Browsing by Author "Kaijage, Shubi"

Now showing 1 - 20 of 60

Air Pollution Monitoring System based on Wireless Networks - Simulation
(Innovative Systems Design and Engineering, 2014) Swagarya, Godbless; Kaijage, Shubi; Sinde, Ramadhani
Air pollution is one of environmental issues that cannot be ignored. Industrial growth and urbanization results in the air pollutants concentrations in many areas. These pollutants can cause damages in human health and other living organisms. The available pollutant emission monitoring systems, such as Opsis, Codel, Urac and TAS-Air metrics are typically expensive. In addition, these systems have limitations to be installed on chimney due to their principle of operation. This causes other areas surrounding the factories being unmonitored and hence cause healthy issues. This paper proposes an industrial air pollution monitoring system based on the technology of wireless sensor networks (WSNs). This system is integrated with the global system for mobile communications (GSM) and its communication protocol used is zigbee. The system consists of sensor nodes, a control center and data base through which sensing data can be stored for history and future plans. The proposed system can be deployed to the industries for monitoring carbon monoxide (CO), sulfur dioxide (SO2) and dust concentration caused by industrial emissions due to process.
Automated Optimization-Based Deep Learning Models for Image Classification Tasks
(Computers, 2023-09-01) Migayo, Daudi; Kaijage, Shubi; Swetala, Stephen; Nyambo, Devotha
Applying deep learning models requires design and optimization when solving multi- faceted artificial intelligence tasks. Optimization relies on human expertise and is achieved only with great exertion. The current literature concentrates on automating design; optimization needs more attention. Similarly, most existing optimization libraries focus on other machine learning tasks rather than image classification. For this reason, an automated optimization scheme of deep learning models for image classification tasks is proposed in this paper. A sequential-model-based optimization algorithm was used to implement the proposed method. Four deep learning models, a transformer-based model, and standard datasets for image classification challenges were employed in the experiments. Through empirical evaluations, this paper demonstrates that the proposed scheme improves the performance of deep learning models. Specifically, for a Virtual Geometry Group (VGG-16), accuracy was heightened from 0.937 to 0.983, signifying a 73% relative error rate drop within an hour of automated optimization. Similarly, training-related parameter values are proposed to improve the performance of deep learning models. The scheme can be extended to automate the optimization of transformer-based models. The insights from this study may assist efforts to provide full access to the building and optimization of DL models, even for amateurs.
Characteristics of Ultrasensitive Hexagonal-Cored Photonic Crystal Fiber for Hazardous Chemical Sensing
(MDPI, 2022-01-10) Maidi, Abdul; Shamsuddin, Norazanita; Wong, Wei-Ru; Kaijage, Shubi; Begum, Feroza
A highly sensitive non-complex cored photonic crystal fiber sensor for hazardous chemical sensing with water, ethanol, and benzene analytes has been proposed and is numerically analyzed using a full-vector finite element method. The proposed fiber consists of a hexagonal core hole and two cladding air hole rings, operating in the lower operating wavelength of 0.8 to 2.6 μm. It has been shown that the structure has high relative sensitivity of 94.47% for water, 96.32% for ethanol and 99.63% for benzene, and low confinement losses of 7.31 × 10−9 dB/m for water, 3.70 × 10−10 dB/m ethanol and 1.76 × 10−13 dB/m benzene. It also displays a high power fraction and almost flattened chromatic dispersion. The results demonstrate the applicability of the proposed fiber design for chemical sensing applications.
