Miracle-dz/UniFAR-FTUs huggingface dataset:
Salient Motion Detection for Spectral Video on the Compressive Domain

Compressive spectral imaging (CSI) systems capture three dimensional (3D) spatio-spectral information of a scene using two-dimensional (2D) coded focal plane array measurements. Some of these architectures have been adapted to effectively capture temporal sequences of dynamic spatial-spectral sources, at high frame rates, which has enabled motion detection in applications such as surveillance. However, the drawback of state-of-the-art compressive motion detection techniques over spectral video is that they first require the reconstruction of each frame. So that, the computational complexity grows with the number of frames. Hence, this work proposes a method to detect the salient motion regions directly on the compressive domain to avoid the reconstruction step. Specifically, the proposed approach uses a recursive instantaneous model that analyzes motion within consecutive CS projections. Simulation results show that the proposed approach reduces the motion detection time in up to 99% with the same precision compared with methodologies that work on the reconstructed data.
Performances of M-ACO-OFDM, DCO-OFDM and M-GLIM OFDM in Visible Light Communication Systems

Orthogonal frequency division multiplexing (OFDM) is a promising modulation used in bandwith limited system due to its improved spectral efficiency. In this work, we propose a modified asymmetrically clipped optical OFDM (M-ACO-OFDM) and modified generalised light emitting diode index modulation OFDM (M-GLIM-OFDM) in order to gain the same bandwith efficiency with conventional direct-current biased optical OFDM (DCO-OFDM). Their performances are compared for a 500 MHz bandwith limited system. Simulation results show that although there are sacrifice of 3dB receiver sensitivity of the proposed M-ACO-OFDM and M-GLIM-OFDM when compared with the original OFDM signal, there is about 8 dB improvement in peak-to-average power ratio (PAPR) reduction.
Flexibility Services Management Under Uncertainties for Power Distribution Systems: Stochastic Scheduling and Predictive Real-Time Dispatch

Over the last years, the role of the distribution system operator (DSO) has largely expanded. This is necessitated by the increased penetration of intermittent energy resources at the distribution level, as well as the new, more complex interactions with the transmission system operator (TSO). As such, to properly manage its system and to have an effective joint cooperation with the TSO, the DSO is required to procure and carefully manage flexibility services from distributed energy resources (DER). This paper introduces a thorough framework on optimal operational planning (day-ahead scheduling) and operational management (real-time dispatch) of active distribution systems under uncertainties, to avoid line congestions and voltage limit violations, and efficiently balance the distribution system. A two-stage stochastic programming model based on weighted scenarios is proposed to optimize the multi-period optimal power flow day-ahead scheduling, i.e., scheduled power flows at the TSO-DSO interface and reserved DER flexibility services. Subsequently, the operational management, realized with a predictive real-time dispatch model based on a constantly updated rolling horizon, aims to efficiently activate the available flexibility services to minimize deviations from the committed schedule. Different sources of flexibility are considered, with their respective response times also taken into account at real-time dispatch. The proposed framework is applied on two distribution systems and investigates the DSO’s level of risk exposure while minimizing its total cost (reservation and activation expenses). The results indicate that a less conservative approach at planning stage, despite the potential risk exposure, can lead to significant reduction in total expenses.
The Visibility Measurement Based on Convolutional Neural Network

Conventional visibility detection instrument has an expensive price and requires for the deployment field strictly. To achieve the goal of low-cost visibility detection, this article studies a low-cost approach which combines monitor camera device and deep learning algorithm. Cameras mounted in the external field are used to capture images, which are the input images of the convolutional neural network (CNN). The scattering visibility detection instrument is used to capture visibility values, which are transferred to the training labels of CNN. Both devices are mounted at the same site outside the meteorological observation station at our school. In advance, 3300 images are filtered from 40000 original sampling data sets to construct a dataset, where 3000 samples are selected as the training set and the rest are as the test set. We don’t adopt the common 7:3 dataset partition strategy, because this 10:1 strategy can promise better training performance in the case of a small-sized dataset. This article constructs the network based on the convolutional neural network of Alex model. During training, two methods are employed for preventing over-fitting. Although there is some fluctuation in the accuracy curve during training, the prediction trend is increasing and stabilized to nearly 85% finally. The result curve comparison shows that in this same period the prediction curve approximately coincides with the detected visibility values, and has a similar variation trend. At last, it is concluded that the low-cost visibility detection method based on cameras and CNN is practicable with relatively high performance, which can comply with the application requirements. The following research will focus on improving the accuracy and decrease the size of CNN.
Digging Into Pseudo Label: A Low-Budget Approach for Semi-Supervised Semantic Segmentation

