Miracle-dz/UniFAR-FTUs huggingface dataset:
PET/CT Motion Correction Exploiting Motion Models Fit on Coarsely Gated Data Applied to Finely Gated Data

Motion correction is imperative to the reduction of blurring and artefacts inherent in PET; due to the relatively long acquisition time, and the temporal difference in the attenuation map acquisition. Registration literature contains many examples of spatial regularisers, however there are few temporal regularisers. Motion models can act as such a temporal regulariser, as well as allowing for the interpolation of unseen motion correction results. In our previous work, we applied a motion modelling approach to high TOF resolution non-attenuation corrected data; where the data was corrected to the space of the attenuation map. However, this approach was challenging, especially when low contrast lung tumours are present. This work seeks to extend previous work, by incorporating an approach suggested by Y. Lu et al. (JNM 2018), to perform an initial MLACF reconstruction for the motion estimation. In this work, we combine these two approaches, with several improvements, including; µ-map alignment, as well as, fitting the motion model on low noise low temporal/gate resolution data, and applying it to high noise high temporal/gate resolution data. To test this, XCAT volumes are constructed, and TOF data simulated. Evaluation compares the results of the proposed method against, where the motion model was fit on data gated more finely, where the motion model was fit on noiseless data, and finally non-motion corrected examples. Results indicate that the incorporation of MLACF, and fitting of the motion model on low noise low temporal/gate resolution data, improves contrast and quantification, while allowing for a relatively fast execution time.
Comparison results of forest cover mapping of Peninsular Malaysia using geospatial technology

Climate change and global warming transpire due to several factors. Among them is deforestation which occur mostly in developing countries including Malaysia where forested areas are converted to other land use for tangible economic returns and to a smaller extent, as subsistence for local communities. As a cause for concern, efforts have been taken by the World Resource Institute (WRI) and World Wildlife Fund (WWF) to monitor forest loss using geospatial technology - interpreting time-based remote sensing imageries and producing statistics of forested areas lost since 2001. In Peninsular Malaysia, the Forestry Department of Peninsular Malaysia(FDPM) has conducted forest cover mapping for the region using the same technology since 2011, producing GIS maps for 2009-2010,2011-2012,2013-2014 and 2015. This paper focuses on the comparative study of the results generated from WRI,WWF and FDPM interpretations between 2010 and 2015, the methodologies used, the similarities and differences, challenges and recommendations for future enhancement of forest cover mapping technique.
General Cephalometric Landmark Detection for Different Source of X-Ray Images

Cephalometric landmarks are specific anatomical points or structures identifiable on a radiograph or X-ray image of the human skull. These landmarks serve as reference points for cephalometric analysis, which involves measuring and assessing skull structures for various clinical and research purposes. Carrying out this marking can lead to errors, such as spending excessive time. Currently, implementations that involve machine learning help to mark these landmarks automatically. However, many of these need to consider that X-Ray images can be obtained from different machines, removing homogeneity in the observations. In this work, we study the influence of cephalometric images from different X-Ray machines on the performance of a model to detect cephalometric landmarks. Performance-based on age and the number of points to be estimated was also studied. We use a universal landmark detection model, a well-known architecture in the state of the art. The results showed that combining specific machines can achieve more minor errors and better generalization to machines not included in the training. On the other hand, the model can train with missing landmarks, but it can only succeed if the number of missing points is low.
Automatic Registration Techniques for Power IoT Terminals

Managing the Power Internet of Things (IoT) presents challenges, particularly in the configuration of terminal devices. Power IoT terminals come in various types. Configuring each terminal on the IoT management platform and edge IoT agents involves a substantial amount of information, which has led to significant labor costs. This paper introduces an automatic registration technique for Power IoT terminals. Firstly, an intelligent identification program is employed to identify device information, including the manufacturer, type, model, and other device information. Subsequently, using this information, the triplet information is retrieved from the IoT management platform and synchronized to the registration server. Lastly, the device establishes communication with the registration server to acquire this information and successfully completes the automatic registration on the IoT management platform using the MQTT protocol. The proposed method addresses both the communication barrier between the IoT management platform and devices and the complex process of manual identification and configuration for terminal device registration. The effectiveness and feasibility of this approach in automatically registering Power IoT terminals provide robust support for the “plug-and-play” capability of new power devices.
An Efficient Method for Leaf Diseases Detection Using Deep Learning Technique

