Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring Tissue Elastic Properties Using Physics Based Neural Networks

Ultrasound elastography is a non-invasive and low-cost imaging technique that is used to detect abnormalities in soft tissues. Elastography detects solid tumors from healthy tissues by observing changes in elasticity of tissues on application of force. Reconstruction of initial tissue modulus distribution based on measured displacement/strain fields is called an inverse elasticity problem which has a wide range of applications in medical diagnosis. This paper tries to measure the elastic properties of tissues using Physics-Informed Neural Networks (PINNs). The input data consists of pre-compression and post-compression images of a phantom. Displacement and strain fields are computed from input data which are fed to our PINN model. The PINN model consists of five independent feed-forward neural networks. The model is trained using a loss function that incorporates physics laws based on linear elasticity along with the input data. Lame constants ($\lambda$ and $\mu$) are considered as network parameters that change during the training phase. The ground truth $\lambda$ value is 920 kPa whereas the value predicted by the model is 925.319 kPa. The results indicated that that PINNs can solve inverse problems in the domain of ultrasound elastography.
Optimal Redundancy Configuration of Hybrid MMC Considering Maintenance Cost of Life Cycle

Modular multilevel converter (MMC) has been widely used in the field of HVDC transmission. In practical application, the hybrid MMC topology with half-bridge submodule(HBSM) and full-bridge submodule(FBSM) gradually shows its advantages. The configuration ratio of the two redundant sub-modules(SMs) in the hybrid MMC affects the reliability and economy of the system. Therefore, this paper proposes the hybrid MMC redundancy optimization strategy based on maintenance strategy. Firstly, the hybrid MMC topology is studied and the system reliability model is established; secondly, combined with the MMC maintenance and replacement strategy, a minimum cost model based on age replacement policy is established; finally, through the case, the minimum maintenance cost corresponding to the different proportion of redundant SMs and the sensitivity of the optimal maintenance cycle to the redundancy ratio parameters are obtained. The results show that under the premise of high reliability, the economic benefit of redundant configuration can be maximized by the above model under the optimal maintenance cycle.
A New Multi-Feature Classification Scheme for Normal and Abnormal Respiratory Sounds Discrimination

During sleep. , breathing-related sleep disorders (BSD) are very probable to cause distortions on human health and even be life-threatening. Among the different types of BSD. , apnea accounts for one of the most common. Many detection algorithms have been proposed for spotting and classifying apneas, using one feature or being designed for binary classification. Also, many proposed clinical setups for respiratory data acquisition are invasive, making the application to patients a non-trial task. In this study, we aim to propose an easy-to-apply and patient-friendly clinical setup with a BSD detection that utilizes a multi-feature classification scheme for binary (apnea, healthy), as well as multiple classes (healthy, central, mixed, and obstructive apneas and hypopneas). Our clinical setup includes a high-resolution microphone attached to the bed at a very close distance to the patient. Our multi-feature approach contains spectral, statistical, and symbolic-based characteristics of respiratory signals of five patients admitted for a first BSD diagnosis and assesses the performance of different classification algorithms iteratively. The results show a high classification performance ($>$ 98% for binary and $>$ 84% for multi-class classification) for either classification scheme. A robust classification scheme is thus proposed, utilizing the entire content of the recorded respiratory signal. Such a classification scheme leads to a promising result towards the design of portable devices with multi-features for real-time detection of BSD.
Modular Solution of Steady-State Values for Small-signal Model of MMC-MTDC System

Modular multilevel converter (MMC) based multi-terminal direct current (MTDC) system has attracted much attentions. MMC-MTDC may contain many MMCs and has large amount of state variables. In order to establish the small-signal model for stability analysis of MMC-MTDC, the steady-state values of state variables of the whole MTDC must be obtained. The current method is to solve all nonlinear equations at one time. However, the large amount of state variables and diverse control strategies of MTDC lead to much programming work and difficulty in solving too many nonlinear equations of the whole MTDC at one time. To solve the above problems, this paper proposes a modular algorithm based on alternating iteration to obtain the steady-state values of the whole MTDC. Based on modularity idea, the algorithm takes single MMC in MTDC as the basic unit for solving the steady-state values of state variables. Each iteration only invokes the fsovle functions in Matlab for several typical control modes of MMC. The algorithm reduces the order of the equation group and computation burden, and meanwhile it makes it easy to modify state variables in single fsolve function. Finally, MMC-MTDC system under different control strategies is constructed in Simulink to validate the accuracy and correctness of the proposed algorithm.
Test results on a super-high-speed amorphous-iron reluctance motor

