Miracle-dz/UniFAR-FTUs huggingface dataset:
Acceleration of ART Algorithm on an FPGA Board with Xilinx SDAccel

Radiation transfer (RT) is an important physical processes of energy transfer in the form of electromagnetic wave, and ART method is a ray-tracing for computing RT of re-connected photons in the simulation of protogalaxies formation. It requires a heavy computational power and complicated memory accesses which are not suitable to acceleration with GPUs. Here, for the future use of FPGA-in-Cloud, the acceleration of ART method with SDAccel on Xilinx's cost efficient FPGA board KCU1500 was tried. Although the execution time was not reduced compared with the host CPU with various combination of derivatives, the energy consumption was reduced to 1/3.35.
Convergence of Satellite and Terrestrial Networks: A Comprehensive Survey

The explosive growth of various services boosts the innovation and development in terrestrial communication systems for the implementation of the next generation mobile communication networks. However, simply utilizing limited resources in terrestrial communication networks is difficult to support the massive quality of service (QoS) aware requirements and it is hard to guarantee seamless coverage in far remote regions. Leveraging the intrinsic merits of high altitude and the ability of multicasting or broadcasting, satellite communication systems provide an opportunity for novel mobile communication networks with its tight interaction and complementary characteristics to traditional terrestrial networks. It is believed that the convergence of satellite and terrestrial networks can solve the problems existing in current mobile communication systems and make a profound effect on global information dissemination. In this paper, we make a comprehensive survey on the convergence of satellite and terrestrial networks. First, motivations and requirements of satellite-terrestrial network convergence are identified. Then, we summarize related architectures of existing literature, classify the taxonomy of researches on satellite-terrestrial networks, and present the performance evaluation works in different satellite-terrestrial networks. After that, the state-of-the-art of standardization, projects and the key application areas of satellite-terrestrial networks are also reviewed. Finally, we conclude the survey by highlighting the open issues and future directions.
Design and Analysis of Dual-Band Microstrip Patch Antenna Array for 5G Cellular Communication Networks with Improved Radiation Characteristics

With the advancement of world to 5th Generation cellular communication system presently commercial implementation of 5G in USA, UK, Switzerland, South Korea and Spain a space for improvement of radiation characteristics is always there for Antenna to accommodate the high gain demands of 5G. In this paper the design and simulation of compact, millimeter wave antenna array for 5G wireless communication network is proposed operating at 30 GHz and 50 GHz. Ka band from 26GHz to 42 GHz and V band from 40GHz to 75GHz need dual band frequency for its application from a single antenna. A conventional patch antenna with dimension 3×3 mm2 is designed on Rogers RT5880LZ substrate material with thickness of 0.787 mm. Two Rectangular and one circular slot is made in the patch to attain another band at 50 GHz frequency. In this proposed antenna array configuration of 2×1 met the requirement s of improved radiation characteristics. In this proposed antenna array configuration meets the commercial demands such as 75 ohm coax cable and substrate material with thickness of 0.787mm. The realized gain of 10.2 dB & 11.6 dB and return loss value of −12.5 & −14.85 at 30 GHz and 50 GHz respectively is achieved. The bandwidth is 2.093 GHz and 1.764 GHz at 30GHz and 50GHz Respectively. The VSWR value measured from simulation is 1.67 and 1.43 at dual bands frequency which is less than 2. The proposed antenna is fed by corporate feed network technique.
A 8-b 1GS/s 2b/cycle SAR ADC in 28-nm CMOS

This paper presents a high-speed 2b/cycle SAR ADC. By resolving their comparison time with a 1-bit TDC, the number of comparators are reduced to two instead of three in a conventional 2b/cycle SAR ADC, which benefits power and linearity simultaneously. The proposed capacitor-halving switching method can realize the same input common-mode (CM) voltage variations on two comparators during the conversion phase. A simple novel high-speed dynamic SAR logic is developed to achieve the direct and fast control from the comparator to DAC arrays. Moreover, a simplified double-tail comparator is presented to accelerate the voltage comparisons. A 8-b prototype ADC is designed in 28-nm CMOS process, operating at a 1-V supply voltage. The simulated (including noise) results show that under a 1-GS/s sampling rate, the ADC achieves a SFDR of 63.4 dB and a SNDR of 46.5 dB at Nyquist frequency and consumes 2.2-mW power, leading to a Walden FoM of 12.8 fJ/conv. -step.
A Novel Algorithm for Top- k Community Detection In Dynamic Social Networks

