The proliferation of large language models (LLMs) has underscored concerns regarding their security vulnerabilities, notably against jailbreak attacks, where adversaries design jailbreak prompts to circumvent safety mechanisms for potential misuse. Addressing these concerns necessitates a comprehensive analysis of jailbreak prompts to evaluate LLMs' defensive capabilities and identify potential weaknesses. However, the complexity of evaluating jailbreak performance and understanding prompt characteristics makes this analysis laborious. We collaborate with domain experts to characterize problems and propose an LLM-assisted framework to streamline the analysis process. It provides automatic jailbreak assessment to facilitate performance evaluation and support analysis of components and keywords in prompts. Based on the framework, we design JailbreakLens, a visual analysis system that enables users to explore the jailbreak performance against the target model, conduct multi-level analysis of prompt characteristics, and refine prompt instances to verify findings. Through a case study, technical evaluations, and expert interviews, we demonstrate our system's effectiveness in helping users evaluate model security and identify model weaknesses.

A magnetic quasi-static (MQS) surface integral equation (SIE) formulation based on the cut set analysis (CSA) is proposed to extract parameters of interconnects in packages. The surface impedance approximation is used to describe the surface current caused by skin effect at high frequencies. To accurately model arbitrarily shaped structures, triangle meshes are selected to discretize surfaces of interconnects in the proposed formulation. After physically interpreting the discretized matrix equation as a circuit, the CSA is tailored to carefully apply the charge conservation condition. Triangles on each terminal are bounded together as a supernode to enforce currents flowing into/out interconnects. In addition, an efficient preconditioner and the pre-corrected Fast Fourier Transform (pFFT) are used to accelerate the convergence and matrix-vector product. Four numerical examples were carried out to validate the effectiveness by comparing it with the industrial solver. Our results show that the proposed formulation is accurate, efficient, and flexible to model complex interconnects at high frequencies.

It is important to estimate the global network traffic data from partial traffic measurements for many network management tasks, including status monitoring and fault detection. However, existing estimation approaches cannot well handle the topological correlations hidden in network traffic and suffer from limited imputation performance. This paper proposes a deep learning approach for network traffic imputation, which well exploits the topological structure of network traffic. We first model the network traffic as a novel graph-tensor and derive a theoretical recovery guarantee. Then we develop an iterative graph-tensor completion algorithm and propose a graph neural network for network traffic imputation by unfolding the iterative algorithm. The proposed graph neural network well captures the topological correlations of network traffic and achieves accurate imputation. Extensive experiments on real-world datasets show that the proposed graph neural network achieves about one-half lower relative square error while at least ten times faster imputation speed than the existing methods.

This article proposes convex optimization of mutual inductance between multiantiparallel coils (MACs) for distance-insensitive wireless charging of air–ground robots. In order to reduce optimization costs, the proposed optimization method is developed by hybridizing analytical and numerical models. First, it is shown that the commonly used analytical model for MAC design becomes inaccurate as frequency increases. Second, a trial mutual inductance is introduced as an optimization variable. From the trial mutual inductance, an MAC is designed using the analytical model. Third, the realized mutual inductance of the analytically designed MAC is computed by a numerical model to correct the error of the analytical model. The difference vector$\bar {\rho }$between the realized and optimal mutual inductances is written as a function of the trial mutual inductance, and the$l_{2}$-norm of$\bar {\rho }$is minimized by changing the trial mutual inductance. The resultant optimization problem is shown to be convex, and it is conveniently solved by using a local searching method. Simulation results show that the proposed design method satisfies design specification much better than the conventional analytical-model-based design method. Using the proposed method, an MAC prototype is designed and fabricated, and a wireless charging system for air–ground robots is constructed and tested. Measurement results show that the efficiency of the designed MAC is maintained at around 80% in the transfer distance range of 15–55 mm, and stable efficiency is obtained when charging real air–ground robots of different heights.

