiments show that our implementation strategy is flexible, automatic, and achieves convincing results.
  • Thumbnail Image
    Item
    Robust edge detector based on anisotropic diffusion-driven process
    (2015-12) Maiseli, Baraka; Gao, Huijun
    Edge detection involves a process to discriminate, highlight, and extract useful image features (edges and contours). In many situations, we prefer an edge detector that distinguishes these features more accurately, and which comfortably deals with a variety of data. Our observations, however, discovered that most edge-defining functionals underperform and generate false edges under poor imaging conditions. Therefore, the current research proposes a robust diffusion-driven edge detector for seriously degraded images. The method is iterative, and suppresses noise while simultaneously marking real edges and deemphasizing false edges. The anisotropic nature of the new functional helps to remove noise and to preserve semantic structures. Even more importantly, the functional exhibits a forward–backward behavior that may sharpen and strengthen edges. Comparisons with some other classical approaches demonstrate superiority of the proposed approach.
  • Thumbnail Image
    Item
    Targets Interaction in Through-The-Wall Radars under Path-Loss Compensated Multipath Exploitation-Based Model for Sparse Image Reconstruction
    (University of Dar es Salaam, 2019) Kokumo, Emmanuel; Maiseli, Baraka; Abdalla, Abdi T
    Multipath caused by reflections from interior walls of buildings has been a long-standing challenge that affects through-the-wall radar imaging. Multipath creates ghost images that introduce confusion when detecting desired targets. Traditionally, multipath exploitation techniques under the compressive sensing framework have widely been applied to address the challenge. However, the multipath component emanating from target-to-target interactions has not been considered–a consequence that may, under multiple target scenarios, lead to incorrect image interpretation. Besides, far targets experience more attenuation due to free space path loss, hence resulting into target undetectability. This study proposes a signal model, based on multipath exploitation techniques, by designing a sensing matrix that incorporates multipath returns due to target-to-target interaction and path loss compensation. The study, in addition, proposes the path loss compensator that, if integrated into the proposed signal model, reduces path loss effects. Simulation results show that the Signal to Clutter Ratio and the Relative Clutter Peak improved by 4.9 dB and 1.9 dB, respectively, compared with the existing model