Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperation of V2I/P2I Communication and Roadside Radar Perception for the Safety of Vulnerable Road Users

Multiple means have been proposed to enhance safety for vulnerable road users (VRUs), especially pedestrians. V2X communication, including vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and vehicle-to-pedestrian (V2P), and roadside sensor perception are two such approaches. V2X communication requires communication devices and is not applicable to VRUs not carrying such a device. On the other hand, for specific areas such as an intersection roadside sensors are getting installed and operable for surveillance purpose. So we investigate the cooperation of the two approaches at an intersection. A 79GHz millimeter radar is used as the roadside sensor to detect vehicles and pedestrians including those without V2X communication ability, while V2I/P2I (pedestrian-to-infrastructure) communication is utilized to assist the radar to recognize hidden vehicles and pedestrians blocked by buildings. In the paper, we first introduce our developed V2P communication and 79GHz radar systems, and then consider object identification and selection based on fusing perception data and communication data. As a new function we also investigate action prediction and collision prediction for pedestrians. Our system is experimented with field tests and the cooperation performance is verified.
User-centered Integration of Automated Air Mobility into Urban Transportation Networks

The existing ground-based transportation network (e.g., rails, streets) has natural limits that are pre-determined by the constellation of the system itself. If these limits are reached or exceeded, the system will no longer be able to manage the existing traffic volume. In order to avoid this situation, different actions can be undertaken such as expanding the network or increasing it's efficiency. Another possible action is the exploitation of a further (third) dimension for locomotion for decoupling from the systematical limits of the existing, ground-based structures. This work addresses a currently upcoming topic: the automated air mobility. Bringing such mobility offers to reality requires work in different fields. Among others, these are especially the integration of the mobility offer into the existing transport infrastructure as well as the integration of a human user into the automated aircraft. The paper contributes to both of these fields. Initially, we analyse requirements on urban airfields and examine existing as well as possible future urban airfields. Secondly, we take a closer look at the interaction of human users and automated aircraft building on knowledge and experiences from human-computer interaction in autonomous cars. This foundation is supplemented by aspects that emerge in the field of air traffic (e.g., required off-board coordination with the air traffic management). Finally, on this basis, we develop a proposal for the human-machine-interaction in automated aircraft.
A STAMP based Method to Synthesize Controller of Safety-Critical Systems

Aeronautical systems are becoming increasingly integrated and complex. The development of these systems is critical due to the need of addressing both system requirements and safety concerns. A key component of these systems is the controller. STAMP is an accident causation model based on systems theory, which treats accidents and unacceptable losses as a dynamic control problem. In this work, we present a STAMP-based design method for synthesizing the controller logic. The control logic is generated based on the model of the system, properties related to the behavior of each non-controller components, and functional and safety system properties. Our method is original as it considers safety properties early in the development - concept of the system. Furthermore, it is based on system-level properties that allow the system designer to focus on a higher level of abstraction during design.
Physics-informed deep generative models

We consider the application of deep generative models in propagating uncertainty through complex physical systems. Specifically, we put forth an implicit variational inference formulation that constrains the generative model output to satisfy given physical laws expressed by partial differential equations. Such physics-informed constraints provide a regularization mechanism for effectively training deep probabilistic models for modeling physical systems in which the cost of data acquisition is high and training data-sets are typically small. This provides a scalable framework for characterizing uncertainty in the outputs of physical systems due to randomness in their inputs or noise in their observations. We demonstrate the effectiveness of our approach through a canonical example in transport dynamics.
Applying Risk Assessment at the Worker Level: Applications to Electrical Safety

Risk management, risk assessment, and the hierarchy of risk control are slowly gaining acceptance in the electrical community as an effective and sustainable approach to improve workplace electrical safety. What are risk management, risk assessment, and the hierarchy of risk control? How can they be applied in a practical way to electrical safety in the petrochemical industry?
The Potential Clinical Utility of Circulating Tumor DNA in Esophageal Adenocarcinoma: From Early Detection to Therapy

Esophageal adenocarcinoma (EAC) is a lethal cancer requiring improved screening strategies and treatment options due to poor detection methods, aggressive progression, and therapeutic resistance. Emerging circulating tumor DNA (ctDNA) technologies may offer a unique non-invasive strategy to better characterize the highly heterogeneous cancer and more clearly establish the genetic modulations leading to disease progression. The presented review describes the potential advantages of ctDNA methodologies as compared to current clinical strategies to improve clinical detection, enhance disease surveillance, evaluate prognosis, and personalize treatment. Specifically, we describe the ctDNA-targetable genetic markers of prognostic significance to stratify patients into risk of progression from benign to malignant disease and potentially offer cost-effective screening of established cancer. We also describe the application of ctDNA to more effectively characterize the heterogeneity and particular mutagenic resistance mechanisms in real-time to improve prognosis and therapeutic monitoring strategies. Lastly, we discuss the inconsistent clinical responses to currently approved therapies for EAC and the role of ctDNA to explore the dynamic regulation of novel targeted and immunotherapies to personalize therapy and improve patient outcomes. Although there are clear limitations of ctDNA technologies for immediate clinical deployment, this review presents the prospective role of such applications to potentially overcome many of the notable hurdles to treating EAC patients. A deeper understanding of complex EAC tumor biology may result in the progress toward improved clinical outcomes.
Machine Learning-based Stereo Vision Algorithm for Surround View Fisheye Cameras

