How Practical Are Fault Injection Attacks, Really?

Fault injection attacks (FIA) are a class of active physical attacks, mostly used for malicious purposes such as extraction of cryptographic keys, privilege escalation, attacks on neural network implementations. There are many techniques that can be used to cause the faults in integrated circuits, many of them coming from the area of failure analysis. In this paper we tackle the topic of practicality of FIA. We analyze the most commonly used techniques that can be found in the literature, such as voltage/clock glitching, electromagnetic pulses, lasers, and Rowhammer attacks. To summarize, FIA can be mounted on most commonly used architectures from ARM, Intel, AMD, by utilizing injection devices that are often below the thousand dollar mark. Therefore, we believe these attacks can be considered practical in many scenarios, especially when the attacker can physically access the target device.

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Software Fault-Proneness Analysis based on Composite Developer-Module Networks

Existing software fault-proneness analysis and prediction models can be categorized into software metrics and visualized approaches. However, the studies of the software metrics solely rely on the quantified data, while the latter fails to reflect the human aspect, which is proven to be a main cause of many failures in various domains. In this paper, we proposed a new analysis model with an improved software network called Composite Developer-Module Network. The network is composed of the linkage of both developers to software modules and software modules to modules to reflect the characteristics and interaction between developers. After the networks of the research objects are built, several different sub-graphs in the networks are derived from analyzing the structures of the sub-graphs that are more fault-prone and further determine whether the software development is in a bad structure, thus predicting the fault-proneness. Our research shows that the different sub-structures are not only a factor in fault-proneness, but also that the complexity of the sub-structure can affect the production of bugs.

*Published in the IEEE Reliability Society Section within IEEE Access.

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A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

Nowadays, with the advancement of technology, world is trending toward high mobility and dynamics. In this context, intersection management (IM) as one of the most crucial elements of the transportation sector demands high attention. Today, road entities including infrastructures, vulnerable road users (VRUs) such as motorcycles, moped, scooters, pedestrians, bicycles, and other types of vehicles such as trucks, buses, cars, emergency vehicles, and railway vehicles like trains or trams are able to communicate cooperatively using vehicle-to-everything (V2X) communications and provide traffic safety, efficiency, infotainment and ecological improvements. In this paper, we take into account different types of intersections in terms of signalized, semi-autonomous (hybrid) and autonomous intersections and conduct a comprehensive survey on various intersection management methods for heterogeneous connected vehicles (CVs). We consider heterogeneous classes of vehicles such as road and rail vehicles as well as VRUs including bicycles, scooters and motorcycles. All kinds of intersection goals, modeling, coordination architectures, scheduling policies are thoroughly discussed. Signalized and semi-autonomous intersections are assessed with respect to these parameters. We especially focus on autonomous intersection management (AIM) and categorize this section based on four major goals involving safety, efficiency, infotainment and environment. Each intersection goal provides an in-depth investigation on the corresponding literature from the aforementioned perspectives. Moreover, robustness and resiliency of IM are explored from diverse points of view encompassing sensors, information management and sharing, planning universal scheme, heterogeneous collaboration, vehicle classification, quality measurement, external factors, intersection types, localization faults, communication anomalies and channel optimization, synchronization, vehicle dynamics and model mismatch, model uncertainties, recovery, security and privacy.

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The Cubli: Modeling and Nonlinear Attitude Control Utilizing Quaternions

This paper covers the modeling and nonlinear attitude control of the Cubli, a cube with three reaction wheels mounted on orthogonal faces that becomes a reaction wheel based 3D inverted pendulum when positioned in one of its vertices. The proposed approach utilizes quaternions instead of Euler angles as feedback control states. A nice advantage of quaternions, besides the usual arguments to avoid singularities and trigonometric functions, is that it allows working out quite complex dynamic equations completely by hand utilizing vector notation. Modeling is performed utilizing Lagrange equations and it is validated through computer simulations and Poinsot trajectories analysis. The derived nonlinear control law is based on feedback linearization technique, thus being time-invariant and equivalent to a linear one dynamically linearized at the given reference. Moreover, it is characterized by only three straightforward tuning parameters. Experimental results are presented.

