Miracle-dz/UniFAR-FTUs huggingface dataset:
A Two-Stage Deep-Learning Based Detection Method for Pipeline Leakage and Transient Conditions

Nowadays, Leakage detection is of great importance as pipelines are the major means of transporting hydrocarbon fluids and gases. In this paper, a novel two-stage detection method is introduced to differentiate normal, leakage and transient conditions of pipelines. In this method, feature vectors are constructed from the flow difference and pressure using leakage characteristics, and are normalized with the modified hyperbolic-tangent estimator. An artificial neural network is used in the first stage of detection to differentiate normal and abnormal conditions with the feature vectors as the inputs. In the second detection stage, a simple logic is used to distinguish leakage and transient from abnormal time-windows. In addition, a pre-set leak-size tolerance is used to trigger alarms for detected leakage time-windows. The results for the cases of using different machine learning methods and varying leak-size tolerances are given. The method has been shown to have higher detection performance and less false alarms in comparison with the line balance and Kantorovich distance methods.
Research and Implementation of Parkinson’s Dyskinesia Monitoring Service System

For Parkinson’s disease, a degenerative neurological disease that is difficult to detect and has a high rate of misdiagnosis, telemedicine technology can undoubtedly bring great improvement to the real limitations of delayed follow-up diagnosis and unsustainable control of the disease. This paper takes the symptom of Parkinson’s Dyskinesia as the focus of attention and designs a complete set of full-process available system which is based on home telemedicine and wearable technology. Finally, a system consists of intelligent hardware facilities, user terminals, and management terminals will be presented to construct a monitoring service scenario where the Parkinson’s Dyskinesia is continuously controllable.
Slow Hypertext Transfer Protocol Mitigation Model in Software Defined Networks

Distributed Denial of Service (DDoS) attacks have been one of the persistent forms of attacks on information technology infrastructure connected to a public network due to the ease of access to DDoS attack tools. Researchers have been able to develop several techniques to curb volumetric DDoS attacks which overwhelms the target with large number of request packets. However, compared to volumetric DDoS, low amount of research has been executed on mitigating slow DDoS. Data mining approaches and various Artificial Intelligence techniques have been proved by researchers to be effective for reduce DDoS attacks. This paper provides the scholarly community with slow DDoS attack detection techniques using Genetic Algorithm and Support Vector Machine aimed at mitigating slow DDoS attack in a Software-Defined Networking (SDN) environment simulated in GNS3. Genetic algorithm was employed to select the features which indicates the presence of an attack and also determine the appropriate regularization parameter, C, and gamma parameter for the Support Vector Machine classifier. Results obtained shows that the classifier had detection accuracy, Area Under Receiver Operating Curve (AUC), true positive rate, false positive rate and false negative rate of 99.89%, 99.89%, 99.95%, 0.18%, and 0.05% respectively. Also, the algorithm for subsequent implementation of the selective adaptive bubble burst mitigation mechanism was presented.
Online Intelligent Music Recommendation: The Opportunity and Challenge for People Well-Being Improvement

In recent decades, fast development of contemporary digital entertainment and Internet technology has dramatically changed how people produce and consume music. This demands design and development of smart online music recommendation. In this paper, four important research (information retrieval and machine learning related) directions including content descriptor generation, personalization, playlist optimization and performance evaluation are identified and discussed to achieve effective online music recommendation. Based on a comprehensive analysis of the directions, we critically review and discuss the opportunities and challenges for IR research and emerging implications for real world practice. To further demonstrate its promises, we present case study on applying VenueMusic system to support people well-being improvement.
Advanced 22 nm FD-SOI devices integration platform

