Miracle-dz/UniFAR-FTUs huggingface dataset:
Secrecy Energy Efficiency Optimization for Artificial Noise Aided Physical-Layer Security in OFDM-Based Cognitive Radio Networks

In this paper, we investigate the power allocation of primary base station (PBS) and cognitive base station (CBS) across different orthogonal frequency division multiplexing (OFDM) subcarriers for energy-efficient secure downlink communication in OFDM-based cognitive radio networks (CRNs) with the existence of an eavesdropper having multiple antennas. For the sake of defending against eavesdropping, artificial noise is used to confuse the eavesdropper at the cost of extra power consumption. For the purpose of improving the energy efficiency (EE) of secure communications, we propose a secrecy energy efficiency maximization (SEEM) scheme by exploiting the instantaneous channel state information (ICSI) of the eavesdropper, called ICSI-based SEEM (ICSI-SEEM) scheme with a given total transmit power budget for different OFDM subcarriers of both PBS and CBS while guaranteeing a certain secrecy rate (SR) for a cognitive user, where a primary user's SR is also taken into consideration for limiting the interference in CRNs at each subcarrier. As for the case when the eavesdropper's ICSI is unknown, we also propose an SEEM scheme by using the statistical CSI (SCSI) of the eavesdropper, namely SCSI-based SEEM (SCSI-SEEM) scheme. Since the ICSI-SEEM and SCSI-SEEM problems are fractional and non-convex, we first transform them into equivalent subtractive problems, and then achieve approximate convex problems by employing the difference of two-convex functions approximation method. Finally, new two-tier power allocation algorithms are proposed to achieve $\varepsilon$-optimal solutions of our formulated ICSI-SEEM and SCSI-SEEM problems. Simulation results illustrate that the ICSI-SEEM has a better secrecy energy efficiency (SEE) performance than SCSI-SEEM, and moreover, the proposed ICSI-SEEM and SCSI-SEEM schemes outperform conventional SR maximization and EE maximization approaches in terms of their SEE performance.
Magnetic RAM based filter design for low power signal processing in IOT applications

Emerging memory technologies have attracted a lot of interest and has witnessed tremendous innovation in the past couple of years due to the continuous demand for greater performance by modern computing systems. This paper presents the design of Magnetic RAM (MRAM) and its implementation in memory based FIR filter. MRAM has obtained significant interest in the past decade due to its non-volatility property. MRAM stores data using spintronic device Magnetic Tunneling Junction (MTJ). In this paper a mathematical model is developed for MTJ using the LLG equation. A new configuration 3T2MTJ for MRAM is proposed and a dual port MRAM is designed using this configuration. A 9-tap FIR filter is constructed using the proposed dual port MRAM design. The performance of MRAM single cell, single and dual port multipliers and FIR filter is evaluated and compared with the conventional SRAM based system. It can be observed that dual port multiplier using MRAM reduces the power consumption by 77% as compared to SRAM based design and the power consumption in FIR filter is reduced by 37% by using MRAM in the place of SRAM.
Feature Reinforcement Network for Image Classification

Deep Learning has attracted much attention these years as it produces fabulous performance in various applications. Most researchers have mainly focused on improving and optimizing the network structure, e.g., deeper and deeper networks are constructed to extract high-level features from raw data. In this paper, we propose a two-wing deep convolutional network, called Feature Reinforcement Networks (FRN). One wing acts as the traditional operation in VGG, ResNet and DenseNet, while the other wing called feature reinforcement block (FRB) also conducts layer-wise convolution operations which share the convolution parameters of the former layer. Then, Relu function is employed in FRB to rectify the feature maps except the output layer. The outputs of these wings are integrated as the input of the next convolution layer. It is confirmed that the proposed FRN is more sensitive to the informative features. Our experiments on a few multimedia datasets prove FRN outperforms the original deep neural networks.
A Genre-Affect Relationship Network with Task-Specific Uncertainty Weighting foR Recognizing Induced Emotion in Music

