Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Physical Layer Security of a Regenerative Relay-Based mixed RF/UOWC

This paper investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. We consider a one-antenna source node ($S$), communicating with one legitimate destination node (D) via a multi-antenna DF relay (R) node. In this context, the relay node receives the incoming signal from $S$ via an RF link, which is subject to Rayleigh fading, then performes selection-combining (SC) followed by decoding and then re-encoding for transmission to the destination over a UOWC link, subject to mixture Exponential-Gamma fading. Under the assumption of eavesdroppers attempting to intercept the S-R (RF side), a closed-form expression for the secrecy outage probability is derived. Our analytical results are corroborated through computer simulations, which verifies their validity.
Data Pattern & Memory Variation Aware Fine-Grained ECC Optimized by Neural Network for 3D-TLC NAND Flash Memories with 2.0x Data-Retention Time Extension and 30% Parity Overhead Reduction

This paper proposes fine-grained ECC optimized by Neural Network (NN) for 3D-TLC NAND flash memories. Among 48 combinations of 8 reliability enhancement techniques (RETs) and 6 BCH ECCs, proposed NN automatically selects the optimal combination of RET and BCH ECC. Errors with complicated data pattern dependence and memory variations are adaptively compensated. In addition, the design trade-off between the high reliability and parity overhead (cost) is resolved. As a result, both high reliability and small parity overhead are realized simultaneously. The acceptable data-retention time increases by 2.0-times and the data overhead decreases by 30%.
Dynamic Topical Community Detection in Social Network: A Generative Model Approach

Social networks that are dynamic contain rich network structure and content information. In dynamic networks, it is necessary to discover communities and their topical meanings. However, existing methods either only discover communities with ignoring their topical meaning in dynamic networks, or they discover communities and their topics in static networks. In this paper, we identify the problem of dynamic topical community detection and propose a dynamic topical community detection (DTCD) method to detect communities and their topical meanings in dynamic networks. The DTCD is a generative model integrating network structure, text, and time. The DTCD considers a community as a mixture of topics and generates the neighbors and documents of the node and their time stamps at the same time via the community. The latent variables are learned by collapsed Gibbs sampling. The DTCD not only can find communities and their topics, but also capture the temporal variations of communities and topics. The experimental results on two real-world datasets demonstrate the effectiveness of DTCD.
A Circular Polarization patch array designed for a Sentinel-1 SAR transponder

This paper reports the design and preliminary test of a circular polarized patch array antenna operating in C band. The antenna was designed as part of a C band Active Corner reflector (ACR). The operating band is 5.405 GHz ± 50 MHz band, the same used by ESA Sentinel-1 SAR. The main requirements for the antenna system are: a simple design to assure an adequate phase stability, simple installation, and low cost, as requested for the proposed ACR.
10-Bit SAR ADC With Novel Pseudo-Random Capacitor Switching Scheme

This paper presents a pseudo-random capacitor switching scheme. Taking 1% of capacitor mismatch into account, after applying the pseudo-random switching scheme to the SAR ADC matlab behavioral model, for 500 Monte Carlo simulations, the missing code (Minimum DNL=-1) occurrence is reduced from 176 to 3, and the other performance indicators are also improved significantly. The circuit was fabricated by using 0.18-μm 1P6M TSMC CMOS process. With a sampling rate of 1KS/s and 50Hz input frequency, the measured SNDR and SFDR achieves 57.11dB and 75.37dB respectively while consuming a power of 7. 68μW. With a sampling rate of 48KS/s and 500Hz input frequency, the measured SNDR and SFDR achieves 56.91dB and 74.47dB respectively while consuming a power of 7. 93μW. With a sampling rate of 1MS/s and 50KHz input frequency, the measured SNDR and SFDR achieves 58.71dB and 77.89dB respectively while consuming a power of 102μW. The measured INL is 0.54/-0.78 LSB, and DNL is 0.67/-0.82 LSB. The circuit is applicable for various bio-medical applications.
Speed and Resource Optimization of BFGS Quasi-Newton Implementation on FPGA Using Inexact Line Search Method for Neural Network Training

Quasi-Newton (QN) method is one of the most effective Neural Network (NN) training methods. However, QN training often needs long time especially when the NN architecture is large. The BFGS-QN has been implemented on FPGA for accelerating the training process. The experimental results show that the line search module of BFGS-QN is the most timeconsuming module because of its frequent objective function evaluation. In order to solve the issue, an inexact line search method, Armijo-Goldstein (AG) method, is implemented to replace the original exact line search method-Golden Section (GS) method. For the highest training speed, an end-to-end FPGA version of BFGS using AG method is implemented. Moreover, the efficiency AG method makes it possible for hardware-software co-design. The objective function evalution unit in line search module which consumes the most computional resource is moved to CPU for a speed and resource tradeoff. The experimental results show that the end-to-end FPGA BFGS-AG implementation achieves up to 239 times speed up compared with software implementation. The FPGA+CPU BFGS-AG implementation is up to 153.1 times faster than the end-to-end software implementation and achieves up to 45% LUT, 29% FF and 64% DSP reduction.
Probe-Corrected Near-Field to Far-Field Transformation Using Multiple Spherical Wave Expansions

