Search results for: visual monitoring

Authors: K. Parandhama Gowd

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The aim of this research paper is to conceptualize, discuss, analyze and propose alternate design methodologies for futuristic Black Box for flight safety. The proposal also includes global networking concepts for real time surveillance and monitoring of flight performance parameters including GPS parameters. It is expected that this proposal will serve as a failsafe real time diagnostic tool for accident investigation and location of debris in real time. In this paper, an attempt is made to improve the existing methods of flight data recording techniques and improve upon design considerations for futuristic FDR to overcome the trauma of not able to locate the block box. Since modern day communications and information technologies with large bandwidth are available coupled with faster computer processing techniques, the attempt made in this paper to develop a failsafe recording technique is feasible. Further data fusion/data warehousing technologies are available for exploitation.

Keywords: flight data recorder (FDR), black box, diagnostic tool, global networking, cockpit voice and data recorder (CVDR), air traffic control (ATC), air traffic, telemetry, tracking and control centers ATTTCC)

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Authors: Isabelle Barrett

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Natural watercourses provide a range of vital ecosystem services, notably freshwater provision. They also offer highly heterogeneous habitat which supports an extreme diversity of aquatic life. Exploitation of rivers, changing land use and flood prevention measures have led to habitat degradation and subsequent biodiversity loss; indeed, freshwater species currently face a disproportionate rate of extinction compared to their terrestrial and marine counterparts. Large woody debris (LWD) encompasses the trees, large branches and logs which fall into watercourses, and is responsible for important habitat characteristics. Historically, natural LWD has been removed from streams under the assumption that it is not aesthetically pleasing and is thus ecologically unfavourable, despite extensive evidence contradicting this. Restoration efforts aim to replace lost LWD in order to reinstate habitat heterogeneity. This paper aims to assess the current state of such restoration schemes for improving fluvial ecological health in the UK. A detailed review of the scientific literature was conducted alongside a meta-analysis of 25 UK-based projects involving LWD restoration. Projects were chosen for which sufficient information was attainable for analysis, covering a broad range of budgets and scales. The most effective strategies for river restoration encompass ecological success, stakeholder engagement and scientific advancement, however few projects surveyed showed sensitivity to all three; for example, only 32% of projects stated biological aims. Focus tended to be on stakeholder engagement and public approval, since this is often a key funding driver. Consequently, there is a tendency to focus on the aesthetic outcomes of a project, however physical habitat restoration does not necessarily lead to direct biodiversity increases. This highlights the significance of rivers as highly heterogeneous environments with multiple interlinked processes, and emphasises a need for a stronger scientific presence in project planning. Poor scientific rigour means monitoring is often lacking, with varying, if any, definitions of success which are rarely pre-determined. A tendency to overlook negative or neutral results was apparent, with unjustified focus often put on qualitative results. The temporal scale of monitoring is typically inadequate to facilitate scientific conclusions, with only 20% of projects surveyed reporting any pre-restoration monitoring. Furthermore, monitoring is often limited to a few variables, with biotic monitoring often fish-focussed. Due to their longer life cycles and dispersal capability, fish are usually poor indicators of environmental change, making it difficult to attribute any changes in ecological health to restoration efforts. Although the potential impact of LWD restoration may be positive, this method of restoration could simply be making short-term, small-scale improvements; without addressing the underlying symptoms of degradation, for example water quality, the issue cannot be fully resolved. Promotion of standardised monitoring for LWD projects could help establish a deeper understanding of the ecology surrounding the practice, supporting movement towards adaptive management in which scientific evidence feeds back to practitioners, enabling the design of more efficient projects with greater ecological success. By highlighting LWD, this study hopes to address the difficulties faced within river management, and emphasise the need for a more holistic international and inter-institutional approach to tackling problems associated with degradation.

Keywords: biological monitoring, ecological health, large woody debris, river management, river restoration

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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Authors: Beloulou Mohamed Lamine, Fedili Benamar, Meliani Walid, Chaid Dalila

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Introduction: The risk factors for the chronicization of postoperative pain are numerous and often intricately intertwined. Among these, the severity of acute postoperative pain is currently recognized as one of the most determining factors. Mastectomy and thoracotomy are described as among the most painful surgeries and the most likely to lead to chronic post-surgical pain (CPSP). Objective: To examine the aspects of acute postoperative pain potentially involved in the development of chronic pain following breast and thoracic surgery. Patients and Methods: A prospective study involving 164 patients was conducted over a six-month period. Postoperative pain (during mobilization) was assessed using a Visual Analog Scale (VAS) at various time points after surgery: Day 0, 1st, 2nd, 5th days, 1st and 6th months. Moderate to severe pain was defined as a VAS score ≥ 4. A comparative analysis (univariate analysis) of postoperative pain intensities at different evaluation phases was performed on patients with and without CPSP to identify potential associations with the risk of chronicization six months after surgery. Results: At the 6th month post-surgery, the incidence of CPSP was 43.0%. Moderate to severe acute postoperative pain (in the first five days) was observed in 64% of patients. The highest pain scores were reported among thoracic surgery patients. Comparative measures revealed a highly significant association between the presence of moderate to severe acute pain, especially lasting for ≥ 48 hours, and the occurrence of CPSP (p-value <0.0001). Likewise, the persistence of subacute pain (up to 4 to 6 weeks after surgery), especially of moderate to severe intensity, was significantly associated with the risk of chronicization at six months (p-value <0.0001). Conclusion: CPSP after breast and thoracic surgery remains a fairly common morbidity that profoundly affects the quality of life. Severe acute postoperative pain, especially if it is prolonged and/or with a slow decline in intensity, can be an important predictive factor for the risk of chronicization. Therefore, more effective and intensive management of acute postoperative pain, as well as longitudinal monitoring of its trajectory over time, should be an essential component of strategies for preventing chronic pain after surgery.

