Miracle-dz/UniFAR-FTUs huggingface dataset:
Atomic Force Microscopy Study of Surfactant Treated CVD Graphene

Surfactants are often used for treating graphitic materials to overcome their hydrophobicity. These treatments have been employed successfully for graphitic materials such as carbon nanotube and graphite. However, there is still a need to investigate the surfactant treatment approach for CVD graphene for device integration. In this work, CVD graphene on silicon substrates was treated with different surfactant concentrations for different times. Atomic force microscopy was used to investigate the surface morphology of the samples. Unlike other derivatives of graphene-like carbon nanotube, it is important for graphene to remain attached to the substrate. Results show that the stability of the CVD graphene on the substrate is highly dependent on the concentration of the surfactant used, and the duration of treatment CVD graphene. The AFM root-mean-square roughness also changes for different treatment concentrations and times. The change in roughness before and after treatment reduces lower surfactant concentration and longer treatment time.
Universal Design Approach for Multiport Filtering Power Divider

This paper presents a universal design approach of multiport filtering power divider with the arbitrary power-division ratio, which is applicable for the designs of the single-ended/balanced circuit or hybrid of them. The power allocation in a specified passband is fulfilled by relying on two key factors in building a filter, i.e., coupling coefficient and external <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-factor (<inline-formula> <tex-math notation="LaTeX">$Q_{\mathrm {e}}$ </tex-math></inline-formula>). After synthesis for the bandpass response, the total input/output <inline-formula> <tex-math notation="LaTeX">$Q_{\mathrm {e}}$ </tex-math></inline-formula> and the total coupling coefficient at each power dividing level can be determined. Subsequently, according to the desired power-dividing ratio, the coupling coefficient between the two coupled resonators and <inline-formula> <tex-math notation="LaTeX">$Q_{\mathrm {e}}$ </tex-math></inline-formula> of each output can be easily determined. Once the parameters for filtering response are fixed, the power allocation can be determined by <inline-formula> <tex-math notation="LaTeX">$Q_{\mathrm {e}}$ </tex-math></inline-formula> without effect on the filtering response. Thus, the two functions of bandpass and power dividing can be designed independently, which provides tremendous freedom for the overall design with the arbitrary order of filtering and number of output ports. The proposed topology has several advantages, such as flexibility, cost efficiency, and simple design procedure, which would be a good candidate for multiple-path operation in the current and future MIMO systems.
Accelerating Key In-memory Database Functionality with FPGA Technology

In-memory databases that support both online transactional and analytical workloads rely on a process called differential updates to manage writes while maintaining the majority of data in an optimized read-only structure. This, however, necessitates the merge of a write optimized delta partition with the read-only main partition, periodically consuming a significant amount of compute and memory resources while also causing a significant database downtime during which incoming transaction requests cannot be accepted. In this paper, we present an FPGA implementation which offloads the most resource intensive component of this merge process from the CPU to the FPGA. The functionality was fully developed using FPGA tools that mirror the typical software development process, with all the inherent advantages over traditional hardware development, such as reduced development times, flexible system level design and a familiar software debugging process. Our FPGA implementation has been fully integrated with a leading enterprise in-memory database and by taking advantage of the FPGA’s streaming based model, its very low latency memory access, its ability to manipulate non-primitive data types as well as its fine-grained and system level parallelism we demonstrate a performance increase of up to ~14x versus the optimized single instruction, multiple data based CPU implementation.
Automated Function Placement and Online Optimization of Network Functions Virtualization