In this work, integration of 22 nm fully depleted silicon-on-insulator (FD-SOI) process with High-k first, metal gate last is demonstrated. The fabricated logic N-type MOSFET (NFET) and P-type MOSFET (PFET) show very good short channel effect (SCE) control. The logic NFET/PFET characteristics under different temperatures are measured and analyzed. Threshold voltage to temperature sensitivity is evaluated for using the back bias to tune device and get performance/power balance. The optimization of static noise margin of SRAM is also studied.
Implementing a DC UPS with Battery’s State of Charge Estimation Based on Coulomb-Counting Method

Embedded applications with requirement of portability need rechargeable battery. A novel DC Uninterruptible Power Supply (UPS) system with battery charging and battery’s State of Charge (SOC) monitoring system has been proposed in this paper. DC UPS systems are evolved in recent times due to requirement of fewer power conversion stages as compared to traditional UPS. This paper describes the DC UPS circuit which transits instantly and automatically from external power supply to battery source and vice versa when external power supply is removed and applied respectively. Modes of operation of DC UPS are explained in this paper. Proposed circuit uses rechargeable battery and thus, a battery charging circuit and battery's SOC estimation circuit are part of the proposed circuit. Battery’s SOC is estimated by Coulomb counter method. Charging circuit diagram and designing of its components are explained. SOC estimation is based on integrating current through the battery, which is done by coulomb counter - LTC4150 which is interfaced with a micro-controller. Initial SOC estimation is one of the important tasks while implementing coulomb counter method. It is estimated by measuring Open Circuit Voltage (OCV) of battery and mapping it to get corresponding initial SOC. Proposed circuit is designed in such a way that, if battery voltage reduces to 3.3V while discharging, circuit disconnects battery from system till external supply is available to charge the battery. Microcontroller is programmed to calculate and display Initial SOC, SOC, and battery voltage on serial monitor.
HyperSLAM: A Generic and Modular Approach to Sensor Fusion and Simultaneous Localization And Mapping in Continuous-Time

Within recent years, Continuous-Time Simultaneous Localization And Mapping (CTSLAM) formalisms have become subject to increased attention from the scientific community due to their vast potential in facilitating motion corrected feature reprojection and direct unsynchronized multi-rate sensor fusion. They also hold the promise of yielding better estimates in traditional sensor setups (e.g. visual, inertial) when compared to conventional discrete-time approaches. Related works mostly rely on cubic, $C^{2}-$continuous, uniform cumulative B-Splines to exemplify and demonstrate the benefits inherent to continuous-time representations. However, as this type of splines gives rise to continuous trajectories by blending uniformly distributed $\mathbb{SE}_{3}$ transformations in time, it is prone to under- or overparametrize underlying motions with varying volatility and prohibits dynamic trajectory refinement or sparsification by design. In light of this, we propose employing a more generalized and efficient non-uniform split interpolation method in $\mathbb{R}\times \mathbb{SU}_{2}\times \mathbb{R}^{3}$ and commence with development of ‘HyperSLAM’, a generic and modular CTSLAM framework. The efficacy of our approach is exemplified in proof-of-concept simulations based on a visual, monocular setup.
A Comparison of Transformer, Recurrent Neural Networks and SMT in Tamil to Sinhala MT

Neural Machine Translation (NMT) is currently the most promising approach for machine translation. The attention mechanism is a successful technique in modern Natural Language Processing (NLP), especially in tasks like machine translation. The recently proposed network architecture of the Transformer is based entirely on attention mechanisms and achieves a new state of the art results in neural machine translation, outperforming other sequence-to-sequence models. Although it is successful in a resource-rich setting, its applicability for low-resource language pairs is still debatable. Additionally when the language pair is morphologically rich and also when the corpora is multi-domain, the lack of a large parallel corpus becomes a significant barrier. In this study, we explore different NMT algorithms – Long Short Term Memory (LSTM) and Transformer based NMT, to translate the Tamil to Sinhala language pair. Where we clearly see transformer outperforms LSTM by 2.43 BLEU score for Tamil to Sinhala direction. And this work provides a preliminary comparison of statistical machine translation (SMT) and Neural Machine Translation (NMT) for Tamil to Sinhala in the open domain context.
A spaceborne camera pose estimate method based on high-precision point cloud model