Because of the high tensile strength and extremely low magnetic losses of amorphous metal, a super-high-speed reluctance motor (unexcited synchronous and unswitched type) was built using this material for both rotor and stator cores. To evaluate the impact of the amorphous metal material in machine performance, the losses of the test machine were segregated. For purposes of comparison the core losses of the silicon iron motor were estimated using equations obtained empirically. At a speed of 48000 r/min, core losses in the amorphous metal machine were reduced by a factor of five compared to the silicon iron machine. The reduction in the core comprised 33% of total losses. The amorphous metal motor showed a six-point efficiency gain over the silicon iron machines. The motor was found to have an efficiency of 85% and a power factor of 0.46 at an output of 372 W. >
An Intelligent Data-Driven Machine Learning Approach for Fault Detection of Wind Turbines

Wind farms have received much attention in recent years due to the ever-increasing demand for clean and renewable energy. In order to effectively meet that demand, wind farms' operation and maintenance abilities must be optimized, and therefore, condition-based maintenance plays an important role. Compared with professional condition monitoring systems, supervisory control and data acquisition (SCADA) systems offer an inexpensive technology that enables wind farm operators to monitor wind turbines' health condition. However, the SCADA dataset is often unlabeled, and therefore, how to effectively detect faults without any prior knowledge is a major concern. In order to solve this problem, this paper proposes an innovative method based on machine learning. The proposed method is validated on real operational wind turbine data. The obtained results indicate the effectiveness and impact of the method when applied in realworld application.
Experimental Assessment of Variability in Junctionless Nanowire nMOS Transistors

In this work, experimental assessment of the variability of threshold voltage and drain current in junctionless nanowire $n$ MOS transistors is presented. Die-to-die variability of threshold voltage and drain current is presented and compared to inversion mode nanowire with the same dimensions. Although the junctionless nanowires have shown larger threshold voltage matching coefficients than inversion mode devices, the variability obtained experimentally has shown to be smaller than predicted by some simulations reported in the literature. Also, it has been shown that as the channel length of junctionless nanowire transistors is reduced, the current variability becomes smaller than in inversion mode nanowires, at the same current level and dimensions.
Multiple-Activation Parallel Convolution Network in Combination with t-SNE for the Classification of Mild Cognitive Impairment

The classification of Mild Cognitive Impairment can be done using 2-D CNN, which take a single slice at a time as input and do not consider pixel information from adjacent slices or spatial correlation amongst the slices of the brain volume or 3-D CNN, which requires huge computation time and memory as a significantly large number of parameters involved in 3D-CNN in comparison to 2D-CNN. To reduce the spatial correlation, computational complexity, and memory requirement, we use t-Distributed Stochastic Neighbor Embedding (t-SNE) on MRI volume to reduce its dimensions. Also, we use parallel CNN instead of sequential to analyze MRI volumes and a combination of RELU, sigmoid, and SIREN activation functions to learn better features for the classification of MCI. To check the efficacy of the proposed t-SNE Multiple-Activation Parallel Convolution Network, experiments are performed on publicly available Alzheimer's Disease Neuroimaging Initiative dataset, and performance is compared with existing methods. We obtain classification accuracy of 94.15 and 94.89 on MCI-C Vs. MCI-NC data and MCI Vs. Controls data respectively.
Visual Fusion Positioning Technology for Safety Management of Urban Power Plants

With the rapid increase of urban electricity demand in recent years, the scale and number of urban power plants are expanding, which poses new challenges to the safety and management of power plant staff, and real-time high-precision localization services can achieve fine-grained management of staff. We propose a vision fusion-based localization technique that combines the traditional visual odometry calculation method based on the feature point method with the optical flow method, and validates it by collecting data sets in a real power plant environment. The experimental results show that the proposed fusion-based visual odometry calculation method is superior to the traditional visual odometry calculation method in terms of comprehensive performance in terms of real-time performance and accuracy of position estimation, which verifies the practical application possibilities of the vision-based localization technology in urban power plants.
Millimeter-Wave Circularly Polarized Staircase Curl Antenna Array with 50% Axial Ratio Bandwidth

