Miracle-dz/UniFAR-FTUs huggingface dataset:
Translational Motion Compensation for Soft Tissue Velocity Images

Purpose: Advancements in MRI Tissue Phase Velocity Mapping (TPM) allow for the acquisition of higher quality velocity cardiac images providing better assessment of regional myocardial deformation for accurate disease diagnosis, pre-operative planning and post-operative patient surveillance. Translation of TPM velocities from the scanner's reference coordinate system to the regional cardiac coordinate system requires decoupling of translational motion and motion due to myocardial deformation. Despite existing techniques for respiratory motion compensation in TPM, there is still a remaining translational velocity component due to the global motion of the beating heart. To compensate for translational motion in cardiac TPM, we propose an image-processing method, which we have evaluated on synthetic data and applied on in vivo TPM data. Methods: Translational motion is estimated from a suitable region of velocities automatically defined in the left-ventricular volume. The region is generated by dilating the medial axis of myocardial masks in each slice and the translational velocity is estimated by integration in this region. The method was evaluated on synthetic data and in vivo data corrupted with a translational velocity component (200% of the maximum measured velocity). Accuracy and robustness were examined and the method was applied on 10 in vivo datasets. Results: The results from synthetic and in vivo corrupted data show excellent performance with an estimation error less than 0.3% and high robustness in both cases. The effectiveness of the method is confirmed with visual observation of results from the 10 datasets. Conclusion: The proposed method is accurate and suitable for translational motion correction of the left ventricular velocity fields. The current method for translational motion compensation could be applied to any annular contracting (tissue) structure.
3D Printed V-band Rod Antenna With Constant Gain Based on Spoof Surface Plasmon Polaritons

A V-band rod antenna with constant gain based on spoof surface plasmon polaritons (SPPs) is proposed in this paper. In this design, a periodic metallic grating operates as a SPP transmission line, which can provide high EM field confinement and achieve a low insertion loss (only 0.079dB/mm at 60GHz). Meanwhile, due to the SPPs unique dispersion characteristic, a broadband rod antenna is designed with high gain. This rod antenna is conveniently fabricated by 3D printing followed by metalization. The simulated results show that the proposed rod antenna can cover V-band from 50 to 75GHz with| S11| < −15dB and achieve a gain of 15.8 dBi with less than 1.2dBi variation. The measured −10dB bandwidth is from 50GHz to 70GHz with 12.87dBi-16.5dBi gain.
Ultra-Wideband Low-RCS Circularly-Polarized Metasurface-Based Array Antenna Using Tightly-Coupled Anisotropic Element

A novel broadband circular-polarized array antenna with ultra-wideband low radar cross section (RCS) is proposed. The array is made up of <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> identically circular-polarized sub-arrays in a chessboard configuration. Furthermore, each sub-array consists of <inline-formula> <tex-math notation="LaTeX">$2\times2$ </tex-math></inline-formula> tightly coupled metasurface-based elements with linear polarization. For radiation, each element is excited and fed with 90° phase shift between adjacent ones to perform broadband circular-polarized operation. For scattering, destructive phase difference is created in an ultra-wide band for orthogonally polarized incidence due to the anisotropic structure. Thus, a considerable ultra-wideband RCS reduction involving in-band and out-of-band is achieved based on energy cancellation. By this means, broadband circular-polarized radiation and ultra-wideband low RCS performance can be achieved in a synthesized design process. The prototype is fabricated and measured. A good agreement between measured and simulated results verifies the validity of our design method.
Identifying Important Criteria for Staff Productivity in Relation to Malaysian Cultural Values: A Comparative Review