This paper proposes a new fully decentralized approach to online placement and optimization of virtual machines (VMs) for network functions virtualization (NFV). The approach is of practical value, as network services comprising a chain of virtual network functions (VNFs) are proposed to be queued on the basis of leading unexecuted VNFs at every server, rather than on the typical basis of services, reducing queues per server and facilitating queue management and signaling. It is also non-trivial because the VNFs of network services must be executed correctly in order at different VMs, coupling the optimal decisions of VMs on processing or offloading. Exploiting Lyapunov optimization techniques, we decouple the optimal decisions by deriving and minimizing the instantaneous upper bound of the NFV cost in a distributed fashion, and achieve the asymptotically minimum time-average cost. We also reduce the queue length by allowing individual VMs to (un)install VNFs based on local knowledge, achieving stable redeployment of VNFs, adapting to the network topology and the temporal and spatial variations of services. Simulations show that the proposed approach is able to reduce the time-average cost of NFV by 71% and reduce the queue length (or delay) by 74%, as compared with existing approaches.
The Effects of Diabetic Duration on Lacrimal Functional Unit in Patients with Type II Diabetes

Purpose To observe ocular surface changes in Type II diabetic patients with different disease durations and to understand the correlations between clinical parameters and diabetic durations. Methods In this cross-sectional, prospective study, 51 healthy controls and 91 patients with Type II diabetes were enrolled. The diabetics were divided into 3 subgroups according to the disease duration, including duration <10 y group, 10 to 20 y group, and ≥21 y group. All subjects underwent clinical ocular examinations, including lipid layer thickness (LLT), blinking rate, tear meniscus height (TMH), noninvasive tear film break-up time (NI-BUT), meibography, superficial punctate keratopathy (SPK) scoring, corneal sensitivity, and Schirmer I test. They were also evaluated using the standard patient evaluation of eye dryness (SPEED) questionnaire. Results SPEED score, meiboscore, SPK score, LLT, Schirmer I test, and corneal sensitivity differed significantly between the diabetic and healthy control groups. Further, SPEED score, Schirmer I test, corneal sensitivity, meiboscore, and blink rate significantly differed among the 3 diabetic subgroups and the control group. In diabetics, the SPEED score correlated with the SPK score, blink rate, TMH, and LLT; NI-BUT with TMH, LLT, and blink rate; TMH with the SPK score; Schirmer I test with the SPK score; and corneal sensitivity with the meiboscore. More importantly, the Schirmer I test, corneal sensitivity, and SPEED score negatively correlated with diabetic duration. Conclusion Diabetic duration is an important factor that affects functions of the lacrimal functional unit in patients with Type II diabetes. The trends of changes in the ocular parameters vary along the course of diabetes.
Cartesian Trajectory Tracking of a 7-DOF Exoskeleton Robot Based on Human Inverse Kinematics

Exoskeleton robots have become an important tool to provide rehabilitation therapy to stroke victims because of their ability to allow rehabilitation exercises, ranging from passive to active-assisted movement, for extended time periods. To generate the desired rehabilitation trajectories and ensure an optimal Cartesian solution, we propose a new solution to the inverse kinematics problem, which is compatible with human upper limb movement and is valid for human arm configuration. In addition, in order to provide passive rehabilitation therapy to the upper extremity of disabled individuals, we implement a robust nonlinear control based on the backstepping technique on the 7-degrees-of-freedom ETS-MARSE robot. The controller was designed to reject the user’s force caused by the subject’s muscular activity. Experimental results validate the stability, robustness, and exactness of the proposed method with the designed tests performed by healthy subjects.
CGQ: Relationship-Aware Contextual Graph Querying in Large Networks

The phenomenal growth of graph data from a wide-variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all previous research has tacitly ignored the presence of relationships and context (usually manifested in the form of node/edge label distributions) in the query. In this paper, we propose CGQ -- Relationship-Aware Contextual Graph Querying} for real-world graphs. Given a query graph and a target graph, CGQ identifies the (top-k) maximal common subgraphs between the query and the target graphs with the highest contextual similarity. We prove that the problem is NP-hard and APX-Hard. To overcome this computational bottleneck, we propose a hierarchical index, CGQ-Tree, with its associated CGQ search algorithm. Empirically, the CGQ search algorithm is capable of achieving speed-ups of up to three orders of magnitude over a baseline strategy. Our experiments show that CGQ is effective, efficient and scalable.
The optimum number of Principles ideal for Enterprise Architecture practice