Keywords: chronic post-surgical pain, acute postoperative pain, breast and thoracic surgery, subacute postoperative pain, pain trajectory, predictive factor

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Authors: Tayyab Hassan, Talha Rehman, Qasim Abdul Aziz, Ahmad Salman

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The constant monitoring of blood glucose level is necessary for maintaining health of patients and to alert medical specialists to take preemptive measures before the onset of any complication as a result of diabetes. The current clinical monitoring of blood glucose uses invasive methods repeatedly which are uncomfortable and may result in infections in diabetic patients. Several attempts have been made to develop non-invasive techniques for blood glucose measurement. In this regard, the existing methods are not reliable and are less accurate. Other approaches claiming high accuracy have not been tested on extended dataset, and thus, results are not statistically significant. It is a well-known fact that acetone concentration in breath has a direct relation with blood glucose level. In this paper, we have developed the first of its kind, reliable and high accuracy breath analyzer for non-invasive blood glucose measurement. The acetone concentration in breath was measured using MQ 138 sensor in the samples collected from local hospitals in Pakistan involving one hundred patients. The blood glucose levels of these patients are determined using conventional invasive clinical method. We propose a linear regression classifier that is trained to map breath acetone level to the collected blood glucose level achieving high accuracy.

Keywords: blood glucose level, breath acetone concentration, diabetes, linear regression

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Authors: Robyn Therese Munnick

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This paper aims to report on a qualitative practice-based research study which explores the notion of nothingness and how it (nothingness) is the conceptual and theoretical foundation for artistic practice. Furthermore, this study explicates how the artist used their mother’s battle with cancer and the subsequent void it created as source material for the artistic expression of nothingness. The diagnosis which was followed by a physical and emotional absence of the matriarch of the artist family led to an emotional trauma that triggered a feeling of nothingness within the artist. The overarching problem in the study is thus: how this ‘nothingness’ could be expressed in visual art? Nothingness, as a product of expectation, is a notion which refers to where something used to be, should be or isn’t anymore, which attempts to grasp what is there by not being there. In attempting to express nothingness, the research aims to build on an exploration of various materials and modes utilized in order to underpin the research objectives. The primary mode of delivery for the art-making process is painting. However, through strengthening the messages and meaning of the hypothesis of nothingness within the art and research, the use of further modes and materials became pivotal. This involves the use of unconventional contrasting modes within a painting such as the cloth doily, thread, tubing, ceramics, food colour, spray paint, polyvinyl acetate paint, plaster, wooden boxes and fragments thereof. These materials and modes were vital in visualising and aestheticising the conceptual underpinnings of the research. As a result, this strengthened and emancipated the art from the traditional bounds of pure painting. Methods of data gathering took the form of artefacts, document analysis, and field notes in the form of photographic journaling. Ultimately the body of work and research validates that the idea of nothingness can be artistically explored.

Keywords: conceptual, nothingness, modes, unconventional

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Authors: Yanjie Zhu, André Jesus, Irwanda Laory

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Structural Health Monitoring (SHM), involving data acquisition, data interpretation and decision-making system aim to continuously monitor the structural performance of civil infrastructures under various in-service circumstances. The main value and purpose of SHM is identifying damages through data interpretation system. Research on SHM has been expanded in the last decades and a large volume of data is recorded every day owing to the dramatic development in sensor techniques and certain progress in signal processing techniques. However, efficient and reliable data interpretation for damage detection under environmental variations is still a big challenge. Structural damages might be masked because variations in measured data can be the result of environmental variations. This research reports a novel method based on single-channel Blind Signal Separation (BSS), which extracts environmental effects from measured data directly without any prior knowledge of the structure loading and environmental conditions. Despite the successful application in audio processing and bio-medical research fields, BSS has never been used to detect damage under varying environmental conditions. This proposed method optimizes and combines Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) together to separate structural responses due to different loading conditions respectively from a single channel input signal. The ICA is applying on dimension-reduced output of EEMD. Numerical simulation of a truss bridge, inspired from New Joban Line Arakawa Railway Bridge, is used to validate this method. All results demonstrate that the single-channel BSS-based method can recover temperature effects from mixed structural response recorded by a single sensor with a convincing accuracy. This will be the foundation of further research on direct damage detection under varying environment.

Keywords: damage detection, ensemble empirical mode decomposition (EEMD), environmental variations, independent component analysis (ICA), principal component analysis (PCA), structural health monitoring (SHM)

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Authors: Akshay Dirisala

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Humans had developed a highly organized and efficient civilization to live in by improving the basic needs of humans like housing, transportation, and utilities. These developments have made a huge impact on major environmental factors. Air pollution is one prominent environmental factor that needs to be addressed to maintain a sustainable and healthier lifestyle. Textiles have always been at the forefront of helping humans shield from environmental conditions. With the growth in the field of electronic textiles, we now have the capability of monitoring the atmosphere in real time to understand and analyze the environment that a particular person is mostly spending their time at. Integrating textiles with the particulate matter sensors that measure air quality and pollutants that have a direct impact on human health will help to understand what type of air we are breathing. This research idea aims to develop a textile product and a process of collecting the pollutants through particulate matter sensors, which are equipped inside a smart textile product and store the data to develop a machine learning model to analyze the health conditions of the person wearing the garment and periodically notifying them not only will help to be cautious of airborne diseases but will help to regulate the diseases and could also help to take care of skin conditions.

Keywords: air pollution, e-textiles, particulate matter sensors, environment, machine learning models

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Authors: Constant Akama, Frank Kulor, Frederick Agyemang

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All over the world, due to increasing population, electric power distribution companies are looking for more efficient ways of reading electricity meters. In Ghana, the prepaid metering system was introduced in 2007 to replace the manual system of reading which was fraught with inefficiencies. However, the prepaid system in Ghana is not capable of integration with online systems such as e-commerce platforms and remote monitoring systems. In this paper, we present a design framework for an automatic metering system that can be integrated with e-commerce platforms and remote monitoring systems. The meter was designed using ADE 7755 which reads the energy consumption and the reading is processed by a microcontroller connected to Sim900 General Packet Radio Service module containing a GSM chip provisioned with an Access Point Name. The system also has a billing server and a management server located at the premises of the utility company which communicate with the meter over a Virtual Private Network and GPRS. With this system, customers can buy credit online and the credit will be transferred securely to the meter. Also, when a fault is reported, the utility company can log into the meter remotely through the management server to troubleshoot the problem.

