Miracle-dz/UniFAR-FTUs huggingface dataset:
Compact Microstrip Lowpass Filter with Ultra-wide Stopband and Sharp Roll- off based on Modified Hairpin Resonator

Saeedeh Lotfi et al. / International Journal of Engineering & Technology Sciences (IJETS) 2 (2): 109121, 2014 110 | P a g e stopband. In [5], a sharp response microstrip LPF with high selectivity and wide stopband using folded stepped impedance open stubs is proposed that comprises two lateral folded open stubs and a central mirrored semicircle ended suppressing cell. A compact LPF using T-shaped resonator with a wide stopband is presented in [6]. In this paper, a new microstrip LPF with compact size, sharp rolloff and ultra-wide stopband based on modified hairpin resonator is presented and implemented. The proposed filter with 3 dB Cut-off frequency at 1.77 GHz has been designed, fabricated and measured. Results indicate that the designed LPF achieves an ultra-wide stopband with overall better than 20 dB attenuation level and a sharp roll-off of 185 dB/GHz. 2. Filter Design 2.1. Main resonator and its characteristics The layout of the proposed main resonator is demonstrated in Figure 1, which is composed of three types of resonators, i.e. resonators 1, 2 and 3. To describe the design theory of the proposed main resonator, the frequency response and proposed LC model of each part has been studied one by one. Resonator 1 Resonator 3 Resonator 2 Fig. 1: Schematic diagram of the proposed main resonator. Figure 2(a) depicts the layout of the resonator 1. As seen; this resonator has a hairpin shape structure and is composed of two main parts: low and high impedance. Parameters of the resonator 1 are as follows: d1 = 6.29 mm, d2 = 7.64 mm, d3 = 8.14 mm, d4 = 4.86 mm, d5 = 4.80 mm, d6 = 4.41 mm, d7 = 1.05 mm, d8 = 2.20 mm, d9 = 0.22 mm, d10 = 0.15 mm, d11 = 0.10 mm. This resonator can create one deep transmission zero at 4.22 GHz with 73.85 dB attenuation which is the first transmission zero as seen in Figure 2(b). Saeedeh Lotfi et al. / International Journal of Engineering & Technology Sciences (IJETS) 2 (2): 109121, 2014 111 | P a g e d1 d2 d3 d4 d5 d6 d8 d9 d10 d11 d7 d9 d8 (a) (b) 0 3 6 9 12 15 18 21 24 27 -70 -60 -50 -40 -30 -20 -10 0 Frequency (GHz) M a g n it u d e ( d B ) |S11| |S21| Fig. 2: The resonator 1, (a) Schematic diagram. (b) Simulated S-parameters. The proposed LC model of the resonator 1 is depicted in Figure 3(a), [4]. Due to symmetry of the resonator’s structure, the half circuit LC analysis is used. In this model the parameters: (L4, L5 and C5), (L2, L3 and C3) and (L1 and C1) are the central transmission lines LC model, C2 and C4 are the capacitance effects of the bends. Moreover, C6 is the capacitance effect of the open subs with the width of d8. L6, also denotes inductance of the stubs with the width of d7 and C7 is the gap’s capacitance effect which is shown by d9. Saeedeh Lotfi et al. / International Journal of Engineering & Technology Sciences (IJETS) 2 (2): 109121, 2014
Modern Technique Of Lecture Attendance Using Face Recognition.

A B S T R A C T Attendance management of students in any institution is a very lengthy process and even time consuming. Furthermore, biometrics attendance system is also available. These methods too are time consuming, since each time students have to form a queue for scanning their thumb impression. In this paper, we have proposed a system which deals in terms of face recognition using real time camera. The work presented in this paper proposes a method to automatically take the attendance of student using face recognition. Continuous observation improves the precision of attendance. The attendance will be recorded by using camera(s), attached in front of the class which is continuously capturing images of the students. It will then compare the faces with the student’s database and marks the attendance. The important key of this paper is to design a better student attendance system with ease interface and accurate results. Regarding to the student and lecturer sides, the system is working without any preparation and no extra effort and the most important thing is to increase the quality of our educational system.
Integrated Deadenylase Genetic Association Network and Transcriptome Analysis in Thoracic Carcinomas

The poly(A) tail at the 3′ end of mRNAs determines their stability, translational efficiency, and fate. The shortening of the poly(A) tail, and its efficient removal, triggers the degradation of mRNAs, thus, regulating gene expression. The process is catalyzed by a family of enzymes, known as deadenylases. As the dysregulation of gene expression is a hallmark of cancer, understanding the role of deadenylases has gained additional interest. Herein, the genetic association network shows that CNOT6 and CNOT7 are the most prevalent and most interconnected nodes in the equilibrated diagram. Subsequent silencing and transcriptomic analysis identifies transcripts possibly regulated by specific deadenylases. Furthermore, several gene ontologies are enriched by common deregulated genes. Given the potential concerted action and overlapping functions of deadenylases, we examined the effect of silencing a deadenylase on the remaining ones. Our results suggest that specific deadenylases target unique subsets of mRNAs, whilst at the same time, multiple deadenylases may affect the same mRNAs with overlapping functions.
A Switched Adaptive Controller for Robotic Gripping of Novel Objects With Minimal Force

