Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonnegatively Constrained Tensor Network for Classification Problems

In the era of big data, massive data are multimodal and highly dimensional, and their processing and analysis is often performed in distributed computing systems, including cloud computing, fog computing as well as ubiquitous network computing. However, a curse of dimensionality strongly limits the usage of fundamental machine learning methods, and hence new state-of-the art models for multi-way data are needed. In this study, we discuss a possibility of using one specific topology of tensor networks for solving multi-label classification problems, under the assumption that the data are gathered in the form of nonnegatively constrained multi-way arrays, and can be represented by a tensor-train model. We propose a new computational algorithm for extracting low-rank and nonnegative 2D features, and show how to apply this algorithm for solving classification problems. The computational experiments demonstrated that our approach outperforms the fundamental and state-of-the art methods for dimensionality reduction and classification problems.
Cognitive Wireless Network Resource Allocation Strategy Based on Effective Capacity

For cognitive wireless networks, it is a crucial task to optimize the communication performance of secondary users while providing effective delay service quality for primary users. In this paper, we propose a resource allocation strategy based on effective capacity under the scenario of secondary user's parallel transmission. Subject to the constraints of total power, minimum secondary user's satisfaction capacity and effective capacity, the model not only guarantees the effective delay service of the main user, but also reasonably distributes the channel and power resources to maximize the secondary user's channel capacity. Genetic algorithm is introduced to solve this model. Simulation results show that the cognitive wireless network resource allocation strategy based on effective capacity has better performance under different transmission power of main users and different resource allocation strategies.
Sum-Rate Maximization for D2D and Cellular Hybrid Networks Enhanced by NOMA

Non-Orthogonal Multiple Access (NOMA) has recently been conceived as a promising technology for the fifth-generation mobile communication system. In this paper, we apply NOMA into the device-to-device (D2D) and cellular hybrid networks to improve the data rate of the D2D links. Specifically, we formulate a D2D transmission rate maximization problem by jointly considering user pairing and power control under the constraints of the decoding threshold of cellular users. To solve the formulated problem, we first analyze the optimal transmission power of the D2D users. According to the obtained power control strategy, the user pairing problem is transformed into a bipartite graph matching problem, which can be solved optimally by the Hungarian algorithm. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of data rate.
Static Back-Stack Transition Analysis for Android

In Android, activity instances at an application’s run time are organized in a back stack, and users always interact with the top instance of the stack. Users’ interactions may trigger activity launchings, which change the state of the back stack. Therefore, back-stack transition analysis is closely related to activity transition analysis, which is fundamental for understanding Android behaviors. Existing activity transition analyses are not sensitive to back-stack-related configurations, which suppose all activities are launched with the default configuration, and thus model infeasible transitions and incorrect back-stack states when there are non-default configurations in an application. In this paper, we present a tool, BSSimulator, to perform the static back-stack transition analysis. The proposed analysis contains two types of graph constructions: the activity launching graph that represents activity transitions with extracted back-stack-related configurations, and the back-stack transition graph that simulates the changes of the back stack triggered by activity launchings and back events. The study conducted on 968 open source apps from F-droid indicates that non-default configurations are in common use. The evaluations on 35 commonly used apps validate the high-precision of the generated activity launching graphs, and indicate that the proposed back-stack transition analysis outperforms the state-of-the-art one by 29.8% in avoiding 3-length infeasible paths.
BFGAN – building footprint extraction from satellite images

Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes. In this work, we have proposed improved generative adversarial networks (GANs) for the automatic generation of building footprints from satellite images. We used a conditional GAN with a cost function derived from the Wasserstein distance and added a gradient penalty term. The achieved results indicated that the proposed method can significantly improve the quality of building footprint generation compared to conditional generative adversarial networks, the U-Net, and other networks. In addition, our method nearly removes all hyperparameter tuning.
A Novel Microfluidic Platform for Label-Free 3D Imaging of Biological Cells

We report a novel microfluidic platform for label-free 3D imaging of cellular and intracellular components of biological cells. The device uses two opposing air-liquid interfaces integrated inside a microchannel which act as tunable constriction, as reported in our earlier work. In this paper, we present a new phenomenon: when the cells approach the air-liquid interfaces, they rotate along the direction of flow owing to velocity gradients generated due to the asymmetric shape of the interface. The rotation of the cell was recorded using a high-speed camera. Cell and nucleus boundary were mapped using image processing and 3D reconstruction was done using the point cloud. We have calculated the volume, surface area and ellipticity of cell and its nucleus.
Contextual Information Based SAR Tomography of Urban Areas