A wideband circularly polarized (CP) staircase curl antenna array with 50% Axial Ratio (AR) bandwidth is proposed for 5G millimeter-wave (mm-wave) applications. Firstly, the principle of the staircase curl element is investigated. It achieves 57.9% 3-dB axial ratio (AR) bandwidth and wider than 71.8% impedance bandwidth. Then, a full-corporate microstrip feeding network is designed to construct the wideband CP antenna array. Finally, an 8×8 wideband planar antenna array is fabricated. Measurement results shows that the AR bandwidth is wider than 53.6%, the impedance bandwidth covers from 22.6 GHz to 45 GHz (66.3%), whereas the peak gain is 18.7 dBic. The proposed antenna array demonstrates a promising candidate for 5G mm-wave wireless systems.
Towards a Sustainable IoT with Last-Mile Software Deployment

Billions of sensor-enabled IoT devices generate extreme amounts of e- waste. Because of the low cost and short lifespan of electronic components, it is often more convenient for consumers to buy a new device instead of re-using or re-purposing the old one. With the increased computing and connectivity capabilities, IoT devices can already receive code updates for new purposes (and thus extend their lifespan), but the cost of such operations often exceeds the price of device replacement due to constrained resources, hindered network connectivity, and distributed placement. This paper describes how these existing capabilities can enable last-mile software deployment at scale. We propose a hierarchical architecture for provisioning software updates from the cloud to terminal devices via edge gateways in a scalable and targeted manner. By enabling such an end-to-end software deployment architecture, the approach promotes hardware re-use via re-purposing and thus contributes to the creation of a more sustainable IoT.
Pentoxifylline-induced protein expression change in RAW 264.7 cells as determined by immunoprecipitation-based high performance liquid chromatography

Although pentoxifylline (PTX) was identified as a competitive non-selective phosphodiesterase inhibitor, its pharmacological effect has not been clearly elucidated. The present study explored the effect of low dose 10 μg/mL PTX (therapeutic dose) compared to high dose 300 μg/mL PTX (experimental dose) in RAW 264.7 cells through immunoprecipitation-based high performance liquid chromatography (IP-HPLC), immunohistochemistry, and western blot. 10 μg/mL PTX increased the expression of proliferation (Ki-67, PCNA, cyclin D2, cdc25A), epigenetic modification (KDM4D, PCAF), protein translation (DOHH, DHPS, eIF5A1), RAS signaling (KRAS, pAKT1/2/3, PI3K), NFkB signaling (NFkB, GADD45, p38), protection (HSP70, SOD1, GSTO1/2), neuromuscular differentiation (NSEγ, myosin-1a, desmin), osteoblastic differentiation (BMP2, RUNX2, osterix), acute inflammation (TNFα, IL-1, CXCR4), innate immunity (β-defensin 1, lactoferrin, TLR-3, -4), cell-mediated immunity (CD4, CD8, CD80), while decreased the expression of ER stress (eIF2α, eIF2AK3, ATF6α), fibrosis (FGF2, CTGF, collagen 3A1), and chronic inflammation (CD68, MMP-2, -3, COX2) versus the untreated controls. The activation of proliferation by 10 μg/mL PTX was also supported by the increase of cMyc-MAX heterodimer and β-catenin-TCF1 complex in double IP-HPLC. 10 μg/mL PTX enhanced FAS-mediated apoptosis but diminished p53-mediated apoptosis, and downregulated many angiogenesis proteins (angiogenin, VEGF-A, and FLT4), but upregulated HIF1α, VEGFR2, and CMG2 reactively. Whereas, 300 μg/mL PTX consistently decreased proliferation, epigenetic modification, RAS and NFkB signaling, neuromuscular and osteoblastic differentiation, but increased apoptosis, ER stress, and fibrosis compared to 10 μg/mL PTX. These data suggest PTX has different biological effect on RWA 264.7 cells depending on the concentration of 10 μg/mL and 300 μg/mL PTX. The low dose 10 μg/mL PTX enhanced RAS/NFkB signaling, proliferation, differentiation, and inflammation, particularly, it stimulated neuromuscular and osteoblastic differentiation, innate immunity, and cell-mediated immunity, but attenuated ER stress, fibrosis, angiogenesis, and chronic inflammation, while the high dose 300 μg/mL PTX was found to alleviate the 10 μg/mL PTX-induced biological effects, resulted in the suppression of RAS/NFkB signaling, proliferation, neuromuscular and osteoblastic differentiation, and inflammation.
Training and Development for Knowledge Workers - Malaysian Scene

