Track circuit is one of the most important parts of train operation control system. In current railway transportation research area, track circuit intelligent maintenance, including fault diagnosis and prediction is mainly achieved by using deep learning (DL). However, the application of DL models in practical has a great limitation. The training of track circuit intelligent maintenance DL models requires huge amount of labeled data. And DL models are too fragile since real data and training data can hardly meet the requirement of independent and identically distributed. In this paper, a new track circuit fault prediction model based on long short-term memory network and attention mechanism is proposed. To enhance the practical performance of this DL model, a deep data adaptation algorithm is applied to make an improved method to overcome degradation problem caused by non-identically distributed of training and real data. Moreover, it can be also applied to reduce data dependence of the DL model training. To evaluate the performance of the proposed DL model and the improved method, experiments were carried out based on real data from China Railway Shenyang Group Company Limited computer monitoring system. The experimental results show that the proposed track circuit fault prediction model can provide fault prediction results with an accuracy better than 95%, the improved method has a better performance than the DL model without deep data adaptation algorithms, and the proposed deep data adaptation algorithm has a better performance than conventional data adaptation algorithms. Compared with the conventional fault prediction DL model without transfer learning, the proposed method can achieve a much better performance, with an improvement, in the test, of over 75%. And the proposed deep data adaptation algorithm can make an accuracy improvement, in the test, of over 30% than conventional data adaptation methods.

In this work we propose a new cooperation strategy between connected autonomous vehicles in on-ramps merging scenarios and we implement the cut-in risk indicator (CRI) to investigate the safety effect of the proposed strategy. The new cooperation strategy considers a pair of vehicles approaching an on-ramp. The strategy then makes decisions on the target speeds/accelerations of both vehicles, possible lane changing, and a dynamic decision-making approach in order to reduce the risk during the cut-in manoeuvre. In this work, the CRI was first used to assess the risk during the merging manoeuvre. For this purpose, scenarios with penetration rates of autonomous vehicles from 20% to 100%, with step of 10%, both connected and non-connected autonomous vehicles were evaluated. As a result, on average a 35% reduction of the cut-in risk manoeuvres in connected autonomous vehicles compared to non-connected autonomous vehicles is obtained. It is shown through the analysis of probability density functions characterising the CRI distribution that the reduction is not homogeneous across all indicator values, but depends on the penetration rate and the severity of the manoeuvre.

This paper presents, a mutual coupling reduction technique between a very closely spaced (1.8 mm) two-element microstrip based MIMO antennas using a modified inverted-fork shaped decoupling (m-IFSD) structure. The m-IFSD consists of an inverted fork and a cross shaped structure with a shorted via. The combined effect of inverted-fork and shorted cross shaped structure results in mutual coupling reduction below −35 dB between adjacent antenna elements. The decoupling technique is analyzed using an approximate transmission-line model and field distribution. Further, the two-element MIMO antenna design is extended to 8-element MIMO configuration to improve the MIMO diversity. To verify the proposed isolation technique a two-element MIMO antenna prototype is fabricated and measured. The proposed MIMO antenna exhibits a low mutual coupling (< −35 dB) with good impedance matching (< −10 dB) at 5.45 GHz. The MIMO antenna provided a total active reflection coefficient (TARC) less than −10 dB and envelop correlation coefficient (ECC) (for isotropic propagation scenario) less than 0.5. Finally, the ECC of the proposed MIMO antenna system is analyzed for a realistic Gaussian/Uniform propagation scenario for various incidence angles and angular spreads to better understand the effect of the mutual coupling reduction technique.

Young’s double slit experiments, which represent the mystery of quantum mechanics, have been described by wave theories such as the electromagnetic wave, quantum probability waves and pilot waves. Recently, the photon chamber has been proposed to detect the traces of the light beams in the 1D-grating diffraction experiments, namely to visualize the passage of the light beams. In this article we apply the photon chamber to the 2D grating. The experiments show that the light beams behave as rays moving through the photon chamber. The existence of the light tracks indicates the particle nature of the light beams in the wave experiments. The challenge is to explain the fact that the light pass through the photon chamber, which may be placed anywhere between the grating and the detecting screen, as photons, while still distribute as waves on the detecting screen.