SAR Tomography (TomoSAR) is a multidimensional imaging technique that has proven its ability in localizing multiple scatterers in the three dimensional observed scene, allowing the reconstruction of the elevation profile of the structures on the ground. Tomographic approaches usually estimate the elevation distribution of the scetterers in each range-azimuth pixel independently from the neighboring ones (local approaches). Then, any relation among the elevations of neighboring pixels is imposed in the tomographic processing. In this paper a local contextual information contained in the data is exploited with the aim of improving the 3D reconstruction (semi-local approaches) and increase the number of reliable reconstructed scatterers in the tomographic scatterers cloud. Results on real data validate the proposed approach.
A Low-Cost Ion Selective Nitrate Sensor Based On Self-Assembled Graphene Microelectrode Arrays

This paper reports a novel micro sensor which used self-assembled graphene microelectrode arrays to selectively detect nitrate solutions. Graphene microelectrode arrays were utilized to sensitively detect the change of ion density. Self-assembly technique provided an effective 3-dimensional platform for sensing applications. An ion selective ionophore film was deposited upon graphene surface to make the sensor selectively sensitive for nitrate ions. As a result, the self-assembled graphene microelectrode arrays showed superb sensing performance for pH with advantages of low cost and label free. After decorating nitrate ionophore film, the selective sensor demonstrated high sensitivity and reproducibility for nitrate ions. This simple, low-cost and selective sensor could lead to wide applications in environmental monitoring.
Big IoT Data Indexing: Architecture, Techniques and Open Research Challenges

As the miniaturization of Internet of Things (IoT) devices has increased, significant quantities of data have been developed in the last decade.This explosion has created the need to manage these volumes of data to make the most of this mass of information for an ever-growing audience. Effective indexing and searching in large data collections is therefore a very topical and urgent issue. Many data, IoT and indexing solutions have given users valuable information about the large volumes of information produced by IoT devices. However, these solutions are still in their initial phases and there is no exhaustive study in the field. This article examines advanced research efforts to index large data on IoT. In addition, this survey shows the study of IoT data on a large scale. In addition, the main types, methods and technologies of IoT data analysis for large-scale data mining are discussed. These include open research challenges, such as privacy protection and large-scale data mining.
Model Predictive Control of Dual Active Bridge Converter Based on the Lookup Table Method

The dual active bridge (DAB) converters are widely used as the pivotal interface in the distributed generation and energy storage systems. In this paper, the lookup table method of the model predictive control (MPC) DAB converter is proposed based on the discrete-time model. The iterative relationship of the state variables is obtained. Part of the complicated predictive process, which hindered the discrete-time model based MPC to be widely applied to the DAB converter before, has been calculated off-line to form a lookup table. Therefore, the accurate predictive control and optimization algorithm can be realized on-line via the proposed lookup table method within one switching cycle. The experiment has also been performed to verify the theoretical analysis and demonstrate the effectiveness of the proposed method.
In Encryption We Don’t Trust: The Effect of End-to-End Encryption to the Masses on User Perception

With WhatsApp's adoption of the Signal Protocol as its default, end-to-end encryption by the masses happened almost overnight. Unlike iMessage, WhatsApp notifies users that encryption is enabled, explicitly informing users about improved privacy. This rare feature gives us an opportunity to study people's understandings and perceptions of secure messaging pre-and post-mass messenger encryption (pre/post-MME). To study changes in perceptions, we compared the results of two mental models studies: one conducted in 2015 pre-MME and one in 2017 post-MME. Our primary finding is that users do not trust encryption as currently offered. When asked about encryption in the study, most stated that they had heard of encryption, but only a few understood the implications, even on a high level. Their consensus view was that no technical solution to stop skilled attackers from getting their data exists. Even with a major development, such as WhatsApp rolling out end-to-end encryption, people still do not feel well protected by their technology. Surprisingly, despite WhatsApp's end-to-end security info messages and the high media attention, the majority of the participants were not even aware of encryption. Most participants had an almost correct threat model, but don't believe that there is a technical solution to stop knowledgeable attackers to read their messages. Using technology made them feel vulnerable.
CSRR Integrated Microwave Humidity Sensor Based Go@Mxene for Breath Monitoring