Cluster based wireless sensor network for forests environmental monitoring
(International Journal of Advanced Technology and Engineering Exploration, 2020-02) Sinde, Ramadhani; Kaijage, Shubi; Njau, Karoli
Monitoring the forest’s weather has been essential to living things over the years. Currently, there is a shortage of information on real-time temporal and spatial environmental conditions of the forest that drive forest health condition. This work focuses on the sensing of humidity and temperature as weather data from the forest. Unlike the traditional systems used to collect weather information, the use of wireless sensor network (WSN) gives real-time data capture from every point of the forest. However, the WSN faces, the number of challenges, including low bandwidth, low power, and short battery lifespan. In this situation, batteries cannot be replaced since nodes are deployed in an inaccessible area. In order to prolong the network lifetime and reduce the network delay, we propose Zone based Clustering (ZbC) scheme and efficient routing to find the best path between source and cluster head. Initially, we deploy sensor nodes in three coronas namely C1, C2 and C3. We place the sink node at the center of the coronas. Based on the center point of the corona, we split each corona into four partitions each with three zones. Our work composed of two phases such as ZbC and Routing. In the first phase, we reduce energy consumption in data aggregation via ZbC scheme. In ZbC scheme, the hybrid Particle Swarm Optimization (PSO) and Affinity Propagation (AP) algorithm are utilized. Network delay is reduced in the second phase using Ant Colony Optimization (ACO) and FireFly Algorithm (FFA). Simulation results confirm that our proposed solution achieves a higher network lifetime up to 30%, reduces delay up to 35% and enhances throughput compared to the existing cooperative Time Division Multiple Access (cTDMA), Dynamic Random Allocation (DRA) and improved Artificial bee colony (iABC) methods.
A Comparative Study of Assistive Technologies for Physically challenged peoples for usability of Powered Wheelchair Mobility Aid
(International Journal of Advances in Scientific Research and Engineering (ijasre), 2022-01) Rwegoshora, Florian; Leo, Judith; Kaijage, Shubi
Physical disabilities have always been a big issue in our communities. Ageing, sickness, and other variables have all had a role in the creation of these issues. That is why Powered wheelchairs were designed to aid people with physical disabilities. Wheelchair users have been exposed to a variety of assistive technologies designed to improve their mobility. As a result, different assistive technologies have recently played a significant role in assisting wheelchair users with movement, this is because technology changes so quickly. The recent trendy of assistive technologies include the joystick, brain-computer interface, voice recognition, tongue drive system, eye tracer, and sip and puff. However, some of the most beneficial assistive technologies become difficult to utilize due to technological gaps among individuals in particular nations. The objective of this research to study and review the comparative study of these assistive technologies for Physical Disabilities. In the study, tongue drive system, eye tracer, voice recognition, and sip and puff technologies are compared to joystick assistive technology. The comparison is made based on selected parameters including usability commands, fatigue, response time, information transfer rate, effects, and costs. Based on review, the researchers propose the design of appropriate wheelchairs with assistive technology for developing countrie
Constraints hindering ICT integration among teachers in enhancing literacy and numeracy skills of learners with hearing impairments in Tanzania
(2025-05-06) Mtani, Hamadi; Kaijage, Shubi; Mduma, Neema
Background The right to education is fundamental for all learners, including those with hearing impairments. The use of ICT makes the learning experience more attractive, accessible, and flexible for these learners. Objective The primary motivation behind this research is to identify various constraints hindering ICT integration among primary school teachers aiming to enhance literacy and numeracy skills among learners with hearing impairments. Methods A qualitative approach is employed in this study, and 10 primary school teachers who teach early-grade learners with hearing impairments were interviewed. Both purposive and convenience sampling were used to select the participants in the research. Thematic analysis was used to analyze the data using a priori technique or deductive approach from the Technology-Organization-Environment (TOE) framework. Results The findings revealed that a lack of ICT tools that support hearing-impaired learners, limited ICT tools that support individualized learning, poor support from school management for ICT facilities, the absence of a curriculum specific to hearing-impaired learners, and limited technological support infrastructure hinder effective ICT integration by teachers. Conclusions These findings highlight the need for comprehensive strategies by the government, educational authorities, and other stakeholders to effectively integrate ICT among teachers, thereby enhancing literacy and numeracy skills among learners with hearing impairment in Tanzania’s special or deaf unit primary schools.
