Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Structured Versus Unstructured Environments Based on Pedestrians' and Vehicles' Motion Trajectories

Trajectory behaviours of pedestrians and vehicles operating close to each other can be different in unstructured compared to structured environments. These differences in the motion behaviour are valuable to be considered in the trajectory prediction algorithm of an autonomous vehicle. However, the available datasets on pedestrians' and vehicles' trajectories that are commonly used as benchmarks for trajectory prediction have not been classified based on the nature of their environment. On the other hand, the definitions provided for unstructured and structured environments are rather qualitative and hard to be used for justifying the type of a given environment. In this paper, we have compared different existing datasets based on a couple of extracted trajectory features, such as mean speed and trajectory variability. Through K-means clustering and generalized linear models, we propose more quantitative measures for distinguishing the two different types of environments. Our results show that features such as trajectory variability, stop fraction and density of pedestrians are different among the two environmental types and can be used to classify the existing datasets.
Evaluation of Safety Control Scheme for Prohibiting EMUs and Freight Trains from Meeting in the Section

In order to ensure safe operation, the China Railway Corporation implemented the principle that Electric Multiple Units (EMUs) and freight trains do not meet in the section on some railway lines when adjusting the train operation chart on June 25, 2021. EMUs and freight trains are prohibited from meetings, which ensures the safety of traffic. However, it will also have a certain impact on operational efficiency. The safety control scheme is added to adjust the train operation plan, so as to avoid the meeting between EMUs and freight trains at the planning level. This paper proposes a quantitative evaluation method for different safety control schemes based on Coloured Petri Nets (CPNs). To exemplify the proposed approach, starting from the dual-track passenger and freight railway, this paper establishes a CPN model to analyze the impact of different safety control schemes on operational efficiency.
Robustifying Car-Following Models with Explicit Reaction Times

Car-following models like the Intelligent Driver Model (IDM) describe the longitudinal behavior of an ego-car that reacts to a leading vehicle. They have been applied on a broad range of mobility-related tasks like the analysis of traffic phenomena, microscopic traffic simulations or single vehicle behavior prediction. Although car-following models can be formulated with explicit delays, the IDM is generally used in form of a time-continuous differential equation without delays. One reason is that in early IDM parameter calibration work it was found that the effect of the explicit delay is negligible. In this paper, we reopen the question of the importance of delays for accurate trajectory modeling, based on an analysis with real trajectories. We show that indeed both trajectory matching and prediction errors are strongly reduced when considering delays, enabling and improving their usage for safety-relevant scenarios like required for behavior prediction in intelligent Adaptive Driving Assistant Systems (ADAS). In addition, from optimizing the match of the model with real trajectories we gain a delay distribution that resembles the distribution of human driver reaction times.
Energy transfer ratio for finite size dipoles in microwave range - FRET analogy

Förster Resonant Energy Transfer (FRET) is a near-field interaction between pairs of quantum emitters that can be modified by their electromagnetic environment. The different approaches to control FRET have been studied analytically and experimentally in the optical frequency range, although, such experiments are challenging. An effective way to investigate this effect is to directly measure an impedances of dipole antennas in the microwave frequency range, which is an analog of the FRET. In this work, we perform an analytical and experimental analysis of the energy transfer between two dipoles near a conductive plane in the microwave range for two dipole orientations: parallel and perpendicular. We analytically investigate the influence of the dipole size on the energy transfer and compare it with the solution for FRET between ideal point dipoles. We show theoretical and experimental evidences of quenching and enhancement of the energy transfer for respectively parallel and perpendicular orientations of the dipoles.
Visibility in Simple Polygons

We present a new method of decomposing a simple polygon which allows the preprocessing of the polygon to e ciently answer queries of various forms. Using O(n3 log n) preprocessing time and O(n3) space, we can, given a query point q inside or outside the polygon, recover the number of vertices visible from q in O(log n) time. Also, we can recover the visibility polygon of q in O(log n+ k) time, where k is the size of the visibility polygon. The key notion behind the decomposition is the succinct representation of visibility regions. We show that there can be (n3) distinct visibility regions, each of which can see (n) points. The main technical theorem is that there can be (n2) regions with minimal visibility sets, thus permitting preprocessing in O(n3 log n) time and O(n3) space. These techniques are extended to handle other types of queries. Given m xed points in the plane, and a polygon of size n, we can, given a query point q inside or outside the polygon, recover the number of xed points unobstructed by the polygon boundary in O(logmn) time and recover the visible set of size k in O(logmn+ k), with O(m2(m+ n) log n) preprocessing time and O(m2(m+ n)) space. We also explore line segment queries. Given a line segment inside or outside a polygon of size n, we can recover its weak visibility set and more generally its weak visibility sequence in O(k log n), using O(n3 log n) preprocessing time and O(n3) space. To answer weak visibility line segment queries on a set of m xed points in the plane, we need O(k logmn) query time, given O(m2(m+n) log n) preprocessing time and O(m2(m+ n)) space. vi
On Mapping in Multilayer Environments: A Robust Graph-SLAM Approach Using LIDAR Intensity and Elevation Data