This paper presents a microwave humidity sensor with a complementary split ring resonator (CSRR) based on composite material of graphene oxide modified MXene (GO@MXENE), which can be used to monitor human breath in real-time. The microwave sensor with CSRR structure were established by software of HFSS to optimize the performance of the sensor. The sensitivity of sensor characterized by resonant frequency and intensity with a wide humidity range from 10% to 95% RH can be improved by using the GO@MXENE composite material as sensitive material of humidity.
Effect of Built-in Stresses on Defects of Graphene Based Gas Sensors

An interesting phenomenon, ID/IG intensity ratios between D mode and G mode increase around micro-wells, is observed in defective graphene sensors. A modified phenomenological model depicting the relation between ID/IG and the built-in stress is developed to reveal the physical mechanism of the above phenomenon. It is demonstrated that the initial defects in graphene gas sensors arising from fabrication processes can further propagate under the built-in tension resulting in increasing the defect density. The underlying mechanism is that the built-in stress provides the energy for the defect propagation of stretched graphene. The work could be helpful for applications of defective graphene-based devices.
Towards combining Satellite Imagery and VGI for Urban LULC classification

In this work we introduce and evaluate a deep learning model, mbCNN, that combines together satellite imagery and Volunteer Geographical Information (VGI) data to deal with different types of built-up surfaces. Differently from most of the previous works that only consider Urban/Non-Urban settings involving only one urban LULC class, here, we investigate the possibility to go a step further and distinguish among several urban land use classes: residential, industrial, sport fields and non-urban. Experiments on a real-world dataset covering the City of Montpellier (South of France) site are reported. Such results demonstrate the quality of Deep Learning approaches to deal with several types of Urban LULC mapping as well as the positive influence to integrate VGI knowledge in the process.
Large-scale building extraction in very high-resolution aerial imagery using Mask R-CNN

Urban areas are hotspots of complex and dynamic alterations of the Earth’s surface. Using deep learning (DL) techniques in remote sensing applications can significantly contribute to document these tremendous changes. Open source building data at a very high level of detail are still scarce or incomplete for many regions, therefore, hindering research and policy to properly provide knowledge on urban structures. In this study, we use a convolutional neural network to extract buildings for the city of Santiago de Chile. We deploy the recently released Mask R-CNN and use a pretrained model (PM) which already has been trained with remote sensing imagery. We fine-tune PM with very high-resolution (VHR) airborne RGB images from our study region and generate the fine-tuned model (FM). To extend the number of training data, we test several data augmentation methods for training purposes and evaluate their performance in context of urban environments. We achieve highest overall accuracy of 92 % by using augmentations and the generated FM. Our findings encourage to use DL methods in the urban context. The presented method can be adapted and applied to other global urban regions, and, help to overcome lacks in open source building data to assess urban environments.
Scalability of LoRaWAN in an Urban Environment: A Simulation Study

The Long-Range Wide Area Network (LoRaWAN) communication protocol can be adapted for the Internet of Things (IoT) due to its long geographical coverage, low energy consumption, high capacity, open business model, and low cost. Recently, both academia and industry are attracted to LoRaWAN because of its recent developments and applicability in the IoT environment. Mostly, the IoT concept involves massive end devices distributed over a wide topographical area, hence establishing a high density and large-scale environment. LoRaWAN makes use of six orthogonal spreading factors (SFs) in order to improve spectral efficiency and increase the capacity. In this paper, we focus on the LoRaWAN class A network end devices and the problem of network scalability under high-density urban environment by using both the confirmed and unconfirmed mode of communication. Simulation results show that the urban environment has a high impact on the distribution of SFs, thus leading to low network performance in terms of success ratio. We also observed that the use of acknowledgment limits the scalability of the LoRaWAN due to the duty cycle constraints. However, this study further reveals that elevating the antenna height can significantly improve the performance in an urban area.
Analysis of Inertia and Damping Characteristics of Grid-connected Photovoltaic Power Generation System Based on Droop Control

The grid-connected photovoltaic power generation system (GPPGS) with high proportion of renewable energy has the ability to load balance. A strategy is proposed for GPPGS based on droop control. The electric torque analysis method (ETAM) is used to establish mathematical model of the system with DC voltage timescale (DVT) and analyze the influence law of inertia, damping and synchronization characteristics from the physical mechanism perspective. The research finds that the equivalent inertia, damping and synchronization parameters of the system are determined by the control parameters, structural parameters and steady-state operating point parameters. Changing the control parameters is the simplest and most flexible way to influence the inertia damping and synchronization ability of the system. The inertia characteristics of the system are greatly influenced by the control parameter Kp and enhanced with the increase of Kp. The damping characteristics of the system are obviously affected by the control parameter Dp and weakened with the increase of Dp. The synchronization effect of the system is only controlled by the parameter Ki and enhanced with the increase of Ki. The effectiveness and correctness of the analysis conclusion are verified by simulation.
Classification of Motor Imagery Signals by Convolutional Neural Network for BCI Applications

