Constructing degree-optimized resilient Boolean functions with high nonlinearity is a significant study area in Boolean functions. In this letter, we provide a construction of degree-optimized n-variable (n odd and n ≥ 35) resilient Boolean functions, and it is shown that the resultant functions achieve the currently best known nonlinearity.

Broadband wireless networks are rapidly expanding over the world. To provide wireless broadband services for Shanghai Expo 2010, pre-research is launched and a test-bed has been developed, in which WiMAX and Wi-Fi mesh are involved. The test-bed shows that Wi-Fi mesh integrated with WiMAX is highly suitable for large-scale activities like the Olympic Games and the World Expo.

Rotation symmetric Boolean functions (RSBFs) that are invariant under circular translation of indices have been used as components of different cryptosystems. In this paper, odd-variable balanced RSBFs with maximum algebraic immunity (AI) are investigated. We provide a construction of n-variable (n=2k+1 odd and n ≥ 13) RSBFs with maximum AI and nonlinearity ≥ 2n-1-¥binom{n-1}{k}+2k+2k-2-k, which have nonlinearities significantly higher than the previous nonlinearity of RSBFs with maximum AI.

The asymmetrical halo and dual-material gate structure is used in the surrounding-gate metal-oxide-semiconductor field effect transistor (MOSFET) to improve the performance. By treating the device as three surrounding-gate MOSFETs connected in series and maintaining current continuity, a comprehensive drain current model is developed for it. The model incorporates not only channel length modulation and impact ionization effects, but also the influence of doping concentration and vertical electric field distributions. It is concluded that the device exhibits increased current drivability and improved hot carrier reliability. The derived analytical model is verified with numerical simulation.

In this paper, an analytical threshold voltage model of the strained gate-all-around MOSFET fabricated on the Si1-xGex virtual substrate is presented by solving the two-dimensional Poisson equation. The impact of key parameters such as the strain, channel length, gate oxide thickness and radius of the silicon cylinder on the threshold voltage has been investigated. It has been demonstrated that the threshold voltage decreases as the strain in the channel increases. The threshold voltage roll-off becomes severe when increasing the Ge content in the Si1-xGex virtual substrate. The model is found to tally well with the device simulator.

Parallelism on heterogeneous machines brings cost effectiveness, but also raises a new set of complex and challenging problems. This paper addresses the problem of estimating the minimum time taken to execute a program on a fine-grained parallel machine composed of different types of processors. In an earlier publication, we took the first step in this direction by presenting a graph-construction method which partitions a given program into several homogeneous parts and incorporates timing constraints due to heterogeneous parallelism into each part. In this paper, to make the method easier to be applied in a scheduling framework and to demonstrate its practical utility, we present an efficient implementation method and compare the results of its use to the optimal schedule lengths obtained by enumerating all possible solutions. Experimental results for several different machine models indicate that this method can be effectively used to estimate a program's minimum execution time.

As a consequence of recent developments in communications, the parameters of communication signals, such as the modulation parameter values, are becoming unstable because of time-varying SNR under electromagnetic conditions. In general, it is difficult to classify target signals that have time-varying parameters using traditional signal recognition methods. To overcome this problem, this study proposes a novel recognition method that works well even for such time-dependent communication signals. This method is mainly composed of feature extraction and classification processes. In the feature extraction stage, we adopt Shannon entropy and index entropy to obtain the stable features of modulated signals. In the classification stage, the interval gray relation theory is employed as suitable for signals with time-varying parameter spaces. The advantage of our method is that it can deal with time-varying SNR situations, which cannot be handled by existing methods. The results from numerical simulation show that the proposed feature extraction algorithm, based on entropy characteristics in time-varying SNR situations,offers accurate clustering performance, and the classifier, based on interval gray relation theory, can achieve a recognition rate of up to 82.9%, even when the SNR varies from -10 to -6 dB.

Ternary content addressable memory (TCAM), which can store 0, 1, or X in its cells, is widely used to store routing tables in network routers. Negative bias temperature instability (NBTI) and positive bias temperature instability (PBTI), which increase Vth and degrade transistor switching speed, have become major reliability challenges. This study analyzes the signal probability of routing tables. The results show that many cells retain static stress and suffer significant degradation caused by NBTI and PBTI effects. The bit flipping technique is improved and proactive power gating recovery is proposed to mitigate NBTI and PBTI effects. In order to maintain the functionality of TCAM after bit flipping, a novel TCAM cell design is proposed. Simulation results show that compared to the original architecture, the bit flipping technique improves read static noise margin (SNM) for data and mask cells by 16.84% and 29.94%, respectively, and reduces search time degradation by 12.95%. The power gating technique improves read SNM for data and mask cells by 12.31% and 20.92%, respectively, and reduces search time degradation by 17.57%. When both techniques are used, read SNM for data and mask cells is improved by 17.74% and 30.53%, respectively, and search time degradation is reduced by 21.01%.

