Miracle-dz/UniFAR-FTUs huggingface dataset:
VLO: Vision-Laser Odometry for Autonomous Flight of Micro Aerial Vehicle

This paper presents an onboard micro aerial vehicle (MAV) localization algorithm VLO using onboard multi-sensor system consisting of a camera, a laser scanner and an inertial measurement unit. On the basis of onboard processor, the VLO can operate in real time without any prior information and ground assistance. Besides, it shows a strong robustness since it can work in both small and large, indoor and outdoor environment. As the main sensing devices of this system, the camera and laser scanner generate different characteristic data. The VLO fuses these two kinds of data for a more sufficient information about environment. A filter and an optimizer are then designed to estimate the MAV poses with extra onboard sensors data. Finally, an incremental dense map is updated. Different with vision-based or laser-based odometry, this system has no requirements for environments such as strong texture or structured surroundings. The Gazebo-based simulated and real MAV systems are built together for algorithm validation. The simulated and real results show that our onboard odometry VLO performs strong robustness without any prior information and basic assumptions.
Lightweight Virtualization Based Security Framework for Network Edge

The interest towards cybersecurity is fast growing over the last years. Accounting for the tremendous increase of security threats, the need for new defense strategies is acquiring an even growing importance. The widespread adoption of Internet of Things (IoT) devices, ranging from smart industrial appliances to simple domestic sensors, will increase the complexity of managing security requirements in a comprehensive way. The provisioning of on-demand security services according to the SECurity-as-a-Service model is gaining notable attention. Nevertheless, the hosting of security functions in remote data-centers will inevitably introduce long routing detours, thus high latency and traffic overhead. To cope with this, edge computing will prove to be useful to process data locally. But the reduced capabilities of edge nodes can negatively impact the overall performance of SECaaS solutions. This paper focuses on the provisioning of virtualized security functions via lightweight virtualization (i.e., container) technologies running in a resource-constrained environment. Our analysis focuses primarily on the feasibility and the performance evaluation of such scenario.
Flexible Use of D Band Spectrum for 5G Transport: a Research Field Trial as Input to Standardization

A flexible use of spectrum at D band (130-174.8 GHz) is presented, as a result of a positive cross connection among industrial research, cooperation with university on a field trial and standardization activity. The research work of the Huawei European Microwave Centre in Milan produced radio unit prototypes operating in the frequency range between 140 and 160 GHz, which have recently been installed in Politecnico di Milano over a short (325 m) link connecting two buildings in the university campus. The targets of the field trial are multiple, ranging from technology validation to propagation study and to the support for regulatory and standardization activities. A new approach to the use of spectrum is proposed with reference to the fixed step frequency division duplexing that is traditionally used in microwave radio links; the proposed scheme, based on the architecture of the radio system with different antennas for TX and RX, allows more flexible and efficient use of the spectrum.
Control OBSS/PD Sensitivity Threshold for IEEE 802.11ax BSS Color

IEEE 802.11ax Spatial Reuse (SR) is a new category in the IEEE 802.11 family, aiming at improving the spectrum efficiency and the network performance in dense deployments. The main and perhaps the only SR technique in that amendment is the Basic Service Set (BSS) Color. It aims at increasing the number of concurrent transmissions in a specific area, based on a newly defined Overlapping BSS/Preamble-Detection (OBSS/PD) threshold and the Received Signal Strength Indication (RSSI) from Overlapping BSSs (OBSSs). In this paper, we propose a Control OBSS/PD Sensitivity Threshold (COST) algorithm for adjusting OBSS/PD threshold based on the interference level and RSSI from the associated recipient(s). In contrast to the Dynamic Sensitivity Control (DSC) algorithm that was proposed for setting OBSS/PD, COST is fully aware of any changes in OBSSs and can be applied to any IEEE 802.11ax node. Simulation results in various scenarios, show a clear performance improvement of up to 57% gain in throughput over a conservative fixed OBSS/PD for the legacy BSS Color and DSC.
A Novel Endmember Extraction Method Using Sparse Component Analysis for Hyperspectral Remote Sensing Imagery

