The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

We consider a time-division multiple access (TDMA) transmission scheme in MIMO broadcast channels. To cope with the fairness issue in heterogeneous networks with slow fading, an opportunistic scheduling algorithm based on the channel eigen-direction is investigated. In the system with sparse users, the mismatch between a random beamforming vector and the principle eigenvector of the channel incurs a throughput penalty. To reduce such a throughput loss, a multiple random beams selection (MRBS) scheme exploiting chordal distances is proposed. Two feedback schemes (unquantized or quantized chordal distances) are considered. The closed-form throughput expressions of the proposed schemes are derived.

This paper presents an accurate and systematic method to simulate the interference imposed on the input/output (I/O) ports of electronic equipment under the electrical fast transients/burst (EFT/B) test. The equivalent circuit of the EFT/B generator and the coupling clamp are modeled respectively. Firstly, a transfer function (TF) of the EFT pulse-forming network is constructed with the latent parameters based on circuit theory. In the TF, two negative real parameters characterize the non-oscillation process of the network while one complex conjugate pair characterizes the damping-oscillation process. The TF of the pulse-forming network is therefore synthesized in the equivalent circuit of the EFT/B generator. Secondly, the standard coupling clamp is modeled based on the scatter (S) parameter obtained by using a vector network analyzer. By applying the vector fitting method during the rational function approximation, a macromodel of the coupling clamp can be obtained and converted to a Spice compatible equivalent circuit. Based on the aforementioned procedures, the interference imposed on the I/O ports can be simulated. The modeling methods are validated experimentally, where the interference in differential mode and common mode is evaluated respectively.

In this paper, the linear complexity and minimal polynomials of Legendre sequences over Fq have been calculated, where q = pm and p is a prime number. Our results show that Legendre sequences have high linear complexity over Fq for a large part of prime power number q so that they can resist the linear attack method.

CMOS SoCs can reduce power consumption while maintaining performance by adopting voltage scaling (VS) technologies. The operating speed of the level converter (LC) strongly affects the effectiveness of VS technologies. However, PVT variations can cause serious problems to the LC, because the state-of-the-art LC designs do not give enough attention to this issue. In this work, we proposed to analyze the impact of PVT variations on the performance of the LC using a previously developed heuristic sizing methodology. Based on the evaluation results from different operating corners with different offset voltages and temperatures, we proposed a variation-tolerant LC that achieves both high performance and low energy with a high tolerability for PVT variations.

In this letter, we study the impact of feedback error on transmit beamforming systems with finite rate feedback. The feedback channel is modeled as a uniform symmetric channel. The outage probability, bit error rate (BER), diversity gain, and array gain formulas are given. Both analytical and simulation results show that feedback error with small probability will make the system behave badly at high signal-to-noise ratios (SNR).

Attenuated and delayed versions of the pulse signal overlap in multipath propagation. Previous algorithms can resolve them only if signal sampling is ideal, but fail to resolve two counterparts with non-ideal sampling. In this paper, we propose a novel method which can resolve the general types of non-ideally sampled pulse signals in the time domain via Taylor Series Expansion (TSE) and estimate multipath signals' precise time delays and amplitudes. In combination with the CLEAN algorithm, the overlapped pulse signal parameters are estimated one by one through an iteration method. Simulation results verify the effectiveness of the proposed method.

In order to realize an Internet-of-Things (IoT) with tiny sensors integrated in our buildings, our clothing, and the public spaces, battery lifetime and battery size remain major challenges. Power reduction in IoT sensor nodes is determined by both sleep mode as well as active mode contributions. A power state machine, at the system level, is the key to achieve ultra-low average power consumption by alternating the system between active and sleep modes efficiently. While, power consumption in the active mode remains dominant, other power contributions like for timekeeping in standby and sleep conditions are becoming important as well. For example, non-conventional critical blocks, such as crystal oscillator (XO) and resistor-capacitor oscillator (RCO) become more crucial during the design phase. Apart from power reduction, low-voltage operation will further extend the battery life. A 2.4GHz multi-standard radio is presented, as a test case, with an average power consumption in the µW range, and state-of-the-art performance across a voltage supply range from 1.2V to 0.9V.

We regard a De Bruijn sequence of order n as a bijection on $mathbb{F}_2^n$ and consider the transition mappings between them. It is shown that there are only two conjugate transformations that always transfer De Bruijn sequences to De Bruijn sequences.

Call admission control (CAC) is becoming vital for multimedia services in the ability of wireless/mobile networks to guarantee Quality of Service (QoS) partially due to the network's limited capacity. In this paper, we propose an optimal call admission control scheme with bandwidth reallocation algorithm (multi-class-CAC-BRA) for multi-classes of adaptive multimedia services in wireless/mobile networks. The multi-class-CAC-BRA approach optimizes revenue for service providers and satisfies QoS requirements for service users. The proposed approach adopts semi-Markov Decision Process to model both call admission control and bandwidth reallocation algorithm. In other words, whenever decisions are made, decisions are made for both call admission control and bandwidth reallocation. Since the non-adaptive multimedia traffic is a special case of the adaptive multimedia traffic, the non-adaptive optimal CAC scheme is a special case of our optimal multi-class-CAC-BRA scheme. Furthermore, the Interior-point Method in linear programming is used to solve the optimal decision problem. Simulation results reveal that the proposed multi-class-CAC-BRA scheme adapts itself well to adaptive multi-class multimedia traffic, achieves optimal revenue, and satisfies QoS requirements that are the upper bounds of handoff dropping probabilities. Our approach solves the optimal adaptive multimedia CAC problem. We believe that this work has both theoretical and practical significance.

