Miracle-dz/UniFAR-FTUs huggingface dataset:
A GRAPHIC PROCESSING UNIT FRAME WORK FOR CONVOLUTIONAL NEURAL NETWORK BASED CLASSIFICATION OF REMOTELY SENSED SATELLITE IMAGES

Abstract. Near real time processing and feature extraction from high-resolution satellite images aids in various applications of remote sensing including segmentation, classification and change detection. The latest generation of satellite sensors are able to capture the data at a very high spatial, spectral and temporal resolution. The processing time required for such a huge data is also large. Disaster monitoring applications such as forest fire monitoring, earthquakes require fast/real time processing of high resolution data to enable response activities. In general, due to the large size of satellite data, the computational time of feature calculation and training neural network is found to be very high. Therefore in order to achieve the aim of near real time processing of such huge data, we developed a parallel implementation. The implementation is performed on NVIDIA’s Graphical Processing Unit. The performance improvement obtained is demonstrated by a GPU implementation on Resourcesat-1 data and compared with the traditional sequential implementation. The results show that the GPU implementation is found to achieve performance improvement in terms of execution time and speedup throughput as compared to the sequential implementation.
New magnetic polyurethane hybrid composite for oil sorption

The new polyurethane/cellulose acetate/magnetic iron oxide nanoparticles composites synthesised presented potential for use as sorbents of mineral oil. The composite synthesised in homogeneous medium in the presence of magnetite nanoparticles presented higher oil sorption capacity than composite synthesised in heterogeneous medium for all analysed conditions, and the values obtained, 9.6 ± 0.1 and 12.2 ± 0.5 grams of oil per gram of sorbent, wet and dry dynamic media, respectively, were comparable to those exhibited by commercial products. In addition, the magnetite facilitates the separation of the composite after the sorption process by external magnetic field.
Sparse Coding for Event Tracking and Image Retrieval

Comparing regions of images is a fundamental task in both similarity based object tracking as well as retrieval of images from image datasets, where an exemplar image is used as the query. In this thesis, we focus on the task of creating a method of comparison for images produced by NASA’s Solar Dynamic Observatory mission. This mission has been in operation for several years and produces almost 700 Gigabytes of data per day from the Atmospheric Imaging Assembly instrument alone. This has created a massive repository of high-quality solar images to analyze and categorize. To this end, we are concerned with the creation of image region descriptors that are selective enough to differentiate between highly similar images yet compact enough to be compared in an efficient manner, while also being indexable with current indexing technology. We produce such descriptors by pooling sparse coding vectors produced by spanning learned basis dictionaries. Various pooled vectors are used to describe regions of images in event tracking, entire image descriptors for image comparison in content based image retrieval, and as region descriptors to be used in a content based image retrieval system on the SDO AIA image pipeline. SPARSE CODING FOR EVENT TRACKING AND IMAGE RETRIEVAL
Popularity Prediction on Online Articles with Deep Fusion of Temporal Process and Content Features

Predicting the popularity of online article sheds light to many applications such as recommendation, advertising and information retrieval. However, there are several technical challenges to be addressed for developing the best of predictive capability. (1) The popularity fluctuates under impacts of external factors, which are unpredictable and hard to capture. (2) Content and meta-data features, largely determining the online content popularity, are usually multi-modal and nontrivial to model. (3) Besides, it also needs to figure out how to integrate temporal process and content features modeling for popularity prediction in different lifecycle stages of online articles. In this paper, we propose a Deep Fusion of Temporal process and Content features (DFTC) method to tackle them. For modeling the temporal popularity process, we adopt the recurrent neural network and convolutional neural network. For multi-modal content features, we exploit the hierarchical attention network and embedding technique. Finally, a temporal attention fusion is employed for dynamically integrating all these parts. Using datasets collected from WeChat, we show that the proposed model significantly outperforms state-of-the-art approaches on popularity prediction.
Skin Blood Flow Response Signal Using Time and Frequency Domain Features for Pressure Ulcer Evaluation