Keywords: access point name, general packet radio service, GSM, virtual private network

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Authors: Ahmed Osama, Hassan A. Mahdy, Khalid A. Kandil, Mohamed Elhabiby

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Automatic Vehicle Location systems (AVL) have been used worldwide for more than twenty years and have showed great success in public transportation management and monitoring. Cairo public bus service suffers from several problems such as unscheduled stops, unscheduled route deviations, and inaccurate schedules, which have negative impacts on service reliability. This research aims to study those problems for a selected bus route in Cairo using a prototype AVL system. Experimental trips were run on the selected route; and the locations of unscheduled stops, regions of unscheduled deviations, along with other trip time and speed data were collected. Data was analyzed to demonstrate the reliability of passengers on the unscheduled stops compared to the scheduled ones. Trip time was also modeled to assess the unscheduled stops’ impact on trip time, and to check the accuracy of the applied scheduled trip time. Moreover, frequency and length of the unscheduled route deviations, as well as their impact on the bus stops, were illustrated. Solutions were proposed for the bus service deficiencies using the AVL system. Finally, recommendations were proposed for further research.

Keywords: automatic vehicle location, public transportation, unscheduled stops, unscheduled route deviations, inaccurate schedule

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Authors: Angela Meyer

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The operation and maintenance cost for wind parks make up a major fraction of the park’s overall lifetime cost. To minimize the cost and risk involved, an optimal operation and maintenance strategy requires continuous monitoring and analysis. In order to facilitate this, we present a decision support system that automatically scans the stream of telemetry sensor data generated from the turbines. By learning decision boundaries and normal reference operating states using machine learning algorithms, the decision support system can detect anomalous operating behavior in individual wind turbines and diagnose the involved turbine sub-systems. Operating personal can be alerted if a normal operating state boundary is exceeded. The presented decision support system and method are applicable for any turbine type and manufacturer providing telemetry data of the turbine operating state. We demonstrate the successful detection and diagnosis of anomalous operating states in a case study at a German onshore wind park comprised of Vestas V112 turbines.

Keywords: anomaly detection, decision support, machine learning, monitoring, performance optimization, wind turbines

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Authors: Cesar Augusto Hoil Rosas, José Luis Vázquez Burgos, José Juan Carbajal Hernandez

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White fish (Chirostoma estor estor) is an endemic species that habits in the Patzcuaro Lake, located in Michoacan, Mexico; being an important source of gastronomic and cultural wealth of the area. Actually, it have undergone an immense depopulation of individuals, due to the high fishing, contamination and eutrophication of the lake water, resulting in the possible extinction of this important species. This work proposes a new computational model for monitoring and assessment of critical environmental parameters of the white fish ecosystem. According to an Analytical Hierarchy Process, a mathematical model is built assigning weights to each environmental parameter depending on their water quality importance on the ecosystem. Then, a development of an advanced system for the monitoring, analysis and control of water quality is built using the virtual environment of LabVIEW. As results, we have obtained a global score that indicates the condition level of the water quality in the Chirostoma estor ecosystem (excellent, good, regular and poor), allowing to provide an effective decision making about the environmental parameters that affect the proper culture of the white fish such as temperature, pH and dissolved oxygen. In situ evaluations show regular conditions for a success reproduction and growth rates of this species where the water quality tends to have regular levels. This system emerges as a suitable tool for the water management, where future laws for white fish fishery regulations will result in the reduction of the mortality rate in the early stages of development of the species, which represent the most critical phase. This can guarantees better population sizes than those currently obtained in the aquiculture crop. The main benefit will be seen as a contribution to maintain the cultural and gastronomic wealth of the area and for its inhabitants, since white fish is an important food and economical income of the region, but the species is endangered.

Keywords: Chirostoma estor estor, computational system, lab view, white fish

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Authors: Bulcha Belay Etana

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Electromyography is used to measure the electrical activity of muscles for various health monitoring applications using surface electrodes or needle electrodes. Recent developments in electromyogram signal acquisition using textile electrodes open the door for wearable health monitoring which enables patients to monitor and control their health issues outside of traditional healthcare facilities. The aim of this research is therefore to develop and analyze wearable textile electrodes for the acquisition of electromyography signals for Parkinson’s patients and apply an appropriate thermal stimulus to relieve muscle cramping. In order to achieve this, textile electrodes are sewn with a silver-coated thread in an overlapping zigzag pattern into an inextensible fabric, and stainless steel knitted textile electrodes attached to a sleeve were prepared and its electrical characteristics including signal to noise ratio were compared with traditional electrodes. To relieve muscle cramping, a heating element using stainless steel conductive yarn Sewn onto a cotton fabric, coupled with a vibration system were developed. The system was integrated using a microcontroller and a Myoware muscle sensor so that when muscle cramping occurs, measured by the system activates the heating elements and vibration motors. The optimum temperature considered for treatment was 35.50c, so a Temperature measurement system was incorporated to deactivate the heating system when the temperature reaches this threshold, and the signals indicating muscle cramping have subsided. The textile electrode exhibited a signal to noise ratio of 6.38dB while the signal to noise ratio of the traditional electrode was 7.05dB. The rise time of the developed heating element was about 6 minutes to reach the optimum temperature using a 9volt power supply. The treatment of muscle cramping in Parkinson's patients using heat and muscle vibration simultaneously with a wearable electromyography signal acquisition system will improve patients’ livelihoods and enable better chronic pain management.

