Miracle-dz/UniFAR-FTUs huggingface dataset:
Pre-ignition Detection Using Deep Neural Networks: A Step Towards Data-driven Automotive Diagnostics

Fault detection in vehicles is currently carried out using model-based or rule-based approaches. Due to advances in automotive technology such as autonomous driving and further connectivity, the complexity of vehicles and their subsystems increases continuously. As a consequence, models and rule-based systems for fault detection become more complex and require more extensive implementation effort and expert knowledge not only within but also across several domains. Besides, vehicles produce rich amounts of data including valuable information for fault detection. These amounts cannot fully be considered by current fault detection approaches. Deep neural networks offer promising capabilities to address these challenges by allowing automated model generation for fault detection without extensive domain knowledge using vast amounts of in-vehicle data. Hence, in this work a data-driven automotive diagnostics approach to fault detection with deep neural networks is proposed in two steps. First, a novel data-driven diagnostics process to learn data-driven algorithms on in-vehicle data is presented. Second, as a key element of this process, a novel fault detection model is proposed using a combination of convolutional and long short-term memory neural networks. To demonstrate the suitability for fault detection, the model is evaluated on internal engine control unit signals for pre-ignition detection in high-pressure turbocharged petrol engines. A classical machine learning processing pipeline and each neural network type separately are used as baselines for a performance comparison. Results show that the proposed model is a promising approach to data-driven automotive diagnostics outperforming all implemented baselines.
Free Will, Moral Responsibility, and Scientific Epiphenomenalism

This article addresses two influential lines of argument for what might be termed “scientific epiphenomenalism” about conscious intentions – the thesis that neither conscious intentions nor their physical correlates are among the causes of bodily motions – and links this thesis to skepticism about free will and moral responsibility. One line of argument is based on Benjamin Libet’s neuroscientific work on free will. The other is based on a mixed bag of findings presented by social psychologist Daniel Wegner. It is argued that both lines of argument are unsuccessful.
Decision Tree Ensemble for Parts-of-Speech Tagging of Resource-poor Languages

Ensemble POS taggers are a good choice to integrate and leverage benefits of various types of POS taggers. This can help the large number (6500+) of resource-poor languages which do not have much annotated training data by providing ways to integrate semi-supervised/unsupervised taggers with supervised taggers. In this paper we present our experiments of developing ensemble POS taggers using a decision tree. We integrate a semi-supervised data mining approach that uses context based lists (CBLs) for POS tagging with supervised (1) Support Vector Machine based POS tagger, called SVMTool and (2) Conditional Random Field based POS tagger. The results are enhanced semi-supervised ensemble POS taggers which outperform the base methods. In these POS taggers, we use a decision tree to decide when to rely on the output of supervised tagger, and when to rely on the semi-supervised CBL method. The CBL based tagger uses rich contextual information which helps in tagging both existing and unseen words and uses no domain knowledge while supervised taggers give good performance for words present in the training model and can include domain based features. Hence, these algorithms have complementary strengths and in our ensemble we are able to combine these strengths. Enhanced performance of our new POS taggers over the base methods suggests that integrating these methods combines the qualities of these in the new tagger which enhances the performance. Therefore, these new semi-supervised ensemble taggers are more suitable for resource-poor languages.
Dual-Band Transmission-Line Resistance Compression Network and Its Application to Rectifiers

In this paper, a dual-band transmission-line resistance compression network (TLRCN) is introduced and applied to the rectifier design to enhance the power conversion efficiency at low input power levels, leading to wider dynamic power range of the rectifier. It can be used to reduce the sensitivity of the rectifying efficiency to input power variation at two frequencies. The analytical expressions for the dual-band TLRCN are derived. In order to validate the proposed idea, two dual-band rectifiers with different frequency spacing (<inline-formula> <tex-math notation="LaTeX">$\Delta f$ </tex-math></inline-formula>) are designed, fabricated, and measured. One works at 0.915 GHz/2.45 GHz (<inline-formula> <tex-math notation="LaTeX">$\Delta f = 1.535$ </tex-math></inline-formula> GHz) and the other at 1.80 GHz/2.40 GHz (<inline-formula> <tex-math notation="LaTeX">$\Delta f = 0.6$ </tex-math></inline-formula> GHz). For comparison, their counterparts, without the dual-band TLRCN, are also designed and implemented. The measured results show that the proposed dual-band TLRCN can be used to extend the input power range at two frequencies without decreasing the maximum power conversion efficiencies.
RFIDCam: a RFID-Aided Visual Tracking System