Aiming at the problem of non-line of sight (NLOS) signal recognition for Ultra Wide Band (UWB) positioning, we utilize the concepts of Neural Network Clustering and Neural Network Pattern Recognition. We propose a classification algorithm based on self-organizing feature mapping (SOM) neural network batch processing, and a recognition algorithm based on convolutional neural network (CNN). By assigning different weights to learning, training and testing parts in the data set of UWB location signals with given known patterns, a strong NLOS signal recognizer is trained to minimize the recognition error rate. Finally, the proposed NLOS signal recognition algorithm is verified using data sets from real scenarios. The test results show that the proposed algorithm can solve the problem of UWB NLOS signal recognition under strong signal interference. The simulation results illustrate that the proposed algorithm is significantly more effective compared with other algorithms.

Pre-trained CNNs on ImageNet have been widely used in object tracking for feature extraction. However, due to the domain mismatch between image classification and object tracking, the submergence of the target-specific features by noise largely decreases the expression ability of the convolutional features, resulting in an inefficient tracking. In this paper, we propose a robust tracking algorithm with low-dimensional target-specific feature extraction. First, a novel cascaded PCA module is proposed to have an explicit extraction of the low-dimensional target-specific features, which makes the new appearance model more effective and efficient. Next, a fast particle filter process is raised to further accelerate the whole tracking pipeline by sharing convolutional computation with a ROI-Align layer. Moreover, a classification-score guided scheme is used to update the appearance model for adapting to target variations while at the same time avoiding the model drift that caused by the object occlusion. Experimental results on OTB100 and Temple Color128 show that, the proposed algorithm has achieved a superior performance among real-time trackers. Besides, our algorithm is competitive with the state-of-the-art trackers in precision while runs at a real-time speed.

In this paper, we give some results towards the conjecture that σ2t+1l-1,2t are the only nonlinear balanced elementary symmetric Boolean functions where t and l are positive integers. At first, a unified and simple proof of some earlier results is shown. Then a property of balanced elementary symmetric Boolean functions is presented. With this property, we prove that the conjecture is true for n=2m+2t-1 where m,t (m>t) are two non-negative integers, which verified the conjecture for a large infinite class of integer n.

In this paper, we investigate the security property of RSA when some middle bits of the private key d are known to an attacker. Using the technique of unravelled linearization, we present a new attack on RSA with known middle bits, which improves a previous result under certain circumstance. Our approach is based on Coppersmith's method for finding small roots of modular polynomial equations.

A novel auto-tuning digital DC--DC converter is presented. In order to reduce the recovery time and undershoot, the auto-tuning control combines LnL, conventional PID and a predictive PID with a configurable predictive coefficient. A switch module is used to select an algorithm from the three control algorithms, according to the difference between the error signal and the two initially predefined thresholds. The detection and control logic is designed for both window delay line ADC and $Sigma Delta$ DPWM to correct the delay deviation. When the output of the converter exceeds the quantization range, the digital output of ADC is set at 0 or 1, and the delay line stops working to reduce power consumption. Theoretical analysis and simulations in the CSMC CMOS 0.5,$mu$m process are carried out to verify the proposed DC--DC converter. It is found that the converter achieves a power efficiency of more than 90% at heavy load, and reduces the recovery time and undershoot.

Controlled optimization of phases (COP) is a core implementation in the future intelligent traffic signal system (I-SIG), which has been deployed and tested in countries including the U.S. and China. In such a system design, optimal signal control depends on dynamic traffic situation awareness via connected vehicles. Unfortunately, I-SIG suffers data spoofing from any hacked vehicle; in particular, the spoofing of the last vehicle can break the system and cause severe traffic congestion. Specifically, coordinated attacks on multiple intersections may even bring cascading failure of the road traffic network. To mitigate this security issue, a blockchain-based multi-intersection joint defense mechanism upon COP planning is designed. The major contributions of this paper are the following. 1) A blockchain network constituted by road-side units at multiple intersections, which are originally distributed and decentralized, is proposed to obtain accurate and reliable spoofing detection. 2) COP-oriented smart contract is implemented and utilized to ensure the credibility of spoofing vehicle detection. Thus, an I-SIG can automatically execute a signal planning scheme according to traffic information without spoofing data. Security analysis for the data spoofing attack is carried out to demonstrate the security. Meanwhile, experiments on the simulation platform VISSIM and Hyperledger Fabric show the efficiency and practicality of the blockchain-based defense mechanism.