Emotion is a core fundamental attribute of humans. Using music to induce emotional responses from subjects to better facilitate human behavior shaping have been effective across domains of health, education, and retail. Computationally model the musically-induced emotion provides necessary content-based analytics for large-scale and wide-applicability of such human-centered applications. In this work, we propose a relationship neural network architecture to learn to regress the induced emotion attributes with an auxiliary task of genre classification. Our proposed Genre-Affect Relationship Network with homoscedastic uncertainty weighting embeds the relationship between affect and genre as tensor normal prior within task-specific layers; the architecture is optimized further by incorporating task-specific uncertainty. The proposed architecture achieves a state-of-art 0.564 average Pearson correlation computed over nine induced emotion ratings in the Emotify database. Furthermore, we provide an analysis to understand the relationship between the induced emotions of these musical pieces and their associated genres.
Improved Analytical Model for Inductance Calculations of a Dual-Rotor Permanent Magnet Reluctance Machine Based on Magnetic Networks

This paper proposed a varying magnetic network modeling method of dual-rotor permanent magnet reluctance (DRPMR) machines based on magnetic circuits considering possible saturation and nonlinearity according to the physical structure, which is put forward to obtain the accurate inductance characteristics of the proposed machine. The feasibility of this analytical model is confirmed by the finite element method. Moreover, experiments are designed to validate the analytical results. It indicates that the analytical inductance characteristics of the DRPMR machine have good agreements with the experimental results.
SVD-Based Low-Complexity Hybrid Precoding for Millimeter-Wave MIMO Systems

For millimeter-wave systems, a low-complexity hybrid precoding method is proposed. Specifically, with the aid of the ordered singular value decomposition of channel matrix, we show that the radio frequency (RF) precoding matrix can be straightforwardly derived based on the right singular vectors (RSVs) of channel matrix by applying the phase angles of entries in the RSVs, and hence, the complicated optimization procedures used to search the near-optimum RF precoding matrix can be avoided. Furthermore, the antenna array response vectors at the transmitter are not required. Simulation results show that the proposed method can achieve the similar performance of that achieved by the fully digital optimum precoding method.
Hierarchical Dropped Convolutional Neural Network for Speed Insensitive Human Action Recognition

Human action recognition using skeleton data has lots of potential applications in content-based action retrieval and intelligent surveillance, with wide usage of depth sensors and robust skeleton estimation algorithms. Previous methods describe spatial temporal skeleton joints as a compact color image and then use Convolutional Neural Network (CNN) to extract more discriminative deep features. However, these methods ignore the effect of speed variation, which is a common phenomenon and can bring severe intra-varieties to same types of actions. To solve this problem, this paper presents a novel hierarchical dropped CNN architecture, which is constructed in two stages. Dropped CNN (d-CNN) is firstly developed to extract deep features from a probabilistic speed insensitive color image. This image expresses both spatial distributions and temporal evolutions of skeleton joints meanwhile avoids the effect of speed variations. To enhance the temporal discriminative power, we extend d-CNN to a hierarchical structure (h-CNN), where multiple scales of temporal information are encoded. Extensive experiments on benchmark MSRC-12 dataset and the largest NTU RGB+D dataset verify the effectiveness and robustness of the proposed method.
Performance Analysis of a PLL-Based Sensor-Less Control of Rotor-Tied DFIG Systems

In this paper, a position/speed sensor-less control strategy of a rotor-tied doubly-fed induction generator (DFIG) is proposed and its performance is analysed. The proposed sensorless control strategy is based on a slip speed/angle estimation technique. The proposed slip speed/angle estimation technique consists of a sliding mode observer (SMO) associated with a phase locked loop (PLL) estimator. The proposed SMO takes the three-phase stator current as state variable. The proposed PLL estimator includes a simple judgement function which allows the PLL to perform in super-synchronous operating conditions. The performance of the proposed sensor-less control strategy is validated through simulations using a 37.3-kW conventional DFIG connected in rotor-tied configuration and the experimental tests using a 5.5-kW custom-designed rotor-tied DFIG.
N‐acetylglucosaminyltransferase‐I as a novel regulator of epithelial‐mesenchymal transition