With the rapid economic development and the substantial improvement of living standards, intelligent video surveillance technology has attracted widespread attention due to its huge natural advantages and has gradually become a research hotspot across the world. RFID is a non-contact automatic identification technology that automatically identifies target objects and obtains relevant data. Besides it requires no human intervention, the identification work can be used in various complicated and harsh environments. By placing RFID tags on valuable objects and installing RFID reader devices near the surveillance cameras, it can monitor whether valuables pass through the monitoring area and integrate RFID technology into the intelligent video surveillance system to help the system accomplish the target recognition and detection track. Therefore, based on the in-depth study of the current intelligent video surveillance technology, this paper proposes an RFID-based intelligent video surveillance system-RFIDCam, and builds a small experimental platform to verify the feasibility and the effectiveness of RFIDCam.
Sidelink Technologies Comparison for Highway High-Density Platoon Emergency Braking

We present a benchmarking framework for different radio access technologies (RATs) in a high density platooning (HDPL) emergency braking use case. We assess the performance of IEEE 802.11p as well as LTE-V managed mode (mode 3) and unmanaged mode (mode 4) for sidelink communications. The performances are studied in terms of delays, packet error rates (PERs) and functional safety indicators. We first vary the number of vehicles, the surrounding traffic and the inter-vehicle distance. Multiple traffic scenarios are then investigated for the most challenging conditions. We find that for reasonable surrounding traffic, the platoon is generally safe in this emergency scenario, although packet error rates are growing for IEEE 802.11p and LTE-V mode 4 as the traffic intensifies, along with delays for the former technology. Thanks to scheduling, LTE-V mode 3 is not affected by this increasing PER and shows a large constant delay: the scheduling delay. With this study, we pave the way for a further study of these radio technologies with more accurate channel models as well as including new 5G components in our benchmarking.
Estimating the fundamental diagram using moving observers

The fundamental diagram (FD) describes the relation between the flow and density in equilibrium conditions. In this paper, we propose an estimation approach to estimate the FD based on data from moving observers. This approach consists of two main steps: (1) estimate flow and density for space-time areas based on trajectories of moving observers and the times and locations they are overtaken or being overtaken and (2) estimate the FD based on the {flow, density}-estimates. To evaluate and gain a deeper understanding of the proposed approach, a simulation study was conducted. This study shows that the {flow, density}-estimates provide valuable information to estimate the FD. Furthermore, the FDs belonging to the simulated traffic flow are estimated accurately. We realize that the second step is expected to be less accurate for traffic that behaves stochastic. Therefore, we provide a potential solution path to extend the second step in future work.
Robust State Estimation of a Quadrotor Based on High-Gain and Sliding-Mode Observers

In this paper, we investigate the problem of joint states and external aerodynamic disturbances estimation for a quadrotor model. The dynamics of longitudinal and angular velocities are corrupted by unknown time varying disturbances whereas only positions and angles are available for measurements. To overcome the problem of observer matching condition, we generate auxiliary outputs to relax the structural properties of the quadrotor system based a high gain approximate differentiator. The latter estimated signals are used by a first order sliding mode observer to reconstruct both the states and the unknown disturbances of the quadrotor. Numerical simulations are depicted to illustrate the effectiveness and the good performances of the nronosed observer.
Alano: An Efficient Neighbor Discovery Algorithm in an Energy-Restricted Large-Scale Network

Neighbor discovery is a fundamental step in constructing wireless sensor networks and many algorithms have been proposed aiming to minimize its latency. Recent developments of intelligent devices call for new algorithms, which are subject to energy restrictions. In energy-restricted large-scale networks, a node has limited power supply and can only discover other nodes that are within its range. Additionally, the discovery process may fail if excessive communications take place in a wireless channel. These factors make neighbor discovery a very challenging task and only a few of the proposed neighbor discovery algorithms can be applied to energy-restricted large-scale networks. In this paper, we propose Alano, a nearly optimal algorithm for a large-scale network, which uses the nodes' distribution as a key input. When nodes have the same energy constraint, we modify Alano by the Relaxed Difference Set (RDS), and present a Traversing Pointer (TP) based Alano when the nodes' energy constraints are different. We compare Alano with the state-of-the-art algorithms through extensive evaluations, and the results show that Alano achieves at least 31.35% lower discovery latency and has higher performance regarding quality (discovery rate) and scalability.
Human-Like Maneuver Decision Using LSTM-CRF Model for On-Road Self-Driving