Pressure Ulcer (PU) is an area of the skin in which cutaneous tissue is compromised and there is progressive damage on the underlying tissue caused by blood flow obstruction due to prolonged external direct pressure. Research has shown that ischemic stress response can be evaluated using skin blood flow response (SBFR) signal features which are useful for pressure ulcer evaluation. Trends of peak reactive hyperemia (RH) were observed for three repetitive loading-unloading cycles in previous animal study to investigate tissue recovery. However, tissue recovery and tissue damage cannot be discriminated by the trends of peak RH for short recovery time. The trends of alternative time-domain SBFR features such i.e total hyperemic response as well as frequency-domain features using Fast Fourier Transform (FFT) and Short Time Fourier Transform (STFT) i.e total power spectrum are further investigated to indicate tissue recovery. The results show that total hyperaemic response outperforms peak RH at detecting insufficient tissue recovery with 72% of samples with increasing trend in the short recovery time group compared to 57% of samples for peak RH. Total hyperemic response is effective at discriminating insufficient recovery time while other investigated features are only effective at detecting sufficient recovery time.
Development and optimization of a low temperature co-fired ceramic suspension for mask-image-projection-based stereolithography

This dissertation has the main goal of developing ceramic materials for Mask-Image-Projection-based Stereolithography (MIP-SLA) technology, focused on electronic applications. To accomplish this aim, a low temperature co-fired ceramic (LTCC) material was selected, mainly used for radio frequency applications. Moreover, the proof of concept of AM technology hybridization and multi-materials printing is also demonstrated, opening the door to new researchers in the field of 3D-2D printing electronic devices, from both the material and technology perspectives. To successfully achieve the main goal, the different steps of the whole process were successfully achieved, i.e, formulation of a LTCC photocurable suspension, its printability by MIP-SLA technology, and the post-thermal treatment of debinding and sintering. The photocurable LTCC suspension consists of ceramic particles dispersed in a suitable photocurable resin, which must polymerize in the visible light range, trapping the ceramic particles. The challenge of the development and optimization of a LTCC photocurable suspension for MIP-SLA with the appropriate rheological and photocurable behavior is accomplished in this work. The printed piece contains the polymeric part, which must be removed (debinding) and then sintered for the densification of the final ceramic piece. This is the most difficult and time-consuming step of the whole process. The so-called debinding is one of the most challenging steps of the SLA-based technology of ceramic materials. In this sense, a detailed study of the debinding process is carried out for a deeper understanding of the degradation of the resin during the thermal debinding. For this to happen, the optimization of the temperature rate and used atmosphere during the thermal treatment is also presented in this work. In fact, the analysis and understanding of thermal treatment parameters and their repercussion on the final results is the key to successfully achieving the main goal, which is to obtain final ceramic pieces without defects. The limits of the MIP-SLA printing process are presented, analyzing the resolution, fidelity of pattern transfer, and accuracy of the printing process using the optimized LTCC suspension. The involved phenomena during the photopolymerization such as light scattering, non-uniformities of the light projection along the building platform and shrinkage during the polymerization, are analyzed and optimized for a fruitful printing process. The greatest achievement of this work is the possibility of printing complex geometry with high resolution with a LTCC material, which has never been demonstrated before in the field of additive manufacturing. This is the beginning of new breakthroughs in multimaterial printing for electronic applications.
Enhanced conductive fiber of multiwall-carbon-nanotubes templated carbonization of cellulose