Malaysia is going towards being a developed country. One of the major benchmark for this is the capability of Malaysia to be a significant place for traders and investors. To achieve this, every company and corporate members in Malaysia has to play their roles to create a harmony environment to attract traders and investors. This can be done by focusing to improve the productivity of the company where this directly relates to staff’s productivity. There are a lot of physical attributes that can be used as a benchmark for productivity such as ability to gain profit and to initiate an innovation of a company, as well as their contribution to the society. Our research involves to two parts; 1. developing framework to identify important criteria from national cultural values involved and 2. to identify the key criteria involved quantitatively. Both parts are important, however, in this paper, we intend to focus on the framework development and the second part will be published separately in the future. For this purpose, some literature surveys and reviews is conducted and analysis is made. A comparative review between criteria is performed to identify which criteria are effective in driving staff productivity. To add on, a hierarchical model for finding the weights for measuring the importance is developed for future reference.
Scintillation Efficiency and Position Sensitivity for Radiation Events in Plastic Scintillators

Plastic scintillators (PLSs) are potential materials for the development of a dosimeter based on linear energy transfer (LET) measurement for mixed radiation fields, such as the surroundings of accelerator facilities and in space because PLSs are composed mainly of hydrocarbon molecules, and have the effective atomic number and density similar to those of water and human tissues. In addition, PLSs have a wide range of application in particle physics experiments, owing to their response to all types of radiation (photon, neutron, and charged particles). To use PLSs in designing and constructing an LET spectrometer, essential characteristics of PLSs, such as light yields as a function of deposited energy and radiation species, and the position sensitivity to observe trajectories of incident radiation must be studied. Previously, we performed a procedure for determining the energy deposited in a PLS (EJ-200) for gamma rays from a 137Cs radiation source using Compton Coincidence Technique. In that measurement, the scintillation efficiency, which was determined as a function of Compton electron energy, approaches a saturation value in the high-energy region and decreases in the low-energy region. The decrease in efficiency possibly suggests the nonproportionality between the light yield and deposited energy. In this paper, the scintillation efficiencies, as well as the absolute light yield of PLSs (EJ-200, EJ-212, and EJ-252) are investigated. In addition, the position sensitivity characteristic is studied by using a PLS rod, and photomultiplier tubes (PMTs) are attached to both ends of the rod, and the position of radiation incidence in the rod can be evaluated by using signals from the PMTs.
Anti-Photoaging Effects of Low Molecular-Weight Fucoidan on Ultraviolet B-Irradiated Mice

Ultraviolet (UV) B exposure induces DNA damage and production of reactive oxygen species (ROS), which causes skin photoaging through signaling pathways of inflammation and modulation of extracellular matrix remodeling proteins, collagens, and matrix metalloproteinase (MMP). As low molecular-weight fucoidan (LMF) has potential antioxidant and anti-inflammatory properties, we examined the protective effects of LMF against UVB-induced photoaging. A UVB-irradiated mouse model was topically treated with myricetin or LMF at 2.0, 1.0 and 0.2 mg/cm2 (LMF2.0, LMF1.0 and LMF0.2, respectively) once a day for 15 weeks. Wrinkle formation, inflammation, oxidative stress, MMP expression, and apoptosis in the treated regions were compared with those in a distilled water-treated photoaging model (UVB control). LMF treatments, particularly LMF2.0 and LMF1.0, significantly inhibited the wrinkle formation, skin edema, and neutrophil recruitment into the photo-damaged lesions, compared with those in the UVB control. While LMF decreased interleukin (IL)-1β release, it increased IL-10. The LMF treatment inhibited the oxidative stresses (malondialdehyde and superoxide anion) and enhanced endogenous antioxidants (glutathione). Additionally, LMF reduced the mRNA expression of MMP-1, 9, and 13. The histopathological analyses revealed the anti-photoaging effects of LMF exerted via its antioxidant, anti-apoptotic, and MMP-9-inhibiting effects. These suggest that LMF can be used as a skin-protective remedy for photoaging.
Energy-Aware Routing in Carrier-Grade Ethernet Using SDN Approach