In dynamic informal networks change the information prevailing inside them in perspective with time. An approach to distinguish the information existed in each nodes was achieved by recognizing the subnetworks or Communities inside the network through Community-Based models. To increase the utility capacity also estimated the degree to networks that are founded. The information about the networks will be not known and consequently the nature of the topology and instability data inside a Dynamic Social Network(DSN). It maximized the cost of finding the communities from the large networks and to reduce the cost expenditure in finding the communities; a novel greedy based approach has been proposed. The K values determine the total number of communities that are to be formed. The Network-based model foresee the individuals from the networks proposed strategy will likewise distinguish the network -based model that can catch the qualities of basic opening spanners productively and decline the connection upkeep cost in the extensive scale systems. An decrease in the cost of finding the optimal communities in comparison with the existing hierarchical and Clique census community detection approaches was achieved by the Top-k greedy approach with an optimal cost percent of 42.6.
AMVG: Adaptive Malware Variant Generation Framework Using Machine Learning

There are advances in detecting malware using machine learning (ML), but it is still a challenging task to detect advanced malware variants (e.g., polymorphic and metamorphic variations). To detect such variants, we first need to understand the methods used to generate them to bypass the detection methods. In this paper, we introduce an adaptive malware variant generation (AMVG) framework to study bypassing malware detection methods efficiently. The AMVG framework uses ML (e.g., genetic algorithm (GA)) to generate malware variants that satisfy specific detection criteria. The use of GA automates the malware variant generations with appropriate modules to handle various input formats. For the experiment, we use malware samples retrieved from theZoo, a collection of malware samples. The results show that we can automatically generate malware variants that satisfy varying detection criteria in a practical amount of time, as well as showing the capabilities to handle different input formats.
Research on Blind Equalization Algorithm of Multipath Interference PCM-FM Signal Based on CMA

This paper simulates and models the PCM-FM signal under multipath effect, and compares the changes of the original signal and the multipath synthesis signal in the time domain and the frequency domain. The analysis results show that the multipath interference causes the PCM-FM signal. In the time domain, the amplitude of the waveform is severely fluctuated, and the spectrum in the frequency domain is distorted. Therefore, the CMA-based PCM-FM signal multipath interference blind equalization algorithm is studied and verified by simulation. The simulation results show the amplitude of the multipath synthesis signal. The equalization effect is obvious, which can effectively reduce the influence of multipath interference on the PCM-FM signal.
S3egANet: 3D Spinal Structures Segmentation via Adversarial Nets

3D spinal structures segmentation is crucial to reduce the time-consumption issue and provide quantitative parameters for disease treatment and surgical operation. However, the most related studies of spinal structures segmentation are based on 2D or 3D single structure segmentation. Due to the high complexity of spinal structures, the segmentation of 3D multiple spinal structures with consistently reliable and high accuracy is still a significant challenge. We developed and validated a relatively complete solution for the simultaneous 3D semantic segmentation of multiple spinal structures at the voxel level named as the S3egANet. Firstly, S3egANet explicitly solved the high variety and variability of complex 3D spinal structures through a multi-modality autoencoder module that was capable of extracting fine-grained structural information. Secondly, S3egANet adopted a cross-modality voxel fusion module to incorporate comprehensive spatial information from multi-modality MRI images. Thirdly, we presented a multi-stage adversarial learning strategy to achieve high accuracy and reliability segmentation of multiple spinal structures simultaneously. Extensive experiments on MRI images of 90 patients demonstrated that S3egANet achieved mean Dice coefficient of 88.3% and mean Sensitivity of 91.45%, which revealed its effectiveness and potential as a clinical tool.
Design of Double Auction Mechanism Based on Social Network

In the traditional double auction market, the number of achievable tradable pairs are usually limited by the market size. To solve this problem, we propose new double auction mechanisms based on social networks to realize market expansion. In this paper, we examine the double auction market, which consists of a group of sellers and a group of buyers, where the sellers provide the same type of items, and the buyers respectively purchase one from them. In addition, every buyer can invite other potential buyers to enter the market through social networks. The goal of this paper is to propose mechanisms such that they can encourage buyers already in the market to invite other potential buyers to join the auction through social networks, and achieve an effective allocation of merchandises and increase profits for sellers, which cannot be achieved under the existing double auction mechanism. We found that the extended McAfee auction mechanism cannot motivate existing buyers to invite other potential ones, while the traditional Vickrey-Clark-Groves (VCG) mechanism cannot guarantee to break even. To solve these problems, we propose two mechanisms, which are called information network auction (INA) mechanism and double network auction (DNA) mechanism. Both of these mechanisms can encourage old buyers in the market to invite new potential customers to participate. Moreover, INA focuses on achieving more effective allocation and DNA focuses on ensuring a balance between income and expenditure.
Efficacy and Safety of Immunotherapy and Standard of Care in High-Grade Gliomas: A Systematic Review and Meta-analysis

