Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Rail Area Detection Using Convolutional Neural Network

Rail area detection is essential in active obstacle perception system of the train. This paper presents an efficient rail area detection method based on the convolutional neural network (CNN). The proposed method is divided into two main parts: extraction of the rail area and further optimization. First, a CNN architecture is established to achieve accurate rail area detection, enabling the pixel-level classification of the rail area. It is notable that the main improvement of our architecture is dilated cascade connection and cascade sampling. Second, an improved polygon fitting method is applied to optimize the contour of the extracted rail area and, thus, obtains a more elegant outline of the rail region. As shown by the experimental results, the excellent accuracy is obtained by using our method, i.e., 98.46% mean intersection-over-union and 99.15% mean pixel accuracy on the BH-rail-dataset, and verified the applicability of our detection method in a large-scale traffic scene video frames of Beijing metro Yanfang line and Shanghai metro line 6.
Robust Unit Commitment and Dispatch Considering with Atmospheric Pollutant Concentration Constraints

With the increasing problem of air pollution, improving atmospheric quality has been attracting more attentions in the worldwide, especially in coal-burning areas. As an important emission source of atmospheric pollutant, the scheduling strategy of coal-fired power plants has a significant impact on atmospheric quality. In this paper, a day-ahead unit commitment and dispatch model of power system considering with atmospheric quality is developed. Due to the fact that atmospheric quality not only depends on pollutant emissions but also depends on meteorological conditions of pollutant diffusion, the atmospheric diffusion model is coupled with the unit commitment and dispatch model. In addition, robust optimization is adopted to ensure the robustness of pollutant concentration constraints. The validity of the proposed model is demonstrated by case study results.
Elastic Services for Edge Computing

Edge computing enables new, low-latency services close to data producers and consumers. However, edge service management is challenged by high hardware heterogeneity and missing elasticity capabilities. To address these challenges, this paper introduces the concept of elastic services. Elastic services are situation aware and can adapt themselves to the current execution environment dynamically to adhere to their Service Level Objectives (SLOs). This adaptation is achieved through Diversifiable Programming (DivProg), a new programming model which uses function annotations as interface between the service logic, its SLOs, and the execution framework. DivProg enables developers to characterize their services in a way that allows a third-party execution framework to run them with the flow and the parametrization that conforms to changing SLOs. We develop a prototype and perform an experimental evaluation which shows that elastic services can seamlessly adapt to heterogeneous platforms and scale with a wide range of input sizes, while adhering to their SLOs with little programming effort.
A Dual Linearly Polarized End-Fire Antenna Array for the 5G Applications

A dual linearly polarized antenna array with end-fire radiation of the 60-GHz band is proposed by combining two types of magneto-electric dipoles (ME-dipole), which operate in vertical polarization (V-pol) and horizontal polarization (H-pol), respectively. Based on the substrate integrated waveguide structure, two kinds of ME-dipole are placed closely by sharing the same metallic post wall between them, achieving an element space of <inline-formula> <tex-math notation="LaTeX">$0.77\lambda _{0}$ </tex-math></inline-formula>. Besides, mutual coupling between the close elements is numerically analyzed and then suppressed by making modifications to the antenna structure. The proposed dual-polarized element achieves bandwidths of 26% and 32% (<inline-formula> <tex-math notation="LaTeX">$\vert \text{S}_{11}\vert < -14$ </tex-math></inline-formula> dB) for the V-pol and H-pol, respectively. A <inline-formula> <tex-math notation="LaTeX">$1\times 8$ </tex-math></inline-formula> element array is designed, fabricated, and measured. Good agreement is achieved between the measurement and simulation. For the V-pol excitation, a bandwidth of 21% is obtained and gain is up to 16.1 dB. For the H-pol excitation, the bandwidth is 18% and peak gain is 15.1 dB. Radiation patterns are stable over the operating frequencies with cross polarizations below −30 dB for both polarizations. The port isolation is very high above 45 dB. With all these advantages of dual polarization, end-fire radiation, stable gain, and high port isolation, the proposed array is a potential candidate for the future 5G applications.
Mitigation of Voltage Deviations in DC Shipboard Microgrids Through the Active Utilization of Battery Energy Storage Systems

DC shipboard microgrids are currently of interest since several benefits can be obtained from their adoption. One of the most important challenges in this type of systems is related to sudden load events that demand significant amounts of power within a short period of time. This kind of events leads to large deviations in voltage magnitude at the main dc busbars. To cope with this problem the usage of storage systems has been identified as a possible solution. This paper proposes a control strategy, based on the participation of a battery energy storage system, that aims to reduce the voltage deviations. In order to carry out the proposed strategy, only few signals are exchanged among the converters, while the required measurements are usually available locally at the installed devices. The proposed strategy can be adapted to the usual schemes used for on-board microgrids in order to effectively mitigates the voltage deviations under sudden load changes, with the aim to ensure secure operations and to increase the stability of the system.
A 1350W Nd: YAG Unstable Ring Laser With Efficient Reverse Wave Suppression