The capability to understand visual scenes with limited labeled data has been widely concerned in the field of computer vision. Although semi-supervised learning for image classification has been extensively studied in some cases, semantic segmentation with limited data has only recently gained attention. In this work, we follow the standard semi-supervised segmentation pipeline for image classification and propose a two-branch network that can encode strong and pseudo label spaces respectively, extracting reliable supervision information from pseudo-labels to assist in training network with strong labels. Our method outperforms previous semi-supervised methods with limited annotation cost. On standard benchmark PASCAL VOC 2012 for semi-supervised semantic segmentation, the proposed approach gains fresh state-of-the-art performance.
Research on Multi-sensor Wireless Network Based on Complex Tunnel Engineering and Its Data Pushing Method

In the process of national construction and development, tunnel construction often occupies an important position. Therefore, to ensure tunnel construction safety, various types of high-sensitivity sensors are used to monitor various risk sources, and various data are collected in real time and sent to the server for monitoring the tunnel environment. Due to the characteristics of the tunnel environment itself, multi-sensors are used for monitoring. In order to comprehensively process and eliminate the useless and erroneous information of multiple information acquisition and representation, multi-sensor fusion technology is used to enhance system survivability and enhance the credibility of the data and the accuracy. The signal transmission uses LoRa wireless transmission mode with excellent functions such as low power consumption, long distance, and self-organizing network. The sensor data is transmitted to the node, and the node transmits the data to the data transmission to the gateway or base station, the gateway or the base station goes to the server or the cloud. At the same time that the output is abnormal, the server will issue a command to trigger an alarm to achieve safe construction.
Direction Finding with Time Modulation for Phased Array

A novel direction of arrival (DOA) estimation method based on the phased array is proposed and verified, by adjusting the phase shifts of all the elements periodically. Because of the periodical modulation, the fundamental component and the harmonic components are generated simultaneously in each element. After the power combiner, the spectrum of the received signal is influenced by the incident direction. Therefore, the incident direction can be calculated out by analyzing the spectrum characteristic of the received signal. The proposed method has a potential application in the wireless communication based on the phased array. Numeric simulations are provided to verify its effectiveness.
Noninflatable Pneumatic Artificial Muscle Requiring Low Space and Consumption Flow Rate

Actuators known as pneumatic artificial muscles are superior for practical use because they do not require maintenance, are inexpensive, and can be assembled with a drive unit and an energy generation unit independently. However, because these actuators expand greatly in the radial direction when pressurized, one problem is that they occupy a large amount of space and consume a significant amount of flow. In this study, we propose a space-saving, constant-axis fiber-reinforced artificial muscle that does not expand and also takes advantage of the benefits of pneumatic artificial muscles. Hence, this paper reports on the driving principle of the proposed noninflatable pneumatic artificial muscle and the basic characteristics are then confirmed using a prototype.
Identifying Outdoor Context by Correlating Air and Noise Pollution Sensor Log

In an urban area, the degree of ambient noise and air pollution play a vital role in determining the quality of human life. The impact of these two pollutants is increasing day-by-day due to rapid urbanization. Although, creating real-time pollution maps and forecasting of pollution levels have been studied extensively, the contextual, spatio-temporal correlation between air and noise pollution has not been investigated thoroughly. This correlation is important to identify the characteristics of an urban area. In this paper, we have highlighted some aspects that are useful to identify a context from different pollutant data. To collect the noise data, we have developed an android based application “AudREC” that uses the inbuilt mobile micro-phone sensor. Moreover, for measuring air pollutants, we have used a ready-made “Flow” device that senses PM2.5 and CO2, etc. The initial outdoor experiments show the feasibility of the platform for recognizing contexts from air and noise pollution information.
Towards Optimal Cyber Defense Remediation in Cyber Physical Systems by Balancing Operational Resilience and Strategic Risk

A prioritized cyber defense remediation plan is critical for effective risk management in cyber-physical systems (CPS). The increased integration of Information Technology (IT)/Operational Technology (OT) in CPS has to lead to the need to identify the critical assets which, when affected, will impact resilience and safety. In this work, we propose a methodology for prioritized cyber risk remediation plan that balances operational resilience and economic loss (safety impacts) in CPS. We present a platform for modeling and analysis of the effect of cyber threats and random system faults on the safety of CPS that could lead to catastrophic damages. We propose to develop a data-driven attack graph and fault graph-based model to characterize the exploitability and impact of threats in CPS. We develop an operational impact assessment to quantify the damages. Finally, we propose the development of a strategic response decision capability that proposes optimal mitigation actions and policies that balances the trade-off between operational resilience (Tactical Risk) and Strategic Risk.
A Linear Array of Skewed Dipoles With Asymmetric Radiation Pattern for Angular Filtering