A type-II analog phase-locked loop without charge pump and analog loop filter is proposed in this paper. A novel discrete proportional-integral-derivative circuit (DPIDC) is proposed to implement phase error integration and frequency compensation with time-domain processing. A phase-to-voltage converter (PVC) with a sample-and-hold is used to convert the phase error processed by the DPIDC to a voltage that controls the voltage-controlled oscillator (VCO). Simulation results in 180nm CMOS technology show the proposed DPIDC and PVC circuits only consume 6.5-μW power at 0.6-V supply voltage. The proposed PLL settles down steadily and achieves a normalized reference-spur rejection better than -84.6dBc.

Equivalent models of antennas are useful for the fast simulation of vehicle-to-everything (V2X) communication. The existing antenna-equivalent models are inflexible because they assume rectangular antenna contour. This article presents a hybrid-equivalent surface-edge current model to overcome the limitation of the existing equivalent models. Based on Huygens' principle, a V2X communication antenna is equivalent to a conducting plate excited by equivalent surface magnetic current (ESMC). The sources of fields reflected by the conducting plate and diffracted by its edges are modeled as the image of ESMC and equivalent edge current (EEC), respectively. A hybrid-equivalent surface-edge current model is thus established, and it consists of ESMC, the image of ESMC, and EEC. The unknown sources in the proposed model are solved from near fields of the antennas, and the proposed model is then used to simulate the radiation performance of antennas installed on vehicles. The transmission coefficient between V2X communication antennas can also be calculated by using the proposed model and the electromagnetic reaction theorem. Simulations and experiments are performed to demonstrate the effectiveness of the proposed method. It is shown that the proposed equivalent model not only accurately models antennas with arbitrarily shaped contour but also significantly accelerates the integrated simulation of antennas and vehicles.

Deep learning has recently garnered significant interest in many applications especially for big data analytics in the edge computing environment. Federated learning, as a novel machine learning technique, aims to build a shared learning model from training data on distributed edge nodes to protect data privacy. However, the model update in federated learning requires parameter exchanges among edge nodes, which is rather bandwidth-consuming. This article proposes a novel distributed hierarchical tensor deep computation model by condensing the model parameters from a high-dimensional tensor space into a set of low-dimensional subspaces to reduce the bandwidth consumption and storage requirement for federated learning. Moreover, an updating approach with a hierarchical tensor back-propagation algorithm is developed by directly computing the gradients of low-dimensional parameters to reduce the memory requirement of training for edge nodes and improve training efficiency. Finally, extensive simulations on classical datasets with different local data distributions are presented for the performance evaluation. The results demonstrate that the proposed model relieves the burden of communication bandwidth and reduces energy consumption at edge nodes for federated learning.

Random frequency diverse array (RFDA) based directional modulation (DM) was proposed as a promising technology in secure communications to achieve a precise transmission of confidential messages, and artificial noise (AN) was considered as an important helper in RFDA-DM. Compared with previous works that only focus on the spot of the desired receiver, in this work, we investigate a secrecy region around the desired receiver, that is, a specific range and angle resolution around the desired receiver. Firstly, the minimum number of antennas and the bandwidth needed to achieve a secrecy region are derived. Moreover, based on the lower bound of the secrecy capacity in RFDA-DM-AN scheme, we investigate the performance impact of AN on the secrecy capacity. From this work, we conclude that: 1) AN is not always beneficial to the secure transmission. Specifically, when the number of antennas is sufficiently large and the transmit power is smaller than a specified value, AN will reduce secrecy capacity due to the consumption of limited transmit power. 2) Increasing bandwidth will enlarge the set for randomly allocating frequencies and thus lead to a higher secrecy capacity. 3) The minimum number of antennas increases as the predefined secrecy transmission rate increases.