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Dynamic Network Slice Scaling Assisted by Attention-Based Prediction in 5G Core Network

Network slicing is a key technology in fifth-generation (5G) networks that allows network operators to create multiple logical networks over a shared physical infrastructure to meet the requirements of diverse use cases. Among core functions to implement network slicing, resource management and scaling are difficult challenges. Network operators must ensure the Service Level Agreement (SLA) requirements for latency, bandwidth, resources, etc for each network slice while utilizing the limited resources efficiently, i.e., optimal resource assignment and dynamic resource scaling for each network slice. Existing resource scaling approaches can be classified into reactive and proactive types. The former makes a resource scaling decision when the resource usage of virtual network functions (VNFs) exceeds a predefined threshold, and the latter forecasts the future resource usage of VNFs in network slices by utilizing classical statistical models or deep learning models. However, both have a trade-off between assurance and efficiency. For instance, the lower threshold in the reactive approach or more marginal prediction in the proactive approach can meet the requirements more certainly, but it may cause unnecessary resource wastage. To overcome the trade-off, we first propose a novel and efficient proactive resource forecasting algorithm. The proposed algorithm introduces an attention-based encoder-decoder model for multivariate time series forecasting to achieve high short-term and long-term prediction accuracies. It helps network slices be scaled up and down effectively and reduces the costs of SLA violations and resource overprovisioning. Using the attention mechanism, the model attends to every hidden state of the sequential input at every time step to select the most important time steps affecting the prediction results. We also designed an automated resource configuration mechanism responsible for monitoring resources and automatically adding or removing VNF instances.

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Orthogonal Chirp-Division Multiplexing for Future Converged Optical/Millimeter-Wave Radio Access Networks

Envisaged network scaling in the beyond 5G and 6G era makes the optical transport of high bandwidth radio signals a critical aspect for future radio access networks (RANs), while the move toward wireless transmission in millimeter-wave (mm-wave) and terahertz (THz) environments is pushing a departure from the currently deployed orthogonal frequency division multiplexing (OFDM) modulation scheme. In this work, the orthogonal chirp-division multiplexing (OCDM) waveform is experimentally deployed in a converged optical/mm-wave transmission system comprising 10 km analog radio-over-fiber (A-RoF) transmission, remote mm-wave generation and 2 m wireless transmission at 60 GHz. System performance is evaluated in terms of both bit error ratio (BER) and error vector magnitude (EVM) for a wideband 4 GHz 16 Gb/s signal and 128/256-Quadrature Amplitude Modulation (QAM) mobile signals compatible with 5G new radio numerology. OCDM is shown to outperform OFDM by offering enhanced robustness to channel frequency selectivity, enabling performances below the forward error correction (FEC) limit in all cases and exhibiting an EVM as low as 3.4% in the case of the mobile signal transmission.

*Published in the IEEE Photonics Society Section within IEEE Access.

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Doppler Spectrum Measurement Platform for Narrowband V2V Channels

This paper describes the implementation of a Doppler spectrum measurement platform for narrowband frequency-dispersive vehicle-to-vehicle (V2V) channels. The platform is based on a continuous-wave (CW) channel sounding approach widely used for path-loss and large-scale fading measurements, but whose effectiveness to measure the Doppler spectrum of V2V channels is not equally known. This channel sounding method is implemented using general-purpose hardware in a configuration that is easy to replicate and that enables a partial characterization of frequency-dispersive V2V channels at a fraction of the cost of a dedicated channel sounder. The platform was assessed in a series of field experiments that collected empirical data of the instantaneous Doppler spectrum, the mean Doppler shift, the Doppler spread, the path-loss profile, and the large-scale fading distribution of V2V channels under realistic driving conditions. These experiments were conducted in a highway scenario near San Luis Potosí, México, at two different carrier frequencies, one at 760MHz and the other at 2,500MHz. The transmitting and receiving vehicles were moving in the same direction at varying speeds, ranging from 20 to 130km/h and dictated by the unpredictable traffic conditions. The obtained results demonstrate that the presented measurement platform enables the spectral characterization of narrowband V2V channels and the identification of their Doppler signatures in relevant road-safety scenarios, such as those involving overtaking maneuvers and rapid vehicles approaching the transmitter and receiver in the opposite direction.