A breakthrough result of a kW-level Nd: YAG slab laser with a unidirectional unstable ring resonator is presented. The reverse wave is efficiently suppressed by a diaphragm placed at the focal point of the negative-branch unstable ring resonator. With specifically developed multi-function sensor and precision placement of the diaphragm, we have achieved the maximum output power of 1350 W with reverse suppression ratio of 54:1 corresponding to an optical–optical efficiency of 21.6% and a slope efficiency of 33.7%. The output power is currently limited by the available pump power. To the best of our knowledge, this is the highest power demonstrated from an Nd: YAG slab laser based on an unstable ring resonator with efficient reverse wave suppression, which is a paramount step to the further development of high power solid-state unidirectional ring lasers.
A 6.25 Mbps, 12.4 pJ/bit DQPSK Backscatter Wireless Uplink for the NeuroDisc Brain-Computer Interface

Wireless brain-computer interfaces (BCIs) used for fundamental neuroscience research in freely moving non-human primates (NHPs) require communication systems capable of transferring large volumes of recorded neural data while consuming minimal power. We introduce a 6.25 Mbps differential quadrature phase-shift keying (DQPSK) backscatter wireless uplink for the NeuroDisc BCI, operating in the 902–928 MHz industrial, scientific, and medical (ISM)-band. The backscatter uplink consumes 77.5 μ$W$ (only 0.06% of the system power budget), yielding a communication energy efficiency of 12.4 pJ/bit, while the measured error vector magnitude of the DQPSK constellation is 9.69%. The neural recording front-end has a measured input-referred noise of 2.35 μVrms at a maximum sampling rate of 20 kSps. We present end-to-end recording and wireless uplink validation with pre-recorded neural data as well as in vivo recordings from a pigtail macaque.
A Novel approach for bootkit detection in Android Platform

Detection of Malware in Android is already a challenging task, even worse Bootkits add more complexity and because it shifts the infection to the early stage of system’s booting, it makes it more stealthy and by far more persistent. In this paper, we discuss the challenge of malware analysis and detection in Android mobile phones platforms, especially at the boot and Kernel levels. To deal with this problem, we present a new approach for Bootkit detection based on malicious behaviour characterisation. Firstly, we identify and extract files from both the boot and the system partition from 3897 different firmware, then we establish a correlation mechanism for each sample. Secondly, we define the main characteristics vector of a normal booting stage behaviours, then a second characterisation vector for malicious boot behaviour. The experiment is performed on 7794 boot files from 87 different vendors and we show that our approach can successfully detect malicious data manipulation. Also, we highlight and give details about several key challenges that need to be addressed in future research.
Synchronous Machine Stability Model, an Update to IEEE Std 1110-2002 Data Translation Technique

IEEE Std 1110-2002 comprises a variety of synchronous machine models and standard methods determining model parameters from standard tests. It also covers the so-called data translation method which links (1)Time-constant-based, operational-inductance and (2)Equivalent-circuit-based models. In this paper, data translation technique for direct axis of well-known second-order synchronous machine dynamic model is reviewed and re-formulated. A new approach based on field resistance measurement is proposed to calculate precisely model parameters in the presence of so-called Canay inductance.
A Biometric Attack Case Based on Signature Synthesis

One of the biggest challenges in on-line signature verification is the detection of malicious and skilled attacks. This paper proposes a new conceivable attack for an on-line signature biometric scheme. The attack consists in interpolating a smoothed 8-connected version of the forged signature and selecting the most relevant salient points, skipping those that belong to tremor or indecisive movements due to the faking procedure. The Sigma-Lognormal model is then used to synthetize the new on-line signature in the hope of obtaining an improved imitation. The experiments aim to prove that the False Acceptance Ratio (FAR) is significantly worsened with the Biosecure-SONOFF public online signature database. These results are expected to elicit new automatic signature verifiers able to cope with this new kind of attack.
Business Process Analytics and Big Data Systems: A Roadmap to Bridge the Gap

