9 research outputs found
Analysis of impedance based sensor to discover the invasive nature of A549 lung cancer cell
Get PDFNumerous studies have been conducted to investigate the effectiveness of impedancebased sensors in detecting the invasive behaviour of cancer cells, specifically through the
use of Electric Cell-substrate Impedance Sensing (ECIS)
methodology. However, the current equivalent circuit models
used to represent the invasive nature of cancer cells have
limitations and inaccuracies, and there has been a lack of utilization of mathematical and data analysis software to better understand the growth and invasive behaviour of these cells. To address these gaps, this research aims to measure the impedance of A549 lung cancer cells, develop a simplified equivalent circuit model, and analyse the results using mathematical and data analysis software. The invasive
behaviour of A549 cells will first be studied through impedance measurements, and then a circuit model will be designed and simulated using software tools to reveal the true invasive nature of these cells. Finally, rigorous mathematical analysis and the use of suitable data analysis software (such as Matlab Powergui and Simulink, EIS Spectrum Analyzer, and Plotly) will be applied to gain a comprehensive understanding of the morphological behaviour of the cells. The experimental results of this research are expected to align with previous findings, and a quadratic equation will be derived to predict the body's resistance of the A549 lung cancer cell using mathematical and data analysis approaches
APPLICATION OF IoT FOR MONITORING WATER QUALITY: SUNGAI PUSU CASE STUDY
Get PDFThe availability of clean water, a critical natural resource essential for supporting diverse ecosystems, is increasingly threatened by sediment accumulation, which negatively impacts rivers, oceans, and coastal environments. During rainy seasons, excessive sediment, including clay particles known as total suspended solids (TSS), contaminates water, compromising its quality, altering its color, and driving up treatment costs. Additionally, sediment can carry pollutants that reduce water clarity, harm aquatic life, and disrupt ecosystem functions. Conventional water treatment methods—such as coagulation, flocculation, sedimentation, and filtration—are commonly employed to address these challenges. However, traditional water quality monitoring relies heavily on laboratory tests that require specialized personnel, chemicals, and expertise, which may not always be adequate for timely and effective intervention. The advent of Internet of Things (IoT) technology offers a promising alternative by enabling the real-time collection of water quality data. Furthermore, integrating soft computing technology into water quality assessment presents a more efficient, rapid, and environmentally sustainable alternative to traditional laboratory-based approaches. An IoT device will be utilized to monitor the performance of a water treatment system and gather data on its water quality indicators. This research can aid in assessing water quality and offering valuable insights to decision-makers on how to maintain or enhance it
Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices
Get PDFRecently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). One promising application of computer vision techniques in ITS is in the development of low- computing and accurate vehicle counting systems that can be used to eliminate dependence on external cloud computing resources. This paper proposes a compact, reliable and real-time vehicle counting solution which can be deployed on low-computational requirement edge computing devices. The system makes use of a custom-built vehicle detection algorithm based on the you only look once version 8 nano (YOLOv8n), combined with a deep association metric (DeepSORT) object tracking algorithm and an efficient vehicle counting method for accurate counting of vehicles in highway scenes. The system is trained to detect, track and count four distinct vehicle classeses, namely: car, motorcycle, bus, and truck. The proposed system was able to achieve an average vehicle detection mean average precision (mAP) score of 97.5%, a vehicle counting accuracy score of 96.8% and an average speed of 19.4 frames per second (FPS), all while being deployed on a compact Nvidia Jetson Nano edge-computing device. The proposed system outperforms other previously proposed tools in terms of both accuracy and speed
Monitoring water quality in Pusu river using Internet of Things (IoT) and Machine Learning (ML)
No full textThe availability of clean water, a vital natural resource that supports diverse ecosystems, is increasingly threatened by sediment accumulation which impacts rivers, oceans, and coastal life, which is in line with sustainable development number goal 6 clean water and sanitation. Rapid industrialization and urbanization have intensified these challenges, leading to the degradation of natural water ecosystems and placing an undue strain on water resources. Pollution from sediments and human activities carries harmful contaminants, reduces visibility, disrupts aquatic life, and impairs ecosystem function. Maintaining the health of rivers and other water bodies requires the timely detection of changing conditions and deterioration, which is crucial for implementing effective countermeasures. However, current water quality monitoring methods primarily rely on laboratory tests, which require specialized staff, chemicals, and expertise. These traditional methods are often insufficient for addressing the complex