Leaf diseases identification and sorting are essential responsibilities in crops, and they pose a significant challenge in today's agriculture and food processing environments since they affect crop inputs and yields. A more accurate and timelier prognosis of agricultural leaf diseases could aid in the creation of an immediate corrective method, greatly reducing economic and production losses. Deep learning technical developments have enabled investigators to significantly improve the performance and accuracy of object identification and recognition systems. The primary objective is to establish how to come up with a more applicable neural network framework for the stated duties. In the present article, we put forward the use of a convolutional neural network (CNN) approach for detecting crop leaf diseases in an assortment of plants employing images taken from plant leaves. As a result, it is able to appropriately assess the detection and classification processes. The proposed crop leaf disease forecasts include four stages: preprocessing to enhance and shrink images using the contrast stretching approach; segmentation using k-means clustering technique; feature extraction and classification using convolutional neural networks. Following that, we detected and displayed in the conversation box several segmentation features, classifier performance metrics, and statistical and structural features of the anticipated disease type. Finally, the acquired findings were compared to existing methodologies, and it was determined that the proposed plant disease prediction method is both accurate and efficient.
Comparative Analysis of Models for Movie Review Sentiment Analysis

Movie evaluations represent an individuals' personal viewpoints and possess the potential to influence the prosperity of a film, therefore, precise categorization of such reviews, through sentiment analysis becomes paramount. This paper compares the effectiveness of five different machine learning models for movie review sentiment analysis using two text preprocessing techniques: TF-IDF and Bag-of-Words. The results indicate that Logistic Regression achieves the highest accuracy in both techniques, with Random Forest as the next best-performing model, followed by Gradient Boosting. This study emphasizes the crucial role of selecting the right preprocessing technique and algorithm for a particular task. It also suggests further research into other vectorization techniques and domain-specific features to improve sentiment analysis model accuracy.
Analysis of Different Wavelet for Removal of Single Channel Mixed Signal Noise

In the past many speech enhancement methods has been developed. These methods improved the signal quality but these techniques enhanced the signal quality but were unable to eliminate the melodic noise that was already present in the signal. To some extent, the wavelet based methods are effective to such type of noises. In this paper, we present the study some mother wavelets function for the for enhancement of mixed noisy signal using binary mask thershoding function. In this paper we decomposed the mother wavelet up to three levels for speech enhancement purpose. Here we make the use of six mother wavelets. These are symlet(order 4 and 18),coifet5, bioorthogonal 3.1 and Deubechies (order 10 and 40) and performance of Hindi speech singnal is analysed on the biases of SNR,FwSNR, Cepstrum distance, PESQ, OVL1. Our findings demonstrate that coifrt5 and Deubechies40 mother wavelets are more successful at removing noise for a given binary mask thresholding function. MATLAB 2018a was used to implement the algorithms.
Area, Distribution and Species Composition of Mangroves in China

This paper systematically summarized the change characteristic of mangroves and their species composition. The geographical distribution of mangrove plants and their growth status are greatly affected by many external factors, including air temperature, ocean currents, waves, bank slopes, salinity, tides, and sediment and so on. Mangroves in China are mainly distributed in the southeast coast of the tropical and subtropical coastal bays,estuaries and other areas under waters. From Hainan Island towards the north, climate zones extend from central tropical zone(the south of Hainan Island), to northern tropical zone(the north of Hainan Island, south of Leizhou Peninsula and Taiwan Island), to southern subtropical zone(Guangxi province, Guangdong province, Fujian province, the south of Taiwan Island and southern coastal areas of Fujian province), to northern subtropical zone(the north of Fujian province and coastal areas of Zhejiang province). The area and population diversity of mangrove plants decline significantly with increasing latitudes, and forest stand also transforms from arbor to shrub, which fully demonstrated the macroscopically control effect of air temperature for the geographical distribution of mangrove plants. In terms of mangrove cover change, the total area of mangroves in China has been dramatically shrunk from 250 thousand ha in history, to 42 thousand ha in 1950, and to 22.8 thousand ha in2001. In 2001, descending order of existing area of mangroves in China's major provinces is: Guangdong province(9 084.0 ha), Guangxi province(8 374.9 ha), Hainan(3 930.3 ha), Fujian province(615.1 ha), Hong Kong Special Administrative Region(510 ha), Taiwan province(278 ha), Zhejiang province(19.9 ha), Macau Special Administrative Region(60 ha). For the species composition of mangrove plants, adding 2 species(Sonneratia apetala and Laguncularia racemosa) of successful introduced true-mangrove plants from abroad, now there is total 38 species of mangrove plants in China, including 26 species of true-mangrove plants and 12semi-mangrove plants.
Data Mining Techniques for Ensuring Security in Large Datasets