Dataset Development for Automated Grade Labelling of Virginia Flue-Cured Tobacco Leaves in Tanzania: A Focus on Stalk Leaf Position
(Indian Journal of Science and Technology, 2025-02-26) Nguleni, Faith; Nyambo, Devotha; Lisuma, Jacob; Kaijage, Shubi
Objectives: This study aimed at developing a dataset for automated grade labeling of Virginia flue-cured tobacco leaves based on stalk leaf position by focusing on quality, colour and anomalies. Methods: Virginia flue-cured tobacco leaves were collected from four Tanzanian tobacco regions: Tabora municipal, Uyui, Urambo and Kaliua. Canon 5D Mark III cameras with a Canon EF 100mm F/2.8L Macro IS USM lens were used to capture tobacco leaves. The collected data concentrated on the upper leaves of the tobacco plant, also known as leaf position. In Tanzania, the tobacco plant position is a very crucial entity during grade labeling processes. Findings: To fulfil the study’s intention, a dataset was created by collecting Virginia flue-cured tobacco leaf images. The study utilized our published dataset of Virginia flue-cured tobacco leaf images, which consisted of 49,779 high-resolution images with 22 grade labels (classes). Novelty: The important findings highlight the dataset's quality, making it crucial to develop automated systems for Virginia flue-cured tobacco leaf grade labeling processes based on stalk leaf position.labeling
Dataset of Virginia Flue-cured Tobacco Leaf images based on stalk leaf position for classification tasks: A case of Tanzania
(Elsevier, 2024-10) Nguleni, Faith; Nyambo, Devotha; Lisuma, Jacob; Kaijage, Shubi
Nicotiana tabacum is a kind of plant cultivated for its leaves used for manufacturing medicine and cigarettes. With the common name, the Tobacco plant is grown in many countries including China, Indonesia, Malawi and Tanzania just to mention a few. Literatures suggest a technical gap in the proper identification of grade labels for various parts of the plant. In addition, manual grading has resulted in various gaps and biases. To mitigate this, a data-driven grading solution is necessary. However, relevant datasets to train grade classifiers from various countries become of the essence. This article presents images concentrated on tobacco leaf plant position namely Leaf position which normally carries 23 grade labels. Due to high rainfall which swiped away the applied fertilizer on the tobacco plants in the farms, we failed to get images of one grade. Therefore, this research could capture and label 22 grade labels. Images of tobacco leaves based on the tobacco plant position were collected in Tanzania through participatory community research. Canon 5D mark III cameras with 100 mm micro lens were used to take pictures of tobacco leaves based on the tobacco plant position. Domain experts were used for image labelling and cleaning according to tobacco grade labels identified in Tanzania. The dataset carries 49,779 images, which can be used to develop machine learning models for tobacco leaf grade label identification. The collected dataset can be used to train models and enhance the performance of pre-trained models in any country of interest.
Deep learning models for enhanced forest-fire prediction at Mount Kilimanjaro, Tanzania: Integrating satellite images, weather data and human activities data
(Elsevier, 2024-12-08) Mambile, Cesilia; Kaijage, Shubi; Leo, Judith
Forest fires (FFs) are a growing threat to ecosystems and human settlements, particularly in vulnerable regions such as Mount Kilimanjaro, Tanzania. Accurate and timely fire prediction is essential to mitigate these risks and improve fire management strategies. This study develops and evaluates advanced Deep Learning (DL) models for FF prediction by integrating spatiotemporal vegetation indices, environmental data, and human activity in- dicators. Specifically, Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNNs), and Convolu- tional Long Short-Term Memory (ConvLSTM) models were employed to analyze Sentinel-2 satellite imagery and weather data, along with anthropogenic factors such as beekeeping, tourism, agriculture, and deforestation rates. Leveraging this diverse, high-dimensional dataset, the ConvLSTM model engineered to capture intricate spatial and temporal relationships delivered superior performance, achieving an AUROC of 0.9785 and Accuracy 98.08%, surpassing the LSTM and CNN models. Integrating human-induced activities with environmental data, these models provide accurate and actionable predictions for fire management in high-risk areas. This study demonstrates the potential of ConvLSTM in developing operational tools for early fire detection, streamlining data-driven decision-making, improving resource allocation, and guiding preventive strategies in fire-prone re- gions such as Mount Kilimanjaro.