The spectral unmixing technique is an effective method of solving the mixed pixel problem in the hyperspectral remote sensed imagery. During the process, endmember extraction algorithm (EEA) is significant for the creation of material abundance maps. However, the traditional EEAs are not very reliable due to the low resolution of sensor and the complex diversity of land cover feature distribution. In addition, the mutually independent assumption of the endmember corresponding abundance will be affected accordingly. In order to overcome the above limitations, a novel endmember extraction method using sparse component analysis for hyperspectral remote sensing imagery (EESCA) has been presented in this paper. EESCA assumes that each pixel in the image scene is a sparse linear mixture of all endmembers. First, the hyperline clustering algorithm is incorporated to consider the subspace clustering of all pixels after the initialization of endmember mixing matrix. It enlarges differences among ground objects and helps finding endmembers with smaller spectrum divergences. After that, the K-SVD is proposed to search the real endmembers for sparse representations with coefficients summarized in the mixing matrix. The method transfers the pure endmember extraction problem into an optimization problem by minimizing the residual errors. The four state-of-the art methods are implemented to make comparisons with the performance of EESCA. The robustness of the proposed algorithm is verified through both simulated images and real satellite images. Experimental results show that the EESCA outperforms other methods in spectral angle distance and root-mean-square error, and especially could identify accurate endmembers for ground objects with smaller spectrum divergences.
A General Framework for Understanding Compressed Subspace Clustering Algorithms

Subspace clustering (SC) refers to the problem of clustering unlabeled high-dimensional data into a union of low-dimensional linear subspaces. In many practical scenarios, one may have access to only the compressed data due to constraints of measurement or computation. In this paper, based on the recently proposed restricted isometric property of Gaussian random projection for low-dimensional subspaces, we propose a general framework for analyzing the performance of various subspace clustering algorithms when applied to the compressed data. Our framework captures the connection between the problems of compressed subspace clustering (CSC) and noisy subspace clustering. With the existing study on noisy SC, our framework makes it possible to easily extend the results in noisy SC to CSC. In this paper, we select the most commonly used subspace clustering algorithms, i.e., sparse SC (SSC), SSC-orthogonal matching pursuit (SSC-OMP), and thresholding based SC (TSC), as representatives and analyze their performance using the proposed framework. Our results are consistent with related work obtained by previous researchers, where our methodology is much more direct and has more universal implications. Finally, the practicability and efficiency of CSC are verified by numerical experiments.
Initial Results of Semisolid Phantoms Based on Synthetic Hydrogels for the cmWave Band

Tissue phantoms are commonly used in experimental assessments related to body area networks. The main bands for these applications are located in the cmWave region, where many attempts to mimic the tissues have been performed. Recent studies showed that acetonitrile mixtures are suitable to obtain liquid phantoms in this band, but semisolids are advisable for some kinds of tests. Natural polymers are normally used for making gels, but they are fragile and cannot be formed with solvents different from water. This paper proposes the use of synthetic polymers instead. The analyses include mechanical and dielectric properties from 0.5 to 26.5 GHz.
Mining Frequent Patterns for Scalable and Accurate Malware Detection System in Android

Nowadays, the high interest of Android applications makes them the target of a huge number of malware. To detect this severe increase of Android malware and help end-users make a better evaluation of apps at install time, several approaches have been proposed such as statistic and dynamic approaches. However, these approaches cannot detect with high accuracy unfamiliar malware types. That inspired us to find a new approach for recognizing a malware basing on the anomalous set of permission it requests. To actualize that idea, we used the theory of frequent patterns, a data mining technique, for mining the frequent combination of requested permissions. We also compare the performance of the proposed system to other malware detection applications. Experimental results show that the proposed system yielded high accuracy with approximately 97 percent of normal applications and 86 percent of abnormal applications.
Unsupervised Feature Extraction for Hyperspectral Imagery Using Collaboration-Competition Graph