Keywords: electromyography, heating textile, vibration therapy, parkinson’s disease, wearable electronic textile

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Authors: A. Kyprianou, A. Tjirkallis

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Condition monitoring of structures in service is very important as it provides information about the risk of damage development. One of the essential constituents of structural condition monitoring is the damage detection methodology. In the context of condition monitoring of in service structures a damage detection methodology analyses data obtained from the structure while it is in operation. Usually, this means that the data could be affected by operational and environmental conditions in a way that could mask the effects of a possible damage on the data. This, depending on the damage detection methodology, could lead to either false alarms or miss existing damages. In this article a damage detection methodology that is based on the Spatio-temporal continuous wavelet transform (SPT-CWT) analysis of a sequence of experimental time responses of a cantilever beam is proposed. The cantilever is subjected to white and pink noise excitation to simulate different operating conditions. In addition, in order to simulate changing environmental conditions, the cantilever is subjected to heating by a heat gun. The response of the cantilever beam is measured by a high-speed camera. Edges are extracted from the series of images of the beam response captured by the camera. Subsequent processing of the edges gives a series of time responses on 439 points on the beam. This sequence is then analyzed using the SPT-CWT to identify damage. The algorithm proposed was able to clearly identify damage under any condition when the structure was excited by white noise force. In addition, in the case of white noise excitation, the analysis could also reveal the position of the heat gun when it was used to heat the structure. The analysis could identify the different operating conditions i.e. between responses due to white noise excitation and responses due to pink noise excitation. During the pink noise excitation whereas damage and changing temperature were identified it was not possible to clearly identify the effect of damage from that of temperature. The methodology proposed in this article for damage detection enables the separation the damage effect from that due to temperature and excitation on data obtained from measurements of a cantilever beam. This methodology does not require information about the apriori state of the structure.

Keywords: spatiotemporal continuous wavelet transform, damage detection, data normalization, varying temperature

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Authors: Ciro Cursio, Pio Luigi Cursio, Giulia Cursio, Isabella Chiardi, Luigi Cursio

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Introduction: Full-face aesthetic treatments often present a difficult task: since different patients possess different anatomical and tissue characteristics, there is no guarantee that the same treatment will have the same effect on multiple patients; additionally, full-face rejuvenation and beautification treatments require not only a high degree of technical skill but also the ability to choose the right product for each area and a keen artistic eye. Method: We present an artificial intelligence-based algorithm that can generate realistic post-treatment 3D models based on the patient’s requests together with the doctor’s input. These 3-dimensional predictions can be used by the practitioner for two purposes: firstly, they help ensure that the patient and the doctor are completely aligned on the expectations of the treatment; secondly, the doctor can use them as a visual guide, obtaining a natural result that would normally stem from the practitioner's artistic skills. To this end, the algorithm is able to predict injection zones, the type and quantity of hyaluronic acid, the injection depth, and the technique to use. Results: Our innovation consists in providing an objective visual representation of the patient that is helpful in the patient-doctor dialogue. The patient, based on this information, can express her desire to undergo a specific treatment or make changes to the therapeutic plan. In short, the patient becomes an active agent in the choices made before the treatment. Conclusion: We believe that this algorithm will reveal itself as a useful tool in the pre-treatment decision-making process to prevent both the patient and the doctor from making a leap into the dark.

Keywords: hyaluronic acid, fillers, full face, artificial intelligence, 3D

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Authors: Ting-Yu Lin, Han-Liang Lin

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This article is about the urban form of visualization by Cityengine. City is composed of different domains, and each domain has its own fabric because of arrangement. For example, a neighborhood unit contains fabrics such as schools, street networks, residential and commercial spaces. Therefore, studying urban morphology can help us understand the urban form in planning process. Streets, plots, and buildings seem as urban fabrics, and they configure urban form. Traditionally, urban morphology usually discussed single parameter, which is building type, ignoring other parameters such as streets and plots. However, urban space is three-dimensional, instead of two-dimensional. People perceive urban space by their visualization. Therefore, using visualization can fill the gap between two dimensions and three dimensions. Hence, the study of urban morphology will strengthen the understanding of whole appearance of a city. Cityengine is a software which can edit, analyze and monitor the data and visualize the result for GIS, a common tool to analyze data and display the map for urban plan and urban design. Cityengine can parameterize the data of streets, plots and building types and visualize the result in three-dimensional way. The research will reappear the real urban form by visualizing. We can know whether the urban form can be parameterized and the parameterized result can match the real urban form. Then, visualizing the result by software in three dimension to analyze the rule of urban form. There will be three stages of the research. It will start with a field survey of Tainan East District in Taiwan to conclude the relationships between urban fabrics of street networks, plots and building types. Second, to visualize the relationship, it will turn the relationship into codes which Cityengine can read. Last, Cityengine will automatically display the result by visualizing.

Keywords: Cityengine, urban fabric, urban morphology, visual simulation

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Authors: Arun C. Gulani, Aaishwariya A. Gulani, Amanda Southall

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Background: Laser Corneoplastique™ as a least interventional, visually promising technique for patients with vision disability from corneal scars of varied causes has been retrospectively reviewed and proves to cause a paradigm shift in mindset and approach towards corneal scars as a Refractive surgery aiming for emmetropic, unaided vision of 20;/20 in most cases. Three decades of work on this technique has been compiled in this 15-year study. Subject and Methods: The objective of this study was to determine the success of Laser Corneoplastique™ surgery as a treatment of corneal scar cases. A survey of corneal scar cases caused by various medical histories that had undergone Laser Corneoplastique™ surgery over the past twenty years by a single surgeon Arun C. Gulani, M.D. were retrospectively reviewed. The details of each of the cases were retrieved from their medical records and analyzed. Each patient had been examined thoroughly at their preoperative appointments for stability of refraction and vision, depth of scar, pachymetry, topography, pattern of the scar and uncorrected and best corrected vision potential, which were all taken into account in the patients' treatment plans. Results: 64 eyes of 53 patients were investigated for scar etiology, keratometry, visual acuity, and complications. There were 25 different etiologies seen, with the most common being a Herpetic scar. The average visual acuity post-op was, on average, 20/23.55 (±7.05). Laser parameters used were depth and pulses. Overall, the mean Laser ablation depth was 30.67 (±19.05), ranging from 2 to 73 µm. Number of Laser pulses averaged 191.85 (±112.02). Conclusion: Refractive Laser Corneoplastique™ surgery, when practiced as an art, can address all levels of ametropia while reversing complex corneas and scars from refractive surgery complications back to 20/20 vision.