Obtaining the position and orientation information of camera or sensor is a key task in many fields such as robot navigation, autonomous driving, DSM (digital surface model) reconstruction, etc. The pose can be recovered by matching a 2D image and a corresponding digital surface model/point cloud model of the scene. A 3D point cloud model of very high spatial accuracy can be created with a combination of stereophotogrammetry and big data processing. So far, the most accurate 3D point cloud model created with satellites imagery can reach the accuracy of 3m@SE90 (3 meters error with SE90, which is the abbreviation for Spherical Error 90%). In this paper, a novel method of estimating the pose of spaceborne cameras based on the fusion of high-resolution point cloud models and remote sensing images. The core of our method is to project a high-precision 3D point cloud model into the image space of a virtual camera, then the 3D-2D pose estimation method is transformed into a 2D-2D registration method. The registration results between two images are used to estimate the camera pose parameters. Simulation experiments were carried out to evaluate the performance of our method. The results showed that acceptable accuracy of camera pose can be achieved by using the proposed approach.
4 mJ Rectangular- Envelope GHz-Adjustable Burst-Mode Fiber-Bulk Hybrid Laser and Second-Harmonic Generation

We demonstrate a high peak power and high burst energy linear-polarization, narrowband, GHz-adjustable burst-mode fiber-bulk hybrid laser with central wavelength of 1064 nm and its second-harmonic generation. The active modulated burst-mode seed provides the tunability of temporal parameters and the pre-compensation of burst envelope distortion. Three-stage polarization maintained fiber amplifier cascading bulk amplifier promote the burst energy to 4 mJ at a burst duration of 100 ns and intra-burst duty of 50%. To the best of our knowledge, this is the highest reported output energy of burst-mode fiber-bulk hybrid laser with ∼ hundred nanoseconds burst duration. Also, the pre-compensation for rectangular burst envelope is investigated by iteration method. Type I, non-critical phase matching lithium borate (LBO) is employed for second-harmonic generation and 1.8 mJ burst energy is achieved, corresponding to a conversion efficiency of 45%. The 3 dB spectral width of green light at maximal burst energy is 90 pm. The immediate application of this laser system is the generation of high peak power frequency-adaptive microwave.
Graphene and Iron based composites as EMI shielding: A Systematic Review

The electromagnetic inference is a problem for a longer span. Various composites were formed to eliminate this problem by developing electromagnetic shielding material. For this purpose, a systematic review was performed to provide a general understanding of graphene and iron-based composites as a shielding material. Scopus database was selected to extract the articles with the only limitation to consider only those articles which were written in the English language. The interpretation of the articles revealed that most of the researchers have worked on the X-band frequency range, where still, there is a need to work on a higher frequency range. Also, the inclusion of polymers into graphene and iron showed promising results, yet still, need further exploration.
An Enhanced Hemostatic Ultrasonic Scalpel Based on the Longitudinal-Torsional Vibration Mode

This paper proposes an ultrasonic scalpel based on the longitudinal-torsional vibration mode to enhance the hemostatic ability. The concentrator of the proposed ultrasonic scalpel has been specially designed with spiral grooves to realize this vibration mode. The change of the working mode has enlarged the vertical motion component of the distal blade to efficiently radiate energy into the dissected tissues, supporting that the blood vessel can receive sufficient energy to be sealed. The electromechanical equivalent method has been employed in the initial design of the scalpel to obtain the desired resonance frequency. The structural optimization based on the finite element method (FEM) has been conducted to further improve the proposed design, as well as investigation of its dynamic performances. Both the conventional and presented ultrasonic scalpel prototypes were fabricated, and their important parameters such as, mechanical quality factor and resonance frequency, were tested and compared using an impedance analyzer. The ex-vivo experiments have been conducted to measure the vertical temperature distribution to investigate their transferring heat effects. The corresponding results indicated that the internal temperature values of tissues cut with the proposed hemostatic-enhanced ultrasonic scalpel are 24.0% higher on the 3mm layer, 16.5% higher on the 6mm layer, and 8.5% higher on the 9mm layer, respectively. The sealing capacity of the ultrasonic scalpels has been investigated through the coagulation dissection experiment on the chicken carotid and their burst pressure tests. The average burst pressure of the sealed vessels by the hemostatic-enhanced ultrasonic scalpel can achieve higher values than that of the conventional ultrasonic scalpel.
Sentiment Analysis of Arabic Tweets Using Supervised Machine Learning