N‐Glycans are involved in numerous biologic processes, such as cell adhesion, migration, and invasion. To distinguish the functions of complex high‐mannose types of N‐glycans, we used the clustered, regularly interspaced, short palindromic repeats/Cas9 system to establish N‐acetylglucosaminyltransferase (GnT)‐I‐knockout (KO) cells. Loss of GnT‐I greatly induced cell‐cell adhesion and decreased cell migration. In addition, the expression levels of epithelial‐mesenchymal transition (EMT) markers such as α‐SMA, vimentin, and N‐cadherin were suppressed, whereas the expression of claudin‐1 was promoted, suggesting a mesenchymal‐epithelial transition‐like phenotype, an opposite process to the EMT, was occurred in the KO cells. The phosphorylation levels of Smad‐2, epidermal growth factor receptor, and integrin‐mediated focal adhesion kinase (FAK) were consistently suppressed. Furthermore, the restoration of GnT‐I in the KO cells suppressed the cell‐cell adhesion and augmented the expression of EMT markers as well as that of FAK activation. The expression levels of integrins were upregulated in the KO cells, although their functions were decreased, whereas their expression levels were downregulated in the rescued cells, which suggests a negative feedback loop between function and expression. Finally, we also found that the expression of GnT‐I was important for cell survival, resistance to cancer drugs, and increased colony formation. The results of the present study demonstrate that GnT‐I works as a switch to turn on/off EMT, which further supports the notion that on most surface receptors, the N‐glycans differentially play essential roles in biologic functions. —Zhang, G. , Isaji, T. , Xu, Z. , Lu, X. , Fukuda, T. , Gu, J. N‐acetylglucosaminyltransferase‐I as a novel regulator of epithelial‐mesenchymal transition. FASEB J. 33, 2823–2835 (2019). www.fasebj. org
Measurement Scale of Student Participation in Community Volunteerism

University students are important personal in making, they are the citizens who will contribute to the well-being of a community. Therefore, students need to be more responsible, care caring and participate involved in community activities. However, nowdaysnowadays students were are found to be less involved in volunteerism, especially at the community level. In order to solve the problem, we need to identify the indicators need to be identified in order to measure the participation behaviours, and acting further steps actions can be taken to improve their engagement. This study was conducted to develop a scale to measure the participation among university students, and testing the validation of the measurement. Conceptualizing of this study was suggested There are 10 indicators for the conceptualization of this study to measure the variable of participation in community volunteerism. This crosssectional survey by using questionnaires was applied distributed inon the east side of peninsular of Malaysia for to 150 respondents, and analyzedanalysed by the structural equation modelling (SEM) by AMOS. The suggested indicators were considered accepted as the measurement elements by observing on the regression weight for standard loading, average variance extracted (AVE) for convergent validity, square root of AVE for discriminant validity, composite reliability (CR) for reliability of items, and at least three fit indexes for model fitness. Finally, a measurement scale of volunteerism participation for students was successfully developed, and suitable to be used in areas of research, evaluation, training, campaign or any type of programme in order to improve students’ engagement with in community activities.
Cancer Pain Management: Opioid Analgesics, Part 2

Opioid analgesics are the cornerstone of moderate to severe cancer pain management, and do not have ceiling doses unless unmanageable adverse effects occur. Oral, short-acting pure μ agonists such as morphine are most frequently used, but other agents and administration formulations allow finding the right opioid and dose for most patients. In addition, clinicians must understand the metabolism, pharmacokinetics, and elimination of particular drugs to individualize opioid selection, select initial doses, and appropriately escalate doses to satisfactory pain relief or uncontrollable toxicity. Anticipation and proactive management of possible adverse effects, particularly constipation, confusion or delirium, opioid-specific adverse effects, and opioid abuse, are also integral to primary and secondary prophylaxis and management.
Infrastructure-Assisted Markov Prediction Routing Protocol for Urban Scenarios