Recently, automated emergency brake systems for pedestrian have been commercialized. However, they cannot detect crossing pedestrians when turning at intersections because the field of view is not wide enough. Thus, we propose to utilize a surround view camera system becoming popular by making it into stereo vision which is robust for the pedestrian recognition. However, conventional stereo camera technologies cannot be applied due to fisheye cameras and uncalibrated camera poses. Thus we have created the new method to absorb difference of the pedestrian appearance between cameras by machine learning for the stereo vision. The method of stereo matching between image patches in each camera image was designed by combining D-Brief and NCC with SVM. Good generalization performance was achieved by it compared with individual conventional algorithms. Furthermore, feature amounts of the point cloud reconstructed by the stereo pairs are utilized with Random Forest to discriminate pedestrians. The algorithm was evaluated for the actual camera images of crossing pedestrians at various intersections, and 96.0% of pedestrian tracking rate with high position detection accuracy was achieved. They were compared with Faster R-CNN as the best pattern recognition technique, and our proposed method indicated better detection performance.
Active Clamp Resonant SEPIC Converter for Local Voltage Regulation in 48V DC Based Energy Management Systems

Recent widespread of dc based renewable energy management systems has been encouraged by technical improvement of power converters used as the local voltage regulators in such systems. SEPIC dc-dc converter is a strong candidate for wide operation voltage range and/or specific selection and we have been developed underlying technologies for soft and high frequency switching of it. In this paper, an active clamp resonant SEPIC dc-dc converter is proposed for local voltage regulation application from 48V dc buses in recent renewable energy management systems. Simulation result proves its feasibility.
A Deep Learning Method for Passing Completely Automated Public Turing Test

CAPTCHAs are used for separating humans and machines. They are tests which can be solved by people but not by computers. With deep learning methods, this tests can be passed automatically. The success of Convolutional Neural Networks is related to the dataset size and quality. However, it is difficult to conclude with an inadequate training data. In this study, a convolutional neural network implementation is proposed for resolving CAPTCHAs without any human interaction with images derived from plugin. An active deep learning approach that is capable of retrain the network and present consequences on an auto-generated captcha dataset proposed. Outcomes show that the performance of the network may be significantly improved with the efficaciously classified test sets.
Real-Time Crash Severity Estimation with Machine Learning and 2D Mass-Spring-Damper Model

Making the right decisions milliseconds before an unavoidable car accident is a challenging, nearly impossible task for a human driver. While it is difficult enough to choose a proper maneuver for oneself in the limited time, most of all, it remains unknown how the opponent will react. Thus, an optimal decision can only exist as a probabilistic quantity. One way to estimate the latter is to evaluate a large number of driver actions. With the number of notable combinations easily reaching the mark of thousands of crash constellations, even the most powerful PCs take minutes, hours or even days to finish simulations such as the highly accurate Finite Element Method (FEM). Too long for a decision making only milliseconds ahead of the collision. In this paper, a real-time capable approach with two parallel paths is proposed. Path A consists of a machine learning component that is trained with FEM-data, enabling the prediction of even complex crash severity measures in less than one millisecond. Path B contains a 2D mass-spring-damper model for estimating the crash forces and accelerations and thus, acting as a fall-back and plausibilization layer for the machine learning. A FEM-database of car-to-car collisions is used to train the machine learning model and tune the parameters of the 2D mass-spring-damper model. Together, both paths provide two diverse perspectives of the same crash scenario, leading to an accurate and reliable prediction result in realtime.
Multipath Mobile Multimedia Streaming Based on Content Delivery Network and Peer-to-Peer Network

Due to the rapid development of mobile communication and web-based applications, more and more mobile devices can access World Wide Web (WWW) and then enjoy various network services, especially multimedia streaming services. Even if the multimedia streaming standards, e.g., MPEG-DASH, can sup-port dynamically adjust video quality according to the network condition, the video quality is still limited to narrow bandwidth or network congestion. This paper proposes one multipath mo-bile multimedia streaming (m3 streaming) to integrate (1) HTTP/2 and (2) WebRTC. That is, the proposed m3 streaming can download required video segments simultaneously from multiple edge servers and peers. Therefore, m3 streaming can reduce video playback delay, increase the video quality and save the limited battery power of mobile devices.
Encryption Over the Air: Securing Two-Way Untrusted Relaying Systems Through Constellation Overlapping