Near-field to far-field transformations constitute a powerful antenna characterization technique for near-field measurement scenarios. In this paper a near-field to far-field transformation technique based on multiple spherical wave expansions is presented. Thanks to its iterative matrix inversion nature, the approach performs the transformation of fields measured on arbitrary surfaces. Also, irregular sampling schemes can be incorporated. The proposed algorithm is based on modelling the antenna fields with not one, but several spherical wave expansions distributed over its geometry. Due to the high number of spherical wave expansions, their truncation number can be arbitrarily reduced. Working with expansions of low order allows to incorporate the probe correction in the transformation in a very simple way, accepting any type of probe and orientation. Only the probe far-field pattern is used, thus working with its full spherical wave expansion is avoided. The algorithm is validated using simulated field data as well as measurements of real antennas.
Location based Authentication Service using 4G/5G Devices

Mobile devices 4G/5G have become an important part of our day to day lives. Accessing internet applications has become much easier due to mobile phones. Mobile phones nowadays have been built with new technologies like location based services like GPS. Mobile phones which make it easy to access applications and are the gateway to authentication failure can be used to provide authentication and protect user’s private and confidential information. As mobiles have become important for our existence its important have them secured and have them authenticated. Location-based technique is one of the new technologies used to check authentication through smart phones. Authentication is one of the building blocks of security measures and mobile phones can compromise it. For example, if a phone is lost anyone can hack into the phone and get all the confidential information like bank account details and can use the credit card or debit card details. To avoid such scenarios location-based authentication services can be used to collect data and analyze the user’s movements and actions. As using internet applications through Smartphones 4G/5G devices has become such a rage, it is necessary to have few methods to help authenticate users.I intend to survey and give out all the methods which use location services to authenticate users and help in maintaining the security. In this paper, I would like to summarize the methods.
Using Decision Tree Classification Algorithm to Predict Learner Typologies for Project-Based Learning

As educational data in tertiary institutions are becoming huge, it is important to deploy Data Mining algorithms in discovering knowledge and improving academic quality. One fast course delivery approach or trend, constructivism in higher education is based on Learner prioritization in the learning process where a learner transforms information, constructs hypothesis and makes decisions using mental models. Similar learner groupings for project-based learning through hidden patterns extraction can aid Active Learning and Instructor Monitoring. In our previous paper, K-means clustering algorithm was used to group learners with similar scores in three assessments. In this paper, we built a classifier model using the J48 Decision Tree Algorithm for predicting learner groupings after getting class labels through the K-means clustering algorithm. This classifier will help in predicting future groupings of learners for the same course and attributes. The weka simulation for the classifier model gave a 99.9% ROC Area curve, which indicates a general performance of the model and a 96.19% of correctly classified instances. The Confusion Matrix has 80% of the members correctly classified. The classification model has an extremely low FP Rate of 2%, another indication of a high performance predictive classifier.
Curvelet Based Watermarking Of Multispectral Images And Its Effect On Classification Accuracy

In this paper, a robust invisible watermarking algorithm for satellite imagery using Curvelet Transform is proposed. Haralick Co-occurrence texture features [1] are used to identify the area for watermarking in the host image. Host image is tiled into smaller non-overlapping blocks. Based on the Haralick texture feature chosen, blocks with high value of chosen texture feature were selected for embedding. Thus, multiple watermarks are embedded in any given image. There are some unstable Curvelet coefficients so a little change of the image will result in a big change of these coefficients. These unstable factors can influence the extracting of watermark. Hence selection of position of embedding in the transformed domain plays a very important role in robustness of the embedding process. This algorithm encourages use of edges and curves for embedding watermarks. The experimental results show that watermark using proposed algorithm is robust against common attacks like Brightness, Contrast, Saturation, Tint adjustments, Low pass Filtering, JPEG Compression attack, Gaussian Noise attack and Laplacian Filtering, Symmetrical and Asymmetrical Image Cropping, Geometric Attacks like scaling and Rotation. Use of SURF features [2] too ensures robustness to geometric attacks. The effect of watermarking on the classification accuracy is also studied.
Attentive Autoencoder Matrix Factorization for Recommender Systems