Signal processing on graph offers the ability to define relationships of high-dimensional data on graph. In this paper, an unsupervised feature extraction method using graph for hyperspectral imagery is proposed, which incorporates collaborative representation using $\ell _2$-norm regularization with locality constrained property into graph construction, named collaboration-competition preserving graph embedding. First, an undirected and weighted graph is constructed to exploit the data structure. Then, a weight matrix of edge in graph is built by formulating the combined collaborative-competitive representation into a convex optimization problem. The constructed graph is expected to reveal local intrinsic manifold and global geometry information of hyperspectral data. The superiority of the proposed graph-based unsupervised feature extraction method, compared with other traditional and state-of-the-art methods, is demonstrated by verifying the classification accuracy on four typical hyperspectral datasets.
Short- Term Prediction of Wind Power Based on Adaptive LSTM

Aiming at the randomness and uncertainty characteristics of wind power time series, a wind power prediction method based on the multivariable adaptive long short-term memory (Adaptive LSTM) is proposed. Firstly, in the case where many influencing factors of wind power coexist, the Pearson correlation analysis method is used to extract the influencing factors that have strong correlation with wind power as the prediction model input. Secondly, in order to overcome the shortcomings of subjective setting of super-parameters during the LSTM predictive model training. it is proposed to use genetic algorithm(GA) to optimize the hyperparameters of predictive models. At the same time, in order to avoid falling into the hyper-parameters local optimum, the genetic algorithm is improved to obtain the optimal prediction model. By comparing the example data with the prediction results of various prediction methods, it is verified that the proposed adaptive LSTM prediction model is not only effective but also has higher wind power prediction accuracy.
Predicting Affective States of Programming Using Keyboard Data and Mouse Behaviors

This study aims at predicting affective states during programming using keyboard and mouse data. The article proposes and evaluates a novel set of features under programming context to predict affective states. Fourteen undergraduate participants performed three programming tasks of varying difficulties. At the completion of each task, participants reported their affective states by viewing webcam videos and screen recordings. Features extracted from keyboard and mouse logs were used to train multiple classifiers. Among trained classifiers, feedforward neural network recognized positive, neutral and negative states with 52.9% accuracy. The overall Cohen's Kappa reached 0.27. Without neutral states, the classifiers were able to differentiate positive and negative states with 74.1% accuracy and 0.48 Kappa. Our approach demonstrates improved ability of predicting self-labelled affective states of programmers from keyboard and mouse data, without using specialized sensors, and potential of emotional feedback to programmers during learning to deliver better experience.
Investigations on the Quality Factor of Lithium Niobate Laterally Vibrating Resonators with Figure of Merit Greater than 1,500

This paper reports on quality factor investigations of high-performance 50 MHz X-cut Lithium Niobate (LN) Laterally Vibrating Resonators (LVR). The effects of resonators' topology on the Figure of Merit (FoM) -product of quality factor at series resonance $(\pmb{Q}_{\mathbf{s}})$ and electromechanical coupling $(\pmb{k}_{\mathbf{t}^{2}})$ - are studied in regard to the most important design variables for single element devices. Vacuum measurements highlighted a consistent increase (+ 25%) in $\pmb{Q}_{\mathbf{s}}$. Low temperature measurements pointed out that electrical loading is one of the most important sources of damping for the devices under study. A Monte Carlo approach was used to simulate the effects of frequency mismatch on $\pmb{Q}_{\mathbf{s}}$ as a function of the number of elements forming parallel arrays of resonators. For individual resonators, $\mathbf{FoMs}$ over 1,500 were recorded in vacuum, with $\pmb{Q}_{\mathbf{s}}$ ranging from 3,000 to 5,000 and $\pmb{k}_{\mathbf{t}^{2}} > 30\%$, the highest ever recorded for resonators in the MHz range.
A Hybrid Control Architecture For Autonomous Driving In Urban Environment