This paper proposes a novel technique called constellation overlapping to secure two-way untrusted relaying systems. We first consider the symmetric case where two terminal users adopt the same modulation type. For this case, a truncated-channel-inversion-based approach is developed to make the signals from different users experience the same equivalent channel, thereby realizing full constellation overlapping at the relay. Consequently, an error floor is created at the untrusted relay, and data confidentiality is protected. Then, the asymmetric case is investigated, where different modulations are employed at the two users and the constellation overlapping cannot be realized. To address this issue, we develop a constellation virtualization method, which exploits the intrinsic user-cooperation mechanism to “virtualize” two identical constellations at the two users. In this manner, the constellation overlapping effect can still be achieved, without sacrificing much reliability performance. We optimize the truncation threshold to maximize the sum rate for end-to-end information exchange, and design the bit-to-symbol mapping criterion and the signal detection method as well. Unlike the existing physical layer security approaches that guarantee secrecy with additional resource consumptions, this method realizes security enhancement by letting different users mutually encrypt their signals over the air, which is more resource-efficient.
The Impact of Demographic Factors on Persuasion Strategies in Personalized Recommender System

A recommender system is an information filtering tool that copes with the growing volume of information and helps the user to make faster decisions by providing products and services matched with their needs and interests. However, a large number of users are not satisfied with the provided recommendations and do not accept them. Based on the Elaboration Likelihood Model (ELM), If supplementary information about recommendations is provided, those users having the low motivation and capability to analyze the usefulness of the recommended item can be persuaded to accept it. This paper focuses on analyzing the impact of demographic factors on increasing the acceptance of recommendations. This study was conducted by a web-based online survey. The movie's recommender system has been developed along with the explanations based on Cialdini's persuasion strategies as the peripheral cues. The collected data are analyzed through statistical techniques using the SPSS software. The results show that the persuasiveness degree of the persuasion strategies differs related to individuals with the different demographic factors.
Oversampling in One-Bit Quantized Massive MIMO Systems and Performance Analysis

Low-resolution analog-to-digital converters (ADCs) have attracted much attention lately for massive multiple-input multiple-output (MIMO) communication and systems with large bandwidth. Especially, 1-bit ADCs are suitable for such systems due to their low-power consumption and cost. In this paper, we illustrate the benefits of using faster than symbol rate (FTSR) sampling in an uplink massive MIMO system with 1-bit ADCs in terms of symbol error rate (SER). We show that the FTSR sampling provides about 5-dB signal-to-noise ratio (SNR) advantage in terms of SER and achievable rate with a linear zero-forcing-type receiver. We also develop analytical bounds on the SER and achievable rate performance of uplink massive MIMO structures with 1-bit quantization for the FTSR scenario for the whole SNR region. The proposed analytical bound holds not only for the FTSR case but also to yield more accurate results compared with some other analytical expressions in the literature. Our results establish a tradeoff between temporal oversampling and the number of receive antennas.
BSSSQS: A Blockchain Based Smart and Secured Scheme for Question Sharing in the Smart Education System

Existing education systems are facing a threat of question paper leaking (QPL) in the exam which jeopardizes the quality of education. Therefore, it is high time to think about a more secure and flexible question sharing system which can prevent QPL issue in the future education system. Blockchain enables a way of creating and storing transactions, contracts or anything that requires protection against tampering, accessing etc. This paper presents a new scheme for smart education, by utilizing the concept of blockchain, for question sharing. A two-phase encryption technique for encrypting question paper (QSP) is proposed. In the first phase, QSPs are encrypted using timestamp and in the second phase, previous encrypted QSPs are encrypted again using a timestamp, salt hash and hashes from previous QSPs. These encrypted QSPs are stored in the blockchain along with a smart contract which helps the user to unlock the selected QSP. An algorithm is also proposed for selecting a QSP for the exam which picks a QSP randomly. Moreover, a timestamp based lock is imposed on the scheme so that no one can decrypt the QSP before the allowed time. Finally, security is analyzed by proving different propositions and the superiority of the proposed scheme over existing schemes is proven through a comparative study based on the different features.
Bio-Inspired Rectangular Shaped Piezoelectric MEMS Directional Microphone