Business processes represent a cornerstone to the operation of any enterprise. They are the operational means for such organizations to fulfill their goals. Nowadays, enterprises are able to gather massive amounts of event data. These are generated as business processes are executed and stored in transaction logs, databases, e-mail correspondences, free form text on (enterprise) social media, and so on. Taping into these data, enterprises would like to weave data analytic techniques into their decision making capabilities. In recent years, the IT industry has witnessed significant advancements in the domain of Big Data analytics. Unfortunately, the business process management (BPM) community has not kept up to speed with such developments and often rely merely on traditional modeling-based approaches. New ways of effectively exploiting such data are not sufficiently used. In this paper, we advocate that a good understanding of the business process and Big Data worlds can play an effective role in improving the efficiency and the quality of various data-intensive business operations using a wide spectrum of emerging Big Data systems. Moreover, we coin the term process footprint as a wider notion of process data than that is currently perceived in the BPM community. A roadmap towards taking business process data intensive operations to the next level is shaped in this paper.
Real- Time Spike Sorting for Multi-Electrode Arrays with Online Independent Component Analysis

When recording neural activity from extracellular electrodes, spike sorting is needed to separate the activity of different neurons. Most of the spike sorting packages are offline and use all the available data to distinguish the activity of single neurons from the recordings. However, when performing an experiment, it is helpful to monitor the activity of recorded neurons. Real-time spike sorting is not a trivial problem and previous approaches usually require the user to manually separate units or an initial offline calibration phase, which can be deleterious in case of non-stationarity of the recordings. In this contribution, we adapted the Online Recursive Independent Component Analysis (ORICA) algorithm for real-time spike sorting of high-density Multi-Electrode Array (MEA) data. Our approach, with its recursive implementation and dimensionality reduction, has the potential to cope with non-stationarity of the signals (e.g. caused by electrode drift) and it yields a better representation of the neural data, which facilitates spike detection and improves the detection accuracy.
Transient Stability-Constrained Optimal Power Flow with Power Flow Routers

Transient stability is important for configuring an optimal and secure operating point for the power system. The consideration of transient stability can make the optimal power flow with degraded performance. It is known that the power flow router (PFR) can manage multiple branch power flows in a more flexible way. In this paper, we study a new transient stability-constrained optimal power flow (TSC-OPF) model with the incorporation of PFRs. The load flow model of PFR is considered and the Single Machine Equivalent method is used for transient stability analysis. We adopt the sequential approach to address TSC-OPF with PFRs and we evaluate the system loadability on the WSCC nine-bus system. The simulation results reveal that the system loadability with PFRs are significantly enhanced and the number of iterations required to stabilize the system are largely decreased. It is verified that PFR is effective for loadability enhancement with the consideration of transient stability of power system.
Reliable Leak Detection in Pipelines Using Integrated DdTS Temperature and DAS Acoustic Fiber-Optic Sensor

Reliable and early detection of leaks in pipelines transporting liquid or gas is highly desirable. In this work, two orthogonal technologies based on high-sensitivity fiber-optic distributed sensing were integrated into one system and tested for their effectiveness in actual field installations. The first technology is based on temperature difference detection using Distributed Differential Temperature Sensor (DdTS or DDTS). The second is based on Distributed Acoustic Sensor (DAS), which uses Coherent Optical Time-Domain Reflectometry (C-OTDR). DdTS can almost instantaneously detect a temperature difference in the optical fiber as low as 0.0005 °C, while C-OTDR can detect minute wide-bandwidth acoustic signals. Both technologies can detect the location of a leak to within a few meters. Experimental results for water and nitrogen gas leaks detected by the integrated system are presented for two different installations in the field - one where the cable was buried directly in the soil and another where the cable was buried in soil inside a 2” conduit for about 29 km. DdTS reliably detected leaks at both installations, while DAS detected leaks only when the cable was buried directly in soil. Test results show that the integration of both DdTS and DAS technologies can increase the probability of leak detection and minimize false/nuisance alarms. The integrated system also detected third party interference (TPI), thus providing a one-system solution for pipeline integrity monitoring and intrusion detection up to 40 km from a central location.
Examining Threat Image Projection Artifacts and Related Issues: A Rating Study

Threat image projection (TIP) is a widely used software function of X-ray machines at airport security checkpoints. TIP projects fictional threat images (FTIs) of actual pre-recorded threat items (mainly guns, knives and improvised explosive devices) into the X-ray images of passenger baggage before they are displayed to security officers (screeners) for screening. TIP increases attention and motivation of screeners and is often used to measure their detection performance. In order to be effective, TIP has to project FTIs in a realistic way. In other words, it should not be possible to detect FTIs by simply detecting visual artifacts resulting from TIP projection. This study was conducted to evaluate TIP quality regarding potential artifacts. First, we interviewed screeners to explore which TIP artifacts they encounter in their day-to-day work. In a second step, we conducted a rating study to quantify the identified artifacts and the quality of TIP images in general. The majority of images (80%) produced by TIP were judged by screeners to appear realistic. However, in some images FTIs were positioned inadequately: the alignment (compared to the surrounding baggage items) appeared artificial (15%) or the placement appeared physically implausible (17%; e.g. an improvised explosive device going through a heel). These two issues also significantly affected the image to appear unrealistic in general. We conclude that in most cases, TIP succeeds in projecting FTIs without creating discernable artifacts. In some cases however, the FTI is positioned inadequately, which could be improved in the future.
Image Enhancement Method Based on Adaptive Fraction Gamma Transformation and Color Restoration for Wireless Capsule Endoscopy