Autonomous driving in an urban environment is one of the most actively studied topics. To date, many studies on autonomous driving can be classified into two main approaches: 1.local perception-based approach 2.global path tracking-based approach. However, each approach has its own limitations for fully autonomous driving. In the case of perception-based approach, it is impossible to autonomously drive to the global destination because it only runs locally within the sensor range. On the other hand, the path tracking-based approach relies heavily on accurate navigation. For accurate navigation, there are many studies using expensive equipment or extremely precise and detailed maps, but they have not been resolved yet, and they are also not practical. In this paper, we address the problem of autonomous driving in an urban environment through the proposed hybrid control architecture. Proposed control architecture is designed to be complementary of local perception-based and global path tracking-based approaches. Especially, end-to-end deep learning based autonomous driving, which mimics human driving, is applied as a local perception-based approach. In addition, path tracking-based autonomous driving is performed in an environment where directional information to the destination is required, such as intersections. At the same time, our hybrid control architecture effectively compensates for the navigation error using ICP matching between the perception-based driven trajectory and the global path to the destination without any highly detailed prior map or expensive equipment. The performance of the proposed architecture on a full-scale autonomous vehicle is verified through experiments in the urban environment.
Hybrid Satellite-Terrestrial Spectrum Sharing Systems with RF Energy Harvesting

In this paper, we propose an energy harvesting-based overlay hybrid satellite-terrestrial spectrum sharing system which exploits both direct and relay links from a primary satellite source to terrestrial user with the coexistence of a secondary transmitter-receiver pair on the ground. It is assumed that an energy-constrained secondary transmitter deploys the time switching-based relaying protocol to harvest energy from the primary satellite's signal and then performs an amplify-and-forward based relay cooperation with the primary satellite network to explore an opportunity for spectrum access. By adopting Shadowed-Rician fading for satellite links and Nakagami-m fading for terrestrial links, we derive analytical expressions for the outage probability of primary satellite network and secondary terrestrial network. In addition, to get useful insights, expressions for throughput and energy efficiency of primary satellite network are also provided assuming a delay-limited scenario. Moreover, our results illustrate the impact of power splitting factor and energy harvesting time fraction on the system performance.
Millimeter Wave Channel Estimation Based on Subspace Fitting

We consider the channel estimation of millimeter wave (mmWave) multiple-input multiple-output systems, where both the transmitter and receiver adopt hybrid beamforming structure. Due to the spatial sparsity of the mmWave channel, it can be reconstructed by estimating the direction and gain of the paths. Leveraging this feature, we propose a channel estimation algorithm based on subspace fitting to estimate the path directions, and the path gains are obtained using the least squares method. However, similar to the most existing mmWave channel estimation schemes, the proposed algorithm requires a two-dimensional search in candidate angle space, which is very complicated. In order to reduce the computational complexity, we develop a low-complexity channel estimation algorithm using the orthogonal matching pursuit (OMP) method, which significantly reduces the computational complexity. However, when the paths are strongly correlated, the channel estimation accuracy will decrease. To overcome this defect, we further develop a low-complexity method based on subspace fitting. This algorithm makes a trade-off between the computational complexity and channel estimation accuracy. Furthermore, the pilot beam pattern for different hybrid beamforming structure is designed using infinitely and quantized phase shifters to improve the signal-to-noise ratio of the pilot signals. In addition, the proposed channel estimation methods can also be used in the multi-user scenario. Simulation results demonstrate that the proposed subspace fitting method outperforms the existing methods when the angular resolution is the same. Meanwhile, the low-complexity OMP and subspace fitting methods have a good performance in single path scenario when compared to subspace fitting method, even if the computation complexity is lower. Moreover, the performance of the low-complexity subspace fitting method is close to the subspace fitting method.
Semantic Change Detection