The information momentum available on social media is an appropriate environment for identifying users' reactions and attitudes towards a particular topic, products, or any issues. To analyze this data and extract useful information, machine learning algorithms are used to categorize data into predefined categories. Analyzing data in the Arabic language is a challenge, and few studies focus on Arabic text mining. This paper focuses on sentiment analysis of Arabic tweets, in which, it conducts a performance comparison between three machine learning classifiers; Logistic Regression (LR), K-Nearest Neighbors (KNN) and Decision Tree (DT). Four Arabic text datasets are used in the experiments to evaluate the performance of the classifiers. For comparing purpose, we used four evaluation metrics: recall, precision, f-measure, and accuracy. The results show that the Logistic Regression achieves a better accuracy rate in the case of large datasets (93%) compared with the other classifiers. LR showed more improvement by increasing the volume of data, unlike other classifiers that recorded a noticeable decrease in accuracy in the last database (74% for KNN and DT when applying on 100K reviews dataset). Also, KNN and LR classifiers outperform DT classifier when applying them on small datasets such as AJGT and ASTD datasets.
Real-Time Greenhouse Environmental Conditions Optimization Using Neural Network and Image Processing

Agricultural business is one of the biggest areas in world economy. With the growth of population losing agricultural lands is the major issue in world food production. Therefore, controlled environment agricultural systems under vertical farming have been introduced with greenhouses. Within greenhouses there is not a mechanism to continuously monitor the growing community and change the climate conditions. Existing systems only predict the required conditions for the plant and once predicted that value is provided to the plants continuously or change the values from season to season. To address these issues, a working prototype of an IoT based smart hydroponic system is introduced, which uses computer vision to gain maximum profits by growing a specific cultivation by providing endemic environmental conditions and addressing the problems over its growing process. There, this research presents a way of external environmental condition optimization. Regression type Feed Forward Neural Network is considered for this research to optimize the required conditions for tomato plants. Based on the current height of the plant, expected height for next 24 hours, and growth date of the plants neural networks predict the CO2, temperature and humidity level for next 24 hours with the accuracy of 88.33%, 89.21% and 92.65% respectively. The objectives of the research can be achieved by this retrieved results. The successful implementation of neural networks results a cost-effective modern farming solution for growers. This research will be supportive to attain a fundamental comprehension on the concept of the research area.
Location of the Leakage From a Simulated Water-Cooling Wall Tube Based on Acoustic Method and an Artificial Neural Network

Water-cooling wall tube leakage accident is an important factor affecting the safe and economic operation of power station boilers. In this article, the acoustic method is applied to locate the leakage from a simulated water-cooling wall tube, and several key technologies are studied in order to identify the precise location of leakage source. Sound wave propagation path in ‘nonuniform temperature field is obtained to overcome the shortcoming that the traditional location technology always assumed that sound waves propagate along the straight line. Considering the refraction effect of sound waves, the reconstruction method based on radial basis function approximation with polynomial reproduction (RBF-PR) is used to reconstruct the temperature field. A generalized cross correlation with second correlation is applied to estimate the time delay of arrival (TDOA). According to the temperature field measured by acoustic method and the propagate time of sound waves, the location fingerprint method including the statistical correlation characteristics between the coordinate of fingerprint points and TDOA are constructed. Radial basis function artificial neural network (RBF ANN) is used to estimate the coordinate of leakage point. Numerical simulations and experimental study are implemented to evaluate the effectiveness of the proposed location system. An acoustic location test platform is constructed to study the location accuracy in different working conditions. The results indicate that considering the refraction effect, the reconstruction performance of temperature field is superior than that without the refraction effect, and acoustic method with RBF ANN algorithm can gain the leakage location with high accuracy and good antinoise ability in the above high-quality reconstructed nonuniform temperature distribution.
Phloroglucinol-Combined Photobiomodulation for Minimizing Burn-Induced Skin Fibrosis