It has been recognized the benefits that utilizing fixed Road Side Units (RSUs) as routing complements to Vehicular Ad-hoc Network (VANET) can improve reliable end to end multi-hop communications. This paper presents Infrastructure-assisted Markov Prediction Routing Protocol (IAMPR) for urban VANET scenarios. IAMPR formulates corresponding routing algorithms for vehicles and RSUs in order to exploit the available heterogeneous network efficiently. Store-and-forward buffering, Markov chains predictor and duplicate transmission techniques are also adopted in the protocol aiming at local minimum problem cause by greedy forwarding strategy. To better evaluate the performance of IAMPR, we complete simulation in NS-3 simulator under a real world topology urban scenario. Simulation results show that our proposed protocol outperforms Greedy V2I2V and GPSR in terms of packet delivery ratio and end to end delay under varying vehicles densities and speeds.
Mural2Sketch: A Combined Line Drawing Generation Method for Ancient Mural Painting

Line drawing is a unique drawing technique developed over millennia in China. Since ancient murals have line drawings of beautiful form and vast history, it is incredibly important to digitally curate these pieces. In this paper, we propose a line drawing generation method named Mural2Sketch (MS) for ancient mural paintings. MS first utilizes heuristic routing to detect the outer edge of a stroke, and then high frequency enhancement filtering is used to extract the information inside the stroke. A complete stroke is then generated by collaborative representation. MS is capable of outputting the result in vector form and producing different artistic styles. Experimental results show that our method is simple but effective. This research has the potential to support digital mural copying, mural protection, as well as related cultural research and application.
Bayesian Network Structure Learning with Side Constraints

Hybrid methods for Bayesian network structure learning that incorporate both observed data and expert knowledge have proven to be important in many fields. Previous studies have presented both exact and approximate hybrid methods for structure learning. In this paper, we propose an approximate method based on local search that is capable of efficiently handling a variety of prior knowledge constraints, including an important class of non-decomposable ancestral constraints that assert indirect causation between random variables. In our experiments, our proposed approximate method is able to significantly outperform an existing state-of-the-art approximate method in finding feasible solutions when hard constraints are imposed. Our approach is able to find nearoptimal networks while scaling up to almost fifty random variables. In contrast, previous exact methods are unable to handle more than twenty random variables. Furthermore, we show that when prior knowledge is integrated, we are often able to produce a network much closer to the ground truth network, particularly when the amount of data is limited.
Deep Learning Based Parking Prediction on Cloud Platform

With the explosive growth of the urban population, an increasing number of cities face the parking problem. Studies shows that more than 30% of the urban traffic is due to these "search berth" traffic, which aggravate the traffic congestion and create more traffic pollutants. The prediction for parking availability plays more and more important role in the urban traffic management system. However, existing parking prediction methods have lower accuracy or require a large number of data and computation cost, which make it difficult for parking prediction to be widely used. In this paper, we propose a deep learning based parking prediction system architecture on cloud platform. The LSTM network is used to predict the availability of parking space, which performs well in time series prediction. In order to improve the prediction accuracy, we comprehensively take more factors into account such as time of day, weather and holiday. In addition, we also propose an economical workflow based on the elastic computing service (ECS) provided by cloud platform. In the optimized workflow, the model training and updating processes need not to be in running state all the time, which can reduce avoidable computation and cost significantly. We implement the proposed system and deploy it on the Ali Could platform. Experimental results show that our prediction model based on LSTM network outperforms BP neural network, and our system has good scalability to apply to the cloud platform.
ReaderTrack: Reader-Book Interaction Reasoning Using RFID and Smartwatch