In real applications, sparseness of user-item rating data significantly causes degrading in recommendation performance. There are several effective techniques which reduce prediction error by auxiliary information. However, these works focus on how to increase the ability of extracting features from a particular part rather than how to fuse features from different parts effectively. And the difference of Gaussian noise is ignored in modeling latent factors of both users and items. In this paper, to address these issues, we propose attentive autoencoder(AAE) to effectively extract features from several different information sources, which is integrated into improved probabilistic matrix factorization (PMF). The improved PMF consider the difference of Gaussian noise between both users and items. We perform extensive experiments on three real-world datasets, and the result shows that our AAEMF significantly outperforms the state-of-the-art models.
Electronically Reconfigurable Unit-Cell and Transmitarray in Dual-Linear Polarization at Ka-Band

This paper presents a multilayer reconfigurable unit-cell operating at Ka-band in dual-linear polarization. It consists of a passive narrow patch and active O-slotted rectangular patch loaded by two p-i-n. diodes. These patches are connected by a metallized via-hole, and the DC bias network is routed in one intermediate layer. This unit-cell exhibits a 1-bit phase resolution for an insertion loss below 1-dB at 29.5 GHz. The interleaving technique is applied to design a dual-polarized transmitarray with independent radiation characteristics between horizontal and vertical polarizations. The -1-dB transmission bandwidth of the unit-cell reaches 7.5 % around 29.5 GHz for both polarizations.
An FPGA Implementation of Robust Matting

Matting is a process of extracting a foreground from background in an image. It is one of the key techniques in many image editing, and many algorithms have been proposed. Robust Matting is one of the powerful matting algorithms. In the Robust Matting, first, a set of pixels in the foreground and background are sampled, and then, for each pixel in the image, its category is determined by comparing it to a number of pairs of a foreground and background sample. Its computational complexity is very high because of a large number of multiply and square operations. In this paper, we propose an FPGA implementation for real-time processing of HD images. In our approach, the image is divided into small blocks and the same pairs of the foreground and background pixels are used for the pixels in the same block in order to reduce the number of the operations. This approach makes it possible to execute multiply operations by simply looking up tables that are dynamically updated block by block.
Modeling of Nonlinear Oscillations of Doped Lamé-mode MEMS Silicon Resonator

Doped silicon MEMS resonators provide an attractive alternate to quartz for frequency reference applications with promising frequency stability, high Q-factor, lower cost and lower size. However, lower size and high Q-factor requires operating with large deformations and higher power, which often induces nonlinear regimes and nonlinear frequency shifts. In this paper, we present an analytical modeling approach to capture the nonlinear dynamics by predicting the nonlinear elastic constants of doped Lame-mode silicon resonator. We investigated the impact of various doping level on both linear and nonlinear frequency response of a single crystal resonator in Lame-mode.
Novel Approach for Designing Broadband Slot Antennas

This paper presents a novel approach for designing broadband slot antennas. The proposed technique involves gradually incrementing the slot length along the propagation direction in order to provide a smoother impedance transition between the waveguide and air. Numerical and experimental results demonstrate up to 52.3% of bandwidth improvement by employing our approach in slotted-waveguide antenna arrays. The technique presents itself as a promising solution for 5G enhanced mobile broadband (eMBB) communications.
Pulsed EM Field Radio: The Low-Power, Ultra-fast Bridge to Ubiquitous Fiber Networks

The suitability of pulsed electromagnetic (EM) field radio as a low-power, ultra-fast instrument for bridging the gap between mobile terminals and ubiquitously deployed fiber-optic networks is investigated. An in-depth study of the present trends in fifth generation (5G) mobile communications strategies is carried out, by highlighting the crucial role of Terahertz (THz) and ‘low-GHz’ ultra-wideband (UWB) radio implementations. Upon recognising the low-power capabilities of ‘low-GHz’ UWB solutions, the contribution zooms in on the pulsed EM field transfer and on the features of a monocycle pulse generator that is deemed conditional for future physical realisations of the concept.
V-Band Beam-Switching Transmitarray Antenna for 5G MIMO Channel Sounding

Beam steering antennas are attractive solutions for beamforming channel sounding applications and multi-beam massive multiple-input multiple-output (M-MIMO) systems. The antenna proposed here consists of a transmitarray antenna fed by a patch antenna array operating in V-band (57 – 66 GHz). The transmitarray is made in PCB technology with 8-phase state unit-cells. The focal array is a four-patch array excited by a standard microstrip corporate feed network. The simulation results demonstrate a wide scan range (72◦) with a peak gain of 22.9 dBi at broadside and with scan loss of about 4.2 dB in E-plane and H-plane for the most offset beams.
Adaptive Encoder Settings for Interactive Remote Visualisation on High-Resolution Displays