In recent years, end-to-end network latency estimation has attracted much attention because of its significance for network performance evaluation. Given the widespread use of personal devices, latency estimation from partially observed samples becomes more complicated due to unstable communication conditions, while measuring the latencies between all nodes in a large-scale network is infeasible and costly. Hence, reducing the measurement cost becomes critical for the latency estimation of personal device network. In this paper, we propose an adaptive sampling scheme based on leverage scores to reduce the measurement cost while achieving high estimation accuracy. Furthermore, we provide theoretical analysis to characterize the performance bounds of the proposed scheme in terms of sampling complexity and estimation error. Finally, we demonstrate the efficiency of the proposed scheme by conducting extensive simulations on both synthetic and real datasets. The results show that the proposed scheme is able to not only improve the estimation accuracy of network latency but also reduce the sample budget compared to the state-of-the-art approaches.

Electrical modeling of vehicular antennas is required for design of modern vehicle-to-everything communication networks. However, full-wave modeling and the existing equivalent source methods still ask for long computation time. To address this issue, a fast simulation method is proposed in this paper: a sparse equivalent source model is developed based on Huygens’ principle where a vehicular antenna is made equivalent to magnetic current placed on a finite conducting plate. The image theory and the uniform theory of diffraction are then applied, a modified Green’s function is derived, and the sparse equivalent source model is constructed to replace the original vehicular antenna. Because of its simple geometry and material, in comparisons with the existing methods, the proposed model uses significantly less expenditures for simulation of vehicular antennas while has better accuracy due to the consideration of the finite conducting plate. Simulations are performed to demonstrate the effectiveness and advantages of the proposed method, and a real-world experiment is designed and conducted to demonstrate the superiority of the proposed method with real measurement data. It is shown that the proposed method can save simulation time by four times, with less than 1-dB error compared to measurement.

Radio frequency (RF) source reconstruction is useful for RF radiation analysis and interference diagnosis in Internet of Things. Near field (NF) sampling is a critical step of RF source reconstruction. Accurate RF source reconstruction usually requires a large number of NF samples, which results in tremendous effort of NF sampling. This article presents an adaptive NF sampling method based on the skeletonization scheme. First, sources to be reconstructed are related to NF samples through integral equation (IE). Second, the IE is discretized with the method of moments, and thus the interaction matrix between source and field points is found. Third, strong rank-revealing QR factorization is applied to the interaction matrix, which results in a permutation matrix, a row skeleton matrix, and a transformation matrix. Finally, a small number of skeleton sampling points are selected by analyzing the permutation matrix. The fields at skeleton sampling points can be used to calculate the fields at other sampling points through the transformation matrix. Hence, one only needs to perform NF sampling at a small number of skeleton sampling points, which significantly reduces the expenditure of NF sampling. Simulations using synthetic and measurement data are presented to show the effectiveness and advantages of the proposed sampling method.

In recent years Wi-Fi fingerprinting has attracted much attention in indoor localization because of the availability of high-quality signal and pervasive deployment of wireless LANs. For fingerprint-based localization, however, offline site survey is usually time-consuming and labor-intensive. Therefore, reducing the burden of offline site survey becomes an important issue for fingerprint-based indoor localization. In this paper, using a low-tubal-rank tensor to model Wi-Fi fingerprints of all reference points (RPs), we propose an adaptive sampling scheme via approximate volume sampling to improve reconstruction accuracy of radio map with reduced expenditure. We propose a rank-increasing strategy to effectively estimate the rank of the underlying fingerprint tensor to alleviate the computation burden for tensor completion. We provide a theoretical foundation to analyze the proposed scheme and derive the performance bounds in terms of sample complexity and reconstruction error. We prove that the proposed scheme can achieve a relative error guarantee. Finally, we validate the effectiveness of the proposed scheme through extensive simulations using both synthetic and real datasets. The simulation results demonstrate that the proposed scheme is able to not only reduce reconstruction error and improve localization accuracy but also reduce running time compared to the state-of-the-art schemes.