In the near future, self-driving vehicles will be frequently tested in urban traffic, and will definitely coexist with human-driving vehicles. To harmoniously share traffic resources, self-driving vehicles need to respect behavioral customs of human drivers. Taking on-road driving for example, self-driving vehicles are supposed to behave in a human-like way to decide when to keep the lane and when to change the lane. This point, however, has not been well addressed in current on-road maneuver decision methods. In this paper, a human-like maneuver decision method based on Long Short Term Memory (LST-M) neural network and Conditional Random Field (CRF) model is proposed for on-road self-driving. Different from previous works, this paper considers the maneuver decision problem as a sequence labeling problem. Its input is a time-series vector which describes a period of neighboring traffic history, and its output is a one-hot vector indicates the suitable maneuver. The proposed model is trained on the NGSIM public dataset, which contains millions of driving maneuvers collected from thousands of human drivers. Simulations with manipulated conditions reveal human-like reasoning for maneuver decision inside the proposed model. Comparative experiments further demonstrate a better human-like performance achieved by the proposed method than that of previous methods.
Joint Radio and Computational Resource Allocation for NOMA-Based Mobile Edge Computing in Heterogeneous Networks

A joint radio and computational resource allocation problem is investigated for non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) in heterogeneous networks (HetNets). The aim is to minimize the energy consumption of all users under the task execution latency constraint with partial task offloading. Due to the non-convexity of the formulated problem, we propose an iterative algorithm by decomposing the optimization problem into two subproblems, where the computational resource allocation is a convex optimization problem, and the uplink power allocation is solved by the sequential convex programming. Simulation results reveal that the partial offloading is superior to local computing-only and full offloading-only and NOMA-based MEC outperforms the orthogonal multiple access-based MEC in HetNets.
Symbolic Distortion Analysis of Multistage Amplifiers

This paper studies the automation of the distortion analysis of multistage operational amplifiers by a symbolic analysis. The goal is to generate the readable closed-form design equations that relate the frequency-dependent harmonic distortion (HD) features, such as dc level, corner frequency, peaking, and zero, to the circuit parameters (including the nonlinearity coefficients). Prior research achieved such kind of characterization by carrying out the traditional signal-flow graph (SFG) analysis, which is highly algebra-involved and depends mainly on the manual derivation. This paper achieves automation by formulating a multiport circuit analysis strategy and applying a topological symbolic analysis method as an intermediate step in generating nodal transfer functions. The frequency-dependent HD factors are then characterized by analytically forming proper virtual current sources associated with the nonlinearity of each stage, modeled by a cubic polynomial. Implementation shows that the proposed tool can generate approximate closed-form HD characterization with comparable accuracy to full transistor-level simulation. The proposed automation tool can substantially mitigate heavy manual derivation work required by the existing SFG-based methods. Theory, software implementation, and circuit application are covered in detail.
Excrescent Lesion: A Diagnosis of Lateral Talar Exostosis In Chronically Symptomatic Sprained Ankles

The excrescent lesion is a symptomatic anterolateral exostosis at the insertion of the anterior talofibular ligament. It is found in patients with chronic ankle pain after inversion injuries. It is most reliably diagnosed by computed tomography scan, but physical examination and oblique radiographs are suggestive in most cases. A technique for surgical excision and, in some cases, repair of the anterior talofibular ligament is described. Five patients having a history of inversion sprains of the ankle, without significant symptomatic improvement for a mean of 21 months after the injury, were evaluated. None had significant instability in the ankle or subtalar joints, clinically or with stress radiographs. The diagnosis of excrescent lesion was confirmed with computed tomography scan in all five patients. Each underwent excision of the exostosis. Removal of the exostosis produced laxity of the anterior talofibular ligament in four of the patients and required an additional modified Broström procedure to tighten the anterior talofibular ligament. Clinical results were evaluated at a mean of 33 months postoperatively, using the Ankle-Hindfoot scale from the American Orthopaedic Foot and Ankle Society. The three patients without pending claims for Workers] Compensation or related litigation all had excellent results (mean score, 93 points). Two patients with active legal claims had fair and poor results (mean score, 53 points).
High‐performance low‐power approximate Wallace tree multiplier

Multipliers are considered critical functional units in many systems like Digital Signal Processing (DSP), machine learning, and so on. The overall performance of such systems are dependent on the efficiency of multipliers. However, multipliers are slow and power inefficient components due to their complex circuits, so we aim to reduce their power consumption by relaxing their accuracy requirements and at the same time enhancing their speed. In this paper, we present a fast and a power‐aware multiplier that targets error‐resilient systems. This is achieved by using our proposed approximation algorithm, a hybrid Wallace tree technique for reducing power consumption, and a hybrid ripple‐carry adder for reducing latency. The proposed approximation algorithm is implemented using both a modified bit‐width aware and carry‐in prediction technique, while the proposed hybrid Wallace tree is implemented using high order counters. These proposed algorithms are implemented using HDL language, synthesized, and simulated using Quartus and Modelsim tools. For a 16‐bit multiplier, a mean accuracy of 98.35% to 99.95% was achieved with a 45.77% reduction in power, a 21.48% drop in latency, and a 34.95% reduction in area. In addition, our design performs even better for larger size multipliers (32‐bit multiplier) where a 61.24% reduction in power was achieved, with an 8.74% drop in latency and a 35.24% reduction in area with almost no loss in accuracy.
Safe and Online MPC for Managing Safety and Comfort of Autonomous Vehicles in Urban Environment