As Malaysia aspires to move ahead into the K-based economy which is knowledge and technologically driven, the government have to focus more on the critical development and management of human capital. Despite the importance of traditional factors of production (labour, capital raw materials and entrepreneurship), knowledge is now considered as the most important critical success factor in driving growth, creating new value and providing the basis to remain competitive in a globalize economy. In line with the government aspiration to be a K-based economy, Faculty of Information Management (FIM) in UiTM is playing its role as an institution of higher learning in striving for a K-based economy. There are many measures which have been taken in creating and preparing the knowledge workers. One of it is by establishing the Masters in Knowledge Management (MKM) programme. This paper gives an overview of KM aspect that is currently practiced at the Faculty. Two core processes of KM will be emphasized. Finally, this paper discusses the challenges faced and potential solutions in the implementation of KM at the Faculty.
Equilibrium of hinged and hingeless structures rotated using surface tension forces

Two different geometries for the rotation of microstructures by the surface tension force of molten solder are investigated theoretically and experimentally. The geometries are based on structures with and without a hinge acting to constrain the motion of a flap to pure rotation. The equilibrium geometry of a hinged structure is first found analytically by considering the surface energy of the solder. An analysis of hingeless structures is then performed that shows a hinge to be unnecessary under certain conditions. Macroscopic experiments performed using printed circuit board parts are then described; the results show that the behaviors of hinged and hingeless structures are similar.
ForestScanner: A mobile application for measuring and mapping trees with LiDAR-equipped iPhone and iPad

Ground-based light detection and ranging (LiDAR) is becoming increasingly popular as an alternative means to conventional forest inventory methods. By gauging the distances to multiple points on the surrounding object surfaces, LiDAR acquires 3D point clouds from which tree sizes and spatial distributions can be rapidly estimated. However, the high cost and specialized skills associated with LiDAR technologies have put them out of reach for many potential users. We here introduce ForestScanner, a free, mobile application that allows LiDAR-based forest inventories by means of iPhone or iPad with a built-in LiDAR sensor. ForestScanner does not require any manual analysis of 3D point clouds. As the user scans trees with an iPhone/iPad, ForestScanner estimates the stem diameters and spatial coordinates based on real-time instance segmentation and circle fitting. The users can visualize, check, and share the scanning results in situ. By using ForestScanner, we measured the stem diameters and spatial coordinates of 672 trees within a 1 ha plot in 1 h 39 min with an iPhone and in 1 h 38 min with an iPad (diameter ≥5 cm; detection rate = 100%). The diameters measured by ForestScanner and a diameter tape were in good agreement; R2=0.963 for iPhone and R2=0.961 for iPad. ForestScanner and a conventional surveying system showed almost identical results for tree mapping (assessed by the spatial distances among trees within 0.04 ha subplots); Mantel R2=0.999 for both iPhone and iPad. ForestScanner reduced the person-hours required for measuring diameters to 25.7%, mapping trees to 9.3%, and doing both to 6.8% of the person-hours taken using a dimeter tape and the conventional surveying system. Our results indicate that ForestScanner enables cost-, labor-, and time-efficient forest inventories. The application can increase the accessibility to LiDAR for non-experts (e.g., students, citizen scientists) and enhance resource assessments and biodiversity monitoring in forests worldwide.
A Deep Learning-based cropping technique to improve segmentation of prostate's peripheral zone