*Published in the IEEE Vehicular Society Section within IEEE Access.

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Rapid and Flexible 3D Printed Finger Prostheses With Soft Fingertips: Technique and Clinical Application

We present a method for fabricating passive finger prostheses with soft fingertips by utilizing 3D scanning and 3D printing with flexible filament. The proposed method uses multi-process printing at varying infill levels to provide soft fingertips to emulate biological fingers. The proposed method also enables rapid prototyping of finger prostheses, and the flexibility to change interphalangeal joint angles to fit the prostheses for different manipulation and occupational therapy tasks. The entire process of designing and fabricating the prostheses can be conducted in one day. The presented technique uses scan data of the intact side fingers to provide the shape and contour of the finger prostheses, while the socket is designed based on the scan data of the amputation side. The paper presents the developed technique and its clinical application. Experiments are conducted to measure the stiffness of the printed material at varying infill levels and the stiffness of the printed fingertips. The results are compared to measurements of biological fingertip stiffness from the literature. The clinical application includes two cases, one case with distal phalanx loss on the thumb, index, and middle fingers, and one case with distal and middle phalanx loss on the middle and ring fingers. Fitting was successful for both recipients and they were both able to use the prostheses successfully.

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A Novel Symmetric Stacked Autoencoder for Adversarial Domain Adaptation Under Variable Speed

At present, most of the fault diagnosis methods with extensive research and good diagnostic effect are based on the premise that the sample distribution is consistent. However, in reality, the sample distribution of rotating machinery is inconsistent due to variable working conditions, and most of the fault diagnosis algorithms have poor diagnostic effects or even invalid. To dispose the above problems, a novel symmetric stacked autoencoder (NSSAE) for adversarial domain adaptation is proposed. Firstly, the symmetric stacked autoencoder network with shared weights is used as the feature extractor to extract features which can better express the original signal. Secondly, adding domain discriminator that constituting adversarial with feature extractor to enhance the ability of feature extractor to extract domain invariant features, thus confusing the domain discriminator and making it unable to correctly distinguish the features of the two domains. Finally, to assist the adversarial training, the maximum mean discrepancy (MMD) is added to the last layer of the feature extractor to align the features of the two domains in the high-dimensional space. The experimental results show that, under the condition of variable speed, the NSSAE model can extract domain invariant features to achieve the transfer between domains, and the transfer diagnosis accuracy is high and the stability is strong.

*Published in the IEEE Reliability Society Section within IEEE Access.

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Dynamic Analysis of Surface-Mounted Permanent Magnet Type Coaxial Magnetic Gear With Damper Bar Considering Magnetic Field Modulation Effect

Coaxial Magnetic Gear (CMG) has unstable dynamic characteristics by hunting or pull-out action of the output rotor when the load or speed are changed, unlike traditional mechanical gears. These dynamic characteristics need to be improved in order to secure the reliability of mechanical power transmission system by fast and accurate response. In this paper, the damper bar used in synchronous machines is considered as a method to improve the dynamic characteristics of CMG. The Surface-mounted Permanent Magnet (SPM) type CMG is selected as the analysis model. The space harmonics of the magnetic flux density of stationary and rotary members of CMG namely, modulating pieces, inner and outer rotor, are analyzed and characterized the influence of them on the improvement of dynamic characteristics as well as torque reduction. Also, the magnetic flux density characteristics and the damping effect are compared according to the position of the damper bars on two rotors and the modulating pieces. In conclusion, the considerations about the perspective for design and application are presented when using the damper bar for SPM type CMG.

*Published in the IEEE Magnetics Society Section within IEEE Access.

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