In recent years, high-resolution 77 GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the trade-off between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77 GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

In recent years, Graph Neural Networks has received enormous attention from academia for its huge potential of modeling the network traits such as macrostructure and single node attributes. However, prior mainstream works mainly focus on homogeneous network and lack the capacity to characterize the network heterogeneous property. Besides, most previous literature cannot the model latent influence link under microscope vision, making it infeasible to model the joint relation between the heterogeneity and mutual interaction within multiple relation type. In this letter, we propose a latent influence based self-attention framework to address the difficulties mentioned above. To model the heterogeneity and mutual interactions, we redesign the attention mechanism with latent influence factor on single-type relation level, which learns the importance coefficient from its adjacent neighbors under the same meta-path based patterns. To incorporate the heterogeneous meta-path in a unified dimension, we developed a novel self-attention based framework for meta-path relation fusion according to the learned meta-path coefficient. Our experimental results demonstrate that our framework not only achieves higher results than current state-of-the-art baselines, but also shows promising vision on depicting heterogeneous interactive relations under complicated network structure.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

In this paper, the multi-user multiple-input multiple-output (MU-MIMO) relay channel is investigated, where the source node provides multi-beams to multi-users via a multi-antenna relay node. In this scenario, linear processing matrix at the relay node is designed around block diagonal (BD) scheme to improve the system sum-rate. Compared with the traditional linear processing matrix with zero-forcing (ZF) scheme at the relay node, the proposed matrix based on BD scheme can not only eliminate the multi-user interference to the same extent as the ZF scheme, but also realize optimal power allocation at the relay node. Numerical simulations demonstrate the BD scheme outperforms the ZF scheme and can significantly improve the sum-rate performance.

In this paper, we first propose a notion of multiple authorities attribute-based designated confirmer signature scheme with unified verification. In a multiple authorities attribute-based designated confirmer signature scheme with unified verification, both the signer and the designated confirmer can run the same protocols to confirm a valid signature or disavow an invalid signature. Then, we construct a multiple authorities attribute-based designated confirmer signature scheme with unified verification. Finally, we prove the correctness and security of the proposed scheme.

In this paper, new classes of binary generalized cyclotomic sequences of period 2pm+1qn+1 are constructed. These sequences are balanced. We calculate the linear complexity of the constructed sequences with a simple method. The results show that the linear complexity of such sequences attains the maximum.

In the past two decades, many generalized cyclotomic sequences have been constructed and they have been used in cryptography and communication systems for their high linear complexity and low autocorrelation. But there are a few of papers focusing on the 2-adic complexities of such sequences. In this paper, we first give a property of a class of Gaussian periods based on Whiteman's generalized cyclotomic classes of order 4. Then, as an application of this property, we study the 2-adic complexity of a class of Whiteman's generalized cyclotomic sequences constructed from two distinct primes p and q. We prove that the 2-adic complexity of this class of sequences of period pq is lower bounded by pq-p-q-1. This lower bound is at least greater than one half of its period and thus it shows that this class of sequences can resist against the rational approximation algorithm (RAA) attack.

We propose a penalty-based and constraint Bayesian optimization methods with an agent-based supply-chain (SC) simulator as a new Monte Carlo optimization approach for multi-echelon inventory management to improve key performance indicators such as inventory cost and sales opportunity loss. First, we formulate the multi-echelon inventory problem and introduce an agent-based SC simulator architecture for the optimization. Second, we define the optimization framework for the formulation. Finally, we discuss the evaluation of the effectiveness of the proposed methods by benchmarking it against the most commonly used genetic algorithm (GA) in simulation-based inventory optimization. Our results indicate that the constraint Bayesian optimization can minimize SC inventory cost with lower sales opportunity loss rates and converge to the optimal solution 22 times faster than GA in the best case.

In this paper, we consider the problem of waveform optimization for multi-input multi-output (MIMO) radar in the presence of signal-dependent noise. A novel diagonal loading (DL) based method is proposed to optimize the waveform covariance matrix (WCM) for minimizing the Cramer-Rao bound (CRB) which improves the performance of parameter estimation. The resulting nonlinear optimization problem is solved by resorting to a convex relaxation that belongs to the semidefinite programming (SDP) class. An optimal solution to the initial problem is then constructed through a suitable approximation to an optimal solution of the relaxed one (in a least squares (LS) sense). Numerical results show that the performance of parameter estimation can be improved considerably by the proposed method compared to uncorrelated waveforms.

In this letter, a micro-expression recognition method is investigated by integrating both spatio-temporal facial features and a regression model. To this end, we first perform a multi-scale facial region division for each facial image and then extract a set of local binary patterns on three orthogonal planes (LBP-TOP) features corresponding to divided facial regions of the micro-expression videos. Furthermore, we use GSLSR model to build the linear regression relationship between the LBP-TOP facial feature vectors and the micro expressions label vectors. Finally, the learned GSLSR model is applied to the prediction of the micro-expression categories for each test micro-expression video. Experiments are conducted on both CASME II and SMIC micro-expression databases to evaluate the performance of the proposed method, and the results demonstrate that the proposed method is better than the baseline micro-expression recognition method.