With the present day trends in technology, greater and more human beings rely on their non-public devices to preserve their sensitive information. Concurrently, the environment in which these devices are linked have grown to develop to be higher dynamic and complex. This opens the communicate of if the modern authentication techniques being used in these devices are reliable adequate to hold these user’s files safe. This paper examines the wonderful purchaser authentication schemes proposed to make larger the safety of great devices. This article is wreck up into two one of a variety avenues discussing authentication schemes that use each be- havioral biometrics or bodily layer authentication. This survey will discuss about every the advantages and challenges that show up with the accuracy, usability, and widespread safety of computing machine getting to recognize techniques in these authentication systems. This article pursuits to beautify in addition look up in this challenge by means of exhibiting the extra than a few existing day authentication models, their schematics, and their results.

We focus on the secure performance metrics at the legitimate users, i.e. secure outage probability (SOP) and secrecy capacity, to quantify the secrecy performance of NOMA-RIS-aided IoT systems. We assume the RIS is placed between the access point and the legitimate devices, and is expected to enhance the link security through the smart phase shift mechanism of metasurface elements in RIS. We first present analytical results for the SOP and secrecy capacity. Next, an iterative search algorithm is adopted to exhibit optimal SOP for further insights and analysis. In order to help the base station allocate power coefficients to NOMA users properly, an efficient deep-neural network (DNN)-based secure metric prediction scheme is adopted to achieve the secure performance. Our derivations and simulations indicate that the number of meta-surface elements of the RIS and the average signal-to-noise ratio at the base station contribute the most to the system performance enhancement.

A typical time-of-flight (ToF) tomography method requires accurate information about the system’s calibration parameters in order to reconstruct the features of interest  within a region of interest (RoI). This paper proposes a constrained joint calibration and tomography (CJCT) method based on a separable least squares (SLS) approach for ToF tomographic systems. Contrary to the existing offline and online calibration methods, the proposed method utilizes prior information about the calibration parameters in the form of constraints. The imposed constraints allow for an efficient search for the minimum of the inherent SLS cost function by reducing the system and measurement ambiguities. Thus, the proposed CJCT method can find the optimum values for both tomography and calibration parameters more efficiently compared to unconstrained methods. The general applicability of the proposed method is demonstrated by evaluating it on two applications: tomographic reconstruction of the permittivity distribution in a RoI using ToF measurements from a high resolution millimeter-wave radar system, and the  temperature distribution inside an industrial blast furnace using measured and simulated acoustic ToF values. The simulation and experimental results show that the proposed method performs significantly better in terms of tomographic reconstruction accuracy compared to unconstrained methods.

Electrification of higher torque density applications in the transportation sector and robotics sector are primarily met with permanent magnet synchronous machines (PMSMs), attributing to their unique properties. However, wide operating temperatures, field weakening controls, and low-quality magnets, lead to uniform demagnetization of rotor magnets. Demagnetization in torque control applications reduce the effective output torque as the output torque is proportional to rotor flux level. Detection of such rotor demagnetization is essential and only second to few key faults from the system perspective. A simple and common approach to detect uniform demagnetization in a sensored-FOC PMSM drive system in torque control mode is discussed in this paper with experimental results emulating uniform demagnetization effect. The false positive nature in the proposed algorithm under a position sensor offset error is discussed, followed by a disambiguation strategy, which are primary contributions of this paper. Analysis, simulation and experimental results are presented to validate the proposed disambiguation strategy.