In this letter, we present a design of a linear array of tilted dipoles to achieve radiation patterns with asymmetric angular filtering characteristics. To realize the asymmetric radiation, the dipole elements are spaced by a distance larger than half a wavelength, thus allowing for grating lobes to occur in the visible region. Moreover, the dipoles are loaded with artificial dielectrics to increase the front-to-back ratio and consequently to enable higher gain in certain desired angular regions. Based on the design, a linear array with ten elements is manufactured and tested. The measured results show the ability of such an array to achieve stable gain from broadside up to 90° scanning while implementing a stopband angular filter for negative scanning angles.
Combined Interference and Communications Strategy as a Defense Mechanism in Cognitive Radio Military Networks

Tactical wireless networks have different requirements and characteristics. Among these requirements, we can highlight physical layer security. An efficient way to increase the secrecy in wireless systems is to degrade the decoding capability of the eavesdroppers by introducing controlled interference or artificial noise. However, in tactical networks, there are situations in which the radio can not interfere, e.g. under severe power supply restrictions or if the enemy can detect radio position if it interferes. Moreover, there are also typical situations in which the radio does not need to interfere and therefore can save energy, e.g. transmission of low cruciality messages or radio usage in enemy-free areas. In this paper, we propose a Combined Interference and Communication strategy as defense against message interception in Cognitive Radio Military Networks. This strategy is grounded on interfering only under specific Cognitive Radio Military Networks requirements and characteristics. Numerical experiments show that we achieve a great energy saving in respect to [3] and [4] in several scenarios, reducing the receiver detection factor.
Non-Uniform Steerable Leaky-Wave Antenna Based on Substrate Integrated Waveguide

In this paper, we introduce a novel electronically steerable antenna. The proposed design is a substrate integrated waveguide based quasi-uniform leaky-wave antenna. Introducing sets of switches over the slots and biasing them non-uniformly enables the scanning capability. Due to non-uniform distribution of the bias voltage of the switches, we achieve large variation in the radiation patterns. The center frequency is chosen as 28.5 GHz to accommodate 5G wireless networks. The width, length, and height of the antenna are 24 mm, 68 mm, and 0.3 mm, respectively. Scanning capability, compactness, and simplicity of the proposed structure makes it a suitable candidate for 5G wireless networks.
Evaluation of Feature and Signature based Training Approaches for Malware Classification using Autoencoders

Malware analysis has become a critical and notable area of research importance due to rapid growth in the development and application of internet based systems. Recent advances in artificial intelligence (AI) particularly with data mining enabled the implementation of AI based malware classification and detection systems. AI based malware analysis systems are predominantly signature based and are built on available malware datasets. This paper tries to evaluate the capability of a feature based malware classification using autoencoders. In so doing, this paper presents a new approach for creating a synthetic malware dataset based on signature and features which could be used to train and test both traditional and artificial intelligence based malware detection systems. Various experiments are carried out using autoencoders training on feature based and signature based datasets and tested on a synthetic dataset. The experiments also carried out with multiple datasets and topologies. The experiment results show that the feature based training is proved to be efficient for synthetic, signature and feature based datasets compared to signature based approach. Feature based stacked autoencoders (5-layered) is able to achieve a classification accuracy of 95.6% more than 11.6% when compared with the signature based system which could achieve only 84.6%.
Object pose rotation by pulling up the edge of object under a constraint of ground contact

An object pose manipulation method is proposed to change the posture in a cluttered space. In this paper, graspless manipulation is focused to rotate the object without hard grasping. In the proposed method, the object is pinched with fingers at the parallel surfaces whose contact points make rotation axis since it is assumed the point contact, and the object posture is controlled by only pulling up the pinching points. The object pose is defined by the height of the pinching position and the contact point of the object with the ground. To make smooth rotation by the gravity force, the pinching position is defined to the point away from the center of gravity of the object. First, the proposed method is simulated in the dynamic simulator GAZEBO, and the relationship between the point of pinching height and the force for pulling up is revealed. From the simulation result, in the case of an artificially shaped object such as a triangle or a rectangle, the height at which the direction of the lifting force is reversed can be found. When the direction of the force is reversed, by releaseing the object from the finger, the object rotates by the gravity force and the grounding surface can be changed to another desired surface. Thus, rotation to adjacent surfaces can be easily achieved simply by observing the pulling force. Finally, experimental result using a hand with force sensor show the feasibly of the proposed object pose manipulation.
A Process Study of Laser Patterning of Different Conductive Layers for Printed Electronics