Soft-defined networking (SDN) is a new approach that enables operators to easily manage all the network elements. In this paper, we address the problem of energy-aware routing in SDN-based carrier-grade Ethernet networks. Our approach is based on turning off network nodes and links to reduce energy consumption, while respecting the rule space capacity for each Openflow switch, and maintaining an allowable maximum link utilization. The problem of identifying the optimal set of network elements to be turned off is NP-hard. We first present an exact model based on an integer linear programming formulation for the problem. Then, we describe a set of first-fit heuristic algorithms suitable for large-sized networks. The exact and heuristic approaches are tested on SNDlib-based instances. Experimentations show the efficiency of both exact and heuristic methods for different network topologies. In particular, our heuristic algorithms are able to achieve a good balance between energy consumption, resource utilization, and network performance.
Exploiting Smallest Error to Calibrate Non-Linearity in SAR Adcs

This paper presents a statistics-optimized organization technique to achieve better element matching in successive approximation register (SAR) analog-to-digital converter (ADC) in smart sensor systems. We demonstrate the proposed technique ability to achieve a significant improvement of around 23 dB on a spurious free dynamic range (SFDR) of the ADC than the conventional, testing with a capacitor mismatch $\sigma _{u}=0.2\%$ in a 14-bit SAR ADC system. For the static performance, the max root mean square (rms) value of differential nonlinearity reduces from 1.63 to 0.20 LSB and the max rms value of integral nonlinearity (INL) reduces from 2.10 to 0.21 LSB. In addition, it is demonstrated that by applying grouping optimization and strategy optimization, the performance boosting on the SFDR can be effectively achieved. Such a great improvement on the resolution of the ADC only requires an off-line pre-processing digital part.
A Group-Oriented Recommendation Algorithm Based on Similarities of Personal Learning Generative Networks

To solve the lack of consideration of the learning time sequence and knowledge dependencies in group-based recommendation, we proposed a novel group-oriented recommendation algorithm which is characterized by mapping the user’s learning log to a personal learning generative network (PLGN) based on a knowledge map. In this paper, we first provide calculation methods of similarity and temporal correlation between knowledge points, where we provide the construction method of the PLGN. Second, a method for measuring the similarities between any two PLGNs is proposed. According to the similarities, we perform the CURE clustering algorithm to obtain learning groups. Third, based on the group clustering, the group learning generative network using a graph overlay method is generated. We calculate the importance of the vertices on the different learning needs and propose a group-oriented recommendation algorithm. Finally, we compare the effect of the proposed recommendation to that of a group-based collaborative filtering recommendation for the aspects of precision rate, recall rate, normalized discounted cumulative gain, and the average accuracy of parameters (MAP). The experimental results show that the group-oriented learning recommendation based on the learning generated network outperforms the group recommendation-based collaborative filtering when the amount of data is large enough.
Tailoring the Greenstone 'Librarian' for seniors: Eliciting metadata and browsing structures

This paper describes a 'cultural probe' study underpinning a user-centered re-design of the Greenstone digital library system's collection creation sub-system (the Greenstone 'Librarian'), with a focus on assisting seniors in developing Greenstone collections representing their personal history. The results of this investigation include a customized metadata set for these users' collections, and a 'personal timeline' browsing structure.
Benchmarking of 3-D MOSFET Architectures: Focus on the Impact of Surface Roughness and Self-Heating

Tremendous improvements in the fabrication technology have allowed to scale the physical dimensions of the transistors and also to develop different promising 3-D architectures that may allow continuing Moore’s law. In this paper, we perform a comparative delay analysis of different 3-D device architectures and study the impact of surface roughness and self-heating on the on-current using a comprehensive in-house simulation framework comprising Schrödinger, Poisson, and Boltzmann transport equation solvers and comprising relevant scattering mechanisms and self-heating. Our results highlight that parasitic capacitance can alter the relative ranking of the architectures from delay point of view. We demonstrate that surface roughness can cause architecture and material-dependent current degradation, and hence, it is necessary to account for it in simulation-based benchmarking different architectures.
Ensemble classifier-based off-line handwritten word recognition system in holistic approach