Online bookstores are capable of capturing readers preferences by analyzing click logs and transaction records, while physical bookstores or libraries still lack effective methods to gather reader behavioral data. Fortunately, the widespread use of mobile wearable devices and RFID technology opens up new possibilities for uncovering in-store experience. In this paper, we propose ReaderTrack, a system that integrates smartwatch and RFID to excavate interactions between readers and books. We first leverage inertial sensors of smartwatch and backscatter signals of RFID tags to infer reader behaviors and book motions, respectively. Then we associate readers with their corresponding books according to previously inferred behaviors and motions. We implement ReaderTrack with COTS devices and evaluate it extensively in our lab environment which mimics a typical reading room. Experimental results show the effectiveness and robustness of ReaderTrack in reader-book interaction reasoning.
Comparative Analysis of Single and Multistep Interferogram Processing Techniques

Digital processing of optical interferograms have been used for wide range of applications in engineering and scientific research. Some of the applications include measurement of step height, tilt angle, micro-displacement, deformation, etc. Two main interferogram processing techniques are phase shifting (PS) and Fourier Transform method (FTM). PS technique has been used to demodulate the phase information from multiple phase shifted interferograms; whereas FT is used to extract phase information from a single interferogram. In this paper, we perform a comparative analysis of PS and FTM technique using simulation analysis. The techniques are assessed based on two common problems, i.e. noise and non-sinusoidal waveforms (harmonics) that arise in the measurement system and reduce the accuracy. A new multi-step imaging technique is proposed by combining the advantages of PS and FTM and is also compared based on the same parameters. Advantages and drawbacks of each technique are also discussed.
The Research on Designs of Multiple Flow Tables in the OpenFlow Protocol

With the increase of network services, the scale of the flow table in the OpenFlow switch tends to increase rapidly, which makes it difficult to store flow tables in the limited memory of the switch, thus becoming a bottleneck of the SDN development. In order to reduce the storage space of internal flow tables in the SDN switch, this paper proposes two novel designs of multiple flow table based on the specific values in the matching field and logical relationship between matching fields.
An intronic genetic variation of MGMT affects enhancer activity and is associated with glioma susceptibility

Purpose O6-methylguanine-DNA methyltransferase (MGMT) plays a crucial role in repairing damaged DNA caused by alkylating agents. A number of cancer susceptibility loci have been recognized as enhancer variants. This study aimed to explore the significance of enhancer variants of MGMT in glioma susceptibility. Patients and methods A retrospective case-control study consisting of 150 glioma patients and 327 controls was conducted to test whether enhancer variants of MGMT are associated with glioma susceptibility. Genotypes were determined by Sequenom MassARRAY technology. Associations were estimated by logistic regression. Biochemical assays were used to examine the function of glioma susceptibility locus. Results We found that the A allele of rs10764901, an intronic variant of MGMT, was associated with a significantly decreased risk of glioma. The rs10764901 AA genotype carriers had an OR of 0.49 (95% CI, 0.24–0.98; P=0.045) compared with the rs10764901 GG genotype. When the rs10764901 AG and AA genotypes were pooled for analysis, a significantly decreased risk of glioma was also found (OR, 0.63; 95% CI, 0.43–0.93; P=0.021). Functional analyses showed that the rs10764901 A allele drove a lower luciferase expression and had higher transcription factor binding affinity than the G allele. Conclusion An enhancer variant of MGMT rs10764901 affects the regulatory activity of enhancer by altering the binding affinity of transcription factors and is associated with glioma susceptibility.
Implementation of 32-Bit Arithmetic Logic Unit on Xilinx using VHDL

In the present day knowledge, there is an massive requisite of developing appropriate data communication interfaces for real time embedded systems. Field Programmable Gate Array (FPGA) gives various means, which can be programmed for constructing an effective embedded unit. The FPGA configuration is generally specified using a hardware description language (HDL). VHDL (VHSIC hardware description language) is a hardware description language used in electronic design automation to explain digital and mixed-signal structures such as field programmable gate arrays (FPGA) and integrated circuits. This work proposes a technique to design and implement a 32 bit ALU which is a digital circuit that performs arithmetic and logical operations on Xilinx ISE using VHDL.