Automatic segmentation of the prostate peripheral zone on Magnetic Resonance Images (MRI) is a necessary but challenging step for accurate prostate cancer diagnosis. Deep learning (DL) based methods, such as U-Net, have recently been developed to segment the prostate and its' sub-regions. Nevertheless, the presence of class imbalance in the image labels, where the background pixels dominate over the region to be segmented, may severely hamper the segmentation performance. In the present work, we propose a DL-based preprocessing pipeline for segmenting the peripheral zone of the prostate by cropping unnecessary information without making a priori assumptions regarding the location of the region of interest. The effect of DL-cropping for improving the segmentation performance was compared to the standard center-cropping using three state-of-the-art DL networks, namely U-net, Bridged U-net and Dense U-net. The proposed method achieved an improvement of 24%, 12% and 15% for the U-net, Bridged U-net and Dense U-net, respectively, in terms of Dice score.
A Stealthier False Data Injection Attack against the Power Grid

We use discrete-time adaptive control theory to design a novel false data injection (FDI) attack against automatic generation control (AGC), a critical system that maintains a power grid at its requisite frequency. FDI attacks can cause equipment damage or blackouts by falsifying measurements in the streaming sensor data used to monitor the grid's operation. Compared to prior work, the proposed attack (i) requires less knowledge on the part of the attacker, such as correctly forecasting the future demand for power; (ii) is stealthier in its ability to bypass standard methods for detecting bad sensor data and to keep the false sensor readings near historical norms until the attack is well underway; and (iii) can sustain the frequency excursion as long as needed to cause real-world damage, in spite of AGC countermeasures. We validate the performance of the proposed attack on realistic 37-bus and 118-bus setups in PowerWorld, an industry-strength power system simulator trusted by real-world operators. The results demonstrate the attack's improved stealthiness and effectiveness compared to prior work.
A Reconfigurable Omnidirectional Circularly Polarized Antenna

A low profile reconfigurable omnidirectional circularly polarized antenna is presented in this paper. The antenna consists of a 1x4 dipole array antenna and a monopole antenna. By designing a reconfigurable feed network to excite the two molecular antennas, the omnidirectional circularly polarized wave which can be switched from left to right can be generated. With a designed feeding network, the antenna can switch between the left-handed circularly polarized (LHCP) and right-handed circularly polarized (RHCP) radiation states. Measurement results show that the antenna's impedance bandwidth (|S11| < - 10 dB) were 21.5% (2.24 to 2.78 GHz) and 19.4% (2.32 to 2.81 GHz). The overlap axis ratio bandwidth (AR < 3 dB) of the full left-right circular polarization state is about 7% (2.44-2.62GHz).
Spherical Harmonic Diagonal Unloading Beamforming with Ego-Noise Reduction for DOA Estimation from Autonomous Systems

A method to improve the localization of a sound source using a spherical microphone array embedded into autonomous systems is presented. The method is based on a low-complexity diagonal unloading (DU) beamforming in the spherical harmonic (SH) domain using a frequency smoothing power transform (FSPT) of the covariance matrices with a novel ego-noise reduction. The attenuation of the ego-noise in the signal-plus-ego-noise broadband FSTP covariance matrix is achieved by estimating the FSPT ego-noise covariance matrix and exploiting the subspace orthogonality property using a diagonal unloading procedure. Experiments with controlled real-world recordings performed by an aerial drone equipped with a 19-microphone spherical array while sensing a flying target drone demonstrate the efficiency of the proposed method.
CFD Analysis of an Elliptical Pin Fin Heat Sink using Ansys Fluent v12.1

The present study carries out numerical physical insight into the flow and heat transfer characteristics. The governing equations are solved by adopting a control volume-based finite-difference method with a power-law scheme on an orthogonal non-uniform staggered grid. The coupling of the velocity and the pressure terms of momentum equations are solved by the computational fluid dynamics. The Elliptical Pin Fin Heat Sink is composed of a plate fin heat sink and some circular pins between plate fins. The purpose of this study is to examine the effects of the configurations of the pin-fins design. The results show that the Elliptical Pin Fin Heat Sink has better unnaturally performance than the plate fin heat sink. Computations of the Elliptical Pin Fin Heat Sink and provides.