1,721,056 research outputs found
Enforcement Cybersecurity Techniques: A Lightweight Encryption over the CAN-Bus
No full textThe continuous search for network connections outside vehicles has increased the surface of cyber-attacks. Indeed, the automotive companies seem to have neglected the protocols of the networks connecting the various electronic components used in any vehicles. The Controller Area Network (CAN), a protocol designed to minimize latency and transmission errors, governs the internal network of vehicles. One of its main features is to use small frames and to transfer the information unencrypted. This last feature, in particular, makes possible attacks in which an attacker can take remote control of the vehicle by inserting a malicious or manipulated message on the communication channel. The design choices made in the first draft of the standard are, however, what has determined the success of this protocol. The confidentiality of the messages exchanged within this network is nevertheless a goal attainable at a higher level: the study of the structure of the transmitted frames shows how it is possible to hide the critical information passing on the communication channel, that is the bits that identify the units responsible for processing a message and the information carried. Such a solution avoids the possibility of large-scale attacks when a pseudo-random factor is introduced into the encryption: with the same message corresponding to two different encodings on two different vehicles, the breaking of the scheme takes place only after appropriate cryptographic analyses. In this article, we want to introduce an encryption approach of the messages exchanged on CAN-Bus through the technique of randomization. As can be seen from the experimental results obtained, this method seems to have a good response in terms of both efficiency and effectiveness
IEEE 1451: Communication among smart sensors using MQTT protocol
No full textSmart sensors, transducers, and sensor networks are undergoing, in recent years, a profound transformation thanks to the spread of new communication protocols based on the Producer-Consumer paradigm rather than on the Request-Response paradigm of Server-Client architectures. In this context, the substandard IEEE 1451.1.6 has been introduced in the IEEE 1451 standard, which aims to standardize the Producer-Consumer based communication protocols, particularly MQTT. The role of this standard is fundamental for the realization of sensor networks with nodes produced by different manufacturers and integrated in a fully automatic way according to the paradigm 'Plug and Play'. This work aims to verify the progress of this sub-standard by testing its reliability with the development and testing of a prototype wireless sensor network based on MQTT with low-cost hardware. The prototype will consist of a Network Capable Application Processor (NCAP) node and a Transducer Interface Module (TIM) node, while the role of MQTT Broker and Wi-Fi Access Point for the physical layer of the Wireless Sensor Network will be covered by the NCAP itself
Classification of coffee bean varieties based on a deep learning approach
No full textIn this article, a coffee beans fraud detection based on a deep learning approach is proposed, which has been achieved after classifying the two coffee varieties to distinguish them in a real-time industrial scenario. The coffee bean quality is typically defined by visual inspection, which is subjective, needing significant effort and time, and susceptible to fault detection. For these reasons, a different method is required to be objective and precise. Therefore, object detection techniques were employed to automatically classify the coffee bean samples according to their specie using an own dataset consisting of over 2500 coffee beans. Furthermore, a convolutional neural network (CNN) based on the YOLO algorithm was employed to categorize the coffee beans automatically. The result of this study has revealed that the object detection technique could be used as an effective method to classify coffee bean species and discover food fraud
Diastereoselective Synthesis of Functionalized 5-Amino-3,4-Dihydro-2H-Pyrrole-2-Carboxylic Acid Esters: One-Pot Approach Using Commercially Available Compounds and Benign Solvents
No full textA novel three-step four-transformation approach to highly functionalized 5-amino-3,4-dihydro-2H-pyrrole-2-carboxylic acid esters, starting from commercially available phenylsulfonylacetonitrile, aldehydes, and N-(diphenylmethylene)glycine tert-butyl ester, was developed. The one-pot strategy delivered this class of amidines bearing, for the first time, three contiguous stereocenters, in good to high yield and diastereoselectivity. The entire sequence was carried out using diethyl carbonate and 2-methyl tetrahydrofuran as benign solvents, operating under metal-free conditions. The process could be conveniently scaled-up, and the synthetic utility of the products was demonstrated
A deep learning approach for the development of an Early Earthquake Warning system
No full textIn the recent period, machine learning approaches have been widely used in many different fields. For example, in such applications where high immunities to noisy conditions are required. This is the case of an Early Earthquake Warning (EEW) system, a common technology used today to issue an alert in case of incoming seismic events. However, since most seismometers are installed in different locations of the Earth's surface, and different mechanical properties characterize them, each interpretation of a seismic earthquake could result in a highly complex task to be done in real-time using traditional approaches. Therefore, the proposed research has investigated the development of an innovative EEW system based on a novel deep learning system using both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The novel approach has been trained on about 5000 events retrieved from the IRIS University consortium. The achieved results have shown the excellent architecture capability in fully discovering the arrival of seismic events and good performance in the scoring of event intensity
Soft Sensors for Instrument Fault Accommodation in Semiactive Motorcycle Suspension Systems
No full textThis article describes the development and experimental verification of an instrument fault accommodation (IFA) scheme for front and rear suspension stroke sensors in motorcycles equipped with electronically controlled semiactive suspension systems. In particular, the IFA scheme is based on the use of nonlinear autoregressive with exogenous inputs (NARX) neural networks (NNs) employed as soft sensors for feeding the suspension control strategy back with measurement even in the presence of faults occurred on the sensors. Different NN architectures have been trained and tuned by considering real data acquired during several measurement campaigns. The performance has been compared with that of the well-known half-car model (HCM). Very satisfying results allow the soft sensor to be really integrated into fault-tolerant control systems. In experimental road tests, an implementation of the proposed IFA scheme on a low-cost microcontroller for automotive applications showed to be in real time. In this article, these experimental results are shown to prove the good performance of the IFA scheme in different motorcycle operating conditions
An LSTM based soft sensor for rear motorcycle suspension
No full textThe increasing development of neural networks for classification and prediction of temporal sequences has opened the way for a new development of mathematical models for soft sensor design. In particular, Long Short-Term Memory (LSTM) networks have greatly improved execution time and reduced error in both single-step and multi-step prediction. In this context, it is therefore possible to improve on the current concept of Instrument Fault Detection and Isolation (IFDI), reducing costs and footprint by not using physical redundancies of sensitive elements but by employing virtual sensors themselves. Therefore, the work aims to develop a soft sensor for rear suspension stroke using an LSTM network. This new approach was trained on over 50000 samples acquired in a real-world environment, and the results were compared with ground truth on a total of over 100000 samples. The results of the analysis showed excellent potential of the method and wide room for improvement in future developments
Fusion Filters between the No Motion No Integration Technique and Kalman Filter in Noise Optimization on a 6DoF Drone for Orientation Tracking
No full textThe paper works on the new combination between the No Motion No Integration filter (NMNI) and the Kalman Filter (KF) to optimize the conducted vibration for orientation angles during drone operation. The drone’s roll, pitch, and yaw with just accelerometer and gyroscope were analyzed under the noise impact. A 6 Degree of Freedom (DoF) Parrot Mambo drone with Matlab/Simulink package was used to validate the advancements before and after fusing NMNI with KF. The drone propeller motors were controlled at a suitable speed level to keep the drone on the zero-inclination ground for angle error validation. The experiments show that KF alone successfully minimizes the variation for the inclination, but it still needs the NMNI support to enhance the performance in noise deduction, with the error only about 0.02°. In addition, the NMNI algorithm successfully prevents the yaw/heading from gyroscope drifting due to the zero-value integration during no rotation with the maximum error of 0.03°
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