It has been reported that a conductivity of 100 S/cm of carbonized cellulose is obtained when the carbonization temperature is higher than 2000 °C. However, such high temperatures require expensive equipment and are energy consuming. In this study, carbonized multi-walled carbon nanotubes (MWCNTs)/cellulose composites fibers with a conductivity of 105 S/cm were prepared. They were prepared based on cellulose (in the carbonization temperature 1000 °C), a naturally abundant and low-cost material. The mechanism of improving electrical conductivity was studied. The MWCNTs played the role of a conductive bridge between the nano-carbonization cellulose blocks. As the template of cellulose graphitization, MWCNTs not only can improve the degree of cellulose graphitization but can also change the microstructure of carbonization cellulose from nano-spheres to sheets. This preparation method can be applied to other carbon materials to make highly conductive fibers or various conductive structure materials, such as a conductive film or an aerogel. This study broadens the application of cellulose in the field of electricity. The obtained conductive fibers have great potential in a range of applications, such as wearable electronics and low-cost energy storage.
Flexural and axial behaviour of CFRP strengthened steel circular hollow section beams and short columns

The use of carbon fibre reinforced polymer (CFRP) sheets for strengthening steel hollow sections is a relatively new technique and provides advantageous of lightweight and strength enhancement. There are presently few applications that concern with composite strengthening of circular tubes with larger diameter-to-thickness ratios, and in the absence of clear theoretical understanding of behaviour to guide engineers, research is much needed to develop analytical and design models for such tubes strengthened by CFRP. This paper summarises a recent investigation at Monash University on the behaviour of circular hollow section (CHS) beams and short columns with varying section slenderness ratios and strengthened by CFRP sheets. Analytical models for determining the ultimate moment capacity and for predicting the momentcurvature response of CFRP-reinforced steel CHS beams are presented. The nonlinear model accounted for material properties of the steel and CFRP, volume fractions of the fibre and adhesive, amount of CFRP, and a wider range of section slenderness. The theoretical models were validated through comparisons with the test results. Design rules for predicting the strength of composite CHS under bending and compression were also proposed.
Zero-Day Aware Decision Fusion-Based Model for Crypto-Ransomware Early Detection

Crypto-ransomware employs the cryptography to lock user personal files and demands ransom to release them. By utilizing several technological utilities like cyber-currency and cloud-based developing platforms, crypto-ransomware has gained high popularity among adversaries. Motivated by the monetary revenue, crypto-ransomware developers continuously produce many variants of such malicious programs to evade the detection. Consequently, the rate of crypto-ransomware novel attacks is continuously increasing. As such, it is imperative for detection solutions to be able to discover these novel attacks, also called zero-day attacks. While anomaly detection-based solutions are able to deal with this problem, they suffer the high rate of false alarms. Thus, this paper puts forward a detection model that incorporates anomaly with behavioral detection approaches. In this model, two types of detection estimators were built. The first type is an ensemble of behavioral-based classifiers whereas the second type is an anomaly-based estimator. The decisions of both types of estimators were combined using fusion technique. The proposed model is able to detect the novel attack while maintaining low false alarms rate. By applying the proposed model, the detection rate was increased from 96% to 99% and the false positive rate was as low as 2.4 %.
Experimental and numerical investigation of strengtheneddeficient steel SHS columns under axial compressive loads

In past years, numerous problems have vexed engineers with regard to buckling, corrosion, bending, and over-loading in damaged steel structures. This article sets out to investigate the possible effects of carbon fiber reinforced polymer (CFRP) and steel plates for retrofitting deficient steel square hollow section (SHS) columns. The effects of axial loading, stiffness, axial displacement, the position and shape of deficient region on the length of steel SHS columns, and slenderness ratio are examined through a detailed parametric study. A total of 14 specimens was tested for failure under axial compression in a laboratory and simulated using finite element (FE) analysis based on a numerical approach. The results indicate that the application of CFRP sheets and steel plates also caused a reduction in stress in the damaged region and prevented or retarded local deformation around the deficiency. The findings showed that a deficiency leads to reduced load-carrying capacity of steel SHS columns and the retrofitting method is responsible for the increase in the load-bearing capacity of the steel columns. Finally, this research showed that the CFRP performed better than steel plates in compensating the axial force caused by the cross-section reduction due to the problems associated with the use of steel plates, such as in welding, increased weight, thermal stress around the welding location, and the possibility of creating another deficiency by welding.
Optimization of the Vibration Isolation Performance of an Impact-testing Machine Using Multi-walled Carbon Nanotubes Reinforced Elastomeric Machine Mounts