Every organization defines Principles for Enterprise Architecture (EA) practice. As there is no set standard, the principles identified exceeds the recommended number 20 by TOGAF. More the number of Principles defined it will be ignored by the Enterprise Architects instead referring for their decision making. In this paper, we identify the ideal number of principles that will motivate Architects to refer to perform their task
The Clustering for Clients in a Bank Based on Big Data

Many technologies about data mining, such as clustering, have been widely applied in the context of bank to understand the behaviors of the clients and investors. Unlike some classic clustering validity index using compactness and separation, we employ Pairing Frequency Clustering Validity Index (PFCVI), which uses pairwise pattern information and focuses more on logical reasoning than geometrical features. We use PFCVI to evaluate the clustering quality under different c and find the optimal value of c is 11 based on the bank’s data, and clients in the 11 classes have different savings value potential levels and different fluctuation patterns. Then, we sum up the above 11 classes into 5 categories with different fluctuation patterns – stabilized savings value category, fluctuating savings value category, rising savings value category, falling savings value category and abnormal savings value category. Finally, we analyze each category with techniques like user profile and give some targeted advice for each category aimed at optimal market segment.
Obstacle Avoidance for Autonomous Driving using Neuro-Fuzzy Architecture in an Urban Landscape

An autonomous vehicle must be able to respond to the state of objects in the surrounding, be it stationary or in motion. This paper outlines the techniques to allow the car to be aware of its immediate surroundings in front of it while it is moving autonomously and to make decision on its next course of action. If the object in front is stationary or moving slowly, the autonomous vehicle could decide to go around it, provided there are no obstacles at the side of the object in front. If it is unable to go around it, it should stop within a safe distance from the object in front. Several sensors are used to ascertain the proximity to surrounding front objects to make decision on the next movement. One of the techniques used is to apply fuzzy logic on the ultrasonic sensor readings to gauge the distance to front surrounding objects in fuzzy terms such as near, medium-distance or far. This method complements the trained neural network in making decisions on obstacle avoidance.
VirNet: Deep attention model for viral reads identification

Metagenomics shows a promising understanding of function and diversity of the microbial communities due to the difficulty of studying microorganism with pure culture isolation. Moreover, the viral identification is considered one of the essential steps in studying microbial communities. Several studies show different methods to identify viruses in mixed metagenomic data using homology and statistical techniques. These techniques have many limitations due to viral genome diversity. In this work, we propose a deep attention model for viral identification of metagenomic data. For testing purpose, we generated fragments of viruses and bacteria from RefSeq genomes with different lengths to find the best hyperparameters for our model. Then, we simulated both microbiome and virome high throughput data from our test dataset with aim of validating our approach. We compared our tool to the state-of-the-art statistical tool for viral identification and found the performance of VirNet much better regarding accuracy on the same testing data.
Estimating Room Occupancy in a Smart Campus using WiFi Soft Sensors

Universities worldwide are experiencing a surge in enrolments, therefore campus estate managers are seeking continuous data on attendance patterns so as to optimize the usage of classroom space. While prior works have measured room occupancy via hardware sensor instrumentation, in this paper we explore the use of pervasive WiFi infrastructure for estimating attendance. In a dense campus environment, WiFi connectivity counts are poor estimators of room occupancy since they are polluted by adjoining rooms, outdoor walkways, and network load balancing. The main contribution of this work is to develop new ways to distinguish and filter out WiFi-connected users outside of the lecture room of interest, and feed such data to a regression analyser to estimate room occupancy. We evaluate our technique across lecture theatres of varying size in our campus, and show that their accuracy approaches that of hardware sensors without incurring cost and effort of installing and maintaining them.
MTJ-Based Nonvolatile Ternary Logic Gate for Quantized Convolutional Neural Networks