This paper describes an implantable wireless microsystem for making embedded ultrasound distance measurements and its applications in real-time bladder volume (BV) estimation. A tuned inductive link at 125 kHz is used for both power transfer and bidirectional data telemetry with the implanted unit. Implant power consumption is minimized by using a single-bit correlation receiver to estimate ultrasound time-of-flight (ToF) from A-mode data acquired by an on-board piezoelectric transducer. In vitro tests of the device (external diameter =30 mm including coil) in saline solution show a maximum operating depth of 3.2 cm from a 10 cm diameter external coil operated at +/-18 V. Wireless BV measurements using both spherical and non-spherical bladder phantoms show estimation errors < 15\% for volumes in the 50-300 mL range.

The purpose of violence detection is to determine whether or not a  violent action has occurred. This topic grew in popularity due to the need to develop appropriate and automatic violence detection systems that investigated visual data obtained from security cameras positioned in various regions. To classify the videos into four different crime categories or non-violent, we downloaded 200 videos from multiple sources such as news channel recordings, YouTube, and online posted videos on all other social networking sites. We created four different classes, such as arrest, assault, arson, and abuse, with each class containing 50 videos. This work used pre-trained deep neural networks to provide a low-complexity strategy for detecting violence.

Free-space optical communication (FSOC) systems require precise pointing, acquisition, and tracking to send and receive optical beams for effective operation. In a lens-assisted beam steering (LABS) system, light is steered by controlling the emission location in a focal plane. The emitted light is directed into the scene by a lens, like a camera but operating in reverse. In this paper, we demonstrate a novel free-space optical communication link using a micro-opto-electro-mechanical-system (MOEMS) based photonic integrated circuit (PIC) for LABS between multiple receive locations. The MOEMS PIC operates via selective electrical actuation of an array of small grating switches (30 μm x 30 μm footprint, 100 μm pitch). Data rates up to 10 Gbps and a 3 dB optical bandwidth covering the infrared C and L bands (1530 nm to 1625 nm) are measured over a 1 m free-space link distance. Eye diagrams indicate a quality communication link for data rates up to 10 Gbps and bit-error-rates < 10-10 are measured for on-off keying (OOK) modulation. A measured beam profile is propagated into the far-field via simulation, and used to calculate link budgets for example CubeSat crosslink and downlink scenarios. Link budget calculations indicate potential > 1 Gbps CubeSat FSOC crosslinks for link lengths > 1000 km and 1 W of input optical power, using identical 90 mm transmit and receive apertures and a commercially-available fiber-coupled InGaAs avalanche photodiode (APD) receive detector.

We study  $P(n,3)$, the size of the largest subset of the set of all permutations $S_n$ with minimum Kendall $\tau$-distance $3$. Using a combination of group theory and integer programming, we reduced the upper bound of $P(p,3)$ from $(p-1)!-1$ to $(p-1)!-\lceil\frac{p} 3}\rceil+2\leq (p-1)!-2$ for all primes  $p\geq 11$.  In special cases where $n$ is equal to $6,7,11,13,14,15$ and $17$ we reduced the upper bound of $P(n,3)$ by $3,3,9,11,1,1$ and $4$, respectively.

This work presents a proof that the existence of a sum of squares (SOS) decomposition for a closed-loop (asymptotic) stability condition valid around the origin is equivalent, for input-affine polynomial systems, to the existence of an SOS certificate for a strict dissipation inequality subject to a certain equality constraint. In addition, a new sufficient condition for feedback asymptotic stabilization of nonlinear systems around nonzero equilibria is proposed using the notion of equilibrium-independent dissipativity (EID). Then, this new condition is applied to polynomial systems, specifically. It is proved in this article that the aforementioned SOS certificate for strict dissipativity exists, with a strongly convex energy function, if  and only if a certain equilibrium-independent strict dissipativity (EISD) condition also has an SOS decomposition. Consequently, not only the origin, but any assignable equilibrium point can be globally stabilized by an appropriate state feedback. As the systems considered are polynomial, semidefinite programming (SDP) can be used to solve this global stabilization problem. A numerical example provides further evidence on the usefulness of the proposed strategy.