Keywords: corneal scar, refractive surgery, corneal transplant, laser corneoplastique

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Authors: David Sinfield, Thomas Cochrane, Marcos Steagall

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While there is much hype around the potential and development of mobile virtual reality (VR), the two key critical success factors are the ease of user experience and the development of a simple user-generated content ecosystem. Educational technology history is littered with the debris of over-hyped revolutionary new technologies that failed to gain mainstream adoption or were quickly superseded. Examples include 3D television, interactive CDROMs, Second Life, and Google Glasses. However, we argue that this is the result of curriculum design that substitutes new technologies into pre-existing pedagogical strategies that are focused upon teacher-delivered content rather than exploring new pedagogical strategies that enable student-determined learning or heutagogy. Visual Communication design based learning such as Graphic Design, Illustration, Photography and Design process is heavily based on the traditional forms of the classroom environment whereby student interaction takes place both at peer level and indeed teacher based feedback. In doing so, this makes for a healthy creative learning environment, but does raise other issue in terms of student to teacher learning ratios and reduced contact time. Such issues arise when students are away from the classroom and cannot interact with their peers and teachers and thus we see a decline in creative work from the student. Using AR and VR as a means of stimulating the students and to think beyond the limitation of the studio based classroom this paper will discuss the outcomes of a student project considering the virtual classroom and the techniques involved. The Atelier learning environment is especially suited to the Visual Communication model as it deals with the creative processing of ideas that needs to be shared in a collaborative manner. This has proven to have been a successful model over the years, in the traditional form of design education, but has more recently seen a shift in thinking as we move into a more digital model of learning and indeed away from the classical classroom structure. This study focuses on the outcomes of a student design project that employed Augmented Reality and Virtual Reality technologies in order to expand the dimensions of the classroom beyond its physical limits. Augmented Reality when integrated into the learning experience can improve the learning motivation and engagement of students. This paper will outline some of the processes used and the findings from the semester-long project that took place.

Keywords: augmented reality, blogging, design in community, enhanced learning and teaching, graphic design, new technologies, virtual reality, visual communications

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Authors: Nabila Dwi Agustin, Ria Septitis Mentari, Nugroho Adi Sasongko

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Indonesia is experiencing a crisis in the development of defense equipment. Most of Indonesia's defense equipment must import its parts from other countries. Moreover, the area of Indonesia is 2/3 of its territory is the sea areas. For the protection of marine areas, Indonesia relies solely on submarines in monitoring conditions and whether or not intruders enter their territory. In fact, we know the submarine has a large size so that the expenses are getting bigger, the time it takes longer and needs a big maneuver to operate the submarine. Indeed, the submarine can only be operated for deeper seas. Many other countries enter the underwater world of Indonesia but Indonesia could not do anything due to the limitations of underwater monitoring system. At the same time, reconnaissance and monitor for shallow seas cannot be done by submarine. Equipment that can be used for surveillance of shallow underwater areas shall be made. This study reviewed the current research and development initiative of the submarine unmanned vehicle (SUV) or unmanned undersea vehicle (UUV) in Indonesia. This can explore underwater without the need for an operator to operate in it, but we can monitor it from a long distance. UUV has several advantages that size can be reduced as we desired, rechargeable ship batteries, has a detection sonar commonly found on a submarine and agile movement to detect at shallow sea depth. In the sonar sensors consisted of MEMS (Micro Electro Mechanical System), the sonar system runs more efficiently and effectively to monitor the target. UUV that has been developed will be very useful if the equipment is used around the outlying islands and outer from Indonesia especially the island frequented by foreign submarines without us know. The impact of this may not be felt now but it will allow foreign countries to attack Indonesia from within for the future. In addition, UUV needs to be equipped with a anti-radar system so that submarines of other countries crossing borders cannot detect it and Indonesia anti-submarine vessels can take further security measures. As the recommendation, Indonesia should take decisive steps in the state border rules, especially submarines of other countries that deliberately cross the borders of the state. This decisive action not only by word alone but also action as well. Indonesia government should show the strength and sovereignty as the entire society unites and applies the principle of universal peace.

Keywords: submarine unmanned vehicle, submarine, development of defense equipment, the border of Indonesia

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Authors: Flora Paganelli

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The radiometric performance of remotely sensed images is important for volcanic monitoring. The Thermal Infrared Sensor (TIRS) on-board Landsat 8 was designed with specific requirements in regard to the noise-equivalent change in temperature (NEΔT) at ≤ 0.4 K at 300 K for the two thermal infrared bands B10 and B11. This study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over the volcanic activity of Kīlauea Volcano and the active East Rift Zone (Hawaii), in order to optimize the use of TIRS data. Results showed that the NEΔTs of the two bands exceeded the design specification by an order of magnitude at 300 K. Both separate bands and split window algorithm were examined to estimate the effect of NEΔT on the land surface temperature (LST) retrieval, and NEΔT contribution to the final LST error. These results were also useful in the current efforts to assess the requirements for volcanology research campaign using the Hyperspectral Infrared Imager (HyspIRI) whose airborne prototype MODIS/ASTER instruments is plan to be flown by NASA as a single campaign to the Hawaiian Islands in support of volcanology and coastal area monitoring in 2016.

Keywords: landsat 8, radiometric performance, thermal infrared sensor (TIRS), volcanology

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Authors: Adkham Paiziev, Fikrat Kerimov

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Near Infrared Spectroscopy (NIRS) is one of the biophotonic techniques which can be used to monitor oxygenation and hemodynamics in a variety of human tissues, including skeletal muscle. In the present work, we are offering tissue oximetry (OxyPrem) to measure hemodynamic parameters of skeletal muscles in rest and exercise. Purpose: - To elaborate the new wireless, portable, noninvasive, wearable NIRS device to measure skeletal muscle oxygenation during exercise. - To test this device on brachioradialis muscle of wrestler volunteers by using combined method of arterial occlusion (AO) and NIRS (AO+NIRS). Methods: Oxyprem NIRS device has been used together with AO test. AO test and Isometric brachioradialis muscle contraction experiments have been performed on one group of wrestler volunteers. ‘Accu- Measure’ caliper (USA) to measure skinfold thickness (SFT) has been used. Results: Elaborated device consists on power supply box, a sensor head and installed ‘Tubis’ software for data acquisition and to compute deoxyhemoglobin ([HHb), oxyhemoglobin ([O2Hb]), tissue oxygenation (StO2) and muscle tissue oxygen consumption (mVO2). Sensor head consists on four light sources with three light emitting diodes with nominal wavelengths of 760 nm, 805 nm, and 870 nm, and two detectors. AO and isometric voluntary forearm muscle contraction (IVFMC) on five healthy male subjects (23,2±0.84 in age, 0.43±0.05cm of SFT ) and four female subjects (22.0±1.0 in age and 0.24±0.04 cm SFT) has been measured. mVO2 for control group has been calculated (-0.65%/sec±0.07) for male and -0.69%/±0.19 for female subjects). Tissue oxygenation index for wrestlers in average about 75% whereas for control group StO2 =63%. Second experiment was connected with quality monitoring muscle activity during IVFMC at 10%,30% and 50% of MVC. It has been shown, that the concentration changes of HbO2 and HHb positively correlated to the contraction intensity. Conclusion: We have presented a portable multi-channel wireless NIRS device for real-time monitoring of muscle activity. The miniaturized NIRS sensor and the usage of wireless communication make the whole device have a compact-size, thus can be used in muscle monitoring.