This study presents a novel ensemble classifier-based off-line handwritten word recognition system following a holistic approach. Here each handwritten word is recognised using two handcrafted features, namely (i) Arnold transform-based feature that addresses local directional feature which depends on the stroke orientation distribution of cursive word and (ii) oriented curvature-based feature which is basically the histogram of curvelet index and one machine generated feature using deep convolution neural network (DCNN). In this study, a new architecture of DCNN is proposed for handwritten word recognition. These features are recognised by three classifiers separately. Finally, the decision of three classifiers is combined to predict the ultimate word class level. To fuse the decision of individual classifiers, the authors have explored three strategies: (i) vote for strongest decision, (ii) vote for majority decision and (iii) vote for the sum of the decisions. The proposed handwritten word recognition system is tested on three handwritten word databases: (i) CENPARMI database, (ii) IAM database and (iii) ISIHWD database. The performance of the proposed system is promising and comparable to state-of-the-art handwriting recognition systems.
MOVE-HF: an internet-based pilot study to improve adherence to exercise in patients with heart failure

Aim: The use of the internet and newer activity monitors such as the Fitbit Charge HR to improve exercise adherence is limited. The primary aim of the Move on Virtual Engagement (MOVE-HF) was to investigate the effects of group social support by internet-based synchronized face-to-face video and objective physical activity feedback on adherence to recommended exercise guidelines. Methods: Thirty stable heart failure patients (New York Heart Association class I–III), aged 64.7±11.5 years, were randomly assigned to an experimental or comparison group. Participants were provided a handout on self-care in heart failure, an exercise routine, a Fitbit Charge HR and were asked to wear the Fitbit Charge HR daily, and record their exercise sessions using both the Fitbit Charge HR and exercise diaries. In addition, participants in the experimental group connected to Vidyo software, once a week, for 8 weeks, for a 45-minute face-to-face online group discussion/education session. Results: Overall Vidyo session attendance was 68%, with 73% of participants attending five or more sessions. Adherence to exercise was 58.8% in the experimental group and 57.3% in the comparison group. The experimental group perceived receiving social support through the internet-based synchronized face-to-face video meetings but due to a small sample size and lack of adequate power, no significant impact on exercise adherence was observed. Participants commented that feedback regarding physical activity from the Fitbit Charge HR was helpful and motivational. Conclusion: Delivering social support by internet-based synchronized face-to-face video is feasible with heart failure patients. However, more investigations are needed to understand its impact on exercise adherence.
Noncontact Pulse Transit Time Measurement Using a Single-Frequency Continuous-Wave Radar

This paper presents a single-frequency continuous-wave (CW) radar that operates concurrently with two different modes to measure pulse transmit time (PTT) without contact. One is self-injection-locked (SIL) CW mode to detect the wrist movement due to pulse. The other is the range-correlated (RC) CW mode to detect the chest movement due to heartbeat. The peak time difference (PTD) between the two resulting output signals in this radar can very well correlate with the PTT that was measured using electrocardiography (ECG) and photoplethysmography (PPG) sensors.
Real-time Neonatal Respiratory Rate Estimation using a Pressure-Sensitive Mat

We present an approach for real-time respiratory rate (RR) estimation in a neonatal intensive care unit (NICU) using a pressure-sensitive mat (PSM). Real patient data were collected in an NICU from four sources simultaneously: a PSM placed under the patient, a Draeger patient monitor, a video camera placed directly above the patient, and a custom bedside event annotation application running on a tablet. The PSM data were used to develop an algorithm for estimating the patient’s RR. The results were evaluated against impedance pneumography (IP) based RR measurements from the patient monitor. In comparison to the IP estimates, we achieved a mean absolute error of 4.51 breaths per minute (bpm) for 3 hours of data collected from a single patient. Moreover, we show that our newer approach performs better than the previous frequency-based approach that was developed based on patient simulator data. The estimation accuracy achieved by the new algorithm meets clinical requirements for the determination of RR.
Active HHV-6 Infection of Cerebellar Purkinje Cells in Mood Disorders

