In this paper, a safe trajectory planning and tracking algorithm for autonomous vehicles is proposed. Specially, the safety problem considers the geometric constraints including the obstacle avoiding and the road side constraints, and the non-convex input constraints defined from the sideslip angle of the wheels and input boundedness. Control barrier function (CBF) is adopted to deal with the state and input constraints and generate nominal trajectory. For this purpose, the nominal dynamics of the autonomous vehicle is defined as the virtual dynamics, from which the CBF safety certificates are derived. By constructing appropriate feedback control, the tracking error of the actual trajectory can be bounded into a tube, which guarantees the geometric safety of the actual vehicle. Two safety certificates, the bearing and the distance safety certificates are derived for multiple-obstacle avoidance. In order to deal with the non-convex input constraints, a safe braking maneuver is carefully considered. The feasible initial velocity set for safe braking is proposed as a part of state constraints. The feasibility and the safety of the overall system is proved. The algorithm to synthesis the CBF certificates for multiple-obstacle avoidance, and the input constraints is proposed. Simulation results from an autonomous vehicle including disturbances demonstrate the feasibility of the algorithm. The software implementation of the proposed algorithm was developed in C++ intended for real-world testing.

BACKGROUND: The objective of this study was to analyze the accuracy of gadolinium-ethoxybenzyl-diethylenetriamine penta-acetic acid enhanced magnetic resonance imaging (Gd-EOB-DTPA-MRI) for predicting microvascular invasion (MVI) in patients with small hepatocellular carcinoma (sHCC) preoperatively. METHODS: A total of 60 sHCC patients performed with preoperative Gd-EOB-DTPA-MRI in the Harbin Medical University Cancer Hospital from October 2018 to October 2019 were involved in the study. Univariate and multivariate analyses were performed by chi-square test and logistic regression analysis. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of Gd-EOB-DTPA-MRI were performed by receiver operating characteristic (ROC) curves. RESULTS: Univariate analysis indicated that alanine aminotransferase (≥ 39.00U/L), poorly differentiated pathology, and imaging features including grim enhancement, capsule enhancement, arterial halo sign and hepatobiliary features (tumor highly uptake, halo sign, spicule sign and brush sign) were associated with the occurrence of MVI (p < 0.05). Multivariate analysis revealed that rim enhancement and hepatobiliary spicule sign were independent predictors of MVI (p < 0.05). The area under the ROC curve was 0.917 (95% confidence interval 0.838-0.996), and the sensitivity was 94.74%. CONCLUSIONS: The morphologies of hepatobiliary phase imaging, especially the spicule sign, showed high accuracy in diagnosing MVI of sHCC. Rim enhancement played a significant role in diagnosing MVI of sHCC.

Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.

At present, the development of most of the granular fuzzy models depends upon some well-established numeric ones. In this study, a layered approach used to directly construct granular fuzzy models based on multidimensional numeric data is presented by engaging design methodology of granular computing. The crux of the approach involves a construction of interval information granules in the output space and the corresponding hyperbox information granules in the input space. A method of constructing these information granules and the hyperbox-based granular fuzzy model formed around them is studied in detail. Two different schemes to decode the formed hyperbox-based granular fuzzy model are also presented. Furthermore, a measure of a composite quality of the formed hyperbox-based granular fuzzy model is proposed along with the concept of coverage and specificity of resulting information granules. A number of experimental studies are reported, which offer a useful insight into the effectiveness of the presented approach, as well as reveal the impact of critical parameters on the performance of the established models.

Enabling high-throughput and cost-effective vehicular communications is important for many emerging vehicular applications, such as safety applications, traffic management, and mobile Internet access. However, dedicated short-range communications (DSRC), as the sole solution so far, would meet significant challenges in the foreseeable future for supporting diverse vehicular applications simply due to the spectrum scarcity. To address this issue, in this paper, we propose an adaptive vehicular data piping framework, which is assisted by a geolocation database, for the joint utilization of DSRC and TV white space (TVWS) spectrum; in this framework, three types of vehicular data pipes (DSRC, TVWS, and cellular) are considered, while the cellular data pipe is only used as a control-plane link in coordinating the dynamic DSRC and TVWS spectrum sharing happened in the data-plane operations. In order to guarantee the optimal dynamic vehicular access to the geolocation database, we first propose a log-sum-exp (LSE) approximation-based TVWS geolocation database access approach, named LSE-WS algorithm. We formulate the adaptive vehicular data piping problem for dynamic DSRC/TVWS spectrum sharing as a coalitional formation game, and it is shown that the proposed coalitional formation approach reaches the optimal and Nash-stable vehicular data pipe selection partition in a distributed way. Through extensive simulations, we demonstrate that not only the proposed LSE-WS algorithm satisfies the dynamic vehicular geolocation database access requirement but also the adaptive multi-radio vehicular data piping approach for dynamic DSRC/TVWS spectrum sharing significantly outperforms the traditional DSRC solution.