Keywords: skeletal muscle, oxygenation, instrumentation, near infrared spectroscopy

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Authors: Giovanni Cicceri, Roberta Maisano, Nathalie Morey, Salvatore Distefano

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The main goal of this paper is to present a solution for a water purification system based on an Environmental Internet of Things (EIoT) platform to monitor and control water quality and machine learning (ML) models to support decision making and speed up the processes of purification of water. A real case study has been implemented by deploying an EIoT platform and a network of devices, called Gramb meters and belonging to the Gramb project, on wastewater purification systems located in Calabria, south of Italy. The data thus collected are used to control the wastewater quality, detect anomalies and predict the behaviour of the purification system. To this extent, three different statistical and machine learning models have been adopted and thus compared: Autoregressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) autoencoder, and Facebook Prophet (FP). The results demonstrated that the ML solution (LSTM) out-perform classical statistical approaches (ARIMA, FP), in terms of both accuracy, efficiency and effectiveness in monitoring and controlling the wastewater purification processes.

Keywords: environmental internet of things, EIoT, machine learning, anomaly detection, environment monitoring

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Authors: Michael Karnes, Alper Yilmaz

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This paper presents the application of the omni-modeler towards pedestrian redetection. The pedestrian redetection task creates several challenges when applying deep neural networks (DNN) due to the variety of pedestrian appearance with camera position, the variety of environmental conditions, and the specificity required to recognize one pedestrian from another. DNNs require significant training sets and are not easily adapted for changes in class appearances or changes in the set of classes held in its knowledge domain. Pedestrian redetection requires an algorithm that can actively manage its knowledge domain as individuals move in and out of the scene, as well as learn individual appearances from a few frames of a video. The Omni-Modeler is a dynamically learning few-shot visual recognition algorithm developed for tasks with limited training data availability. The Omni-Modeler adapts the knowledge domain of pre-trained deep neural networks to novel concepts with a calculated localized language encoder. The Omni-Modeler knowledge domain is generated by creating a dynamic dictionary of concept definitions, which are directly updatable as new information becomes available. Query images are identified through nearest neighbor comparison to the learned object definitions. The study presented in this paper evaluates its performance in re-identifying individuals as they move through a scene in both single-camera and multi-camera tracking applications. The results demonstrate that the Omni-Modeler shows potential for across-camera view pedestrian redetection and is highly effective for single-camera redetection with a 93% accuracy across 30 individuals using 64 example images for each individual.

Keywords: dynamic learning, few-shot learning, pedestrian redetection, visual recognition

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Authors: Annika J. Meyer, Tom Piechotta

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Unmanned Aerial Vehicles (UAVs) have increasingly become vital tools in various applications, including surveillance, search and rescue, and environmental monitoring. One common approach to ensure redundant communication systems when flying beyond visual line of sight is for UAVs to employ multiple mobile data modems by different providers. Although widely adopted, this approach suffers from several drawbacks, such as high costs, added weight and potential increases in signal interference. In light of these challenges, this paper proposes a communication framework intermeshing mobile networks and LoRa (Long Range) technology—a low-power, long-range communication protocol. LoRaWAN (Long Range Wide Area Network) is commonly used in Internet of Things applications, relying on stationary gateways and Internet connectivity. This paper, however, utilizes the underlying LoRa protocol, taking advantage of the protocol’s low power and long-range capabilities while ensuring efficiency and reliability. Conducted in collaboration with the Potsdam Fire Department, the implementation of mobile network technology in combination with the LoRa protocol in small UAVs (take-off weight < 0.4 kg), specifically designed for search and rescue and area monitoring missions, is explored. This research aims to test the viability of LoRa as an additional redundant communication system during UAV flights as well as its intermeshing with the primary, mobile network-based controller. The methodology focuses on direct UAV-to-UAV and UAV-to-ground communications, employing different spreading factors optimized for specific operational scenarios—short-range for UAV-to-UAV interactions and long-range for UAV-to-ground commands. This explored use case also dramatically reduces one of the major drawbacks of LoRa communication systems, as a line of sight between the modules is necessary for reliable data transfer. Something that UAVs are uniquely suited to provide, especially when deployed as a swarm. Additionally, swarm deployment may enable UAVs that have lost contact with their primary network to reestablish their connection through another, better-situated UAV. The experimental setup involves multiple phases of testing, starting with controlled environments to assess basic communication capabilities and gradually advancing to complex scenarios involving multiple UAVs. Such a staged approach allows for meticulous adjustment of parameters and optimization of the communication protocols to ensure reliability and effectiveness. Furthermore, due to the close partnership with the Fire Department, the real-world applicability of the communication system is assured. The expected outcomes of this paper include a detailed analysis of LoRa's performance as a communication tool for UAVs, focusing on aspects such as signal integrity, range, and reliability under different environmental conditions. Additionally, the paper seeks to demonstrate the cost-effectiveness and operational efficiency of using a single type of communication technology that reduces UAV payload and power consumption. By shifting from traditional cellular network communications to a more robust and versatile cellular and LoRa-based system, this research has the potential to significantly enhance UAV capabilities, especially in critical applications where reliability is paramount. The success of this paper could pave the way for broader adoption of LoRa in UAV communications, setting a new standard for UAV operational communication frameworks.