An accurate map generation in multilevel environments is very challenging especially in cities packed with longitudinal bridges. This is because of obstruction and deflection of satellite signals of GNSS/INS-RTK (GIR) systems by the complex road structures. Even though using SLAM technologies, capability and implementation are rarely discussed in the literature because of difficulties to determine true correspondences between vehicle positions. In addition, these environments impose to address the issue of maintaining consistency in the Absolute Coordinate System between layers in order to enable many applications such as accurate estimation of elevation errors and sharing precise traffic information between autonomous vehicles. Accordingly, this paper proposes a Graph SLAM framework to generate precise LIDAR maps regardless the road structure complexity. The essence is to conduct the entire process in the image domain instead of 3D point cloud domain such as loop-closure detection, relative position error compensation and edge calculation. Moreover, a new concept of virtual layer edges is introduced and investigated to efficiently express the layer consistency in the optimization process. A critical highway and multilevel course in Tokyo has been scanned to check the reliability of addressing the above issues. The experimental results have verified the robustness, scalability and reliability of the proposed GS framework to generate accurate, coherent and consistent maps and outperform expensive GIR systems.
Work-in-Progress: Efficient Low-latency Near-Memory Addition

Near-memory computing provides energy and time saving for performing bitwise-parallel operations in computing systems. Bitwise parallelism of addition, however, is restricted due to carry propagation. In this work, we propose new circuits for performing digit-wise near-memory addition for ternary data. The proposed architecture can perform carry-free addition and, despite the number of digits of the input operands, its latency is 21 memory cycles which is the lowest latency compared to prior work.
Triple Exploit Chain with Laser Fault Injection on a Secure Element

This work presents three vulnerabilities identified in the ATECC608B secure element. This circuit is the latest silicon revision of the ATECC devices family, which is widely deployed in IoT devices. When chained, the three vulnerabilities exploitation lead to a protected secret data extraction from the secure element. For this work, three different commands of the chip are faulted with laser illumination. The first attack recovers internal secret EEPROM masking keys. With the knowledge of those keys, further attacks are leveraged: we show how authentication and session key generation can be hijacked with laser assistance to finally gain authorized access to a secret data slot. We also used very long laser pulses for our attacks in order to fault multiple memory accesses with high efficiency. Our study was done in a black box approach, and shows multiple exploit attacks using laser fault injection can be practical. In particular, this allowed us to recover secret data on a real test device.
Evaluation and Optimization of Lane Allocation at Isolated Intersections Using Trajectory Data

Traditional optimization methods for lane allocation focus on increasing the intersection capacity or reducing the degree of saturation for approach lanes in a separate stage of traffic design. However, lane allocation and signal timing need to be considered at the same stage in order to fully utilize the lane-resources at isolated intersections. To accurately evaluate the effectiveness of a lane allocation plan, this paper proposes an evaluation framework based on vehicular trajectory data, considering eight assessment indexes in three segments of the intersection. With the key assessment indexes, an optimization model for signal timing and lane allocation of isolated intersections is established to minimize the critical degree of saturation of the approach lanes. A case study is conducted to evaluate the performance of the proposed model. The results show that the proposed model could improve the lane-level operation efficiency of the intersection.
Uniaxially Symmetrical T-Junction OMT with 45-Degree Tilted Branch Waveguide Ports

A T-junction Ortho-Mode-Transducer (OMT) is known as one of waveguide components that separate two orthogonal linear polarizations in the same frequency band. One of the advantages of the T-junction OMT is short axial length. However, the two rectangular ports need to be orthogonal, and it is difficult to realize the wideband performances for its asymmetry. We propose a uniaxially symmetrical T-junction OMT to realize the wideband performances. The proposed configuration has two branch waveguides which are 45-degree tilted to the short circuit. The uniaxially symmetrical configuration enables to realize the same performances for the two ports, and the impedance matching is easier compared to the conventional one. The polarization separation principle can be explained using principles of Ortho-Mode-Junction (OMJ) and turn-style OMT. The design results demonstrated a return loss of 25dB, a polarization isolation of 30dB, an interterminal isolation of 21dB and a loss of 0.25dB, with a bandwidth of 15% in the K-band for satellite communication systems. The OMT was then fabricated as a single piece via 3D printing and evaluated against the calculated performance indices.
Residual Networks and Deep-Densely Connected Networks for the Classification of retinal OCT Images