A ternary logic gate based on MTJ-based nonvolatile logic-in-memory (NV-LIM) architecture for ternary neural networks (TNNs) is proposed. The NV-LIM-based implementation achieves reduced computational cost and data transfer cost related to the inference function of deep neural networks. Through an experimental evaluation of a basic component of TNNs, its impact on the energy, delay and area overhead reduction is demonstrated.
Cinnamomum zeylanicum, Origanum vulgare, and Curcuma longa Essential Oils: Chemical Composition, Antimicrobial and Antileishmanial Activity

The resistance mechanisms of bacteria and protozoans have evidenced the need of discover new compounds with potential pharmaceutical activity against pathogenic microorganisms. Medicinal plants have been for centuries a promising alternative as sources of new drugs. The objective of this work was to evaluate the chemical composition, antimicrobial and antileishmanial activities of Cinnamomum zeylanicum, Origanum vulgare, and Curcuma longa essential oils. Chemical analysis was performed by gas chromatography-mass spectrometry. Antimicrobial activity was performed by disk diffusion and minimum inhibitory concentration (MIC) test. Antileishmanial activity was performed against antipromastigote and intracellular amastigote of Leishmania amazonensis. Cytotoxic and nitrite production were realized in BALB/c peritoneal macrophages. The major compounds of the essential oils were cinnamic aldehyde (46.30%) in C. zeylanicum, cis-p-menth-2-en-1-ol (33.88%) and linalyl acetate (13.90%) in O. vulgare, and turmerone (55.43%) in C. longa. The MIC showed significant antimicrobial activity of C. longa essential oil against S. aureus (83.3 ± 14.43 µg/mL). Antipromastigote activity showed IC50 values >500 µg/mL to C. zeylanicum, 308.4 ± 1.402 µg/mL to O. vulgare, and 405.5 ± 1.119 µg/mL to C. longa essential oil. Activity against intracellular amastigote of L. amazonensis showed IC50 of 63.3 ± 1.369 µg/mL and cytotoxic was not observed, resulting in selectivity index higher than 15.79 to parasite. C. longa essential oil decreased nitrite production in peritoneal macrophages, but not in Leishmania-infected cells. The chemical composition of the three essential oils is directly associated to its potential biological action, as the antimicrobial activity. C. longa presented a potent antileishmanial activity against promastigote and intracellular amastigote of L. amazonensis, although this activity is not linked to nitric oxide, since C. longa essential oil inhibits its production.
Deep Reinforcement Learning for Traffic Light Optimization

Deep Reinforcement Learning has the potential of practically addressing one of the most pressing problems in road traffic management, namely that of traffic light optimization (TLO). The objective of the TLO problem is to set the timings (phase and duration) of traffic lights in order to minimize the overall travel time of the vehicles that traverse the road network. In this paper, we introduce a new reward function that is able to decrease travel time in a micro-simulator environment. More specifically, our reward function simultaneously takes the traffic flow and traffic delay into account in order to provide a solution to the TLO problem. We use both Deep Q-Learning and Policy Gradient approaches to solve the resulting reinforcement learning problem.
Partial Discharges and Breakdown of Protrusion in GIS under Oscillating Lightning Impulses

