Miracle-dz/UniFAR-FTUs huggingface dataset:
Dissolved organic carbon in the lower Namoi River, New South Wales : determing responses to flow, loads and food web linkages

VI STRUCTURE OF THESIS VIII CHAPTER 1. BACKGROUND TO THE STUDY 1 1.1. ECOLOGY OF AUSTRALIAN RIVERS 1 1.2. WATER MANAGEMENT IN AUSTRALIA 3 1.3. WATER IN THE NAMOI RIVER 5 1.4. INTEGRATED MONITORING OF ENVIRONMENTAL FLOWS 7 1.5. CONSTRAINTS 8 1.6 AIMS & HYPOTHESES OF RESEARCH 9 CHAPTER 2. A REVIEW OF THE LITERATURE 11 2.1. FORMS OF DISSOLVED ORGANIC MATTER 11 2.2. SOURCES OF DOC 12 2.3. DOC RESPONSE TO FLOWS / FLOOD EVENTS 18 2.4. DOC AND FOOD WEBS 24 2.5. DOC SOURCES AND UTILISATION IN AUSTRALIAN AQUATIC ENVIRONMENT 29 CHAPTER 3. USING FLOW MANAGEMENT TO INCREASE DELIVERY OF ALLOCHTHONOUS DERIVED DISSOLVED ORGANIC CARBON TO THE HIGHLY REGULATED LOWER NAMOI RIVER 39 3.
Waffle

The bathroom has consistently ranked among the most perilous rooms in households, with slip and fall incidents during showers posing a critical threat, particularly to the elders. To address this concern while ensuring privacy and accuracy, the mmWave-based sensing system has emerged as a promising solution. Capable of precisely detecting human activities and promptly triggering alarms in response to critical events, it has proved especially valuable within bathroom environments. However, deploying such a system in bathrooms faces a significant challenge: interference from running water. Similar to the human body, water droplets reflect substantial mmWave signals, presenting a major obstacle to accurate sensing. Through rigorous empirical study, we confirm that the interference caused by running water adheres to a Weibull distribution, offering insight into its behavior. Leveraging this understanding, we propose a customized Constant False Alarm Rate (CFAR) detector, specifically tailored to handle the interference from running water. This innovative detector effectively isolates human-generated signals, thus enabling accurate human detection even in the presence of running water interference. Our implementation of "Waffle" on a commercial off-the-shelf mmWave radar demonstrates exceptional sensing performance. It achieves median errors of 1.8cm and 6.9cm for human height estimation and tracking, respectively, even in the presence of running water. Furthermore, our fall detection system, built upon this technique, achieves remarkable performance (a recall of 97.2% and an accuracy of 97.8%), surpassing the state-of-the-art method.
Unobtrusive Air Leakage Estimation for Earables with In-ear Microphones

Earables (in-ear wearables) are gaining increasing attention for sensing applications and healthcare research thanks to their ergonomy and non-invasive nature. However, air leakages between the device and the user's ear, resulting from daily activities or wearing variabilities, can decrease the performance of applications, interfere with calibrations, and reduce the robustness of the overall system. Existing literature lacks established methods for estimating the degree of air leaks (i.e., seal integrity) to provide information for the earable applications. In this work, we proposed a novel unobtrusive method for estimating the air leakage level of earbuds based on an in-ear microphone. The proposed method aims to estimate the magnitude of distortions, reflections, and external noise in the ear canal while excluding the speaker output by learning the speaker-to-microphone transfer function which allows us to perform the task unobtrusively. Using the obtained residual signal in the ear canal, we extract three features and deploy a machine-learning model for estimating the air leakage level. We investigated our system under various conditions to validate its robustness and resilience against the motion and other artefacts. Our extensive experimental evaluation shows that the proposed method can track air leakage levels under different daily activities. "The best computer is a quiet, invisible servant. " ~Mark Weiser
Image is First-order Norm+Linear Autoregressive