Distributed Generation (DG), especially photovoltaic distributed generation (PVDG), has contributed significantly to serving the growing demand for active power. However, the necessity to meet the demand for reactive power is still challenging. In addition to active power generation, photovoltaic inverters can be used to provide ancillary services to the grid, including reactive power compensation. This paper proposes a metaheuristic approach based on Particle Swarm Optimization (PSO) for allocation and sizing of photovoltaic inverters performing the complementary function of static synchronous compensator (PV-STATCOM) units. The objective function of the above-mentioned approach is to minimize the initial investment in the acquisition of PV-STATCOM units. The proposed methodology takes into account both daily load curve and generation and is applied to the 33 Bus Test System. The results show improved grid voltage profile throughout the day. Also, the photovoltaic inverter did not fail to perform its main function, i.e., that of generating active power. One of the major contributions of the proposed methodology is that it will assist investors in determining the allocation and sizing of PV-STATCOM units to perform the ancillary service of reactive power compensation in the grid.

The highly dynamic nature of cognitive radio (CR) systems and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transport systems (ITS). In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology in a secure manner. Specifically, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs’ secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of the SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. Furthermore, we develop an offline blockchain framework to store the information of participating relays and make it available for future operations, in order to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. The stored reputations of the participating relays are used to develop a self-learning algorithm to exclude the non-reputable relays from the selection group. Finally, we present thorough numerical results to demonstrate the superiority of the proposed system in terms of security, credibility, and integrity.

MIMO antenna for 5G applications A quad-element multiple-input multiple-output (MIMO) antenna operating in 3.5 GHz (3.4GHz-3.6GHz) and 4.9GHz (4.8GHz-4.96GHz) frequency bands for the fifth-generation (5G) smartphone applications is presented. Each single radiating element consists of an L-shaped strip and a modified C-shaped strip. To keep enough space for second-, third-and fourth-generation (2G/3G/4G) antennas in a limited space, four antenna elements are printed respectively at four corners of a system board. The total size of the proposed antenna is only 145×75×5mm3. The design steps and the analysis of key parameters of an individual antenna element are derived to demonstrate the design methodology of the proposed antenna. The measured total radiation efficiency is better than 60% at both the operating frequency bands. The measured isolation is better than 16 dB and the calculated envelope correlation coefficient (ECC) is smaller than 0.02. The measured results demonstrate that the proposed MIMO antenna is a good alternative for sub-6 GHz 5G smartphone applications.

We developed a new connectivity measure using an multiview brain decoder to efficiently predict the links between anatomical regions while performing the visual object recognition task.

The work in this paper is an extended research of our previous work: M. Delianidi, K. Diamantaras, G. Chrysogonidis, and V. Nikiforidis,“Student performance prediction using dynamic neural models,” in Fourteenth International Conference on Educational Data Mining (EDM 2021), 2021, pp. 46–54. In both works we study the task of predicting a student’s performance in a series of questions based, at each step, on the answers he/she has given on the previous questions. We propose a recurrent neural network approach where the dynamic part of the model is a Bidirectional GRU layer. In this work, we differentiate the model architecture from the earlier paper by imposing that the dynamic part is based exclusively on the history of previous question/answers, not including the current question. Then, the subsequent classification part is fed by the output of the dynamic part and the current question. In this way, the first part estimates the student’s knowledge state and represents it with a dynamically generated vector considering only the student’s previous questions and responses. We call this part the “Knowledge State Representation subnet’‘. Using this representation, the following “Tracing subnet’‘ which is a static multi-layer classifier can predict the correctness of the answer to any following question. Therefore, this architecture is suitable not only for described prediction task but also for recommendation tasks which were not possible with the previous architecture. In addition, in this work, we also study the effectiveness of our models against each other, as well as against the previous state-of-the-art models on seven datasets including a new multi-skills dataset derived from the NeurIPS 2020 education challenge. Both our models compare favorably against the state-of-the-art methods in almost all datasets, while the newly proposed architecture achieves very similar results compared to our earlier model in most cases except for two cases, including the multi-skill “NeurIPS-2020-small” dataset, where it achieves considerably better results.