We present the design and implementation of an algorithm, equipped with a switched adaptive controller, for grasping unknown objects using a robot gripper. A Lyapunov-based analysis demonstrates that the switching controller is indeed asymptotically stable with both the translational and rotational slip velocities converging to the origin. Experimental results using a novel sensorized gripper prototype and objects of different sizes, shapes, and weights show that the proposed algorithm not only ensures the prevention of slippage of the grasped objects, but is also able to apply the minimal force needed to safely grasp these objects without causing excessive deformation.
Multicriteria mapping manual - version 2.0

Multicriteria mapping (MCM) is an interactive appraisal method for exploring contrasting perspectives on complex strategic and policy issues. The tool aims to help 'open up' technical assessment by systematically 'mapping' the practical implications of alternative options, issues, uncertainties and values. MCM uniquely bridges qualitative and quantitative approaches, and helps to get the best of both. It allows great flexibility, offering an appraisal method that is strongly grounded in theory but highly unconstrained in practice. This versatility has been reflected in its use internationally to explore contentious decisions in the fields of energy strategy, food production, environmental policy, radioactive waste management and public health. This MCM Manual offers basic advice on how to do multicriteria mapping. It suggests how to: design and build a typical MCM project; engage with participants; and analyse results – and get the most out of the online MCM tool. The online MCM software tool provides its own operational help so the manual is more focused on the general approach.
Demanding on Many Dimensions: Validating the Interactivity-As-Demand
 Measurement Model for VR-Based Video Games

Video games represent exemplar interactive experiences, and this direct user control over the form and content of on-screen information is one of many reasons that games are such enjoyable and meaningful experiences for players. However, interactivity also requires players to serve as co-authors of the experience, which brings with it certain demands on player’s limited attentional resources. Previous research has found evidence for at least four such demands: cognitive, emotional, physical (subdivided into controller intuitiveness and physical exertional) and social demands. Taken together, this five-factor structure is presented as the interactivity-as-demand model and has been used in a variety of cross-cultural contexts (among English-, German-, and Mandarin Chinese-speaking populations). These five dimensions explain unique variance in focal interactive user experiences but have yet to be applied to VR-based applications such as VR-based video games. Results on N = 144 VR video gamers confirmed the a priori factor structure of the Video Game Demand Scale (VDGS) held when applied to VR-based video games. More importantly, discrete VGDS dimensions predicted relevant VR outcomes: spatial presence was higher when VR controls were more intuitive (less demanding) and social presence was higher when experiences were more emotionally socially demanding. Likely due to the centrality of natural user interfaces and broader body movement in VR games, perceptions of physical demand (both controller demand and physical exertion) played a more central role on several entertainment outcomes. Non-gaming and social VR use was rare, hindering broader comparisons.
Effects of flushing and various doses of β-Carotene and vitamin E applications on the reproductive performance of ewes

The aim of this study was to determine whether fertility could be enhanced in Kıvırcık ewes during the anoestrous period by flushing or by administering an injectable (Ovostim) containing β-Carotene and vitamin E. Three-year-old Kıvırcık ewes (n = 99) were used. The ewes were randomly divided into four groups: flushing, a single dose of Ovostim (5 ml/ewe) before the breeding season, a dose of Ovostim 10 days before the breeding season and a second dose at its start, and an untreated control. Results showed that pregnancy rate was higher in the treated groups than in the control (P <0.001). The single dose of Ovostim produced more multiple births (72%) and the highest litter size (1.64) compared with the other treatments. Although the double-dose of Ovostim influenced the ewes in the anoestrous period to the point of becoming pregnant, it failed to favourably affect the number of lambs born per ewe lambing. In conclusion, flushing the ewes or administering a single dose of Ovostim was shown to be advantageous in comparison with untreated ewes or those given two doses of Ovostim.
Multilevel Scattering Center and Deep Feature Fusion Learning Framework for SAR Target Recognition