This paper reveals that every image can be understood as a first-order norm+linear autoregressive process, referred to as FINOLA, where norm+linear denotes the use of normalization before the linear model. We demonstrate that images of size 256 × 256 can be reconstructed from a compressed vector using autoregression up to a 16 × 16 feature map, followed by upsampling and convolution. This discovery sheds light on the underlying partial differential equations (PDEs) governing the latent feature space. Additionally, we investigate the application of FINOLA for self-supervised learning through a simple masked prediction technique. By encoding a single unmasked quadrant block, we can autoregressively predict the surrounding masked region. Remarkably, this pre-trained representation proves effective for image classification and object detection tasks, even in lightweight networks, without requiring fine-tuning. The code will be made publicly available.
Impact Analysis of Open Cast Coal Mining on Land Use/Land Cover using Remote Sensing and GIS Technique in Ledo-Margherita Region of Assam, India

: Ledo-Margherita area of Tinsukia District of Upper Assam is the prime coal mining area of the state of Assam. Field investigation reveals that large scale geomorphological changes took place in the region due to open cast coal mining. Therefore, an investigation on the changes of Land-use/Land-cover (LULC) in the area, especially due to open cast mining has been made for a period of 20 years (1996 – 2016) by using remote sensing and GIS techniques. Twelve LULC classes were identified in the region. Significant increase were observed in case of settlement (2214%), mining (130%), shifting cultivation (127%) and grassland (74%) areas; on the contrary decrease in wetland (-79%), sandbar (-43%), water body (-33%) and forest cover (-30%) were observed. Indirect impact of open-cast mining on shifting of agricultural activities to other places due to impact on soil fertility was also observed in the region.
Explainable Artificial Intelligence for Highlighting and Searching in Patent Text 12-21

The verbose content and redundant information present in patents often add complexity to reading and understanding them. Individual subject matters related to an invention and its decisiveness are scattered throughout patent documents. Moreover, these matters could provide relevant key arguments for an effective examination or critical assessment of an invention. To address these complexities and facilitate patent practitioners’ efficient reading and in-page semantic searches ofpatents,wegeneratedamulticlassdatasetrepresentingkeyargumentsofpatentsonasentencelevel. Essentially,thesekey argumentsaretheconcretedetailsrelatedtoaninvention, suchastheproblemitsolvesorthetechnicaleffectsoradvantages it achieves. We fine-tuned Transfer Learning models on this novel dataset and developed two Chromium extensions. One extension automatically highlights these key arguments using our fine-tuned model, and the other steers semantic search withinanyopenedpatentdocumentinthebrowser. Thedataandcoderelatedtothisworkarereleasedtothecommunityvia a GIT repository. The empirical test cases and manually labeled gold truth data provide evidence supporting our hypothesis regarding in-page patent search and efficient reading, respectively.
Effect of Photoplethysmogram Signal Preprocessing on Predicting Blood Pressure by Deep Learning

Photoplethysmography is a convenient and practical health monitoring method. However, its ability to predict blood pressure has not yet reached medical-grade standards. Previous research has rarely delved into signal preprocessing. Therefore, we enhanced predictive accuracy using improved preprocessing methods. In the first phase of this research, we reviewed articles on photoplethysmography (PPG) signal-based blood pressure prediction and improved the preprocessing techniques to solve the problems of missing values, outliers, feature extraction, and data stratification. Then, we verified that these improvements yielded positive results. In the second phase, we reduced the prediction error in blood pressure by increasing the volume of training data and the number of patients by introducing new indicators in data stratification. Due to significant individual differences in photoplethysmography waveforms, we divided the experimental results into two groups: prediction errors for patients used during model training and prediction errors for patients not included in training for diastolic and systolic blood pressure. The results indicated that for patients used during deep learning model training, the standard deviation of diastolic blood pressure decreased from 13.59 to 5.20, and the mean absolute error decreased from 10.43 to 3.44. For systolic blood pressure, the standard deviation decreased from 20.53 to 8.23, and the mean absolute error decreased from 15.84 to 5.68. For patients not included in training but directly predicted, the standard deviation of diastolic blood pressure also decreased from 15.98 to 10.86, and the mean absolute error decreased from 12.58 to 8.76. For systolic blood pressure, the standard deviation decreased from 29.19 to 18.29, and the mean absolute error decreased from 24.34 to 14.90.
Random Forest Method Based Research on the Anxiety Differences of Different Investors Facing Financial Product Default