The objective of this paper is to investigate the vibration isolation performance of an impact testing machine mounted on elastomeric nanocomposite mounts. An effective analytical-experimental test method was implemented in this paper to characterize the vibration isolation performance of acrylonitrile-butadiene rubber (NBR) mounts reinforced with multi-walled carbon nanotubes (MWCNTs). This method utilizes modal tests in order to measure experimental transfer functions (TFs) of a impact testing machine as a correlation parameter to analyticalexperimental determined TFs. The optimization procedure is carried out using a genetic algorithm (GA) by minimizing the difference between the experimental data from modal tests and the calculated response. A series of NBR mounts were manufactured with different concentrations of MWCNTs. The vibration isolation capacity of the machine mounts was determined through the transmissibility of a suitably designed test system. Elastomers’ vibration isolation performance was ameliorated with the inclusion of MWCNTs, signifying that the enhancement of the elastomers with MWCNTs was rather effective. It is also shown that the stiffness of an elastomer and its damping capacity can be tuned by adjusting the proportion of MWCNTs. The vibration level of the impact-testing machine was decreased to 91% by incorporating the optimal concentration of MWCNTs in NBR mounts. © 2019 Published by Faculty of Engineering Corresponding author:
Perancangan Dan Implementasi Aplikasi Penerjemah Bahasa Isyarat Menjadi Suara Berbasis Kinect Menggunakan Metode Hidden Markov Model

Suatu isyarat dapat dikenali melalui gerakan tubuh dan gerakan mulut seseorang. Dengan mengenali ciri-ciri khusus pada masing-masing isyarat, maka dapat diterjemahkan. Pada tugas akhir ini dilakukan pengenalan sejumlah kata dalam bahasa isyarat, agar orang-orang dengan kemampuan berbicara secara verbal lebih mudah menerjemahkan isyarat seorang tunarungu. Masukan secara realtime yang digunakan berupa data gambar (image) pose bahasa isyarat (berdasarkan Kamus Umum Bahasa Isyarat Indonesia) menggunakan perangkat sensor kinect. Data tersebut kemudian diolah sehingga mendapatkan data skeleton. Dari data skeleton kemudian didapatkan skeleton joint. Dalam hal ini menggunakan 6 skeleton joint untuk dijadikan data karakteristik dari setiap isyarat. Berikutnya 6 skeleton joint akan dimodelkan dengan Hidden Markov Model (HMM), dan dilakukan pelatihan sehingga dihasilkan sebuah basis data untuk seluruh HMM. Keluaran dari sistem ini berupa suara yang merupakan terjemahan dari isyarat pada masukan. Hasil pengujian pada tugas akhir ini mendapat akurasi rata – rata 90%, trendline akurasi untuk berbagai jumlah kata mengalami penurunan jika ditambah isyarat baru ke database, dan waktu komputasi sistem rata-rata 22. 8μs. Kata Kunci: Bahasa Isyarat, Kinect, Hidden Markov Model
Recent Advances in TiO2 Films Prepared by Sol-gel Methods for Photocatalytic Degradation of Organic Pollutants and Antibacterial Activities

Photocatalysis has recently emerged as an advanced, green, and eco-friendly process for the treatment of wastewater and air, and antimicrobial disinfection applications. In this context, TiO2 nanostructures have been shown to be the prominent photocatalyst candidates due to their low cost, non-toxicity, and ease of fabrication. This review highlights the investigation and development of TiO2 photocatalyst film by sol-gel method with special emphasis on the photodecolorization of synthetic dyes and antibacterial activities. Furthermore, various synthesis methods for the preparation of TiO2 films and their advantages, as well as limitations, are summarized. Finally, recent advances in TiO2 films by sol-gel method for dye degradation and antibacterial activities, challenges, and future perspective are discussed.
THE POSSIBILITY OF USING WOOD FIBER MATS IN PRODUCTS MANUFACTURING MADE OF POLYMER COMPOSITES BASED ON NUMERICAL SIMULATIONS