Early-life infections and associated neuroinflammation is incriminated in the pathogenesis of various mood disorders. Infection with human roseoloviruses, HHV-6A and HHV-6B, allows viral latency in the central nervous system and other tissues, which can later be activated causing cognitive and behavioral disturbances. Hence, this study was designed to evaluate possible association of HHV-6A and HHV-6B activation with three different groups of psychiatric patients. DNA qPCR, immunofluorescence and FISH studies were carried out in post-mortem posterior cerebellum from 50 cases each of bipolar disorder (BPD), schizophrenia, 15 major depressive disorder (MDD) and 50 appropriate control samples obtained from two well-known brain collections (Stanley Medical Research Institute). HHV-6A and HHV-6B late proteins (indicating active infection) and viral DNA were detected more frequently (p < 0.001 for each virus) in human cerebellum in MDD and BPD relative to controls. These roseolovirus proteins and DNA were found less frequently in schizophrenia cases. Active HHV-6A and HHV-6B infection in cerebellar Purkinje cells were detected frequently in BPD and MDD cases. Furthermore, we found a significant association of HHV-6A infection with reduced Purkinje cell size, suggesting virus-mediated abnormal Purkinje cell function in these disorders. Finally, gene expression analysis of cerebellar tissue revealed changes in pathways reflecting an inflammatory response possibly to HHV-6A infection. Our results provide molecular evidence to support a role for active HHV-6A and HHV-6B infection in BPD and MDD.
Non-contact Beat-to-beat Blood Pressure Measurement Using Continuous Wave Doppler Radar

Ahstract-This paper presents a non-contact beat-to-beat blood pressure (BP) measurement system using a continuous wave (CW) Doppler radar. The tiny displacement on body surface induced by the central aortic artery is acquired by a digital-IF CW Doppler radar. Then the pulse transit time (PTT) is extracted to calculate the BP. Experiments have been carried out with three human subjects to evaluate the performance. Compared with the sphygmomanometry, the results demonstrate that the proposed system can acquire the beat-to-beat BP accurately.
CNN with coarse-to-fine layer for hierarchical classification

Most of the traditional convolution neural network (CNN)-based classification models are flat classifiers, which have an underlying assumption that all classes are equally difficult to distinguish. However, visual separability between different object categories is highly uneven in the real world. Recently, hierarchical classification has been proven effective for CNNs, more and more attempts have been made to exploit category hierarchies in CNN models. In this study, the authors propose a novel hierarchical CNN architecture, called coarse-to-fine CNN. It is simple, with a proposed coarse-to-fine layer on the top of a generic CNN. The coarse-to-fine layer is inspired by the Bayesian equation, where the coarse prediction can affect the fine prediction directly. Arbitrary CNNs can perform the hierarchical classification by adding the proposed layer. The training of a coarse-to-fine CNN is end-to-end, it can be optimised by typical stochastic gradient descent. In the test phase, it outputs multiple hierarchical predictions simultaneously. Experimental results on the benchmark datasets MNIST, CIFAR-10, and CIFAR-100 show clear advantages over the compared baselines.
Adaptive Weighting with SMOTE for Learning from Imbalanced Datasets: A Case Study for Traffic Offence Prediction

This paper proposes to augment the prediction capability of a classifier or of an ensemble of classifiers for an imbalanced set using a combination of informed sampling based on SMOTE (Synthetic Minority Oversampling Technique) and a post-classification adaptive weighting that takes into account a priori knowledge about a dataset. As a case study, the paper analyzes the relationship between traffic tickets (provincial offence notices), their types and the trends in attributes such as vehicle type, offence type, location, ticket status for the city of Ottawa, Canada with the purpose of enabling a proactive traffic enforcement.
Optimal Full-Rank Signaling Over MIMO Wiretap Channels Under Interference Constraint

A closed-form full-rank solution for an optimal transmit covariance matrix over a strictly degraded MIMO wiretap channel and its secrecy capacity are obtained under an interference constraint, in addition to the transmit power constraint. A number of its properties are pointed out. The regimes when one of the constraints is inactive are studied. The interference constraint affects significantly the optimal covariance and induces its new properties, which cannot be found under the total transmit power constraint only.