Diabetic macular edema (DME) and drusen macular degeneration (DMD) are two se-vere vision-threatening diseases affecting the mac-ula of the eye. This study presents deep learning based classification models for retinal optical co-herence tomography (OCT) images that distinguish between healthy eyes, DME and DMD cases. The work involves the use of residual models, and deep and densely connected networks for the analysis of OCT images. This involves pre-initialisation of the model using the ImageNet dataset, followed by fine-tuning with OCT images. Both models are very powerful and suitable for real-time use in clinical practise. ResidualNets use skip connections, where the output of the previous layer is added to the layer before it. DenseNets use dense connections between the convolutional layers of the network, which allows deeper supervision between layers. This makes it easier for the model to learn the complex feature maps of the images of OCT in each layer of the network. The models are trained and evaluated using the Mendeley OCT dataset, a publicly available SD-OCT dataset for the retina. We calculate the F1 score, accuracy, precision and recall to evaluate the models. The models provide excellent performance without requiring any pre-processing steps. The promising performance of the computerised systems prove that they can serve as automatic recognition tools to assist ophthalmologists.
Traffic State Estimation with Loss Constraint

Traffic state estimation is relevant for real-time traffic control, providing travel information as well as for expost analysis of traffic patterns. While the output is usually the average speed and vehicle flow along street segments, the type of input data and the existing methods to obtain the output are diverse. Recently, physics-informed data-driven approaches started to emerge that enrich the estimation process with information taken from physical models. In traffic, so far, these have been the continuity equation and the fundamental diagram, designed to describe fully the traffic dynamics along links and corridors. In this paper, we propose a simpler and practice-ready physics-informed machine learning approach that informs the estimation through the well-established fundamental diagram in a loss constraint. It is designed for a link-level analysis where traffic homogeneity along the considered link is assumed. We apply the proposed method to full-trajectory drone data from Athens, Greece, demonstrate the applicability of our proposed approach, and point out its potential to future applications, e.g., a filter for control algorithms.
DPL: A Language for GDPR Enforcement

The General Data Protection Regulation (GDPR) regulates the handling of personal data, including that personal data may be collected and stored only with the data subject's consent, that data is used only for the explicit purposes for which it is collected, and that is deleted after the purposes are served. We propose a programming language called DPL (Data Protection Language) with constructs for enforcing these central GDPR requirements and provide the language's runtime operational semantics. DPL is designed so that GDPR violations cannot occur: potential violations instead result in runtime errors. Moreover, DPL provides constructs to perform privacy-relevant checks, which enable programmers to avoid these errors. Finally, we formalize DPL in Maude, yielding an environment for program simulation, and verify our claims that DPL programs cannot result in privacy violations.
Research on Intelligent Question Answering System for Chemical Safety Based on Knowledge Graph

In recent years, the chemical industry has developed rapidly, and the production usage of chemical has continued to increase. In the face of variety of chemicals and ever-changing chemical reactions, people’s cognition of knowledge and damage characteristics lags behind, leading to frequent chemical accidents. For facilitating and assisting people to make reasonable judgments in daily work or when accidents happen, this paper utilizes the existing structured data of chemical safety constructs an intelligent question answering system based on knowledge graph, which is applied to chemical safety for the first time. Firstly, to deal with the problem of user intent understanding in question answering system, a multi-classifier combining NB (Naive Bayes) algorithm and Bert-BiLSTM-CRF model is constructed to complete the task of question classification. Secondly, according to the classification result, the most similar question template is matched. Finally, the semantic information of the template and the intention words in the question are mapped to the chemical safety knowledge graph to retrieve the answers. In addition, this paper utilizes SVM (Support Vector Machines) to compare the effect of question classification, and utilizes BiLSTM-CRF model to compare the effect of feature words recognition. The experimental results demonstrate that 91% of the questions are answered correctly, indicating that question answering system can answer the questions related to chemical safety in real time and accurately.
Frequency Agile, Sub-wavelength, Metamaterial-inspired Huygens Dipole Antenna

