Biblio

Multi-factor user authentication (MFUA) becomes increasingly popular due to its superior security comparing with single-factor user authentication. However, existing MFUAs require multiple interactions between users and different authentication components when sensing the multiple factors, leading to extra overhead and bad use experiences. In this paper, we propose a secure and user-friendly MFUA system, namely BioDraw, which utilizes four categories of biometrics (impedance, geometry, composition, and behavior) of human hand plus the pattern-based password to identify and authenticate users. A user only needs to draw a pattern on a RFID tag array, while four biometrics can be simultaneously collected. Particularly, we design a gradient-based pattern recognition algorithm for pattern recognition and then a CNN-LSTM-based classifier for user recognition. Furthermore, to guarantee the systemic security, we propose a novel anti-spoofing scheme, called Binary ALOHA, which utilizes the inhabit randomness of RFID systems. We perform extensive experiments over 21 volunteers. The experiment result demonstrates that BioDraw can achieve a high authentication accuracy (with a false reject rate less than 2%) and is effective in defending against various attacks.

Physical interaction of robots with their environment is a challenging problem because of the exchanged forces. Hybrid position/force control schemes often exhibit problems during the contact phase, whereas impedance control appears to be more simple and reliable, especially when impedance is shaped to be energetically passive. Even if recent technologies enable shaping the impedance of a robot, how best to plan impedance parameters for task execution remains an open question. In this paper we present an optimization-based approach to plan not only the robot motion but also its desired end-effector mechanical impedance. We show how our methodology is able to take into account the transition from free motion to a contact condition, typical of physical interaction tasks. Results are presented for planar and three-dimensional open-chain manipulator arms. The compositionality of mechanical impedance is exploited to deal with kinematic redundancy and multi-arm manipulation.

Security and privacy of smart grid communication data is crucial given the nature of the continuous bidirectional information exchange between the consumer and the utilities. Data security has conventionally been ensured using cryptographic techniques implemented at the upper layers of the network stack. However, it has been shown that security can be further enhanced using physical layer (PHY) methods. To aid and/or complement such PHY and upper layer techniques, in this paper, we propose a PHY design that can detect and locate not only an active intruder but also a passive eavesdropper in the network. Our method can either be used as a stand-alone solution or together with existing techniques to achieve improved smart grid data security. Our machine learning based solution intelligently and automatically detects and locates a possible intruder in the network by reusing power line transmission modems installed in the grid for communication purposes. Simulation results show that our cost-efficient design provides near ideal intruder detection rates and also estimates its location with a high degree of accuracy.

The distribution of video contents generated by Content Providers (CPs) significantly contributes to increase the congestion within the networks of Internet Service Providers (ISPs). To alleviate this problem, CPs can serve a portion of their catalogues to the end users directly from servers (i.e., the caches) located inside the ISP network. Users served from caches perceive an increased QoS (e.g., average retrieval latency is reduced) and, for this reason, caching can be considered a form of traffic prioritization. Hence, since the storage of caches is limited, its subdivision among several CPs may lead to discrimination. A static subdivision that assignes to each CP the same portion of storage is a neutral but ineffective appraoch, because it does not consider the different popularities of the CPs' contents. A more effective strategy consists in dividing the cache among the CPs proportionally to the popularity of their contents. However, CPs consider this information sensitive and are reluctant to disclose it. In this work, we propose a protocol based on Shamir Secret Sharing (SSS) scheme that allows the ISP to calculate the portion of cache storage that a CP is entitled to receive while guaranteeing network neutrality and resource efficiency, but without violating its privacy. The protocol is executed by the ISP, the CPs and a Regulator Authority (RA) that guarantees the actual enforcement of a fair subdivision of the cache storage and the preservation of privacy. We perform extensive simulations and prove that our approach leads to higher hit-rates (i.e., percentage of requests served by the cache) with respect to the static one. The advantages are particularly significant when the cache storage is limited.

The presence of security vulnerabilities in automotive networks has already been shown by various publications in recent years. Due to the specification of the Controller Area Network (CAN) as a broadcast medium without security mechanisms, attackers are able to read transmitted messages without being noticed and to inject malicious messages. In order to detect potential attackers within a network or software system as early as possible, Intrusion Detection Systems (IDSs) are prevalent. Many approaches for vehicles are based on techniques which are able to detect deviations from specified CAN network behaviour regarding protocol or payload properties. However, it is challenging to detect attackers who secretly connect to CAN networks and do not actively participate in bus traffic. In this paper, we present an approach that is capable of successfully detecting unknown CAN devices and determining the distance (cable length) between the attacker device and our sensing unit based on Time Domain Reflectometry (TDR) technique. We evaluated our approach on a real vehicle network.