Gas insulated switchgear (GIS) is important equipment in power system. To ensure its safety, alternatingcurrent (AC) voltage withstand test is usually carried out in field. However, for GIS of high voltage level and big scale, it becomes non-effective to expose all defects for the low sensitivity of partial discharge (PD) detection. Then field impulse voltage withstand test, as an important supplement, is becoming increasingly important. Oscillating lightning impulse (OLI) is suitable for field use because of its easy generation. Sharp protrusions on HV conductor are typical defects in GIS. They are more critical under lightning impulses. This paper therefore studied its discharge characteristics under OLI. Four OLIs are generated whose wavefront was 1.2 µs, 3 µs, 7 µs and 13 µs respectively. The protrusion was 6 mm-long and was set on the inner conductor in a GIS chamber of 330 kV. An internal ultra high frequency (UHF) sensor was used to measure partial discharge and a capacitive voltage divider to measure applied voltage. Then the breakdown characteristics were experimentally studied. The results show that breakdowns always occur in the vicinity of each wave peak. 50% probability breakdown volage (BDV50) is lower for positive polarity. Wavefront has strong influence on the discharge characteristics. BDV50 increase with the increase of wavefront. BDV50 of 13 µs wavefront is more than 10 % higher than that of 1.2 µs wavefront. Short wavefront indicates that first streamer will directly lead to breakdown. When wavefront is longer, first streamer becomes pre-breakdown discharge and forms corona stabilization, which makes it harder to breakdown. The results of the study are expected to be beneficial for the field impulse voltage withstand test for GIS.
Antenna Polarization Alignment Using Intermodulation for Multi-Sine Wireless Power Transfer

The efficiency of wireless power transfer (WPT) always suffers polarization misalignment between transmitting and receiving directional antennas. In this work, an implantable antenna polarization alignment using the useless intermodulation from a multi-sine excited rectifier is proposed, which enables a maximum wireless power link for the WPT applications. Two directional transmitting and receiving antennas are implemented to establish a two-tone multi-sine power link for an implantable rectifier. Then the intermodulation is generated accompanied with the RF-DC rectification. Two resonating coils are utilized to build up an inductive coupling of the intermodulation, by which the implantable antenna polarization can be aligned. The proposed idea is validated by the theoretical analysis and fabrication.
User-Centric Energy Efficiency Optimization for MISO Wireless Powered Communications

We consider a multiple-input single-output wireless powered communication network (WPCN), where the single-antenna users harvest energy from a multi-antenna access point (AP) and then transmit information back to the AP. With multiple antennas, the AP can perform energy beamforming in the downlink and exploit multiplexing- or (receive) beamforming-gain in the uplink. We consider maximizing the sum of the users’ energy efficiencies (EEs) by jointly optimizing the energy beamforming of the AP, transmit powers of the users, and time allocation. We formulate EE maximization problems for both TDMA-based WPCN (T-WPCN) and SDMA-based WPCN (S-WPCN), which are non-convex because they have the sum-of-ratios objective functions. We optimally solve the former by reformulating it into an equivalent parametric problem, whose solution can be obtained by iteratively solving convex problems, while the latter is optimally solved by convexifying it using the so-called feasible set reduction scheme. We show that the T-WPCN outperforms S-WPCN when there is no minimum throughput requirement, while we can observe that the S-WPCN outperforms T-WPCN when minimum throughput requirement is high. The simulation results verify our theoretical findings and demonstrate the effectiveness of our proposed schemes.
A Novel Feature Enhancement Method Based on Improved Constraint Model of Online Dictionary Learning

Online dictionary learning (ODL) is an emerging and efficient dictionary learning algorithm, which can extract fault features information of fault signals in most occasions. However, the typical ODL algorithm fails to consider the interference of noise and the structural features of the fault signals, which leads to the fault features of weak fault signals that are difficult to extract. For that, a novel feature enhancement method based on an improved constraint model of an ODL (ICM-ODL) algorithm has been proposed in this paper. For the stage of dictionary learning, the elastic-net constraint is used to promote the anti-noise performance of the dictionary atoms. For the stage of signals sparse coding, the $l_{2,1} $ norm constraint is added to learn the structural features of fault signals. In addition, a variational mode decomposition algorithm is used to reduce the impact of noise on the signal initially. Taking the weak fault signals of bearing as examples for analysis, the results show that the feature enhancement of the weak fault signals is fulfilled by using the ICM-ODL algorithm. Compared with the typical ODL method, the ICM-ODL algorithm can not only improves the anti-noise performance of the dictionary atoms, but also removes the noise compositions of the reconstructed signal significantly.