We present an approach that dynamically adapts encoder settings for image tiles to yield the best possible quality for a given bandwidth. This reduces the overall size of the image while preserving details. Our application determines the encoding settings in two steps. In the first step, we predict the quality and size of the tiles for different encoding settings using a convolutional neural network. In the second step, we assign the optimal encoder setting to each tile, so that the overall size of the image is lower than a predetermined threshold. Commonly, for tiles that contain complicated structures, a high quality setting is used in order to prevent major information loss, while quality settings are lowered for others to keep the size below the threshold. We demonstrate that we can reduce the overall size of the image while preserving the details in areas of interest using the example of both particle and volume visualisation applications.
A Review of Semantic Segmentation Based on Context Information

Semantic image segmentation, which is one of the basic tasks in computer vision, is the basis of instance segmentation. It predicts the dense labeling of each pixel in an image and is a very important task that helps us to understand the application scenario. This paper mainly studies the fully convolutional network (FCN) of end-to-end training in semantic segmentation. At the same time, it analyzes the problem that FCN can not handle the context well and the segmentation accuracy is low. There summarize the classical methods which are Conditional Random Fields(CRF), dilated convolutions, Multi-scale feature fusion and deconvolution networks, analyze their performance of semantic segmentation algorithm and explore the possible research directions in the future.
Hybrid Control of Networked Battery Systems

Battery systems are inherently networked systems, in which battery cells or packs are grouped by serial and parallel connections to meet power and capacity specifications. In real-time operation, battery packs must be controlled collaboratively so that their capacities can be fully utilized. By employing both analog charge/discharge currents and discrete-event bypass switches in networked battery systems, this paper develops a new hybrid control methodology to control the states of charge (SOCs) and optimize performance. The paper first establishes necessary and sufficient conditions for controllability under hybrid control mechanism. These conditions are then used to derive control strategies to achieve coordinated SOC control, state consensus, and performance optimization. Control algorithms are developed, their convergence properties proved, and optimality established. Simulation examples are used to demonstrate the methods and their performances using a production battery system.
Joint Received Signal Strength, Angle-of-Arrival, and Time-of-Flight Positioning

This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters.
Citation Network Visualization of Reference Papers Based on Influence Groups

Figure1:Citation Network Visualization of Reference Papers based on Influence Groups. In the circular main view (a), the research paper published according to the year can be identified by the node (b), and the size of the node represents the influence of the paper. The red node (c) is the bridge research paper that links the paper to the paper. If you click the node, you can check the detail information of the research paper, the number of citations and the ego network in the subview (d). (http://13.124.91.164)Survey of relevant research papers are very important in academic research to fully understanding the research trends in a given field of interest. Many researchers use keyword searches to investigate their related research. However, it is often difficult to find the desired research papers only through a keyword search. Here we propose a citation network visualization technique that constructs the genealogy of a research paper. It allows the user to trace the genealogy of the reference papers that form the basis of their research via large citation data sets, while also providing a visualization of how the research paper influences its research field.
Improved Edge Detection Algorithm of High-Resolution Remote Sensing Images based on Fast Guided Filter

In order to achieve fast and accurate extraction of edge and contour features of remote sensing images, a high-resolution remote sensing image edge detection algorithm based on fast guided filtering is proposed in this paper. The algorithm overcomes the disadvantage of losing a large number of edge information when the traditional Canny operator uses Gauss filter to smooth the image. We use fast guided filter instead of Gauss filter to reduce the loss of image edge information and keep the edge information of the image. Then the improved Sobel operator is used to accurately calculate the gradient and direction of the gradient. Finally, the edge of the remote sensing image is detected by the Canny operator. Compared with the previous edge detection methods, the edge detection contour information is more obvious and the pseudo edge is significantly reduced.
Comparison of coaxial open-ended probe based dielectric measurements on ex-vivo thermally ablated liver tissue

This paper compares an in-house dielectric properties measurement system based on the "Stuchly and Stuchly" method with the broadly commercialised Keysight setup and existing literature for the characterization of ablated and non-ablated liver tissue. Measurements were carried out on ex-vivo ovine liver tissue across the frequency range 0.5 – 4.5 GHz. Results show that the in-house "Stuchly & Stuchly" method using calibration standards such as deionised water, methanol and open-circuit conditions, offers comparable performance to the proprietary Keysight system. Moreover, results are also comparable to measurements at 2.45 GHz documented in the literature. Being the in-house system suitable for portable setups as reported in the literature, this study shows that measurement of dielectric properties of tissue can be performed even during the thermal ablation procedure.