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CDN (Content Delivery Network) has become an important infrastructure of the Internet. However, building an anomaly detection system to monitor and guarantee CDN service quality is non-trivial. Current anomaly detection system usually suffers from undesirable performance in terms of high rate of false positive and false negative, which consequently impacts on its practical deployment. Identifying the root cause of a false detection is critical for diagnosing and improving the performance of anomaly detection. In this paper, we propose a novel feedback mechanism for prediction-based anomaly detection in CDN . Specifically, we introduce a carefully-designed metric named Fittingscore to diagnose whether the prediction model can fit the data well. Further, a threshold adjustment mechanism is proposed to dynamically adjust the thresholds of residual errors. Extensive experiments employing a three-month real CDN dataset collected from a top ISP-operated CDN in China show our proposed method can significantly improve the performance of anomaly detection.

DNS tunnel is a technology used to bypass firewalls for data exfiltration. It takes advantage of the characteristic of firewalls that always allow DNS traffic pass. Most of the studies have realized the detection of DNS tunnels from the perspective of traffic and payload. In recent years, due to the popularity of machine learning and data mining methods, researchers began trying to use them to detect DNS tunnels. However, these detection methods require a lot of professional knowledge to extract and construct feature set, and the detection results are often unsatisfactory. In order to solve the problem of manual feature extraction limitation and improve the detection accuracy of DNS tunnels, we propose a deep learning method, called Byte-level CNN, to detect the DNS tunnels in this paper. Furthermore, we propose to represent DNS packets with bytes for the first time, as the converted computable data to the CNN model. Compared with traditional machine learning methods, our byte-level CNN method can learn full information in the whole DNS packets, especially the sequential and structural information, besides common statistical information, and therefore achieves good performance results. The simulation showed that the detection accuracy and recall rate are significantly improved, and the complex manual feature extraction process is avoided as well.

More and more CDN (Content Delivery Network) or video content providers are deploying their cache nodes at the edge of networks in order to improve user perceived QoE (Quality of Experience). However, state-of-the-art ABR (Adaptive Bitrate) algorithms do not take into account the presence of a cache on video delivery path. In this paper, we firstly investigate how these ABR algorithms behave in the context of edge caching system and how caching factors such as hit ratio and access bandwidth impact on the performance of ABR algorithms. Extensive analysis results show introducing an edge cache on video delivery path cannot necessarily improve the performance of ABR algorithms in terms of average QoE. Furthermore, we propose a cache-aware ABR approach taking into account caching information including a cache hit indicator for the past chunks and the availability of the next video chunk on the cache. Experimental results validate the significant benefits of the approach which helps to make a more accurate throughput estimation and is more likely to select the video chunks stored in the cache to increase cache utilization.

This paper presents a detailed investigation of the timing variation due to random channel dopants for 22nm Complementary-Metal-Oxide-Semiconductor (CMOS) inverter. Consequently, the timing parameters such as propagation delay, rise time, fall time, high-to-low transition, low-to-high transition and overshooting time and their variations have been extracted and estimated with channel dopants fluctuations. Furthermore, the influence of load capacitance and power supply voltage on timing characteristic also has been studied. This investigation was performed by HSPICE simulation with a high accuracy, which is important for yield improvement and the optimization of CMOS circuit design.

With harmonic resonant vibration synthesis, resonant-type piezoelectric impact motors show a great improvement of power output capability against traditional piezoelectric impact motors. In this paper, a resonant-type inertia drive linear motor with piezoelectric asymmetric tuning-fork actuator is proposed. Structural design and working principle of the motor is descripted. A simplified mechanical model of the piezoelectric asymmetric tuning-fork actuator is established to investigate the frequency trends of the anti-phase and in-phase bending vibration modes of the structure. With FEA simulation, modal analysis of the structure is used to determine the optimal sizes of the components, resulting 1:2 frequency ratio of the two bending vibration modes. A prototype motor is manufactured to test the characteristics of the two vibration modes and their frequency trends.

The linear FM Doppler fuze extract Doppler using secondary mixing method, which need more analog circuits and lead difficult to parameter adjustment. Direct IF sampling need higher sampling rate resulting in a large amount of data processing. Aiming at the problem, the linear FM fuze Doppler extraction method based on band-pass sampling is proposed in this paper. The method used band-pass sampling extract Doppler of linear FM Doppler fuze directly, not need secondary mixing and need subsequent analog circuits. The method not only extracts Doppler of linear FM Doppler fuze direct but also reduces the sampling rate. Simulation analysis and actual verification showed that the linear FM fuze Doppler extraction method based on band-pass sampling could extract Doppler, do not need secondary mixing and subsequent analog circuits, the amount of data after sampling is small and amount of data to be processed is small to.