The noise floor of a piezoelectric MEMS directional microphone largely depends on piezoelectric materials and transducer modes. A careful choice of these parameters (piezoelectric materials and transducer modes) can help to significantly improve the difficulties of noise floor. Here, we present a unique diaphragm-based biomimetic MEMS directional microphone having multiple-port sensing schemes. The diaphragm is separated into two wings and supported by two torsional beams, which are hinged to a fixed support as to imitate the unique inter-tympanal feature of the fly Ormia ochracea. In this microphone, the thermal-mechanical noise (Johnson noise) is minimized by adopting aluminium nitride-based piezoelectric sensing. The d33 mode is incorporated with a prime focus on sensitivity enhancement which leads to signal-to-noise ratio improvement. Measured directivity patterns of both wings show a strong agreement with the direction of applied acoustic pressure as expected for an ideal bi-directional microphone. The measured noise floor at 1-kHz frequency and overall A-weighted noise floor across the audio frequency are 31.35 dB SPL and 32.5 dBA, respectively, which are quite less than some notable works on directional microphones. The experimental results verify the uniqueness of this work.
Fine-Grained Vehicle Representations for Autonomous Driving

The long-term goal of autonomous driving will require a detailed understanding of complex traffic scenes, in particular the state and possibly intentions of other traffic participants, the most prominent one being non-ego vehicles. An intermediate step and cornerstone to this understanding is a precise estimate of a vehicle's pose and category as well as potential other cues that facilitate predicting its future behavior. The output representations of current state-of-the-art computer vision algorithms, e.g. detection or semantic segmentation, hold little pose information and generally do not straightforwardly allow for any vehicle state analysis. In this work we focus on new vehicle representations that can be learned by semantic segmentation algorithms. We present three vehicle fragmentation levels that divide a vehicle into part areas based on a mixture of material and function, jointly aiming at vehicle state analysis and embedding pose information. To avoid expensive manual labeling we annotate the representations on CAD models to feed a semi-automated labeling pipeline, requiring 3D bounding box labels and a calibrated camera. We evaluate the fine-grained segmentations on two publicly available datasets demonstrating that current state-of-the-art convolutional networks are capable of learning such complex representations and can, hence, exceed straightforward appearance learning.
Traffic Signal Control Based on Reinforcement Learning with Graph Convolutional Neural Nets

Traffic signal control can mitigate traffic congestion and reduce travel time. A model-free reinforcement learning (RL) approach is a powerful framework for learning a responsive traffic control policy for short-term traffic demand changes without prior environmental knowledge. Previous RL approaches could handle high-dimensional feature space using a standard neural network, e.g., a convolutional neural network; however, to control traffic on a road network with multiple intersections, the geometric features between roads had to be created manually. Rather than using manually crafted geometric features, we developed an RL-based traffic signal control method that employs a graph convolutional neural network (GCNN). GCNNs can automatically extract features considering the traffic features between distant roads by stacking multiple neural network layers. We numerically evaluated the proposed method in a six-intersection environment. The results demonstrate that the proposed method can find comparable policies twice as fast as the conventional RL method with a neural network and can adapt to more extensive traffic demand changes.
Detection of Texting While Walking by Using Smartphone’s Posture and Acceleration Information for Safety of Pedestrians

In this paper, we focus on a new activity estimation method of pedestrians with coordination between intersections in P-VCASS. In this method, it is assumed that pedestrians always confirm whether the vehicle is coming before crossing. But pedestrians act variously in actual traffic environment, so it is difficult to adapt to cases that pedestrians pay no attention to confirm whether the vehicle is coming. By focusing on the problem of texting while walking, we propose a new detection method without cameras that are used in the various previous studies. In this paper, we detect texting while walking by using the smartphone’s posture and the inclination of his head. We show the effectiveness of the proposed method by experiments. We show that the proposed method can detect texting while walking with 77.5% of accuracy.
An Adaptive Algorithm for Online Interference Cancellation in EMG Sensors

One of the biggest issues encountered in the analysis of sensitive electromyography (EMG) sensor data is the power line interference (PLI). Conventional methods in literature either lose valuable sensor data or inadequately decrease the power line noise. Instead of filtering out predetermined frequencies, adaptively estimating the spectrum of PLI can provide better performance. This paper introduces an online adaptive algorithm that removes the power line interference in real time without disturbing the true EMG data. Our method sequentially processes the biomedical signal to properly estimate and remove the PLI component. Through experiments with real EMG data, we compared our method to the five state-of-the-art techniques. Our algorithm outperformed all of them with the highest SNR gain (3.6 dB on average) and with the least disturbance of the true EMG signal (0.0152 dB loss on average). Our method reduces the PLI the most while keeping the valuable sensor data loss at its minimum in comparison to the state-of-the-art. Reducing the noise without disturbing the valuable sensor data provides higher quality signals with decreased interference, which can be better processed and used in biomedical research.