Investors may experience anxiety when facing financial product defaults. There are significant differences in the level of anxiety among different investors. This difference may be related to the Socioeconomic attributes of investors. We collected the Socioeconomic attributes of investors as independent variables based on an investment institution and then measured the anxiety level of investors as the dependent variable on an anxiety scale. Then, a classification model for anxiety level was constructed using machine learning methods. The test results showed that the Precision, Recall, and F1-score of the Decision tree model were 85.1, 84.1, and 84.6%, respectively. The Precision, Recall, and F1-score of the Random forest model were 88.0, 87.3, and 87.6%, respectively. The experimental results validated the hypothesis that there is indeed a correlation between the level of anxiety among investors when facing financial product defaults and their Socioeconomic attributes. The level of anxiety among investors when facing financial product defaults was classified and predicted based on their socioeconomic attributes. The classification and regression decision tree (CART) model output strong explicit rules, making it more suitable for clinical application.
Evaluate the Brand Style of Automated Design Product by Machine Learning

Automatic design technology is becoming mature and applied to generate various new concept designs. However, with many new concept designs, it is necessary to select the optimal plan effectively. Therefore, we proposed an evaluation method that combined design automation-generated techniques and t-distributed stochastic neighbor embedding (t-SNE) of machine learning. In the design experiment, we used automated design to generate 200 samples with the image generation of the 2D concept, sample management to build the correlation between 2D and 3D, and complete the 3D model. Finally, we used the brand style classification system to evaluate the sample. In the existing classification system, two computer mouse brand styles were classified: Mad Catz and Genius brand style. The system was based on the t-SNE of machine learning and data visualization to predict generated samples. The generated computer mice of design automation were evaluated effectively. Moreover, the brand style presented by the appearance design of the new mouse generated by design automation conformed to the design assumption of the original brand style.
An Arduino Based Modular Battery Management System for Protection and Real Time Monitoring of Lithium-Ion Cell

Battery management systems (BMS) are widely used for proper protection and utilization of battery systems. In this paper, a modular BMS circuit is proposed for the design of modular battery stacks that can be reconfigured depending upon utilization. The real-time evaluation is carried out using four local modules and one central module. An Arduino nano with an analog-digital converter (ADC) multiplexer and voltage divider circuit is used as central module in the BMS system. The BMS voltage measurement accuracy is evaluated with the measured value. The state of charge (SOC) measurement technique is developed to calculate the SOC of each cell. A protection circuit is designed for under-voltage, over-voltage, and short-circuit in battery cells. The circuit also provides passive cell balancing. The hardware results verify the operation of the circuit as well as the cell balancing in the battery cells during the charging and discharging cycle.
Imaginary Coordinate System Based Space Vector Pulse Width Modulation of a Cascaded H-Bridge Multilevel Inverter

This paper mainly focusses on the imaginary coordinate transformation technique based Space Vector Pulse Width Modulation (SVPWM) of a multilevel inverter. Imaginary coordinate system based SVPWM involves simpler calculations. It does not use any look-up table or trigonometric calculations and eliminates redundant vectors. The SVPWM scheme based on the imaginary coordinate system proposed in this paper works on minimum device switching frequency which in turn reduces the switching loss, thereby increasing the overall efficiency of the inverter. A step-by-step implementation of an imaginary coordinate transformation technique based pulse width modulation of a cascaded H-Bridge multilevel inverter is detailed here. The proposed technique is simulated for a five-level inverter and validated using MATLAB/Simulink. The simulation results show that the proposed technique gives better performance when compared with the conventional SVM techniques. The proposed technique is perfectly general and can be extended to any n level multilevel inverter.
Electrically Switched Resistance Element Based on the Thin Film of Vanadium Dioxide