A variance detection (VD)-based impulse radio ultra-wideband (IR-UWB) system has the potential of mitigating narrowband interference (NBI), even in multipath environments. However, as a highly nonlinear system, which involves fourth-order multiplications, comprehensive analysis and evaluations of its performances have not been seen so far. In this paper, we address the issues by developing a log-normal random distribution model for the VD-based IR-UWB receiver and deriving the analytical bit-error-rate formulas for the system in both additive white Gaussian noise and CM1 multipath channels with and without NBIs for the first time. Furthermore, through both theoretical analysis and simulations, we show that the presented VD-based receivers can outperform the conventional energy-detection-based and the fourth-order detection-based receivers and have a stronger inherent ability to mitigate destructive performance degradation caused by strong NBIs. This makes the VD-based IR-UWB system a good candidate as a non-coherent IR-UWB receiver for low power and low complexity applications.

Abstract Because of the surging demands of multimedia services, quality‐of‐experience (QoE) is becoming an important metric to evaluate network quality from users' perspective. In this paper, resource optimisation to achieve optimal tradeoff between QoE and energy consumption in bidirectional orthogonal frequency‐division multiple‐access relaying networks is addressed so as to provide satisfactory multimedia delivery quality and support green communications. We first formulate a QoE‐energy efficiency tradeoff optimisation where QoE requirements and relaying traffic balance are considered and prove that QoE‐energy efficiency is quasiconcave on QoE, which suggests the existence of a unique global optimal tradeoff point. We then propose an optimisation framework to achieve the optimal tradeoff efficiently. With the framework, we develop resource allocation approaches for two specific relaying strategies, that is, two‐phase decode‐and‐forward relaying with dynamic XOR network coding and compute‐and‐forward relaying with physical network coding via structured codes. Numerical results validate theoretical findings and demonstrate the effectiveness of the proposed optimisation solution for achieving the tradeoff between QoE and energy consumption. Copyright © 2015 John Wiley & Sons, Ltd.

In this paper, a unified framework, denoted as generalized nonlinear detection (GND), is developed to encompass existing nonlinear detection technologies as well as further optimize the performance of nonlinear detection for noncoherent impulse radio (IR) ultra-wideband (UWB) receivers. Computer simulations show that independent of absence or presence of narrowband interferences (NBIs), the GND-based receivers can achieve much better bit-error rate (BER) performance than the energy detection (ED) based receivers under both AWGN and multipath channels. Furthermore, as compared to the existing nonlinear detection algorithms, the GND algorithm with a well selected β value always has the best BER performance and the strongest ability to resist NBIs.

In this paper, we investigate the joint optimization of power allocation and subcarrier assignment in the OFDM bidirectional systems where two users exchange data assisted by multiple decode-and-forward (DF) relays. We assume the availability of imperfect channel state information (CSI) that contains a probabilistic uncertainty component and consider its worst effect when performing resource allocation. Moreover, we adopt dynamic network coding to exploit channel diversity. The objective of the resource allocation problem is to maximize the weighted sum rates of the two users subject to quality-of-service (QoS) requirements and individual power constraints. We formulate the problem as a mixed integer programming problem and solve it efficiently in an asymptotic manner by using the dual method. Simulation results show that the proposed scheme is robust to imperfect CSI and provides better performance compared to the scheme with static network coding.

In this paper, by modeling a narrowband interference (NBI) as a wide-sense stationary (WSS), zero-mean Gaussian bandpass random process, a systematic theoretical framework is developed for the first time to characterize the operation and performance improvement of energy detection (ED) based impulse radio ultra-wideband (IR-UWB) receivers that employ a nonlinear Teager-Kaiser operator (TKO) and a highpass filter. Furthermore, through both theoretical analysis and simulations, we have identified (1) that the TKO technique can effectively mitigate not only the performance degradation caused by an NBI but also that caused by additive white Gaussian noise and (2) that a larger bandwidth of the UWB signal leads to better mitigation. Therefore, the TKO technique is considered as a very promising technique to improve the overall performance of ED-based IR-UWB receivers no matter an NBI is present or not.