Wireless capsule endoscopy (WCE) plays an important role in gastrointestinal (GI) tract observation and disease diagnosis. However, due to the limited illumination and complex environment in GI tract, endoscopic images are usually not clear enough to be interpreted by doctors or used for further processing. Although many algorithms have been proposed to improve image quality from different aspects, the balance of algorithm complexity and image quality is still a challenge worth exploring. In this paper, we propose a novel function named adaptive fraction gamma transformation (AFGT) to enhance the quality of images captured by the WCE. Characteristics of AFGT are adaptive to different luminance in an image and self-tunable to the statistical characteristics of different images. Besides, HSI color space is adopted for color restoration, namely, the saturation component is linearly scaled according to the enhanced intensity component. Experimental results show that performances of the proposed method in both visual quality and objective assessments are better than that of the state-of-art methods.
A Feasibility Study of Digital Beamforming for 5G mmWave Massive MIMO Base Station Receivers

The future 5G network is promising a performance increase by a factor of 100 to 1000 compared to the previous generation. The foreseen technical solutions to provide this gain are multiple. One of them is to use the large available or under-utilized spectrum in the millimeter wave domain combined with large antenna array beamforming systems in a dense deployment of small cells, the so called massive MIMO. Many previous works have performed system analysis evaluating the performances of the three different architectures, analog, hybrid and digital beamforming, and the impact of design parameters. In this work, in order to evaluate the feasibility of DBF, we propose a simplified methodology to derive, from the 5G key performance indicators, a circuit level specification for such systems. In particular the proposed method takes into consideration, in the presence of strong out of band interferers (OoBI), the impact of the Rx nonlinearity and the local oscillator noise floor. The OoBIs power level is evaluated based on a multiple operator scenario. The resulting specifications were compared to the state of the art to perform a power consumption evaluation and demonstrate the feasibility of digital architectures.
600mW Active Rectifier with Shorting-Control for Wirelessly Powered Medical Implants

An integrated active-rectifier including overvoltage protection for wireless power transfer has been fabricated in 180nm HV-CMOS. The rectifier operating at 600kHz can provide up to 600mW output power and achieves greater than 90% efficiency with output powers from 40mW to 600mW. This power range is suitable for a lifetime implantable pressure monitoring telemeter for patients with hydrocephalus. It also enables the use of small piezoelectric pumps or actuators for active shunt control. As reliability is a key concern for a lifetime implantable medical device, shorting-control has been implemented on chip for over-voltage protection and voltage regulation to maintain the output voltage within safe operating limits despite changes in load or coil coupling. Minimal additional power is dissipated while shorting-control is active.
Uncertain Optimal Planning of Distributed Generators Based on DS Evidence Theory

In view of the insufficient decision-making ability in the existing distributed generator (DG) optimal planning methods, this paper proposes a multi-objective DG optimal allocation model and algorithm by combining evidence theory and genetic algorithm considering its uncertainty. This model takes evidence theory as the inner layer to evaluate the configuration scheme generated by outer layer and guide the evolutionary direction of outer layer genetic algorithms. In this method, multi-attribute assignment problem with attribute value including determined value and interval value are deeply studied in the configuration scheme, and the influence of uncertain factors on the optimal allocation of DGs is quantitatively analyzed. The simulation is conducted on a typical distribution network as an example. The results validate the constructed models and the proposed methods. The conclusion is superior to the TOPSIS method based on the interval numbers through contrastive analyses.
Muscle Synergy Adaptation During A Complex Postural Stabilization Task*

The organization of encoded motor modules or motor primitives in the central nervous system and their combination leads to different aspects of natural motor behavior. It is believed that neural stimulation of these coded sections activates specific groups of muscles to achieve a behavioral goal. We use the muscle synergy (MS) hypothesis to compare activation patterns during overground walking and slackline walking for a small group of highly proficient slackliners and beginners. Synchronous MS were extracted using factor analysis (FA) for rhythmic and arrhythmic repertoire of movement. The results revealed no significant difference between slackliners and non-slackliners as the extracted synergies were dependent on the variability of the task. Besides, the shared dimensional space revealed the task-specific higher loading of the quadriceps muscles for walking with such postural constraints.