Fibrosis is a prevalent problem that pathologically accompanies chronic inflammatory diseases. Despite the availability of various fibrosis treatments, therapeutic efficacy must be further improved to achieve better clinical outcomes. The purpose of this study is to develop a novel treatment by combining phloroglucinol (PHL) with photobiomodulation (PBM) to prevent skin fibrosis. In in vitro, 635 nm PBM at 8 J/cm2 significantly reduced TNF-alpha levels, and 100 μg/ml of PHL suppressed the expression of α-smooth muscle actin and type I collagen due to the downregulation of SMAD 2/3 and MAPK signaling pathways. The 1470 nm laser light created the initial wound (97.1 mm2), leading to scar formation after complete wound closure (21 days) on rodent skin. In comparison to other groups, the dual treatment accelerated wound closure and achieved rapid and complete healing in 17 days. Histological analysis presented the reduced fibrotic scar and collagen deposition in the dual treatment group. PHL-combined PBM treatment enhanced the anti-inflammatory effect by 36% in comparison to single treatment groups. Overall, the proposed combined treatment promoted wound healing and enhanced anti-inflammatory and anti-fibrotic effects in the overexpressed in vitro and in vivo models.
Efficiency Versus Accuracy: A Review of Design Techniques for DNN Hardware Accelerators

Deep neural networks (DNNs) have surpassed other algorithms in analyzing today’s abundant data. Due to the security and delay requirements of the given applications, analytical data extraction should happen on edge devices. Edge devices, however, struggle to support the increasingly complex DNNs because of the models’ high computational load and number of employed parameters. Thus, edge devices need support by efficient accelerators to process DNNs. However, the design of DNN accelerators remains challenging, as there is a lack of established design techniques directed towards a specific design point in terms of energy budget, area, time-to-solution, and classification accuracy. This article fills this gap by providing a quantitative, large-scale, state-of-the-art comparison of DNN accelerators building on published data subjected to technology scaling and benchmark normalization. The leveled comparison stimulates learning from previous designs by considering the impact of each technique on energy, area, and time-to-solution. Furthermore, the key design techniques used in the DNN accelerators are classified according to their influence on the classification accuracy. Finally, we provide a discussion of hardware accelerators to support future designers in considering the trade-off between efficiency and accuracy in order to identify the most suitable techniques for certain benchmarks.
Model Predictive Control with Active Damping Capability for Induction Machine Driver based on Indirect Matrix Converter

In this paper, a model predictive control approach with active damping capability for input filter is presented for an indirect matrix converter-based induction machine driver. The indirect matrix converter is a two-stage converter without using an intermediate bulky energy storage component between the rectifier side and the inverter side. An induction machine is connected to the inverter side and model predictive based indirect field-oriented control is adopted. To control the rectifier side with the unity input power factor, a current-controlled predictive approach in the dq-frame is used. To mitigate the unwanted harmonics in grid current, an active damping technique without requiring the selection of optimum value for fictitious damping resistor is also included in the proposed control scheme. The cost function just employs current terms for controlling input and output, and no weighting factor is required since all the terms are in the same nature and identical ranges. The presented control technique provides a good driving for the induction machine and unity input power factor with low THD for the grid side.