Experimental results of the planar resistive element based on thin-film vanadium dioxide are presented in this article. Samples of thin films of vanadium dioxide with thickness 100 nm were obtained by magnetron sputtering. Triangular copper electrodes were formed on the film to provide a gap between vertexes of about $80\mu\mathrm{m}$. This gap is the place of the formation of the current-conducting channel during the phase transition. The change of the element resistance more than 3 orders was observed. Switching of the element occurred at 4 V across the gap at temperature stabilization at 322 K with the scale of the transient time of less than 1 ms.
Power Line EMI Filter Design of Power Converters to Meet the Conducted Emission Specifications of MIL-STD-461G

This paper presents the investigation and mitigation of the conducted emissions generated by the power converter in any system with and without an EMI filter to meet the EMC limits. The conducted emissions of the converter are measured with a standard setup, and depending upon the qualification limit line (MIL-STD-461G standard), the insertion loss requirement and cutoff frequency are calculated for differential mode and typical mode emissions. The conductions will exceed the limit line w.r. t the general power converter operation, and these emissions will travel towards the power source or grid. An EMI filter is designed to mitigate this by selecting the appropriate L and C elements. This EMI filter is placed at the input of the power converter to mitigate the conducted emission from the converter. The EMI filter and the power converter are designed and validated in the laboratory under different load conditions and different frequencies of operation.
Aluminum Flake Defect Detection Based on Improved Less Sample Generation Algorithm

In the production of aluminum profiles, aluminum sheet defects reduce the quality of the products. However, in practical engineering, there is a lack of sample data, which limits the performance of defect detection models. To solve this problem, we developed an improved few-sample generation algorithm for defect detection of aluminum sheets. We used a defect library to extract sample information of different defect types and randomly generate a large number of non-repeated high-quality datasets. This method enhanced the diversity and complexity of the data. Experiment results showed that the model accuracy was improved by 2.8% after using the few-sample generation algorithm, and the overall Map value reached 97.61%. Such results provide a valuable reference for the application of few-sample data processing in defect detection.
Dual Encoder–Decoder Network for Land Cover Segmentation of Remote Sensing Image

Although the vision transformer-based methods (ViTs) exhibit an excellent performance than convolutional neural networks (CNNs) for image recognition tasks, their pixel-level semantic segmentation ability is limited due to the lack of explicit utilization of local biases. Recently, a variety of hybrid structures of ViT and CNN have been proposed, but these methods have poor multiscale fusion ability and cannot accurately segment high-resolution and high-content complex land cover remote sensing images. Therefore, a dual encoder–decoder network named DEDNet is proposed in this work. In the encoding stage, the local and global information of the image is extracted by parallel CNN encoder and transformer encoder. In the decoding stage, the cross-stage fusion module is constructed to achieve neighborhood attention guidance to enhance the positioning of small targets, effectively avoiding intraclass inconsistency. At the same time, the multihead feature extraction module is proposed to strengthen the recognition ability of the target boundary and effectively avoid interclass ambiguity. Before outputting, the fusion spatial pyramid pooling classifier is proposed to merge the outputs of the two decoding strategies. The experiments demonstrate that the proposed model has superior generalization performance and can handle various semantic segmentation tasks of land cover.
Fault Detection in DC Nanogrid System using Advanced Signal Processing Techniques

With the penetration of DERs (Distributed Energy Resources), conventional transmission and distribution systems are getting increasingly replaced with converter based topologies along with localized transfer of power to loads. These are extremely dynamic systems, exposed to numerous intermittent sources of energy such as solar power, wind energy etc. The converters which interface the power generation sources with the load have to tightly maintain the bus voltage in order to maintain supply reliability and power quality. They also need to be resilient enough when faced with faults and disturbances in the system. However, these power converters which consist mostly of power electronic components which have very high switching frequencies, when faced with an unprecedented condition might give in to failures due to their low tolerance to very high currents that might cause damage to these components. These kinds of faults need to be accurately detected and isolated within time then to create a smart, reliable, energy efficient power system.
Study of Cloud-based Monitoring and Feeding System for Smart Aquaculture Farming in Infanta, Quezon