Patterning of different conductive materials utilising nanosecond laser ablation was studied. The materials are namely printed silver (Ag) electrodes, laminated aluminium (Al) films, ITO thin films and PEDOT:PSS conductive polymer thin layer of different thicknesses. The authors investigated the feasibility of using 1064 nm wavelength, 20 nanosecond pulsed laser beam to strip off the conductive layers in order to electrically isolate the large area conductive materials or traces into appropriate segments or patterns. More importantly, the ablation process could be controlled to induce minimal interference to the underlying supporting materials while removing completely the conductive layer with very little residue. The ablated track widths the authors achieved in this project depends on the coating material and process parameters. Typically, the minimum line widths obtained using the laser patterning system were $60\ \mu \mathrm{m}$ for printed silver, $33\ \mu \mathrm{m}$ for Al, $28\ \mu \mathrm{m}$ for ITO layer and $32\ \mu \mathrm{m}$ for PEDOT:PSS, respectively.
Delay Prediction and Abnormal Detection for Hybrid Cloud Based on GBDT

Cloud computing is the product of the integration of traditional computing. For Hybrid Cloud managers, delay prediction plays an important role in Hybrid Cloud management. Machine learning has shown good predictive performance in many areas. In this paper, we have proposed a training model based on machine learning algorithm, and predicted the delay using this model. The experimental results reflect the GBDT model with the strongest prediction ability compared to RF and SVM. According to the delay prediction, the dynamic threshold is more likely to find network traffic abnormalies.
SDBF-Net: Semantic and Disparity Bidirectional Fusion Network for 3D Semantic Detection on Incidental Satellite Images

In this paper, we propose a conceptually simple, flexible, and general framework for the semantic stereo task on incidental satellite images. Our method efficiently detects the objects in an incidental satellite image for generating a high-quality segmentation map, and more accurately match the left-right incidental satellite images for obtaining a more accurate disparity map at the same time. The method, called semantic and disparity bidirectional fusion network (SDBF-Net), consists of three main modules: the Semantic Segmentation Module (SSM), the Stereo Matching Module (SMM), and the Fusion Module (FM). The semantic segmentation module takes advantage of the capacity of global context information by extending the receptive field to produce the initial segmentation map. The stereo matching module applies the 3D convolutional operation to regularize the feature map of left-right images to generate the initial disparity map. The fusion module fuses the initial segmentation and disparity map to obtain the refined segmentation and disparity map. Extensive quantitative and qualitative evaluations on the US3D dataset demonstrate the superiority of our proposed SDBF-Net approach, which outperforms state-of-the-art semantic stereo approaches significantly.
Analysis of Underwater Acoustic Channel based on Angle of Arrival Distribution

Owing to the unique geometry of the underwater acoustic (UWA) channel, analysis based on the angle of arrival (AoA) distribution is fundamental. The energy in the UWA channel remains confined due to the geometry, and the number of interactions with the surface and bottom (also referred to as bounce) tends to be substantial. The acoustic signal undergoes coherent or incoherent scattering at the sea surface, depending upon the surface roughness before reaching the receiver. In this work, we derive the AoA distribution for incoherent scattering for a single bounce path. Channel characteristics such as the autocorrelation function (ACF) and Doppler spread (DS) based on the AoA distribution have been evaluated. Their significance and effect under different underwater geometries have been studied.
Toward Smart Vehicular dc Networks in the Automotive Industry: Process, computational tools, and trends in the design and simulation of vehicle electrical distribution systems

The automotive industry is experiencing unprecedented requirements to fulfill higher expectations from customers and include new functionalities in vehicles, such as driving assistance, gadget connectivity, sharing capabilities, and electrified traction. The electrical network is currently one of the most challenging onboard systems to be designed and prototyped because these demands add to its intrinsic complexity.
Modeling Multi-source Information Diffusion: A Graphical Evolutionary Game Approach

Modeling of information diffusion over social networks is of crucial importance to better understand how the avalanche of information overflow affects our social life and economy, thus preventing the detrimental consequences caused by rumors and motivating some beneficial information spreading. However, most model-based works on information diffusion either consider the spreading of one single message or assume different diffusion processes are independent of each other. In real-world scenarios, multi-source correlated information often spreads together, which jointly influences users' decisions. In this paper, we model the multi-source information diffusion process from a graphical evolutionary game perspective. Specifically, we model users' local interactions and strategic decision making, and analyze the evolutionary dynamics of the diffusion processes of correlated information, aiming to investigate the underlying principles dominating the complex multi-source information diffusion. Simulation results on synthetic and Facebook networks are consistent with our theoretical analysis. We also test our proposed model on Weibo user forwarding data and observe a good prediction performance on real-world information spreading process, which demonstrates the effectiveness of the proposed approach.