BACKGROUND: Immunotherapy combined with standard of care (SOC) is often used for high-grade glioma (HGG). There are few comparisons of immunotherapy with SOC treatment or without SOC treatment (IMT) versus SOC. It is important to understand what interventions exist and their relative effectivenesses. METHODS: The Cochrane Library, Embase, Medline, and the Web of Science Core Collection were systematically searched by two librarians. Retrieved hits were screened for inclusion. Subgroup analysis was used to examine the main factors associated with overall survival (OS) and progression-free survival (PFS), which were used as primary endpoints to assess the efficacy and safety of IMT. This study was registered with PROSPERO, number CRD42019112356. RESULTS: The search yielded 2,315 results, of which 11 met the eligibility criteria. We identified 6 publications through exploration. Compared to SOC alone, IMT including viral therapy (VT) and dendritic cell vaccination improved the OS (HR = 0.55, 95% CI 0.42-0.72; p < 0.0001) and PFS (HR = 0.67, 95% CI 0.53-0.84; p = 0.0005). CONCLUSIONS: We found that IMT in the era of TMZ has an improved effectiveness over SOC. Our findings support the use of immunotherapy for brain tumors to improve HGG outcomes.
A Wideband Large Dynamic Range Logarithmic RF Power Detector With 50 mV Input Offset Cancellation Range

This paper presents a 100 MHz to 4 GHz logarithmic power detector. The detector is implemented as a successive approximation logarithmic power detector utilizing modified Cherry-Hooper amplifier stages. Input DC offset cancellation is achieved using a dual feedback loop approach. Balanced source-coupled pairs are employed as rectifying transconductance stages to convert the output voltages of the limiting gain stages to current. The detector is realized using a SiGe BiCMOS process and achieves up to -52 dBm sensitivity and 52 dB dynamic range. An input DC offset cancellation range larger than 50 mV is achieved.
A Novel Heuristic for Handover Priority in Mobile Heterogeneous Networks

Handover (HO) is designed to facilitate user mobility and ensure quality of service in mobile networks. In multiple base station (eNodeBs) scenarios, the HO priority process is a problem that has been studied in many surveys, as neglecting the use of priority-based schemes can result in high amounts of HO and, consequently, a decrease in the quality of services provided. This paper presents a Heuristic for Handover based on AHP-TOPSIS-FUZZY (H2ATF), which generates a priority ranking of eNodeBs from the use of (a) the analytical hierarchical process (AHP) to define the weights of the criteria; (b) the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the selected target cells; and (c) the use of an adaptive hysteresis calculated through a fuzzy inference system based on parameters that directly impact the HO process. Through this proposal, it was possible to define the best time and, together, the best antenna to perform the HO. The results demonstrate a decrease of up to 43% in HO ping pong (HPP), a widely used metric in the literature to evaluate HO heuristics.
A Generalized MPPT Controlled DC-DC Boost Converter for PV System Connected to Utility Grid

This paper presents a Generalized MPPT control based DC-DC boost converter to generate continuous power with its preserved dynamic control system. The PV array is modeled with algebraic loop to achieve accurate solution with iterations in large sample of times. The larger time steps are utilized in this model to obtain fast simulation. MPPT controller embedded with Generalized P and O technique in PV system to regulate duty cycle through which maximum continuous power will be obtained. The MPPT controller with proposed converter is designed in a strategy to extract continuous power regardless of irradiance and temperature. 100kW PV array connected 25kV utility grid is examined in MATLAB SIMULINK and compared with other models in available literature to show effectiveness of designed model.
Practical Issues of Processing Synchrophasor Measurement Data in the Analysis of Low-Frequency Oscillations in Power System

One of the important problems in ensuring the quality of power systems operation is the analysis of their oscillatory stability [1–3]. The poorly damped low-frequency oscillations (LFO) negatively affecting the reliability of the system are particularly interesting [1, 2, 4]. One of the main reasons for their occurrence is a significant power imbalance, and one of the factors of their continuation and development is the incorrect work of the regulators of the excitation system of electric generators [1, 2]. The poor damping of these oscillations leads to negative consequences, for example, the degradation of power quality, undue stress on equipment, system separation, blackouts, and others. The mentioned oscillations are observed in the low-frequency part of the spectrum (to 4 Hz) of the measured quantities (power, frequency) [5, 6]. The corresponding components of the measurement signals are called low-frequency modes. The example of measured signal and two low-frequency modes are shown in Fig. 1.
Nonlinear Nonstationary Analysis Techniques for Mode Shape Estimation in Power Systems

Inspection of Low frequency oscillation is highly crucial in the context of modern power system operation and control. A dynamic approach is needed for exact estimation of the mode shape at low-frequency modes. Signal decomposition techniques and spectral analysis were adapted in this paper. Since the signal quality is weak, it is needed to identify the low-frequency mode prior to spectral analysis. The power spectral density is used to process dominant modes which indicate centre frequencies in the spectrum. Variational mode decomposition is proposed in place of empirical mode decomposition so as to provide a better-quality way of analysis of low-frequency mode and mode shape determination, and it is tested on real-time data from the power grid.
Comparative Ageing Study of CC-CV Fast Charging for Commercial 18650 Li-Ion Cells: Impact of Environmental Temperature