In this paper, a novel combining weight estimation algorithm including a capability for inter-symbol interference (ISI) cancelation is proposed for monobit impulse radio (IR) ultra wideband (UWB) receivers. By applying the proposed algorithm to a monobit weighted transmitted-reference IR-UWB receiver as a case study, we have demonstrated through computer simulations that the algorithm can effectively mitigate the destructive effects of ISI and achieve considerable performance improvement over the algorithm without ISI cancellation capability. Furthermore, because most calculations required by the proposed algorithm can be performed offline and the obtained results can be saved into lookup tables for online operations, the system complexity to implement the algorithm is low. Therefore, this ISI-included combining weight estimation algorithm enables the employment of monobit IR-UWB receivers for high data rate applications under non-line-of-sight channels, and can be considered as a promising technology for IR-UWB applications demanding for low system complexity, low power consumption, and high data rates.

In this paper, a low complexity suboptimal monobit receiver, denoted as Q-function estimation (QFE) based monobit receiver, is proposed for transmitted-reference (TR) based impulse radio (IR) ultra wideband (UWB) systems to mitigate the performance degradation caused by severe quantization noise due to employing monobit ADCs. Through accumulating reference samples over multiple blocks, the QFE-based monobit receiver can considerably reduce the noise-on-noise effect and optimize the contribution of each sample to the decision statistic. Computer simulations demonstrate that in both line-of-sight (LOS) and non-LOS (NLOS) channels the QFE-based monobit weighted TR receiver can achieve a close-to-optimal bit-error rate (BER) performance without requiring full channel state information. Furthermore, because the optimal combining weights can be obtained offline and saved into a lookup table, the complexity of the proposed suboptimal monobit receiver is quite low. Therefore, it is considered as a promising technology for IR-UWB applications requiring low system complexity and low power consumption.

In this paper, a systematic framework is developed for the first time for theoretically analyzing performance of the nonlinear Teager-Kaiser operator (TKO) employed in energy detection (ED) based impulse radio ultra-wideband (IR-UWB) receivers. Closed-form formulations are derived for various nonlinear terms in the TKO output as well as for the performance gain of the IR-UWB systems with the TKO technique (over those without the TKO technique). Both theoretical analysis and computer simulations show that the TKO technique can effectively mitigate not only performance degradation caused by narrowband interference (NBI) but also that by additive white Gaussian noise. As a result, the TKO technique can be used to improve overall performance of ED based IR-UWB receivers regardless of an NBI being present or not.

In this paper, two novel digital signal processing technologies, denoted as polarity-invariant square law (PISL) technology and forgetting factor based successive reference enhancement (SRE) technology, are proposed for transmitted reference (TR) based monobit receivers to compensate the quantization noise and enhance the quality of reference pulses. Since weighted TR (WTR) system can achieve a superior performance as well as a high data-rate under severe multipath environments, the PISL and SRE technologies are applied to the WTR receiver over intra-vehicle channels. The results show that compared to the SRE technology, the PISL technology enables a fixed summation time for all multipath channels, and significantly enhances the performance of the monobit ADC WTR receiver. Therefore, PISL technology is a promising technology to compensate the information loss caused by monobit quantization insuring high-performance, power efficient, and low complexity implementation for TR-based UWB receivers.

In this paper, a differential code-shifted reference ultra-wideband (CSR UWB) transceiver has been proposed to reduce the power spent to transmit the reference pulse sequence so as to improve the bit-error rate performance of the systems. In the differential CSR UWB transceiver, by differentially encoding M information bits to be transmitted simultaneously, one data pulse sequence can be used as the reference sequence of another data pulse sequence. Therefore, the power used to transmit the reference pulse sequence can be reduced from half to 1/(M+1) of the total power. The evaluation results obtained from the computer simulations have shown that as compared to the original CSR UWB transceiver, the differential CSR UWB transceiver has much better bit-error rate performance with comparable system complexity.