In this work the calculations for predicting the properties of wood fiber mats – polyester resin composite using numerical homogenization method were performed. For this purpose, the microstructural strength properties were calculated using DIGIMAT FE commercial code. In addition, for comparative purposes a calculation of polyester resin – glass fiber composites was conducted. This allowed to compare the properties of two types of com-positions. In addition, the obtained strength properties were used to simulate the work of product made of these composites. This study was performed using the Ansys commercial code. Usability of the polyester resin – wood fiber mat composite and knowledge of its properties will allow to find a correct application of this composite type and can provide an alternative way to other polymeric resin reinforced by mat.
Learning Media Based on Augmented Reality Applied on the Lesson of Electrical Network Protection System

Technology is created and developed to help people work more efficiently. One of them is Augmented Reality technology. Augmented Reality is a technology that combines two or three dimensional virtual objects into the real three-dimensional environments, then it is projecting the virtual objects in real time, in this case it was possible to help ease the learning process for displaying props and also to increase students attention. In this study, the learning media based on Augmented Reality applied to the subject of Electrical Network Protection System.
Development of a Two-Stage Segmentation-Based Word Searching Method for Handwritten Document Images

Abstract Word searching or keyword spotting is an important research problem in the domain of document image processing. The solution to the said problem for handwritten documents is more challenging than for printed ones. In this work, a two-stage word searching schema is introduced. In the first stage, all the irrelevant words with respect to a search word are filtered out from the document page image. This is carried out using a zonal feature vector, called pre-selection feature vector, along with a rule-based binary classification method. In the next step, a holistic word recognition paradigm is used to confirm a pre-selected word as search word. To accomplish this, a modified histogram of oriented gradients-based feature descriptor is combined with a topological feature vector. This method is experimented on a QUWI English database, which is freely available through the International Conference on Document Analysis and Recognition 2015 competition entitled “Writer Identification and Gender Classification. ” This technique not only provides good retrieval performance in terms of recall, precision, and F-measure scores, but it also outperforms some state-of-the-art methods.
Concurrent transcranial magnetic stimulation and magnetic resonance spectroscopy in phantoms and humans

Transcranial magnetic stimulation (TMS) is an increasingly popular treatment for psychiatric diseases including depression, the most common cause of psychiatric disability worldwide. TMS is an effective treatment for depression even in patients where conventional pharmacological and therapeutic treatments for the disease have been unsuccessful. As the mechanism of TMS relies on electromagnetic induction, it is a completely noninvasive treatment. While TMS is known to be effective in treating depression, exactly how TMS affects the brain is an active area of research. To properly study the effects of TMS in vivo, non-invasive methods are required. As such, magnetic resonance (MR) modalities present an attractive option due to their non-invasive nature. There is interest in studying the effects of TMS using MR concurrently with TMS, however, concurrent TMS/MR is challenging due to TMS-related distortions to the MR scanners magnetic field. MR Spectroscopy (MRS) is ideal for TMS/MR applications as not only does MRS provide information about brain chemicals thought to change during TMS, it also is sensitive enough to characterize distortions that are present in other MR images but not as easy to recognize. In this thesis, non-water-suppressed MRS experiments were performed concurrently with TMS to characterize distortions to the MRS water signal. Phantom experiments were performed under a wide range of experimental conditions, varying MRS voxel positioning and parameters related to TMS pulsing. Distortions investigated included signal-noise ratio, free induction decay spikes, B0 inhomogeneity, eddy currents, and frequency modulation sidebands. As a proof of concept, concurrent TMS/MRS results from a human experiment are presented. The dominant source of signal distortion was found to be related to the presence of the TMS coil itself, and the magnitude of the distortions depended most strongly on the position of the MRS voxel relative to the TMS coil. To a lesser extent, TMS pulses further distorted MRS signal, particularly at higher pulse amplitudes and when there was a smaller time delay between the TMS pulse and 90◦ radiofrequency pulse.