This work presents first results of an on-going investigation on three commercial 18650 li-ion cell references. Accelerated cycle ageing tests are performed at recommended fast charge current in commercial specifications and at three temperatures (5, 25 and 45 °C). First experimental results reveal that the impact of recommended fast charging current varies significantly between different cell reference and environmental temperature. In general, cells with higher energy density show reduced cycle life under fast charging, in contrast with high power cells. Moreover, the temperature condition at which degradation is minimal varies from one cell reference to the other. These observations suggest that optimized charging protocols and thermal management, specific to each cell reference, can better the trade-off between charging time and durability.
Predictive Freeway Overtaking Strategy for Automated Vehicles Using Deep Reinforcement Learning

This paper aims to combine the deep learning (DL) and reinforcement learning (RL) techniques to formulate the predictive overtaking strategy for autonomous vehicles in freeway scenario. First, the real-world driving data is extracted from the Next Generation SIMulation (NGSIM) dataset. The long short-term memory (LSTM) model is leveraged to forecast the longitudinal and lateral motion of vehicles. Then, the freeway overtaking scenario is constructed, wherein two-lane transportation is considered. Based on the predicted driving trajectories of the surrounding vehicles, RL is utilized to guide the target vehicle passes through the scenario as soon as possible. Results indicate that the presented decision-making strategy could enhance the mobility and safety of the studied automated vehicle. The availability of the proposed method in uncertain and complex driving situations is also demonstrated.
Dynamic 3D Environment Perception Using Monocular Vision and Semantic Segmentation

This paper presents a complete system for traffic environment perception based on a single color image source. The acquired color images are segmented into road and obstacle areas using a U-Net style Convolutional Neural Network (CNN). The segmented image is mapped into a bird’s-eye view using the automatically calibrated camera parameters. Obstacle scans are extracted from the bird’s-eye view image, highlighting the contact points between the obstacles and the road. The scans are postprocessed to increase the connectivity between obstacle parts. The processed scans are used to generate the measurement likelihood values for all observable cells of a dynamic occupancy grid, taking into consideration the expected measurement errors with respect to the distance from the camera. A particle-based occupancy grid is used to track the environment at cell level, and then the occupied cells are grouped into individual objects. The system is able to estimate stable cuboids with measured width, length, orientation and speed for the moving individual objects such as vehicles and pedestrians, and also to identify generic occupied areas for the continuous structures such as fences or barriers.
FPGA-Based Moving Object Detection with Interferences

Visual interferences caused by bad weather conditions can have a negative impact on the performance of a visual surveillance system. How can we detect the moving object in a quickly and accurately manner is a very important step for further analysis, e.g., object recognition, object tracking, event detection, or behavior analysis, in a visual surveillance system. However most of the moving object object detection systems are based on a high-level micro-processor for its highly complex algorithm. Moreover, the detection accuracy is often affected by the environment of the system used. In this paper, we proposes using an FPGA (Field Programmable Gate Array) to realize a visual surveillance system so that the real-time requirement can be achieved. Besides, the proposed system can adapted itself to a variety of environmental situations, especially for the condition of light changes, such as the existence of a shadow, raindrop, etc. As we will see in the experiment that a very good performance in terms of power consumption, memory usage, throughput, as well as the capability of adapt to interference can be achieved in the proposed system
Performance-Boosting Sparsification of the IFDS Algorithm with Applications to Taint Analysis

The IFDS algorithm can be compute-and memoryintensive for some large programs, often running for a long time (more than expected) or terminating prematurely after some time and/or memory budgets have been exhausted. In the latter case, the corresponding IFDS data-flow analyses may suffer from false negatives and/or false positives. To improve this, we introduce a sparse alternative to the traditional IFDS algorithm. Instead of propagating the data-flow facts across all the program points along the program’s (interprocedural) control flow graph, we propagate every data-flow fact directly to its next possible use points along its own sparse control flow graph constructed on the fly, thus reducing significantly both the time and memory requirements incurred by the traditional IFDS algorithm. In our evaluation, we compare FLOWDROID, a taint analysis performed by using the traditional IFDS algorithm, with our sparse incarnation, SPARSEDROID, on a set of 40 Android apps selected. For the time budget (5 hours) and memory budget (220GB) allocated per app, SPARSEDROID can run every app to completion but FLOWDROID terminates prematurely for 9 apps, resulting in an average speedup of 22.0x. This implies that when used as a market-level vetting tool, SPARSEDROID can finish analyzing these 40 apps in 2.13 hours (by issuing 228 leak warnings) while FLOWDROID manages to analyze only 30 apps in the same time period (by issuing only 147 leak warnings).