Braille code classifications tool based on computer vision for visual impaired

Blind and visually impaired people (VIP) face many challenges in writing as they usually use traditional tools such as Slate and Stylus or expensive typewriters as Perkins Brailler, often causing accessibility and affordability issues. This article introduces a novel portable, cost-effective device that helps VIP how to write by utilizing a deep-learning model to detect a Braille cell. Using deep learning instead of electrical circuits can reduce costs and enable a mobile app to act as a virtual teacher for blind users. The app could suggest sentences for the user to write and check their work, providing an independent learning platform. This feature is difficult to implement when using electronic circuits. A portable device generates Braille character cells using light- emitting diode (LED) arrays instead of Braille holes. A smartphone camera captures the image, which is then processed by a deep learning model to detect the Braille and convert it to English text. This article provides a new dataset for custom-Braille character cells. Moreover, applying a transfer learning technique on the mobile network version 2 (MobileNetv2) model offers a basis for the development of a comprehensive mobile application. The accuracy based on the model reached 97%

Exploring the Impact of Preprocessing Techniques on Retinal Blood Vessel Segmentation Using a Study Group Learning Scheme

The segmentation of retinal vessels in retinal images is vital for automated diagnosis of retinal diseases. This is a challenging task because it requires accurate manual labeling of the vessels by expert clinicians and the detection of tiny vessels is difficult due to limited samples, low contrast, and noise. In this study, we explore the use of preprocessing techniques such as contrast-limited adaptive histogram equalization (CLAHE), grad-cam analysis and min-max contrast stretching to improve the performance of a study-group learning (SGL) segmentation model. We evaluate the impact of these preprocessing techniques on the accuracy, sensitivity, specificity, AUC, IoU, and Dice scores using four publicly available datasets, DRIVE, CHASE, HRF and IOSTAR. Our findings indicate that the utilization of the Min-Max technique resulted in a notable enhancement in the accuracy of both the DRIVE and CHASE datasets, with an approximate increase of 3% and 2% respectively. Conversely, the impact of the CLAHE method was discernible solely in the DRIVE dataset, demonstrating an improvement in accuracy of 1%. In addition, our results demonstrated superior accuracy performance for both the DRIVE and CHASE datasets compared to the findings of the reviewed studies. The GitHub repo for this project is available at Link

Current advances in imaging spectroscopy and its state-of-the-art applications

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy.Anam Zahra, Rizwan Qureshi, Muhammad Sajjad, Ferhat Sadak, Mehmood Nawaz, Haris Ahmad Khan, Muhammad Uzai

The Moving Junction Protein RON8 Facilitates Firm Attachment and Host Cell Invasion in Toxoplasma gondii

The apicomplexan moving junction (MJ) is a highly conserved structure formed during host cell entry that anchors the invading parasite to the host cell and serves as a molecular sieve of host membrane proteins that protects the parasitophorous vacuole from host lysosomal destruction. While recent work in Toxoplasma and Plasmodium has reinforced the composition of the MJ as an important association of rhoptry neck proteins (RONs) with micronemal AMA1, little is known of the precise role of RONs in the junction or how they are targeted to the neck subcompartment. We report the first functional analysis of a MJ/RON protein by disrupting RON8 in T. gondii. Parasites lacking RON8 are severely impaired in both attachment and invasion, indicating that RON8 enables the parasite to establish a firm clasp on the host cell and commit to invasion. The remaining junction components frequently drag in trails behind invading knockout parasites and illustrate a malformed complex without RON8. Complementation of Δron8 parasites restores invasion and reveals a processing event at the RON8 C-terminus. Replacement of an N-terminal region of RON8 with a mCherry reporter separates regions within RON8 that are necessary for rhoptry targeting and complex formation from those required for function during invasion. Finally, the invasion defects in Δron8 parasites seen in vitro translate to radically impaired virulence in infected mice, promoting a model in which RON8 has a crucial and unprecedented task in committing Toxoplasma to host cell entry

Genome-wide screens identify Toxoplasma gondii determinants of parasite fitness in IFNγ-activated murine macrophages

Macrophages play an essential role in the early immune response against Toxoplasma and are the cell type preferentially infected by the parasite in vivo. Interferon gamma (IFNγ) elicits a variety of anti-Toxoplasma activities in macrophages. Using a genome-wide CRISPR screen we identify 353 Toxoplasma genes that determine parasite fitness in naїve or IFNγ-activated murine macrophages, seven of which are further confirmed. We show that one of these genes encodes dense granule protein GRA45, which has a chaperone-like domain, is critical for correct localization of GRAs into the PVM and secretion of GRA effectors into the host cytoplasm. Parasites lacking GRA45 are more susceptible to IFNγ-mediated growth inhibition and have reduced virulence in mice. Together, we identify and characterize an important chaperone-like GRA in Toxoplasma and provide a resource for the community to further explore the function of Toxoplasma genes that determine fitness in IFNγ-activated macrophages

Principles, Characteristics and Applications of Magneto Rheological Fluid Damper in Flow and Shear Mode

AbstractMagneto rheological (MR) fluid has been attracting great research attention because it can change its characteristics very rapidly and controlled easily in the presence of an applied magnetic field. The devices using MR fluid like dampers, clutches, polishing machines, hydraulic valves, etc., have a great promising future. Magneto-rheological (MR) dampers are semi active control devices that use MR fluids to suppress the vibrations. In this paper, the various modes of usage and characteristics are discussed. Mathematical modelingof the MR fluid dampers based on Bingham plastic model and Herschel Bulkley model are presented

simulation-optimization approach

In this study, a new simulation-optimization approach is proposed to solve the multi-pollutant waste load allocation (WLA) problems in surface water systems. The proposed approach simulates the fate and transport of multiple pollutants utilizing the AQUATOX model. Since AQUATOX is an independent model and its integration into the optimization process is computationally not efficient, a multi-pollutant concentration-response matrix (mpCRM) is developed by using the results of the AQUATOX model. This mpCRM is then integrated into an optimization model where a nonlinear generalized reduced gradient (GRG) optimization method is used. Unlike in previously conducted studies, a new optimization formulation is proposed wherein the multiple pollutant loads are allocated among the source locations depending on their pre-assigned load allocation weights. This formulation allows for an equal or variable pollutant load allocation plan among source locations depending on the water management strategy for the watershed. The applicability of the proposed approach is evaluated on a sub-watershed of the Kucuk Menderes River Basin (KMRB) in Turkey by considering different load allocation scenarios. Furthermore, a detailed sensitivity analysis is conducted to evaluate the model results for different problem parameters. Identified results suggest that the proposed simulation-optimization approach is an effective way to solve the multi-pollutant WLA problem.C1 [Sadak, Derya] Sakarya Univ, Dept Civil Engn, Sakarya, Turkey.[Ayvaz, M. Tamer] Pamukkale Univ, Dept Civil Engn, Denizli, Turkey.[Elci, Alper] Dokuz Eylul Univ, Dept Environm Engn, Izmir, Turkey