Deep Reinforcement Learning based Handover Management for Millimeter Wave Communication
(International Journal of Advanced Computer Science and Applications,, 2021) Mollel, Michael; Kaijage, Shubi; Michael, Kisangiri
The Millimeter Wave (mm-wave) band has a broad-spectrum capable of transmitting multi-gigabit per-second date-rate. However, the band suffers seriously from obstruction and high path loss, resulting in line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions. All these lead to significant fluctu-ation in the signal received at the user end. Signal fluctuations present an unprecedented challenge in implementing the fifth gen-eration (5G) use-cases of the mm-wave spectrum. It also increases the user’s chances of changing the serving Base Station (BS) in the process, commonly known as Handover (HO). HO events become frequent for an ultra-dense dense network scenario, and HO management becomes increasingly challenging as the number of BS increases. HOs reduce network throughput, and hence the significance of mm-wave to 5G wireless system is diminished without adequate HO control. In this study, we propose a model for HO control based on the offline reinforcement learning (RL) algorithm that autonomously and smartly optimizes HO decisions taking into account prolonged user connectivity and throughput. We conclude by presenting the proposed model’s performance and comparing it with the state-of-art model, rate based HO scheme. The results reveal that the proposed model decreases excess HO by 70%, thus achieving a higher throughput relative to the rates based HO scheme.
Design and Analysis of Smart Sensing System for Animal Emotions Recognition
(International Journal of Computer Applications, 2017-07) Massawe, Erick Alphonce; Michael, Kisangiri; Kaijage, Shubi; Seshaiyer, Padmanabhan
Recently, animal emotion recognition has become an important field for developing intelligent systems for tracking and monitoring rhinos and elephants. In this work, a smart sensing system that helps in detecting animal emotions based on information from physiological parameters obtained from sensors attached on animal body, has been designed. The signals are continuously obtained from a heart rate sensor,galvanic skin resistance sensorand body temperature sensor. After amplifying and filtering of the signals from the sensors are done, they are processed in the microcontroller and transmitted wirelessly using GSM modem and ZigBee technologies.The signals which are received from the system are displayed and stored in the database where they are analyzed visually for patterns. The four basic emotions parameters observed in this project are happy (excited), sad, angry and neutral (relaxed). In this research dog have been used for the pilot study.
Design and Simulation of Photonic Crystal Fiber for Liquid Sensing
(MDPI, 2021-01-12) Maidi, Abdul; Yakasai, Izaddeen; Abas, Emeroylariffion; Nauman, Malik; Apong, Rosyzie; Kaijage, Shubi; Begum, Feroza
A simple hexagonal lattice photonic crystal fiber model with liquid-infiltrated core for different liquids: water, ethanol and benzene, has been proposed. In the proposed structure, three air hole rings are present in the cladding and three equal sized air holes are present in the core. Numerical investigation of the proposed fiber has been performed using full vector finite element method with anisotropic perfectly match layers, to show that the proposed simple structure exhibits high relative sensitivity, high power fraction, relatively high birefringence, low chromatic dispersion, low confinement loss, small effective area, and high nonlinear coefficient. All these properties have been numerically investigated at a wider wavelength regime 0.6–1.8 μm within mostly the IR region. Relative sensitivities of water, ethanol and benzene are obtained at 62.60%, 65.34% and 74.50%, respectively, and the nonlinear coefficients are 69.4 W−1 km−1 for water, 73.8 W−1 km−1 for ethanol and 95.4 W−1 km−1 for benzene, at 1.3 μm operating wavelength. The simple structure can be easily fabricated for practical use, and assessment of its multiple waveguide properties has justified its usage in real liquid detection.