Change detection is the study of detecting changes between two different images of a scene taken at different times. The change detection methodology can provide us information in which area images changed time by time. However, for application use, especially on disaster investigation, it is highly required to understand not only where but also what changes are occured in high precision and resolution. The paper proposes the concept of semantic change detection, which involves intuitively inserting semantic meaning into detected change areas. We mainly focus on the novel semantic segmentation in addition to a conventional change detection approach. In order to solve this problem and obtain a high-level of performance, we propose an improvement to the hypercolumns representation, hereafter known as hypermaps, which effectively uses convolutional maps obtained from convolutional neural networks (CNNs). We also employ multi-scale feature representation captured by different image patches. We applied our method to the TSUNAMI panoramic change detection dataset (TSUNAMI dataset), and re-annotated the changed areas of the dataset via semantic classes. The results show that our multi-scale hypermaps provided outstanding performance on the re-annotated TSUNAMI dataset.
High-Throughput Microchannels for Single Cell Immobilization

Nowadays single cell analysis becomes a more and more important method to gather the information of individual cells and study the heterogeneity of cells caused by random expression of gene, protein and the level of metabolism. Many device and technologies of single cell analysis have been developed to meet these needs. In this paper, we presented a high-throughput microchannel for single cell immobilization with small sheer pressure. It features high density arrays, which can accommodate up to 130∼300 traps within 1∼2 mm2. According to our experiment, about 91% of capture unit can be occupied by the single cell in 40 s, using the optimized structure of microchannels. Therefore, we expect that the high throughput microchannels can be of great importance for the biological research.
Mosaic-CNN: A Combined Two-Step Zero Prediction Approach to Trade off Accuracy and Computation Energy in Convolutional Neural Networks

In convolutional neural networks (CNNs), convolutional layers consume dominant portion of computation energy due to large amount of multiply-accumulate operations (MACs). However, those MACs become meaningless (zeroes) after rectified linear unit when the convolution results become negative. In this paper, we present an efficient approach to predict and skip the convolutions generating zero outputs. The proposed two-step zero prediction approach, called mosaic CNN, can be effectively used for trading off classification accuracy for computation energy in CNN. In the mosaic CNN, the outputs of each convolutional layer are computed considering their spatial surroundings in an output feature map. Here, the types of spatial surroundings (mosaic types) can be selected to save computation energy at the expense of accuracy. In order to further save the computations, we also propose a most significant bits (MSBs) only computation scheme, where a constant value representing least significant bits compensates the MSBs only computations. The CNN accelerator supporting the combined two approaches has been implemented using the 65-nm CMOS process. The numerical results show that compared with the state-of-art processor, the proposed reconfigurable accelerator can achieve energy savings ranging from 16.99% to 29.64% for VGG-16 without seriously compromising the classification accuracy.
An Automatic Clustering Algorithm Based on Region Segmentation

Clustering as an unsupervised learning technique has been widely used in practice. In this paper, a novel clustering algorithm based on region segmentation (CRS) is proposed. It aims to automatically evolve the optimal number of clusters as well as the clusters of the data sets based on the data density. First, a new data density is given based on the reverse near neighbor enhancement which can make the clusters detection more effectively. Then, the multiple sub-region centers can be determined through the data density. Moreover, a merge criterion is proposed to make the relevant regions be merged and obtain the final clustering results. The proposed algorithm does not need to know the number of clusters in advance and no threshold limit. Therefore, it can be used more widely. In the experiments, we compare the performance of our CRS algorithm with DBSCAN, IS-DBSCAN, STClu, DP, and SCDOT algorithms on synthetic, and real-world data sets. Experimental results demonstrated that the NMI, ACC, F1 and ARI obtained by CRS algorithm is always better than that obtained by the other algorithms for the same data sets.