Due to the complicated composition of cloud and its disordered transformation, the rendering of cloud does not perfectly meet actual prospect by current methods. Based on physical characteristics of cloud, a physical cellular automata model of Dynamic cloud is designed according to intrinsic factor of cloud, which describes the rules of hydro-movement, deposition and accumulation and diffusion. Then a parallel computing architecture is designed to compute the large-scale data set required by the rendering of dynamical cloud, and a GPU-based ray-casting algorithm is implemented to render the cloud volume data. The experiment shows that cloud rendering method based on physical cellular automata model is very efficient and able to adequately exhibit the detail of cloud.

The crossbar-based switch fabric is widely used in today's high performance switches, due to its internally nonblocking and simply implementation properties. Usually there are two main switching architectures for crossbar-based switch fabric: internally bufferless crossbar switch and crosspoint buffered crossbar switch. As internally bufferless crossbar switch requires a complex centralized scheduler which limits its scalability to high speeds, crosspoint buffered crossbar switch has gained more attention because of its simpler distributed scheduling algorithm and better switching performance. However, almost all the scheduling algorithms proposed previously for crosspoint buffered crossbar switch either have unsatisfactory scheduling performance under non-uniform traffic patterns or show poor service fairness between input traffic flows. In order to overcome the disadvantages of existing algorithms, in this paper we propose two novel high performance scheduling algorithms named MCQF_RR and IMCQF_RR for crosspoint buffered crossbar switches. Both algorithms have a time complexity of O(log N), where N is the number of input/output ports of the switch. MCQF_RR takes advantage of the combined weight information about queue length and service waiting time of input queues to perform scheduling. In order to further reduce the scheduling complexity and make it feasible for high speed switches, IMCQF_RR uses the compressed queue length information instead of original queue length information to schedule cells in input VOQs. Simulation results show that our novel scheduling algorithms MCQF_RR and IMCQF_RR can demonstrate excellent delay performance comparable to existing high performance scheduling algorithms under both uniform and non-uniform traffic patterns, while maintain good service fairness performance under severe non-uniform traffic patterns.

This paper describes the robust design of the 30 V high voltage NMOS (HVNMOS) structure implemented in a 0.6 µm 5 V standard CMOS processes without any additional masks or process steps. The structure makes use of the field oxide (FOX) and light doping N-well to increase the drain to gate and drain to bulk breakdown voltages, respectively. By varying the six spacing parameters: the channel length, gate overlap FOX, N-well overlap channel length, poly to the active area of the drain (OD2), metal extend beyond the OD2 and N-well extend beyond the OD2 in HVNMOS structure, the breakdown voltage can be improved. The experimental results show that the breakdown voltage of the normal NMOS is 11 V, and the breakdown voltage of the HVNMOS is increased to over 30 V. With the optimized layout parameters of the HVNMOS, it can be increased to 38 V.

In this paper, we propose a texture descriptor based on amplitude distribution and phase distribution of the discrete Fourier transform (DFT) of an image. One dimensional DFT is applied to all the rows and columns of an image. Histograms of the amplitudes and gradients of the phases between adjacent rows/columns are computed as the feature descriptor, which is called aggregated DFT (ADFT). ADFT can be easily combined with completed local binary pattern (CLBP). The combined feature captures both global and local information of the texture. ADFT is designed for isotropic textures and demonstrated to be effective for roughness classification of castings. Experimental results show that the amplitude part of ADFT is also discriminative in describing anisotropic textures and it can be used as a complementary descriptor of local texture descriptors such as CLBP.

The aim of this paper is to propose effective attentional regions for fine-grained visual recognition. Based on the Spatial Transformers' capability of spatial manipulation within networks, we propose an extension model, the Attention-Guided Spatial Transformer Networks (AG-STNs). This model can guide the Spatial Transformers with hard-coded attentional regions at first. Then such guidance can be turned off, and the network model will adjust the region learning in terms of the location and scale. Such adjustment is conditioned to the classification loss so that it is actually optimized for better recognition results. With this model, we are able to successfully capture detailed attentional information. Also, the AG-STNs are able to capture attentional information in multiple levels, and different levels of attentional information are complementary to each other in our experiments. A fusion of them brings better results.

In this letter, we propose a novel framework to estimate the joint distribution of multiple Local Binary Patterns (LBPs). Multiple LBPs extracted from the same central pixel are first encoded using handcrafted encoding schemes to achieve rotation invariance, and the outputs are further encoded through a pre-trained Restricted Boltzmann Machine (RBM) to reduce the dimension of features. RBM has been successfully used as binary feature detectors and the binary-valued units of RBM seamlessly adapt to LBP. The proposed feature is called RBM-LBP. Experiments on the CUReT and Outex databases show that RBM-LBP is superior to conventional handcrafted encodings and more powerful in estimating the joint distribution of multiple LBPs.