In the contemporary digital landscape, the security of large datasets is paramount, given the increasing volume, variety, and velocity of data generation and collection. This paper provides an intricate examination of the application of various data mining techniques to bolster security measures in handling expansive datasets. Through a systematic exploration of methods including clustering, classification, and association, we aim to elucidate the efficacy and adaptability of these techniques in identifying and mitigating potential security threats. We critically analyze real-world scenarios and case studies, drawing comparative insights to underscore the nuanced applications and challenges inherent in diverse data environments. A detailed discussion is presented on the role of machine learning algorithms and artificial intelligence in augmenting data mining processes, enhancing predictive accuracy, and real-time responsiveness to security breaches. The findings indicate a significant positive correlation between advanced data mining applications and enhanced data security. However, they also highlight existing vulnerabilities and areas necessitating further research and innovation. We conclude with recommendations for integrating adaptive, scalable, and robust data mining frameworks to ensure comprehensive security in managing large datasets, paving the way for future research intersections between data mining and cybersecurity.
A Novel State Estimation Algorithm for Wheel-legged Multi-mode Vehicle

Wheel-legged multi-mode vehicles exhibit the capability to effectively execute specialized tasks in complex environments, relying on the maintenance of high maneuverability control. State estimation serves as the foundation for achieving this dynamic control. This paper proposes a novel state estimation algorithm that considers wheel dynamic and reliable contact state based Kalman Filter (CWRKF) for multi-mode vehicle, mainly focusing on wheel velocity estimation and vehicle state estimation. The CWRKF integrates wheel velocity estimation to address the issue of inaccurate velocity estimation during driving and braking stages resulting from wheel slip. Simultaneously, a trust model is implemented to ensure that motion calculations are performed based on the reliable contact between the wheels and the ground. Various numerical and experimental simulations are carried out on simulation and actual vehicle platforms to verify the effectiveness of the proposed algorithm. The results demonstrate that the CWRKF algorithm has superior accuracy and robustness in various scenarios, making it a promising approach for state estimation in multi-mode vehicles.
Determining Average Error of Flank Wear in Turning Duplex Stainless Steel Using Canny's Edge Detection Algorithm in Comparison with Fuzzy Logic Algorithm

To obtain the average error of flank wear in turning Duplex Stainless Steel (DSS) using Canny's Edge Detection Algorithm and compare it with Fuzzy Logic Algorithm. The average error accuracy of flank wear is computed for Canny's Edge Detection Algorithm and Fuzzy Logic Algorithm separately and are categorized into Group 1 and Group 2 respectively, each consisting of 27 samples, providing a total sample size of 54. It was computed using clinicalc.com with an enrollment ratio of one, an alpha error of 0.05, a G-power of 80%, and a confidence interval of 95%. The average error accuracy determined using the Novel Canny's Edge Detection Algorithm (92.78%) is more accurate and statistically significant than the Fuzzy Logic Algorithm (88.91 %) and the significance value obtained is p = 0.000 (p<0.05). The proposed Novel Canny's Edge Detection Algorithm is significantly better at predicting flank wear and the average error accuracy than the Fuzzy Logic Algorithm.
Calibration of the TRMM Precipitation Radar Using the Active Radar Calibrator (ARC)

Active radar calibrator (ARC) are generally used for calibration of spaceborne or airborne SAR. The Japan Aerospace Exploration Agency (JAXA) and National Institute of Information and Communications Technology (NICT) developed the ARC for calibration of the first spaceborne Precipitation Radar (PR) onboard the Tropical Rainfall Measuring Mission (TRMM) satellite. The main characteristic of this ARC is three function of radar receiver, beacon-signal transmitter, and delayed-transponder function. On the development phase of TRMM-PR, it was confirmed that the ARC is useful for calibration of active array radar such as TRMM-PR. Since the TRMM launch in 1997, TRMM-PR has been calibrated with the ARC per a few months, and its calibration results have been reflected in the PR data processing system as calibration coefficients. At first, TRMM-PR used to be calibrated only by the radar receiver and beacon-signal transmitter function of the ARC, but we established a new calibration technique using delayed-transponder function which can calibrate radar cross section of TRMM-PR directly. Through the five year calibration period using the ARC, transmittion and receiption gains of TRMM-PR have been calibrated to an accuracy of ±0.5 dB or better, and the PR-measured radar cross section has been calibrated to an accuracy of within•}1 dB.
Radar Object Detection Using 3D-2D Encoder-Decoder Network