Near-field resonant parasitic based Huygens sources have been of special interest in many antenna applications recently. Unidirectional patterns with high directivity, high radiation efficiencies and large front-to-back-ratio values, as well as compact dimensions, have made them an attractive choice for space-limited wireless platforms. It is demonstrated with CST simulations that the frequency of operation of a sub-wavelength, metamaterial-inspired Huygens dipole antenna and, hence, its cardioid pattern, can be easily adjusted across a broad bandwidth using tunable lumped reactive elements.
SmartAct: Energy Efficient and Real-Time Hand-to-Mouth Gesture Detection Using Wearable RGB-T

Researchers have been leveraging wearable cameras to both visually confirm and automatically detect individuals’ eating habits. However, energy-intensive tasks such as continuously collecting and storing RGB images in memory, or running algorithms in real-time to automate detection of eating, greatly impacts battery life. Since eating moments are spread sparsely throughout the day, battery life can be mitigated by recording and processing data only when there is a high likelihood of eating. We present a framework comprising a golf-ball sized wearable device using a low-powered thermal sensor array and real-time activation algorithm that activates high-energy tasks when a hand-to-mouth gesture is confirmed by the thermal sensor array. The high-energy tasks tested are turning on the RGB camera (Trigger RGB mode) and running inference on an on-device machine learning model (Trigger ML mode). Our experimental setup involved the design of a wearable camera, 6 participants collecting 18 hours of data with and without eating, the implementation of a feeding gesture detection algorithm on-device, and measures of power saving using our activation method. Our activation algorithm demonstrates an average of at-least 31.5% increase in battery life time, with minimal drop of recall (5%) and without impacting the accuracy of detecting eating (a slight 4.1% increase in F1-Score).
E-orientation system socio-psychological data sensitive

Nowadays, the diversity of specialty choices in the different fields of study is a challenge for students. orientation has taken place at different levels of education and has become a crucial step in deciding the future career of students. In the majority of cases, the choice of the field of study is made subjectively, which sometimes results in failure or dropping out of educational institutions. In order to overcome this problem, we have proposed a specialty recommendation system that proposes the most appropriate field of study to students according to their academic and psycho-social background. In order to realize this recommendation system, we followed the construction methodology of recommendation systems, which is divided into three phases, starting with the first phase of data collection and preprocessing, during which we applied different techniques of data analysis, namely, principal component analysis for the reduction of the dimensionality of the database, followed by the second phase, the learning phase, whose objective is the construction of the students’ profiles through a clustering algorithm. The obtained profiles are used in the last phase, which is the prediction phase, where we have used a neural network to predict the appropriate recommendation. To validate our proposal approach, we developed a prototype using the Portuguese database that allowed us to analyze the relationship between different social data and the performance of students. The results obtained from the prototype are very interesting and reveal a strong correlation between the different types of data (numerical, psycho-social, or a combination of both) and the performance of students in a specialty.
Multipartite Unlockable Bound Entanglement In The Stabilizer Formalism

We find an interesting relationship between multipartite bound entangled states and the stabilizer formalism. We prove that, if a set of commuting operators from the generalized Pauli group on n qudits satisfy certain constraints, then the maximally mixed state over the subspace stabilized by them is an unlockable bound entangled state. Moreover, the properties of this state, such as symmetry under permutations of parties, undistillability, and unlockability, can be easily explained from the stabilizer formalism without tedious calculation. In particular, the four-qubit Smolin state [Smolin, Phys. Rev. A 63, 032306 (2001)] and its recent generalization to even numbers of qubits [Bandyopadhyay et al., Phys. Rev. A 71, 062317 (2005); Augusiak et al., ibid 73, 012318 (2006)] can be viewed as special examples of our results. Finally, we extend our results to arbitrary multipartite systems in which the dimensions of all parties may be different.
Timeliness Improvement of Information Interaction for IoVs Using Mini-Batch Identification Strategy

When it comes to IoVs, BSM plays a crucial role since BSM-based exchanges between entities in the IoV are essential for traffic flow management and collision prevention. The IoVs are primarily concerned with ensuring that the BSMs are received by other entities in a timely fashion. A number of schemes with batch verification have been investigated in an effort to improve the verification efficiency. However, despite the improvement in verification efficiency, the batch verification failure cannot be settled with these schemes, which drastically reduces the benefit of batch verification. Therefore, to deal with the above problem, this paper proposes a new BSM identification mechanism based on batch verification to find invalid BSMs faster by shortening the batch length according to the historical invalid signature rate, which is called as mini-batch identification strategy. Simulations indicate that the proposed mechanism has the potential to reduce the latency by 60% in the ideal case, compared with several existing identification algorithms, while maintaining the ability to find invalid signatures efficiently despite a large base number of signature conditions.