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%.

This paper presents CrtSmile--a CAD tool for the automatic extraction of layout-dependent substrate effects for RF MOSFET modeling. CrtSmile incorporates a new scalable substrate model, which depends not only on the geometric layout information of a transistor (the number of gate fingers, finger width, channel length and bulk contact location), but also on the transistor layout and bulk patterns. We show that this model is simple to extract and has good agreement with measured data for a 0.35 µm CMOS process. CrtSmile reads in the layout information of RF transistors in the CIF/GDSII format, performs a pattern-based layout extraction to recognize the transistor layout and bulk patterns. A scalable layout-dependent substrate model is automatically generated and attached to the standard BSIM3 device model as a sub-circuit for use in circuit simulation. A low noise amplifier is evaluated with the proposed CrtSmile tool, showing the importance of layout effects for RF transistor substrate modeling.

The abnormal crowd behavior detection is an important research topic in computer vision to improve the response time of critical events. In this letter, we introduce a novel method to detect and localize the crowd gathering in surveillance videos. The proposed foreground stillness model is based on the foreground object mask and the dense optical flow to measure the instantaneous crowd stillness level. Further, we obtain the long-term crowd stillness level by the leaky bucket model, and the crowd gathering behavior can be detected by the threshold analysis. Experimental results indicate that our proposed approach can detect and locate crowd gathering events, and it is capable of distinguishing between standing and walking crowd. The experiments in realistic scenes with 88.65% accuracy for detection of gathering frames show that our method is effective for crowd gathering behavior detection.

A signal-model-based SAR image formation algorithm is proposed in this paper. A model is used to describe the received signal, and each scatterer can be characterized by a set of its parameters. Two parameter estimation methods via atomic decomposition are presented: (1) applying 1-D matching pursuit to azimuthal projection data; (2) applying 2-D matching pursuit to raw data. The estimated parameters are mapped to form a SAR image, and the mapping procedure can be implemented under application guidelines. This algorithm requires no prior information about the relative motion between the platform and the target. The Cramer-Rao bounds of parameter estimation are derived, and the root mean square errors of the estimates are close to the bounds. Experimental results are given to validate the algorithm and indicate its potential applications.

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.

Concurrent transmission (CT) is a revolutionary multi-hop protocol that significantly improves the MAC- and network-layer efficiency by allowing synchronized packet collisions. Although its superiority has been empirically verified, there is still a lack of studies on how the receiver survives such packet collisions, particularly in the presence of the carrier frequency offsets (CFO) between the transmitters. This work rectifies this omission by providing a comprehensive evaluation of the physical-layer receiver performance under CT, and a theoretical analysis on the fading duration of the beating effect resulting from the CFO. The main findings from our evaluations are the following points. (1) Beating significantly affects the receiver performance, and an error correcting mechanism is needed to combat the beating. (2) In IEEE 802.15.4 systems, the direct sequence spread spectrum (DSSS) plays such a role in combatting the beating. (3) However, due to the limited length of DSSS, the receiver still suffers from the beating if the fading duration is too long. (4) On the other hand, the basic M-ary FSK mode of IEEE 802.15.4g is vulnerable to CT due to the lack of error correcting mechanism. In view of the importance of the fading duration, we further theoretically derive the closed form of the average fading duration (AFD) of the beating under CT in terms of the transmitter number and the standard deviation of the CFO. Moreover, we prove that the receiver performance can be improved by having higher CFO deviations between the transmitters due to the shorter AFD. Finally, we estimate the AFD in the real system by actually measuring the CFO of a large number of sensor nodes.

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.

Genetic algorithms have been shown to be very useful in a variety of search and optimization problems. In this paper, we propose a modified genetic channel router. We adopt the compatible crossover operator and newly designed compatible mutation operator in order to search solution space more effectively, where vertical constraints are integrated. By carefully selected fitness function forms and optimized genetic parameters, the current version speeds up benchmarks on average about 5.83 times faster than that of our previous version. Moreover the total convergence to optimal solutions for benchmarks can be always obtained.

Duty-cycle MAC protocols achieve high energy-efficiency. However, duty-cycle MACs introduce significant end-to-end delivery latency. Recently proposed protocols such as RMAC and PRMAC improve the latency of duty-cycle MAC protocols by employing a mechanism of multi-hop wakeup reservation to allow a packet to be forwarded over multiple hops in a single communication cycle. However, these protocols can not efficiently handle cross traffic bursts which are common in applications with space-correlated event detection. If there are multiple packets to send in each flow, most of the data packets will be seriously postponed. This paper proposes a multi-channel pipelined routing-enhanced MAC protocol, called MPR-MAC, to handle this problem. By jointly employing channel diversity and time diversity, MPR-MAC allows cross data flows to forward multiple packets respectively in a single communication cycle without interfering with each other. Simulation results show the advantage of MPR-MAC in handling cross data flows and the significant performance upgrade in terms of end-to-end latency and energy efficiency.

In this paper we propose MR-MAC, a new multiple reservation MAC protocol for asynchronous duty cycling wireless sensor networks. In MR-MAC, the receiver transmits a collection packet to the senders when it wakes up that asks for the number of packets each sender wants to send. Then each sender replies to the receiver according to the scheduled sequence with a short report packet. After getting the number of packets from each sender, the receiver assigns multiple batch transmission (MBT) for the senders and begins to initiate the transmissions. The senders then transmit packets to the receiver in a batch style as scheduled so that packets can be delivered to the receiver as fast as possible. Experiments on a Tmote-sky testbed show that our protocol outperforms other protocols in diverse performance metrics such as throughput, latency and energy efficiency.

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.

Vector Coding (VC) is a novel vector modulation scheme that partitions a SISO (Single-Input Single-Output) channel into orthogonal subchannels by singular value decomposition (SVD). Because the orthogonal transmissions enabled by VC cannot cope with inter block interference (IBI) that is inevitable in delay spread channels, this paper proposes an IBI cancelling demodulator which can remove IBI by an iterative technique. We also show that code elimination in which insignificant eigencodes with lowermost eigenvalues are intentionally removed from transmission vectors greatly reduces BER (Bit Error Rate). The VC which utilizes the IBI cancelling demodulator and code elimination to reduce BER is compared with the original VC in not only delay spread SISO channels but also delay spread MIMO (Multi-Input Multi-Output) channels while emphasis is placed on the MIMO cases. Simulation results show that, under a predetermined BER, the enhanced MIMO-VC can improve effective transmission rate than the natural extension of VC to delay spread MIMO channels.

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.