Exploiting physical layer characteristics to enhance or complement authentication strength in wireless networks has been attracting research attention recently. Existing physical layer authentication mechanisms mainly tackle single-hop communications. In this paper, we propose two physical layer challenge-response authentication mechanisms for wireless networks with relay. One mechanism, named PHY-CRAMR, is an extension of the existing PHY-CRAM protocol. It fully utilizes the randomness, reciprocity, and location decorrelation features of the wireless fading channel to hide/encrypt the challenge response messages at the physical layer, and is immune to outside attacks with a trusted relay. The other novel mechanism, named PHY-AUR, exploits randomness, coherence, and location decorrelation properties of wireless fading channel to securely convey the product of the channel state information on consecutive links and uses the fading channel to encrypt challenge and response messages. PHY-AUR is immune to both outside and inside attacks with an untrusted relay. Both PHY-CRAMR and PHY-AUR adopt OFDM technique to modulate the authentication key and challenge-response messages on subcarriers. Physical layer pilots and preambles are eliminated to prevent an attacker from gaining knowledge about the channel state information, and as a result prevent the authentication key from being revealed to untrusted attackers. We analyze the security strength of both mechanisms and conduct extensive simulations to evaluate them. It shows that both PHY-CRAMR and PHY-AUR can achieve both a high successful authentication rate and low false acceptance rate, and the performance improves as the signal to noise ratio (SNR) increases.

Cognitive radio (CR) implements dynamic spectrum access (DSA) mainly through performing spectrum sensing. In some circumstances, spectrum sensing is regarded as not sufficient for CR systems without generating unacceptable impact on primary users. For example, for wireless microphone (WM) systems when the transmitters either are turned off or stay idle while receivers are actively listening, CR systems decide to transmit message over the idle channel after spectrum sensing. At this moment, active WM receivers still can detect narrow-band BPSK/QPSK signals transmitted by customer premise equipments (CPEs) leading to unexpected audio signals. In this paper, we first address and study such a hidden busy channel (HBC) situation. The degree of audio interferences generated by narrow-band BPSK/QPSK signals on HBCs is evaluated through field testing. To detect HBCs, we propose a novel technique named iSens. Experiments show that detection the rate of iSens is higher than 90% while the false alarm rate is lower than 10%.

Exploiting the unique properties of the physical layer to enhance or complement authentication strength in wireless networks has attracted a lot of research attention recently. In this paper, we propose a novel PHYsical layer Challenge-Response Authentication Mechanism (PHY-CRAM) for wireless networks. PHY-CRAM is suitable for both one-way and mutual authentication. It fully utilizes the randomness, reciprocal, and location decorrelation features of the wireless fading channel, and is immune to various passive and active attacks. In the authentication procedure, challenge-response signals are exchanged at the physical layer, which allow two devices to verify their shared secrets while not revealing these secrets to attackers. PHY-CRAM adopts orthogonal frequency-division multiplexing (OFDM) technique which separately modulates the higher layer information and shared keys on subcarriers' phases and amplitudes respectively, in order to prevent channel probing from traffic-related information. We conduct extensive simulation study and develop a prototype using field-programmable gate array (FPGA) and discrete radio frequency (RF) components to evaluate PHY-CRAM in real-world environments. It shows that PHY-CRAM achieves both high successful authentication rate and low false acceptance rate in various channel conditions and under various attacks.

In this demonstration, we will reveal a full functional wireless access for vehicular environments (WAVE) prototype, which achieves the complete functions defined by the IEEE 802.11p standard through integrating effective and novel base-band algorithms including fast time-synchronization, multiple-stage multiple Doppler detection, accurate channel estimate, and joint QAM demodulation and convolution decoding, all upon the Cyclone III field programmable gate array (FPGA) development kit from the Terasic Company. The WAVE prototype has been extensively tested under various road conditions showing satisfied performances. The WAVE prototype has an Ethernet interface to connect with a computer, upon which media control access (MAC) protocols and a vehicular network simulator operate. Meanwhile, the prototype can also connect with an Acer Iconia Tablet running on the Android system through a universal series bus (USB) to support various intelligent transportation system (ITS) applications based on vehicular communications and networks. This activity is funded by several NSF grants and the CISCO Research Grant.