Design of an Integrated Android Mobile Application and Web-Based System (IAMAWBS) as a Solution to Concerns of Passengers Using Bus Rapid Transit System for Public Transportation in Dar Es Salaam
(Modern Education and Computer Science Press, 2019-02-08) Alfred, Reuben; Kaijage, Shubi
The rapid population growth in Dar Es Salaam has prompted the demand of effective transport system in the city. This tremendous rise of population led to serious road traffic congestions, which brings a number of challenges into the city and other growing urban areas. City authorities attempted various solutions to control the traffic congestions such as construction of new roads, expansion of existing roads, installation of traffic lights and other transportation infrastructures such as reestablishment of commuter train to operate within the city but they couldn’t effectively relieve the problem. Eventually, the Government of Tanzania (GoT) supported the city’s effort by establishing the organ called Dar Es Salaam Rapid Transit (DART) to supervise the implementation and operation of Bus Rapid Transit (BRT) system. The BRT system provided direct benefits to passengers such as minimal travel time, improved reliability as compared to other public transport commonly known as daladala, and reduced accident as BRT buses travel in their dedicated lanes. Despite these benefits there still persist transportation challenges with the BRT, where passengers still suffer from waiting on very long queue during ticket booking, shortage of smart cards, they are unable to check balance direct from their mobile phones, as well as they fail to top-up onto their card’s balance using their smart phones. This paper presents a software technology approach that would help passengers to check balance, send request specifying station to board a bus and check the bus arrival time at any station.
Design of an Interactive Geo-Location Mobile Application for Civil Societies in East Africa
(Scientific Research Publishing Inc, 2021-10-13) Patrick, Emil; Leo, Judith; Kaijage, Shubi
This paper reports the developed mobile application through the use of ad- vanced technologies such as mobile computing, cloud computing and Global Positioning System (GPS) in order to solve the challenges. The purpose of this study is to find out how the recent advances and mass adoption of ICT by the public can best be leveraged to enhance the performance of Civil Society Organizations (CSOs) in East Africa. The data collection techniques used are through questionnaires, observations and conducting interviews with differ- ent stakeholders in the civil society arena i.e., the donors, the CSOs and the people in society that are given a voice by civil societies. In the system devel- opment phase, agile development methodology was used. Analysis of the data collected showed that there is a gap in the availability of a single or a centra- lized platform which can be easily accessed, user-friendly and reliable where different actors can readily get reliable and up-to-date information about the available CSOs they are interested in. To address this problem, an interactive online directory of CSOs has been developed. The platform is mobile based and enables CSOs to register and fill up their current details, thus ensuring that there is always correct and updated information. The platform is equipped with, among other features, a geo-mapping facility which enables users of the system to correctly geo-locate their civil societies of interest on a map view. The results of system evaluation showed that 88.125% of users were satisfied with the system basing on the evaluation criteria.
Design of an Interactive Mobile Application for Maternal, Neonatal and Infant Care Support for Tanzania
(Scientific Research Publishing, 2018-12-27) Mramba, Bernard; Kaijage, Shubi
Reducing maternal and infant deaths’ rates in the developing countries, particularly in sub-Sahara Africa, remain a big challenge. Despite efforts by governments, the reductions have been unsatisfactory. To accelerate the reduction in maternal and infant deaths, m-health has been proposed as a viable, economical and effective intervention, able to reach the low income and disadvantaged groups. Mobile phone-based applications are among the m-health interventions that have been found to have positive outcomes for different healthcare challenges, such as improving clinical attendance and skilled delivery, and reducing perinatal mortality. However, the adoption of smartphone-based applications for health in Tanzania has been slow. Some of the likely contributing factors might be low technology exposure by the majority of the population and English language skills’ limitations. In this work, we developed a mobile application for providing interactive support to users, thus complementing other solutions available such as SMS and other smartphone apps. The main advantage of this app is the presence of interactive features that enable patient-provider communication. We adopted the Rapid application development (RAD) model for developing the application. We used UML modeling language tools for designing the application. The mobile application’s technical architecture uses various technologies and system development tools such as PHP programming language for the web application, MySQL database manage