and dynamic issues of water quality. Fortunately, the advent of the Internet of Things (IoT) technology has enabled real-time collection of water quality data. In addition, the application of soft computing technology for water quality assessment offers a more efficient, faster, and environmentally friendly alternative to conventional laboratory-based techniques. In this dissertation, we propose the use of an IoT device to monitor the performance of a water treatment system and collect data on key water quality indicators. Machine learning (ML) tools will be employed to analyze and simulate these data, enabling the prediction of future water quality parameters. The water quality dataset was collected in two stages. During the first iteration, data were gathered using sensors that measured four parameters: pH, turbidity, temperature, and total dissolved solids (TDS). In the subsequent iteration, the dataset was expanded to include a dissolved oxygen sensor in addition to the initial four sensors. The data collection process for turbidity and other water quality parameters involved more than just 879 data points, the data collection process was comprehensive, and the dataset was validated and analyzed with seasonal changes in mind, systematic approach ensured that the water quality parameters data collected were reliable, accurate, and actionable for monitoring water quality in the river. The dataset encompasses samples from three distinct potability classes: potable water sources, free-flowing river water from the Pusu River, and stagnant water from the puddles, and potholes. Nine proven classification algorithms were applied to the datasets, successfully classifying the water quality conditions with up to 98% accuracy. The best-performing model was then deployed and integrated into a graphical user interface (GUI) for rapid water condition testing, thereby facilitating the instantaneous assessment of water quality
Water quality monitoring using machine learning and IoT: a review
Get PDFWater remains one of the most essential natural resources. With the ever-increasing population, the demand for water across various sectors, including agriculture, industry, and power, as well as the growing prevalence of pollution, has led to a significant strain on water supplies. The availability of fresh and usable water is becoming increasingly limited, making quality monitoring and analysis crucial for sustainable use and environmental protection. Traditional water quality monitoring techniques involve manual sampling, testing, and investigation, which may not always be reliable and are often inefficient in providing early warnings of water quality deterioration. However, with the emergence of machine learning (ML) and Internet of Things (IoT) technologies, the process of water quality monitoring and analysis has become more efficient, accurate, and cost-effective. ML algorithms can analyze large volumes of water quality data, enabling data-centric approaches to designing, supervising, simulating, assessing, and refining various water treatment and management systems. This review paper provides an overview of the past and current applications of machine learning and IoT in water quality monitoring and analysis. Long-term cost savings can be seen in different ways as reduced labor costs, lower operational costs, early detection and intervention prevent costly repairs and emergencies, minimized infrastructure costs, distributed IoT sensors reduce the need for extensive physical infrastructure, optimized resource allocation and efficiency improvements with IoT and Machine Learning in water quality monitoring can be highlighted in the following points, real-time monitoring: immediate data analysis allows for prompt adjustments and decision-making, enhanced accuracy, advanced sensors and algorithms improve data precision and reliability, scalability, systems can be easily expanded or adapted to meet evolving needs, predictive maintenance, automated systems proactively address issues before they escalate, reducing manual oversight. The paper explores various ML algorithms, including supervised and unsupervised learning and deep learning, along with their applications, and discusses the use of IoT sensors for real-time monitoring of water quality parameters such as pH, dissolved oxygen, temperature, and turbidity
A Large Multi-Target Dataset of Common Bengali Handwritten Graphemes
Get PDFLatin has historically led the state-of-the-art in handwritten optical
character recognition (OCR) research. Adapting existing systems from Latin to
alpha-syllabary languages is particularly challenging due to a sharp contrast
between their orthographies. The segmentation of graphical constituents
corresponding to characters becomes significantly hard due to a cursive writing
system and frequent use of diacritics in the alpha-syllabary family of
languages. We propose a labeling scheme based on graphemes (linguistic segments
of word formation) that makes segmentation in-side alpha-syllabary words linear
and present the first dataset of Bengali handwritten graphemes that are
commonly used in an everyday context. The dataset contains 411k curated samples
of 1295 unique commonly used Bengali graphemes. Additionally, the test set
contains 900 uncommon Bengali graphemes for out of dictionary performance
evaluation. The dataset is open-sourced as a part of a public Handwritten
Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for
multi-target grapheme classification. The unique graphemes present in this
dataset are selected based on commonality in the Google Bengali ASR corpus.