Electroencephalography (EEG) signals have been using for clinical purposes for many years. However, studies on the use of EEG signals in brain computer interface (BBA) applications are increasing. It is possible to control machines using only mental activities, especially for patients with limited mobility. Motor imagery signals (MIS) which are formed as a result of the imagination of moving a limb are one of the most common signal used for this purpose. In this study, it is aimed to classify MIS signals with Convolutional Neural Network by using BCI-IV 2b dataset. As a result, higher (%75,7) performance was obtained with lower number of parameters compared to similar previous studies.
Deep Q-Network Based Decision Making for Autonomous Driving

Currently decision making is one of the biggest challenges in autonomous driving. This paper introduces a method for safely navigating an autonomous vehicle in highway scenarios by combining deep Q-Networks and insight from control theory. A Deep Q-Network is trained in simulation to serve as a central decision-making unit by proposing targets for a trajectory planner. The generated trajectories in combination with a controller for longitudinal movement are used to execute lane change maneuvers. In order to prove the functionality of this approach it is evaluated on two different highway traffic scenarios. Furthermore, the impact of different state representations on the performance and training process is analyzed. The results show that the proposed system can produce efficient and safe driving behavior.
Energy-efficiency Random Network Coding Scheduling Based on Kalman prediction under Mobile Network

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless networks. However, when the users move randomly in the network, the transmitting distance between the transceivers changed randomly, causing the time-varying channel conditions. To fit this situation, in this paper, we considered the heterogenous wireless channels of the users caused by user movement and analyzed the energy consumption of overall network. Then, we introduce the Kalman filter to predict the locations of the users and proposed a new power control metric that both considered the energy consumption and the network throughput. Based on the new metric and the Kalman prediction, we optimized the transmitting power of the BS and proposed an energy-efficient ARNC scheduling to improve quality of service. The simulation results also showed the effectiveness of the optimization and proposed methods compared with the traditional methods.
Application of Modern Data Analysis Methods to Cluster the Clinical Pathways in Urban Medical Facilities

Patient flow modeling in healthcare plays a large role in understanding the operation of the system and its characteristics. Besides, modeling techniques can significantly improve the effectiveness of the medical facilities. The existing level of automation in these facilities enables the accumulation of large amounts of various data. Therefore, the collected data might be considered as the resource of new valuable knowledge. A novel approach to automatically identify the groups of similar clinical pathways based on event hospital data is presented in the paper. More specifically, the approach summarizes the most frequent pathways by implementing hard and soft clustering algorithms in order to describe the behavior patterns. The obtained clusters of clinical pathways serve as a starting point for the development of a personalized approach in modelling the heterogeneous patient flow in urban medical facilities. The results indicate the suitability of multidimensional time series clustering and Additive Regularization of Topic Models (ARTM) for the clinical event data.
Exploiting reconfigurable computing in 5G: a case study of latency critical function: Invited Paper

The fifth generation of mobile communications (5G) is expected to dramatically improve performance compared to preceding standards by offering very high bandwidths and low latencies. To provide this performance, heavy processing is required and must meet strong timing constraints. Reconfigurable computing, managing processing in software and exploiting reconfigurable hardware acceleration, is an innovative approach that should be considered for 5G for its capacity to combine high throughput and high flexibility. This paper presents a case study for Orthogonal Frequency Division Multiplexing (OFDM) computation reconfigurable offloading onto an Field Programmable Gate Array (FPGA). The implementation is based on Open Computing Language (OpenCL) that represents a versatile solution, as this language can be compiled for several architectures, provided that a Host+Accelerator structure is used. The objective of our study is to demonstrate that, by means of hardware offloading, the 5G architecture resources can reach high computational load, avoiding processing stalls and latency increase. Results show that around 15% of the software processing can be freed through hardware acceleration and reallocated to support other tasks.
Evaluation of Agricultural Precision Algorithms on UAV Images

The monitoring of agricultural soils has become a necessary need for good soil quality. For this reason several parameters have been studied to solve problems in agriculture. On the other hand we find several tools for this monitoring, generally the most known are based on UAVs. In this article we will focus on evaluating the two most known parameters the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Water Index (NDWI). The evaluation of these parameters was done in a heterogeneous embedded ODROID-XU4 architecture based on two CPUs and a GPU.