Design of the data-driven software application for identification, population monitoring, and risk assessment for lions in Serengeti Tanzania
(International Academy of Ecology and Environmental Sciences, 2024-09) Okey, Ambokile; Nyambo, Devotha; Kaijage, Shubi; Masenga, Emmanuel; Levi, Matana
This study presents a design of a Data-Driven software application for identification, population monitoring, and risk assessment for lions in Serengeti Tanzania. Lions’ populations have been declining due to poaching, overhunting, and other ecosystem factors resulting in unmet demands for tourism and ecological balance. Data-driven techniques can lower the negative consequences by providing mechanisms for lions’ management, risk assessment, and monitoring in selected wildlife reserves. Lion’s whisker spots, poaching rates, prey availability, human-conflict incidences, and pride size are key elements for achieving management, identification, monitoring, and risk assessment for lions. The software application design aimed at providing conceptual and logical requirements for the development of the application that will enhance lions’ monitoring and management efforts to protect their existence and contribution to the ecosystem. The study was conducted in the Serengeti ecosystem, including ecologists from the Tanzania Wildlife Research Institute Serengeti Wildlife Research Center, and information systems analysts. Through a mixed research methods approach, qualitative methods and incremental prototyping software development life cycle model were used to develop the specific requirements. Unified Modeling Language (UML) was used to model the requirements and led to the realization of design diagrams: application framework, database design, and artificial intelligence model workflows. The application should equip ecologists with tools to add and identify specific lions, monitor sightings, estimate population trends, assess risks for individual lions, and produce reports on monitoring and sightings. This design serves as a foundation for developing the data-driven software application for identification, population monitoring, and risk assessment for lions in Serengeti National Park Tanzania which will enhance monitoring and management activities of lions’ population non-invasively.
Detectron2-enhanced mask R-CNN for precise instance segmentation of rice blast disease in Tanzania: Supporting timely intervention and data-driven severity assessment
(Elservier, 2025-08-09) Alfred, Reuben; Leo, Judith; Kaijage, Shubi
Rice blast, caused by Magnaporthe oryzae, poses a significant threat to rice production in Tanzania and across Africa, affecting food security and farmers’ livelihoods. Traditional inspection methods are slow and often overlook early symptoms, leading to delayed responses. Although progress has been made with deep learning diagnostics, many approaches still depend on whole-image classification or broad bounding boxes, lacking them pixel-level detail needed to assess infection severity. This study introduces a Mask R-CNN instance segmentation model developed within the Detectron2 framework to accurately detect and segment blast lesions (BL), blast- infected leaves (BIL), and healthy leaves (HL) at the pixel level. In addition to detection, the model quantifies the lesion severity by computing the proportion of infected leaf area, supporting informed evaluation and improved disease management decisions. Built on a ResNet-50 backbone with a Feature Pyramid Network (FPN), it achieved a mean average precision (mAP) of 89.4 %, with an AP50 of 94.6 % and an AP75 of 90.5 %. The model exhibited consistent performance across object scales, achieving an AP of 81.31 % for small objects and 86.06 % for large objects. Furthermore, testing on unseen images (images not used in the training process) demonstrated strong generalization, with detection confidence above 99 % and accurate masks that provide reliable severity scores. By enabling pixel-level severity assessment without expensive sensors or UAVs, this study offers a practical and affordable solution for disease monitoring in resource constrained farming communities. It equips Tanzanian smallholder farmers with timely, accessible tools for effective blast detection and data-driven decision making.