In synthetic aperture radar (SAR) automatic target recognition (ATR), there are mainly two types of methods: the physics-driven model and the data-driven network. The physics-driven model can exploit electromagnetic theory to obtain physical properties, while the data-driven network will extract deep discriminant features of targets. These two types of features represent the target characteristics in the scattering domain and the image domain, respectively. However, the representation discrepancy caused by the different modalities between them hinders the further comprehensive utilization and fusion of both features. In order to take full advantage of physical knowledge and deep discriminant feature for SAR ATR, we propose a new feature fusion learning framework SDF-Net to combine scattering and deep image features. In this work, we treat the attributed scattering centers (ASC) as set-data instead of multiple individual points, which can well mine the topological interaction among scatterers. Then, multiregion multiscale subsets are constructed at both component and target levels. To be specific, the most significant scattering intensity and overall representation in these subsets are exploited successively to learn permutation-invariant scattering features according to a set-oriented deep network. The scattering representations can provide mid-level semantic and structural features that are subsequently fused with the complementary deep image features to yield an end-to-end high-level feature learning framework, which helps enhance the generalization ability of networks especially under complex observation conditions. Extensive experiments on the Moving and Stationary Target Acquisition and Recognition database verify the effectiveness and robustness of the SDF-Net compared against both typical SAR ATR networks and ASC-based models.
A two‐stage deep learning framework for image‐based android malware detection and variant classification

With the popularity of the internet and smartphones, malware on smartphones has increased dramatically. In addition, the ubiquity and openness of the Android operating system have made it a lucrative platform for cybercriminals to develop malware. Traditional malware detection techniques require a lot of time and manual effort to classify malware accurately. Recently, deep learning (DL) based malware detection and classification techniques have been developed to solve this issue. This article proposes a DL‐based two‐stage framework that detects Android malware and classifies its variants using image‐based malware representations of the Android DEX files. The framework uses the EfficientNetB0 convolutional neural network (CNN) to extracts relevant features from the malware color images. The extracted features are then passed through a global average pooling layer and fed into a stacking classifier. The stacking classifier employs linear support vector machine (SVM) and random forest (RF) algorithms as base‐level classifiers and logistic regression as the meta‐level classifier. This method obtained an accuracy of 100% in the binary classification of Android malware images and a 92.9% accuracy in 5‐class (Adsware, Adware + Adware, Clicker + Trojan, Spyware, and Benign) classification, and an 88.6% accuracy in 4‐class (Adsware, Adware + Adware, Clicker + Trojan, and Spyware) classification. We compared our method with 26 state‐of‐the‐art pretrained CNN models (including the original EfficientNetB0) and large‐scale learning classifiers such as EfficientNetB0‐SVM and EfficientNetB0‐RF. The proposed framework outperformed the compared methods in all performance metrics. Experiments also demonstrate that substituting the softmax layer of CNNs with a large‐scale learning classifier or stacking classifier results in an enhanced performance over the original network.
Characterization of a Plain Broadband Textile PIFA

Bandwidth characteristic of a wearable antenna is one of the major factors in determining its usability on the human body. In this work, a planar inverted-F antenna (PIFA) structure is proposed to achieve a large bandwidth to avoid serious antenna reflection coefficient detuning when placed in proximity of the body. The proposed structure is designed based on a simple structure, in order to provide practicality in application and maintain fabrication simplicity. Two different types of conductive textiles, namely Pure Copper Polyester Taffeta Fabric (PCPTF) and ShieldIt, are used in order to proof its concept, in comparison with a metallic antenna made from copper foil. The design is spaced and fabricated using a 6 mm thick fleece fabric. To cater for potential fabrication and material measurement inaccuracies, both antennas' performance are also investigated and analyzed with varying physical and material parameters. From this investigation, it is found that the proposed structure's extended bandwidth enabled the antenna to function with satisfactory onbody reflection coefficients, despite unavoidable gain and efficiency reduction.
The Impact of Information and Communication Technology on Marketing Performance:

Technology advancement has supported the market performance around the world even in the developing world. Moreover, firms in the market seek finding best means to reach to customers and also develop their business gradually and efficiently. This study is a quantitative study, the survey aims to identify and clarify how information and communication technology affect marketing performance at Majid Mall in Sulaimania. The survey has close-ended question, seeking the answers of the questions aimed at examining all of the hypotheses tested in this study. The research community in this study consisted of employees at Majid mall supermarket in Sulemani. This survey is descriptive and correlational. This survey was a random sampling for employees in Majid mall, in which 120 participants from the administrative staff of the mall have taken part voluntarily. For analyzing the collected empirical data the researchers used the software package for statistical analysis SPSS V.21 and for testing all hypotheses through the regression method. The results showed that the statistical process of hypothesis testing has revealed the remarkable role of ICT based on using (network, database, and software) is the greatest significance where (Beta= 0.366, Sig= 0.004) it positively and straightway retreats on marketing performance. The researchers recommend improving ICT facilities in Majid mall shopping for the sake of improving their market performance in the long-run. Moreover, it supports employee's development in terms of quantity, quality, and promotes the ICT culture through the formulation and implementation of more frequent technology-specific training programs for their staff.
An Ameliorated Multiattack Network Anomaly Detection in Distributed Big Data System-Based Enhanced Stacking Multiple Binary Classifiers