The province of Quezon is the top agricultural area in the region because of its vast aquaculture industry. Producers of fish and seafood consumers are disturbed by the inadequate food safety and security of aquaculture operations. In fish farming, particularly with Nile tilapia, overfeeding, and subpar monitoring contaminate water due to feed leftover, poor health of fish, and fish with a higher mortality rate. Fish keepers can be alerted by the proposed monitoring method in this study when the tilapia in their fish farms display low- and high-risk behaviors on the Blynk app. Six sensors were used in the system: total dissolved solids (TDS), pH, dissolved oxygen (DO), turbidity, temperature, and water level. Additionally, it contained a fish feeder, allowing fish caretakers to feed their fingerlings anytime. The results showed that the TDS significantly affected the system’s performance and the growth of Nile tilapia in fishponds. The study’s findings showed that the TDS sensor’s and fish feeder’s actuator’s functionality, the exact data, and the continuous notification on the Blynk application significantly influenced fish farming. Additionally, the fabricated prototype was reliable and accurate in alerting and collecting sensor data.
IoT-based Indoor Air Quality Surveillance and Purifier

The air quality of the surrounding area needs to be monitored and air needs to be purified if air quality levels exceed a certain level. The recent developments in technologies allow for combining different types of devices into a single device. In the past few years, more people have been aware of air quality indoors and outdoors. This study aimed to design an Internet-of-Things (IoT) system to detect humidity, PM2.5, and temperature and determine the air quality. The air purifier sent the essential data to the user via Wi-Fi. The system collected data from various sensors such as DHT22 and PMS5003. The current indoor air quality was evaluated to determine standard indoor air quality values. Fuzzy logic was used to accurately evaluate the level of air quality based on the data and the standard values and control the fan’s power of the air purifier. The air purifier effectively eliminated airborne particles and filtered dust particles and smoke. The prototype built for this research was compared with two different types of commercially available air purifiers in terms of size and cost. A t-test was used for comparison. The prototype’s t-score was 0.242 which was below the critical value of 2.101. Statistically, in the time of filtering the air in an enclosed environment, there were no significant differences between the commercial air purifiers and the prototype.
Machine Learning-Based High-Resolution Estimation of Global Catch Distribution of Taiwan’s Distant Water Fisheries

Taiwan’s overseas fishing fleet is well-known for its size and operation efficiency. Due to technological advancement, Taiwan has an excellent coastal and offshore mapping system of precision of 0.01 and 0.001° in locating fishing grounds and plotting catch distribution. However, for overseas fisheries, traditional methods for mapping catch distribution use only the trajectory of the fishing vessel with eLogbooks to distribute the catch on 1.0° using the geographic information system (GIS) [1]. Thus, we developed an approach to map catch distributions of Taiwan’s overseas fishing fleet using artificial intelligence (AI) and high-resolution geographic information system (GIS) technology in 0.5 and 0.25°. We integrated vessel monitoring system (VMS) data and eLogbooks records to recognize fishing activities and improve the accuracy of fishing locations. We combined the VMS data and eLogbook catch records of Taiwan fishing vessels to recognize the species of catch of each fishing vessel. After obtaining the species for each fishing vessel, we used machine learning to identify fishing and non-fishing areas to assign weighted catches using a machine learning algorithm. The result was analyzed globally on a high-resolution scale of 0.25 °. The findings showed that the system mapped the catch volumes of different fish species and differentiated between fishing and non-fishing areas while the fishing vessel sailed. This effectively determined catch distributions in areas where vessels were not fishing, providing more accurate fishing areas and enhancing resource assessment. In addition, a distinctive feature to visualize the distinct catch and species in a high resolution was provided.