From competition proceedings, we see that deep-learning methods can generalize
to a large span of out of dictionary graphemes which are absent during
training. Dataset and starter codes at www.kaggle.com/c/bengaliai-cv19.Comment: 15 pages, 12 figures, 6 Tables, Submitted to CVPR-2
From the river to the sea? : honour, identity and politics in historical and contemporary Palestinian rejectionism
No full textThe present thesis seeks to understand and explain the rhetoric and
behaviour of the rejectionist 'current' within the Palestinian national
movement. It proceeds from the view that extant scholarship, primarily from
within the fields of terrorism and security studies, has profoundly
misunderstood rejectionist speech and behaviour by ignoring the
explanatory capacity of Emic - the research subject's perception - as well as
the influence of the sociocultural milieu within which rejectionism exists.
The thesis proceeds to set up a 'socioculturally sensitive' analytical
framework drawn from social identity theory, a heuristic, non-reductionist
model for understanding group interaction and conflict. Emphasizing
cultural norms and cues identified by anthropologists as salient in the
eastern Mediterranean, the thesis suggests that the social value of honour,
patron-client dynamics and a firmly entrenched group orientation must be
significant elements of a model for understanding rejectionist behaviour.
The main analytical narrative suggests that for reasons derived from
ideology, patron-client relations and group dynamics, what has distinguished
the rejectionists from the mainstream have been a qualitatively different set
of preconditions for, and objectives of diplomatic negotiations. To the main
rejectionist factions the goal of liberating Palestine has always been
inextricably intertwined with the goal of restoring national honour; one
without the other has been impossible and to claim otherwise would mean a
depletion of factional and personal honour. To the rejectionists, there has
never been any question of deviating from the fundamental goals - national
recognition, repatriation, self-determination and independent statehood, not
even for tactical reasons. This 'higher standard' likely derives from their
structurally and politically subordinate position within the national
movement, and the need to creatively enhance their own social status and
appeal
Application of IoT for monitoring water quality Sungai Pusu case study
No full textThe availability of clean water, a critical natural resource essential for supporting diverse ecosystems, is increasingly threatened by sediment accumulation, which negatively impacts rivers, oceans, and coastal environments. During rainy seasons, excessive sediment, including clay particles known as total suspended solids (TSS), contaminates water, compromising its quality, altering its color, and driving up treatment costs. Additionally, sediment can carry pollutants that reduce water clarity, harm aquatic life, and disrupt ecosystem functions. Conventional water treatment methods—such as coagulation, flocculation, sedimentation, and filtration are commonly employed to address these challenges. However, traditional water quality monitoring relies heavily on laboratory tests that require specialized personnel, chemicals, and expertise, which may not always be adequate for timely and effective intervention. The advent of Internet of Things (IoT) technology offers a promising alternative by enabling the real-time collection of water quality data. Furthermore, integrating soft computing technology into water quality assessment presents a more efficient, rapid, and environmentally sustainable alternative to traditional laboratory-based approaches. This research utilizes an IoT device to monitor the performance of a water treatment system and gather data on its water quality indicators. Integrating IoT devices into water treatment systems enables real-time monitoring, automated control, and predictive maintenance, leading to improved efficiency, cost savings, and proactive water quality management. These technologies enable real-time monitoring and analysis, reducing the need for manual sampling and laboratory testing, which lowers labor and operational costs. Early detection and automated intervention help prevent costly repairs and environmental damage by addressing issues before they escalate. A novel IoT-based monitoring system of integrating soft computing into water quality assessment has been achieved
Real-time vehicle counting using custom YOLOv8n and DeepSORT for resource-limited edge devices
Get PDFRecently, there has been a significant increase in the use of deep learning and low-computing edge devices for analysis of video-based systems, particularly in the field of intelligent transportation systems (ITS). One promising application of computer vision techniques in ITS is in the development of low-computing and accurate vehicle counting systems that can be used to eliminate dependence on external cloud computing resources. This paper proposes a compact, reliable and real-time vehicle counting solution which can be deployed on low-computational requirement edge computing devices. The system makes use of a custom-built vehicle detection algorithm based on the you only look once version 8 nano (YOLOv8n), combined with a deep association metric (DeepSORT) object tracking algorithm and an efficient vehicle counting method for accurate counting of vehicles in highway scenes. The system is trained to detect, track and count four distinct vehicle classeses, namely: car, motorcycle, bus, and truck. The proposed system was able to achieve an average vehicle detection mean average precision (mAP) score of 97.5%, a vehicle counting accuracy score of 96.8% and an average speed of 19.4 frames per second (FPS), all while being deployed on a compact Nvidia Jetson Nano edge-computing device. The proposed system outperforms other previously proposed tools in terms of both accuracy and speed