Keywords: LoRa communication protocol, mobile network communication, UAV communication systems, search and rescue operations

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Authors: Linda Richter

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Background and Significance: The influence of parents on their children’s attitudes and behaviors is immense, even as children grow out of what one might assume to be their most impressionable years and into teenagers. This study specifically examines the potential that parents have to prevent or reduce the risk of adolescent substance use, even in the face of considerable environmental influences to use nicotine, alcohol, or drugs. Methodology: The findings presented are based on a nationally representative survey of 1,014 teens aged 12-17 living in the United States. Data were collected using an online platform in early 2018. About half the sample was female (51%), 49% was aged 12-14, and 51% was aged 15-17. The margin of error was +/- 3.5%. Demographic data on the teens and their families were available through the survey platform. Survey items explored adolescent respondents’ exposure to addictive substances; the extent to which their sources of information about these substances are reliable or credible; friends’ and peers’ substance use; their own intentions to try substances in the future; and their relationship with their parents. Key Findings: Exposure to nicotine, alcohol, or other drugs and misinformation about these substances were associated with a greater likelihood that adolescents have friends who use drugs and that they have intentions to try substances in the future, which are known to directly predict actual teen substance use. In addition, teens who reported a positive relationship with their parents and having parents who are involved in their lives had a lower likelihood of having friends who use drugs and of having intentions to try substances in the future. This relationship appears to be mediated by parents’ ability to reduce the extent to which their children are exposed to substances in their environment and to misinformation about them. Indeed, the findings indicated that teens who reported a good relationship with their parents and those who reported higher levels of parental monitoring had significantly higher odds of reporting a lower number of risk factors than teens with a less positive relationship with parents or less monitoring. There also were significantly greater risk factors associated with substance use among older teens relative to younger teens. This shift appears to coincide directly with the tendency of parents to pull back in their monitoring and their involvement in their adolescent children’s lives. Conclusion: The survey findings underscore the importance of resisting the urge to completely pull back as teens age and demand more independence since that is exactly when the risks for teen substance use spike and young people need their parents and other trusted adults to be involved more than ever. Particularly through the cultivation of a healthy, positive, and open relationship, parents can help teens receive accurate and credible information about substance use and also monitor their whereabouts and exposure to addictive substances. These findings, which come directly from teens themselves, demonstrate the importance of continued parental engagement throughout children’s lives, regardless of their age and the disincentives to remaining involved and connected.

Keywords: adolescent, parental monitoring, prevention, substance use

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Authors: Sabiha Shahid Antora, Young Ki Chang

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Along with various farm management technologies, imagery is an important tool that facilitates crop assessment, monitoring, and management. As a consequence, drone imaging technology is playing a vital role to capture the state of the entire field for yield mapping, crop scouting, weed detection, and so on. Although it is essential to inspect the cultivable lands in real-time for making rapid decisions regarding field variable inputs to combat stresses and diseases, drone imagery is still evolving in this area of interest. Cost margin and post-processing complexions of the image stream are the main challenges of imaging technology. Therefore, this proposed project involves the cost-effective field programmable gate array (FPGA) based image processing device that would process the image stream in real-time as well as providing the processed output to support on-the-spot decisions in the crop field. As a result, the real-time FPGA-based image processing system would reduce operating costs while minimizing a few intermediate steps to deliver scalable field decisions.

Keywords: real-time, FPGA, drone imagery, image processing, crop monitoring

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Authors: Areti Okalidou, Paul D. Hatzigiannakoglou, Aikaterini Vatou, George Kyriafinis

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Young children are nowadays adept at using technology. Hence, computer-based auditory training programs (CBATPs) have become increasingly popular in aural rehabilitation for children with hearing loss and/or with cochlear implants (CI). Yet, their clinical utility for prognostic, diagnostic, and monitoring purposes has not been explored. The purposes of the study were: a) to develop an updated version of the auditory rehabilitation tool for Greek-speaking children with cochlear implants, b) to develop a database for behavioral responses, and c) to compare accuracy rates and reaction times in children differing in hearing status and other medical and demographic characteristics, in order to assess the tool’s clinical utility in prognosis, diagnosis, and progress monitoring. The updated version of the auditory rehabilitation tool was developed on a tablet, retaining the User-Centered Design approach and the elements of the Virtual Reality (VR) serious game. The visual stimuli were farm animals acting in simple game scenarios designed to trigger children’s responses to animal sounds, names, and relevant sentences. Based on an extended version of Erber’s auditory development model, the VR game consisted of six stages, i.e., sound detection, sound discrimination, word discrimination, identification, comprehension of words in a carrier phrase, and comprehension of sentences. A familiarization stage (learning) was set prior to the game. Children’s tactile responses were recorded as correct, false, or impulsive, following a child-dependent set up of a valid delay time after stimulus offset for valid responses. Reaction times were also recorded, and the database was in Εxcel format. The tablet version of the auditory rehabilitation tool was piloted in 22 preschool children with Νormal Ηearing (ΝΗ), which led to improvements. The study took place in clinical settings or at children’s homes. Fifteen children with CI, aged 5;7-12;3 years with post-implantation 0;11-5;1 years used the auditory rehabilitation tool. Eight children with CI were monolingual, two were bilingual and five had additional disabilities. The control groups consisted of 13 children with ΝΗ, aged 2;6-9;11 years. A comparison of both accuracy rates, as percent correct, and reaction times (in sec) was made at each stage, across hearing status, age, and also, within the CI group, based on presence of additional disability and bilingualism. Both monolingual Greek-speaking children with CI with no additional disabilities and hearing peers showed high accuracy rates at all stages, with performances falling above the 3rd quartile. However, children with normal hearing scored higher than the children with CI, especially in the detection and word discrimination tasks. The reaction time differences between the two groups decreased in language-based tasks. Results for children with CI with additional disability or bilingualism varied. Finally, older children scored higher than younger ones in both groups (CI, NH), but larger differences occurred in children with CI. The interactions between familiarization of the software, age, hearing status and demographic characteristics are discussed. Overall, the VR game is a promising tool for tracking the development of auditory skills, as it provides multi-level longitudinal empirical data. Acknowledgment: This work is part of a project that has received funding from the Research Committee of the University of Macedonia under the Basic Research 2020-21 funding programme.