The growth of the Internet of Things (IoT) generates new processing, networking infrastructure, data storage, and management capabilities. This massive volume of data may be used to provide high-value information for decision support and data-intensive science research, etc. However, owing to the nature of IoT in distribution, virtualisation, cloud integration, and internet connectivity, the IoT environment is prone to various cyber-attacks and security issues. Hence, the increasing frequency and potency of recent attacks and constantly evolving attack vectors necessitate the development of improved detection methods. Therefore, this study proposes a distributed computing-based security model to safeguard big data systems. The proposed ensemble multi binary attack model (EMBAM) is an intrusion detection system (IDS) that offers a unique anomaly based IDS to detect normal behaviour and abnormal attack(s), for example, threats in a network. EMBAM ensembles multiple binary classifiers into a single model through stacking. The core binary model is a decision tree classifier with hyperparameters optimised using the grid search method. The use of multiple binary classifiers allows each binary classifier to adopt the limitations of the others. Empirical analysis of the experimental profile of the EMBAM has been discussed with eight-plus state-of-the-art methods using performance metrics, such as accuracy, detection rate, precision, specificity, false alarm rate, and F1-score. EMBAM can recognise multiple attack types as a star plug and play advantageous in a highly dynamic scheme. The proposed approach outperforms existing approaches on the UNSW-NB15 dataset and yields competitive results on the CICIDS2017 dataset.
Improved Configuration of Multilevel Inverter with Reduced Semiconductor Devices

Abstract In DC/AC power conversion, multi-level inverter (MLI) incorporates a large number of semiconductor devices, which increases its cost and complexity. Moreover, the recently introduced topology uses a large variety of semiconductor switches. Therefore, a great attention is paid toward increasing output voltage levels with less variety and reduced number of switches as compared to conventional topologies. In this paper, improved configuration of symmetrical and asymmetrical MLI is proposed. Improvement is brought on both quantitative and qualitative basis, which led to a reduction in number of semiconductor devices and its variety. Analysis of power losses of the proposed topology is carried out and compared with CHB. A wide range of comparison with recently proposed topologies is made in order to show the novelty and contribution of the proposed topology. Multi-Carrier Pulse Width Modulation strategy is adopted for generating the switching pulses. The detailed simulation study of the proposed topology has been carried out using MATLAB/SIMULINK and validated experimentally for 7-level and 81-level inverter.
Stability Analysis of 6t and Schmitt Trigger Based SRAM Cell

Due to wide-spread use of electronic devices, embedded memories play a significant role in modern era. Due to fast and random access, SRAM is mainly used as a cache memory. But in low power VLSI, Stability degrades due to technology scaling and fluctuations in process parameters that is oxide thickness, diffusion depth and density of impurity concentration etc Thus to measure stability, static noise margin is the main performance parameter in SRAM cell. In this paper, we present two SRAM topologies, conventional 6t and Schmitt Trigger based SRAM bit cell. These two topologies are characterized and compared on the basis of their Read and Write SNM with dynamic power supply. The tool used for simulation purpose is IC station by Mentor Graphics using TSMC 180nm CMOS technology.
Direct dynamic analysis of steel braced frame constructions

Aseismic design of steel frame constructions defines usage of capable mechanical and geometrical material characteristics during the seismic action with controlled energy dissipation. This paper presents dynamic nonlinear time history analysis of several steel frame models with comparison of results. The accent is pointed on brace application with different shape which are placed concentrically and eccentrically wit aim to investigate construction response and make conclusions about better energy dissipation of models. In this analysis accelerograms of two earthquakes NorthRidge and Kobe were used, which caused great damages in steel constructions.
Large scale integration of CVD-graphene based NEMS with narrow distribution of resonance parameters

We present a novel method for the fabrication of the arrays of suspended micron-sized membranes, based on monolayer pulsed-CVD graphene. Such devices are the source of an efficient integration of graphene nano-electro-mechanical resonators, compatible with production at the wafer scale using standard photolithography and processing tools. As the graphene surface is continuously protected by the same polymer layer during the whole process, suspended graphene membranes are clean and free of imperfections such as deposits, wrinkles and tears. Batch fabrication of 100 μm-long multi-connected suspended ribbons is presented. At room temperature, mechanical resonance of electrostatically-actuated devices show narrow distribution of their characteristic parameters with high quality factor and low effective mass and resonance frequencies, as expected for low stress and adsorbate-free membranes. Upon cooling, a sharp increase of both resonant frequency and quality factor is observed, enabling to extract the thermal expansion coefficient of CVD graphene. Comparison with state-of-the-art graphene NEMS is presented.