Loss of field (LOF) relay, with ANSI code 40, is one of the most important protection functions for synchronous generators in power plants. Although many LOF protection schemes have been presented in the literature during the last decades, a few numbers of them such as impedance and admittance based schemes are accepted by the industry. This paper explores and compares the performances of some industrial LOF protection schemes through simulation studies and from speed, reliability and security viewpoints. The simulation studies are carried out in the real-time-digital-simulator, where a realistic power generation unit is developed by employing the phase domain model of synchronous generator. Using such a realistic system, various types of LOF events can be simulated in accordance with IEEE Standard C37.102-2006, so that the performance of any method can be evaluated through careful LOF studies.

According to the technical difficulties of the air-coupled piezoelectric ultrasonic transducer, 1-3 type piezoelectric composites and double matching layers structure are adopted in order to solve the acoustic impedance mismatch at the interface between the piezoelectric materials and air. The optimal design of the matching layer thickness for double matching layers structure air-coupled ultrasonic transducer is also completed through experiments. Based on this, 440 kHz flat-plate and focused air-coupled piezoelectric ultrasonic transducer are designed, fabricated and characterized. Finally, surface cracks are detected using the focused air-coupled piezoelectric ultrasonic transducer.

Analysis of acoustic wave scattering by a finite flat impedance strip grating is presented. The associated two-dimensional (2-D) boundary-value problem is considered in the full-wave manner and cast to a set of coupled integral equations. Based on them, we build several mathematical models. The focus of research is on the acoustic plane wave scattering by a grating of narrow impedance strips that has explicit asymptotic solution.

Wireless transmission of power has been in research for over a century. Our project aims at transmitting electric power over a distance of room. Various methods using microwaves, lasers, inductive coupling, capacitive coupling and acoustic medium have been used. In our project, we are majorly focusing on acoustic method of transferring power. Previous attempts of transferring power using acoustic methods have employed the usage of ultrasonic sound. In our project, we are using infrasonic sound as a medium to transfer electrical power. For this purpose, we are using suitable transducers and converters to transmit electric power from the 220V AC power supply to a load over a considerable distance. This technology can be used to wirelessly charge various devices more effectively.

In order to solve the problem of millimeter wave (mm-wave) antenna impedance mismatch in 5G communication system, a optimization algorithm for Particle Swarm Ant Colony Optimization (PSACO) is proposed to optimize antenna patch parameter. It is proved that the proposed method can effectively achieve impedance matching in 28GHz center frequency, and the return loss characteristic is obviously improved. At the same time, the nonlinear regression model is used to solve the nonlinear relationship between the resonant frequency and the patch parameters. The Elman Neural Network (Elman NN) model is used to verify the reliability of PSACO and nonlinear regression model. Patch parameters optimized by PSACO were introduced into the nonlinear relationship, which obtained error within 2%. The method proposed in this paper improved efficiency in antenna design.

Although connecting a microgrid to modern power systems can alleviate issues arising from a large penetration of distributed generation, it can also cause severe voltage instability problems. This paper presents an online method to analyze voltage security in a microgrid using convolutional neural networks. To transform the traditional voltage stability problem into a classification problem, three steps are considered: 1) creating data sets using offline simulation results; 2) training the model with dimensional reduction and convolutional neural networks; 3) testing the online data set and evaluating performance. A case study in the modified IEEE 14-bus system shows the accuracy of the proposed analysis method increases by 6% compared to back-propagation neural network and has better performance than decision tree and support vector machine. The proposed algorithm has great potential in future applications.

One of the primary motivations for using ultrasound reflectometry for fingerprint imaging is the promise of increased spoof resistance over conventional optical or capacitive sensing approaches due to the ability for ultrasound to determine the elastic impedance of the imaged material. A fake 3D printed plastic finger can therefore be easily distinguished from a real finger. However, ultrasonic sensors are still vulnerable to materials that are similar in impedance to tissue, such as water or rubber. Previously we demonstrated an ultrasonic fingerprint reader operating with 1.3GHz ultrasound based on pulse echo impedance imaging on the backside silicon interface. In this work, we utilize the large bandwidth of these sensors to differentiate between a finger and materials with similar impedances using the frequency response of elastic impedance obtained by transducer excitation with a wideband RF chirp signal. The reflected signal is a strong function of impedance mismatch and absorption [Hoople 2015].