With the continuous development of autonomous driving technology, accurate 3D object detection in complex scenarios has garnered significant attention. Among commonly utilized sensors, radar is often considered a robust and cost-effective sensor that can operate effectively even in challenging lighting or adverse weather conditions. In this paper, we propose a 3D-2D encoder-decoder network for millimeter-wave radar object detection, which processes multiple chirps input from a single frame to extract the location and class of objects. Our extensive experiments demonstrate that the proposed algorithm achieves better average recall and average precision than previous works in all testing scenarios, significantly improving real-time performance. Our code is available at: https://github.com/luffydo/3D-2DEncoder-Decoder-Network.
Disease Classification in X-Ray Images using Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have emerged as a revolutionary tool in the domain of disease categorization, fundamentally changing the landscape of medical imaging diagnostics. This study deeply investigates the application of CNNs to automatically and with high precision classify diseases using medical imagery, including X-rays, MRIs, and CT scans. The utilization of CNNs in this research takes advantage of their proficiency in recognizing intricate patterns and features within medical images, thereby simplifying the disease diagnosis process. The incorporation of CNNs in disease classification represents a promising advancement in the healthcare sector. Through extensive training on extensive datasets, these neural networks can discern intricate patterns and subtle irregularities in medical images, resulting in improved precision and efficiency in disease diagnosis. By automating the categorization process, healthcare providers can minimize the likelihood of human errors and enhance the speed of diagnosis, ultimately leading to improved patient outcomes. This study emphasizes the potential of CNNs to reshape disease classification, offering a glimpse into a healthcare future where advanced technology plays a pivotal role in ensuring accurate and timely disease identification. It also underscores the ongoing necessity for research and development in the field of medical image analysis, as CNNs continue to reshape the healthcare landscape with their ability to revolutionize disease diagnostics.
Improvement and Evaluation of a Block-based Language Environment for Introductory C# Programming Course

This research improved and evaluated a blockbased tool for teaching novices introductory C# programming. The hybrid approach of teaching C# was applied in the study. Consequently, a novice-oriented application, called CSBlock, was developed based on Blockly. Participants enrolled in a four-week online C# course to verify its effectiveness in teaching settings. After the course, participants completed four tasks using C# and submitted a final questionnaire. By analyzing the tasks and questionnaires submitted by the participants, the study found that CSBlock did not improve participants' learning effect and performance, specifically even performed worse in loop statements.
Mice PET/CT Dataset Augmentation Using a 3D-Progressive Growing GAN

In this study, we applied GANs for synthetic 3D mice generation, to be used in PET/CT preclinical deep learning (DL) based research. The lack of available datasets makes it difficult for researchers to apply DL to solve tasks that involve small-animal data, such as emission-based attenuation correction in small-animals’ PET/MR. We applied a 3D-Progressive Growing GAN (3D-PGAN) to generate synthetic CT scans of mice. The 3D-PGAN generated a 64 x 64 x 160 CT scan of a mouse in approximately 0.10 seconds with a Fréchet inception distance (FID), which measures the similarity between two datasets of images, of 103.1 on the coronal plane. For reference, a FID of 0 indicates perfect similarity, and a score of 103.1 is comparable to related works’ results. The generated CT scans resembled real CT scans; however, the generated images were often missing finer structures like a mouse’s ribcage.
A Novel Paradigm of Indoor Navigation System using Li-Fi Technology

According to a 2022 survey by the World Health Organization (WHO), nearly 2.2 billion people across the globe are living with visual impairments, and this number is poised to grow, especially as the global population ages. When these individuals visit unfamiliar indoor locations, they face many difficulties. While navigation indoors is convenient for sighted individuals, it is essential for the visually impaired to have aiding tools for their daily activities. Electronic Travel Aids (ETAs) were developed to detect obstacles and guide users through acoustic signals for simple navigation. To address these challenges, an indoor navigation system utilizing Li-Fi technology was developed. This technology uses LED light bulbs for data transmission and operates on a free, unlicensed spectrum that is unaffected by RF noise. Li-Fi provides high-speed, low-cost wireless communication, is highly secure, and cannot be easily intercepted. Additionally, it offers a larger bandwidth compared to Wi-Fi. To enhance user convenience, the system is integrated with an ultrasonic sensor for obstacle detection and an accelerometer to monitor the user’s angular velocity. This integration makes the system well-suited for guiding visually impaired individuals in unfamiliar indoor environments.
Scalable Fabrication of Nano-yarn-based Strain Sensor for Motion Sensing

Nanomaterials have gained significant attention in fabricating on-skin and wearable electronic devices, offering diverse applications in interactive touch screens, pressure sensors, strain sensors, and medical devices. Researchers have explored stretchable conductive fibers embedded in soft polymers for their lightweight, flexible nature and fabric attachment capabilities. Various natural materials, including flax, cotton, wool, and silk, have created flexible sensors. High conductivity in natural materials can be achieved through carbonization or coating processes using conductive particles like graphene, carbon nanotubes, and liquid metal. However, these processes have drawbacks such as elevated working temperatures, complex setups, lengthy durations, and labor-intensive procedures. The dipping coating technique, while promising, also faces challenges in achieving precise thickness control, uniform particle coverage, and proper adhesion for long-term durability. This study introduces an automated machine and a layer-by-layer dip coating approach to address these challenges in fabricating polyurethane yarn (PUY)-based strain sensors. The novel technique significantly reduces production time, increases success rates, and demonstrates the potential for efficient and scalable fabrication of PUY-based strain sensors.