In this paper is presented a linear MPC controller design for autonomous cars navigation. It combines both the lateral and longitudinal control. The MPC cost function has been designed to account for human driving behaviours, i.e., it smoothes out coarse reference trajectories. Furthermore, a safety monitoring module has been implemented. It computes an estimated time before reaching an unacceptable situation (w.r.t. comfort constraints and tracking performance) under the current tracking conditions. The overall benefit of this controller is to guarantee trajectory smoothness while outputting information on its performance. This information will later be used to re-plan safe trajectories in dynamic environments. The proposed linear MPC controller has been tested in a typical urban scenario based on a realistic simulator.
Modeling Traffic Accidents Caused by Random Misperception

Understanding the formation of accidents is of major importance to the automotive industry, its related businesses and policymakers. This is not a trivial task considering the current stream of innovations driven by the development of autonomous vehicles. Historical accident data are inadequate for gauging the safety of future traffic systems. To cope with this challenge, we propose a microscopic traffic model that introduces small errors due to random misperception as an omnipresent cause for accidents - an issue affecting both human drivers and control systems of autonomous vehicles. We model errors dynamically by stochastic processes and investigate their impact on the safety and the efficiency of traffic systems by Monte Carlo simulations. We focus on two case studies: a simple one-lane road segment and a t-junction with turning vehicles.
Memristor-Based Circuit Implementations of Recognition Network and Recall Network With Forgetting Stages

This paper proposes a novel memistor-based neuron circuit, in which the memristor-CMOS hybrid synaptic circuit simply utilizes the positive voltage to change the memristance. At the same time, in order to obtain the memristance and output voltage changes in the neuron circuit, a mathematical deduction is implemented according to the circuit structure. Then, the memristor-based recognition and recall network circuits are constructed based on the proposed neuron circuit. The recognition and recall functions are realized by associative learning between the unconditional stimuli and the conditional stimuli (CS). After the learning stages, the single presentation of CS activates forgetting stages in the two networks. Furthermore, the related parameter changes in the learning and forgetting stages can be calculated by the deduced equations approximately. The PSPICE simulations are implemented to demonstrate the effectiveness of the proposed circuits and the deduced equations.
Stabilization of a DC-Link of Microgrids feeding a Inverter-BLDC motor drive using a PI-Fuzzy

The stabilization of the DC-link supplying 3phase inverter which drives Brushless DC motor based on Fuzzy-PI controller in the microgrid is presented. This work is included the study of a mathematical model for each inverter and motor. In addition, the stabilization analysis of motor driver based on Fuzzy-PI controller. The purposed controller is validated by simulation in MATLAB/Simulink for a motor driver under speed and voltage source variations. The simulation results prove that the controller makes the motor speed matching with its reference speed as well as constant and stable of DC-link voltage.
A Blockchain-Based Network Security Mechanism for Voting Systems

As technology advances, many countries have now opted for electronic voting systems. Any voting system must follow principles of transparency and impartiality in order to achieve fairness; the electronic voting process must also be protected against cyberattacks or denial-of-service attacks (DDOS) because such attacks may affect the processing time in voting procedures and even hinder the fairness in voting. This study establishes a network security mechanism for voting systems based on blockchain technology. The blockchain mechanism employs a distributed architecture that can prevent system shutdown resulting from malicious cyberattacks; additionally, any user in the blockchain can authenticate data integrity, which satisfies requirements of transparency and impartiality in voting systems. This study utilizes bilinear pairing to establish network security in voting systems, which call for anonymity, authenticity, integrity, and non-repudiation. When authenticating voting integrity, the user's anonymity must be ensured to prevent identity revelation, and the data must be protected against malicious tampering such security measures also fulfill blockchain requirements. The proposed voting system relies on the basis of blockchains to create a trustworthy voting system. In current blockchain technology, smart contracts allow the establishment of voter-related regulations to prevent controversies during voting processes. Additionally, in order to establish both a secret ballot and an open ballot system, the study also implements a bilinear pairing security mechanism to ensure the overall security of a voting procedure.