Development and Testing of Adaptive Vehicle Speed Monitoring System integrated with Alcoholic Detector for Public Buses: A case of Tanzania
(International Journal of Computer Applications, 2015-10-07) Ramju, Farhan; Sinde, Ramadhani S.; Kaijage, Shubi
21 Development and Testing of Adaptive Vehicle Speed Monitoring System integrated with Alcoholic Detector for Public Buses: A case of Tanzania Farhan Ramju Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. Ramadhani S. Sinde Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. Shubi Kaijage Department of Communication Science and Engineering Nelson Mandela African Institution of Science and Technology (NM-AIST), Arusha, Tanzania. ABSTRACT Road accidents are the serious humanity and public health issue in Tanzania. The problem is increasing day by day. Apart from the loss of many lives, the effect of the road crashes on the country’s economy is massive. In Tanzania Human factors is the main contribution of major road crashes while over-speeding and drinking driving is one of the accelerating factors to the increase of road casualties. Existing measures to limit these problems have been unsuccessful to diminish the road accidents thus only use of handheld devices such as the speed radar gun and breath analyzer is applicable during inspection on the road or check points. Since these devices are not automatic in the sense that they would need to be operated manually by the Traffic Police, they lack the continuous monitoring of speed and therefore their efficiency in speed detection is low. To address these challenges, an adaptive vehicle speed monitoring system integrated with alcohol detector is utmost important. This chapter attempts to develop an effective solution for vehicle speed monitoring and alcohol detection on a real time basis. The main objective of this paper is to develop an adaptive vehicle speed monitoring integrated with an alcoholic detection system able to monitor the vehicle speed into defined speed limits and driver’s alcoholic content (Blood Alcoholic Content) during the journey on the road. The system consists of GPS module that measures the distance and calculates the accurate speed of moving objects and also provides a location in term of latitude and Longitude, sensor nodes to measure the level of alcoholic content through breath, Arduino controller also used to drive the operation of the system. The system is integrated with LCD display for the driver and GSM network to send the message to the database to be stored for future uses and constantly updating the law enforcers (traffic policies) on what is going on in the roads and take prompt action in case of misbehaving. The system will help most of traffic police in finding out driver’s behavior on the road and also public buses that are daily victims of road accidents results due to the Human factors.
Development of Self Speaking Body Weight Scale for Visually Impaired People in Tanzania
(International Journal of Advances in Scientific Research and Engineering, 2021-07) Nzasangamariya, Gloriose; Sinde, Ramadhani; Kaijage, Shubi
Speaking weight scale is an important low vision health aid that measures and announces the measured weight. It is valuable in numerous applications such as Bathroom scale, Kitchen scale, and more. Different talking scales have been developed for the blindcommunity. Many talking scales have language options for English, German, French, or Spanish. However, only limited work exists for Selfspeaking visually impaired community in EAC given the fact that no talking scale can announce weight in Selfwhich is the common language in EAC. Therefore, this project aims to develop a Self-speaking weighing machine to assist visually impaired people in Tanzania. The developed device is divided into two major parts. On the front end of the design, sensors are used to capture weight parameters. The captured values are mapped onto a sequence of voice patterns. The back-end consists of transferring a sequence of voice patterns to a loudspeaker whereby the voice patterns are stored on an SD card. Finally, the developed device has been evaluated on several objects with known weights. The results show that the developed device accurately measures weight, displays weight, and announces it in the Selflanguage. However, blind people still need assistance from sightedpersons to be directed to the scale’s platform. The developed device has great potential as a low vision health aid for Selfspeakers. Moreover, the features of this device can be further improved by integrating iBeacon technology to increase the autonomy of blind people to use the scale and navigate to the device’s location safely.
Extremely low material loss and dispersion flattened TOPAS based circular porous fiber for long distance terahertz wave transmission
(Elsevier, 2016) Islam, Saiful; Sultana, Jakeya; Rana, Sohel; Islam, Mohammad; Faisal, Mohammad; Kaijage, Shubi; Abbott, Derek
In this paper, we present a porous-core circular photonic crystal fiber (PC-CPCF) with ultra-low material loss for efficient terahertz wave transmission. The full vector finite element method with an ideally matched layer boundary condition is used to characterize the wave guiding properties of the proposed fiber. At an operating frequency of 1 THz, simulated results exhibit an extremely low effective material loss of 0.043 cm 1, higher core power fraction of 47% and ultra-flattened dispersion variation of 0.09 ps/THz/cm. The effects of important design properties such as single mode operation, confinement loss and effective area of the fiber are investigated in the terahertz regime. Moreover, the proposed fiber can be fabricated using the capillary stacking or sol-gel technique and be useful for long distance transmission of terahertz waves.