Keywords: VR serious games, auditory rehabilitation, auditory training, children with cochlear implants

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Authors: Tawfig Falani, Abdulalaziz Saleh

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Air pollution has become a major environmental issue with definitive repercussions on human health. Global concerns have been raised about the health effects of deteriorating air quality due mainly to widespread industrialization and urbanization. To assess the quality of air in Brega, air quality indicators were calculated using the U.S. Environmental Protection Agency procedure. Air quality was monitored from 01/10/2019 to 28/02/2021 with a daily average measuring six pollutants of particulate matter <2.5µm (PM2.5), and <10µm (PM₁₀), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), ozone (O₃), and carbon monoxide (CO). The result indicated that air pollution at general air quality monitoring sites for sulphur dioxide, carbon monoxide, PM₁₀ and PM2.5 and nitrogen dioxide are always within the permissible limit. Referring to a monthly average of Pollutants in the Brega Industrial area, all months were out of AQG limit for NO₂, and the same with O₃ except for two months. For PM2.5 and PM₁₀ 7, 5 out of 17 months were out of limits, respectively. Relative AQI for ozone is found in the range of moderate category of general air pollution, and the worst month was Nov. 2020, which was marked as Very Unhealthy category, then the next two months (Dec. 2020 and Jan. 2021 ) were Unhealthy categories. It's the first time that we have used the AQI in SOC, and not usually used in Libya to identify the quality of air pollution. So, I think it will be useful if AQI is used as guidance for specified air pollution. That dictate putting monitoring stations beside any industrial activity that has emissions of the six major air pollutants.

Keywords: air quality, air pollutants, air quality index (AQI), particulate matter

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Authors: Ghada Ahmed

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Feminist translation is one of the strategies adopted in the field of translation studies when a gendered content is being rendered from one language to another, and this strategy has been examined in previous studies on written texts. This research, however, addresses the practice of feminist translation in audiovisual texts that are concerned with the screen, dialogue, image and visual aspects. In this thesis, the objectives are studying feminist translation and its adaptation in subtitling from Arabic into English. It addresses the connections between gender and translation as one domain and feminist translation practices with particular consideration of feminist translation strategies in English subtitles. It examines the visibility of the translator throughout the process, assuming that feminist translation is a product directed by the translator’s feminist position, culture, and ideology as a means of helping unshadow women. It also discusses how subtitling constraints impact feminist translation and how the image that has a narrative value can be integrated into the content of the English subtitles. The reasons for conducting this research project are to study language sexism in English and look into Arabic into English gendered content, taking into consideration the Arabic cultural concepts that may lose their connotations when they are translated into English. This research is also analysing the image in an audiovisual text and its contribution to the written dialogue in subtitling. Thus, this research attempts to answer the following questions: To what extent is there a form of affinity between a gendered content and translation? Is feminist translation an act of merely working on a feminist text or feminising the language of any text, by incorporating the translator’s ideology? How can feminist translation practices be applied in an audiovisual text? How likely is it to adapt feminist translation looking into visual components as well as subtitling constraints? Moreover, the paper searches into the fields of gender and translation; feminist translation, language sexism, media studies, and the gap in the literature related to feminist translation practice in visual texts. For my case study, the "Speed Sisters" film has been chosen so as to analyze its English subtitles for my research. The film is a documentary that was produced in 2015 and directed by Amber Fares. It is about five Palestinian women who try to break the stereotypes about women, and have taken their passion about car-racing forward to be the first all-women car-racing driving team in the Middle East. It tackles the issue of gender in both content and language and this is reflected in the translation. As the research topic is semiotic-channelled, the choice for the theoretical approaches varies and combines between translation studies, audiovisual translation, gender studies, and media studies. Each of which will contribute to understanding a specific field of the research and the results will eventually be integrated to achieve the intended objectives in a way that demonstrates rendering a gendered content in one of the audiovisual translation modes from a language into another.

Keywords: audiovisual translation, feminist translation, films gendered content, subtitling conventions and constraints

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Authors: Zheng Wang, Zhenhong Li, Jon Mills

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Ground-based synthetic aperture radar (GBSAR) represents a powerful remote sensing tool for deformation monitoring towards various geohazards, e.g. landslides, mudflows, avalanches, infrastructure failures, and the subsidence of residential areas. Unlike spaceborne SAR with a fixed revisit period, GBSAR data can be acquired with an adjustable temporal resolution through either continuous or discontinuous operation. However, challenges arise from processing high temporal-resolution continuous GBSAR data, including the extreme cost of computational random-access-memory (RAM), the delay of displacement maps, and the loss of temporal evolution. Moreover, repositioning errors between discontinuous campaigns impede the accurate measurement of surface displacements. Therefore, a versatile package with two complete chains is developed in this study in order to process both continuous and discontinuous GBSAR data and address the aforementioned issues. The first chain is based on a small-baseline subset concept and it processes continuous GBSAR images unit by unit. Images within a window form a basic unit. By taking this strategy, the RAM requirement is reduced to only one unit of images and the chain can theoretically process an infinite number of images. The evolution of surface displacements can be detected as it keeps temporarily-coherent pixels which are present only in some certain units but not in the whole observation period. The chain supports real-time processing of the continuous data and the delay of creating displacement maps can be shortened without waiting for the entire dataset. The other chain aims to measure deformation between discontinuous campaigns. Temporal averaging is carried out on a stack of images in a single campaign in order to improve the signal-to-noise ratio of discontinuous data and minimise the loss of coherence. The temporal-averaged images are then processed by a particular interferometry procedure integrated with advanced interferometric SAR algorithms such as robust coherence estimation, non-local filtering, and selection of partially-coherent pixels. Experiments are conducted using both synthetic and real-world GBSAR data. Displacement time series at the level of a few sub-millimetres are achieved in several applications (e.g. a coastal cliff, a sand dune, a bridge, and a residential area), indicating the feasibility of the developed GBSAR data processing package for deformation monitoring of a wide range of scientific and practical applications.

Keywords: ground-based synthetic aperture radar, interferometry, small baseline subset algorithm, deformation monitoring

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