To better evaluate the stability of filtering algorithm, the stability evaluation index filtering algorithm is proposed based on the analysis of the factors affecting factors. Then the index system of stability evaluation is constructed with its mathematical model established. Taking the application of Kalman filtering used in initial alignment in integrate INS/GPS navigation system as an example, stability of Kalman filtering is evaluated according to the index system. Simulation results show that the stability evaluation results are in consistent with the theoretical analysis.

In view of the fact that the radio fuze detector will not work properly or even be damaged under strong electromagnetic environment, a radio fuze detector reinforced by RF MEMS components is proposed, a RF MEMS switch is integrated between VCO and the antenna, and the MEMS switch control module controls its working status according to the electromagnetic environment where the detector is located. A RF MEMS filter is integrated between the MEMS switch and the mixer to suppress out-of-band interference signals. The radio fuze detector reinforced by RF MEMS components was tested by electromagnetic impact test and ground dynamic detection test, which proved that the performance of the radio fuze detector reinforced by RF MEMS components is normal. It improves the survivability of the radio fuze detector under strong electromagnetic environment.

The distance adjustment of triangle-wave FM harmonic ranging fuze is complex and difficult to implement. Aiming at this problem, the distance adjustment method of triangle wave FM harmonic fixed range fuze is proposed in this paper. The method used the adjustment of triangle wave frequency to adjust the ranging of triangle wave FM harmonic ranging radio fuze. The adjustment of triangle wave frequency changed the harmonic times and the ranging. It is proved by the simulation and actual verification that the distance adjustment method of triangle wave FM harmonic ranging radio fuze is feasible and simple. just the triangle wave frequency need to changed, other parameters are unchanged.

A small wire structure log-periodic antenna used for mobile phones and wi-fi signal enhancement is designed and studied. The radiation and matching characteristics of the antenna are analyzed in detail by means of the CST simulation technology. The experimental model of the antenna is constructed and measured. It is shown that the experimental results are agreement with the simulation and the antenna has a good engineering application.

Visual tracking algorithms based on deep learning have robust performance against variations in a complex environment because deep learning can learn generic features from numerous unlabeled images. However, due to the multilayer architecture, the deep learning trackers suffer from expensive computational costs and are not suitable for real-time applications. In this paper, a low-complexity visual tracking scheme with single hidden layer neural network is proposed based on denoising autoencoder. To further reduce the computational costs, feature selection is applied to simplify the networks and two optimization methods are used during the online tracking process. The experimental results have demonstrated that the proposed algorithm is about six times faster than the trackers based on deep nets and rapid enough for real-time applications with encouraging accuracy.

Fault injection attacks have posed serious threat to cryptographic integrated circuits (crypto-ICs). Heavy ion is one of the most powerful fault injection source due to its high energy and focused beam sizes. However, the ion has to strike on the certain transistor at the exact time instances. We choose the certain transistor among tens of thousands of transistors on the chip. And we choose the exact time instances during the whole decryption period. Only this kind of hit can cause a successful ion fault attack on crypto-ICs. The success rate is niche and often highly relies on experiences. This paper proposes a prediction model considering the ion density, crypto-ICs and the cryptographic algorithms. The model helps to indicate the appropriate Poisson intensity and beam spot size to maximize the success rate, so as to ease the ion fault injection test. Experimental results prove the feasibility of our model.

Urban traffic flow has the property of complexity, uncertainty, and time-varying, which bring large difficulty to real-time and accurately forecast the traffic flow for traffic control and route guidance. In this paper, according to the characteristics of existing traffic flow prediction model for long processing time and memory constraints, a parallel multivariate linear regression model was designed based on MapReduce to real-time predict traffic flow. The model is composed of three MapReduce process to estimate the regression parameters. Furthermore, the authors design and implement a series of experiments to verify the effectiveness of the proposed parallel multivariate linear regression model through empirical research. Experimental results show that the multivariate linear regression prediction model based on MapReduce has better performance in both speedup and scaleup, and suit for analysis and prediction of large-scale multi dimensional and time-series traffic data.

Despite improved device performance over traditional Poly-SiON technology, high-K metal gate flow introduces additional device variations not previously seen in Poly-SiON process, especially impacting large dimensional (WxL) devices for matching critical applications. For the first time, we report a comprehensive analysis of device variations introduced from metal gate process, GDIM and GGIM, and their sensitivity to circuit layout. Design optimization and verification mechanisms are developed to mitigate metal gate process induced variations in analog matching circuits. After co-optimization, DAC Vt mismatch is reduced by 2.1X and ADC comparator speed is improved by 23.5% in the analog blocks of an advanced mobile SoC currently in production.

Wireless Sensor Network is a new self-organization and multi-hop network which deploys a lot of nodes which communicating with each other wirelessly, so the design of a appropriate routing protocol for wireless sensor networks is essential. In this script, the performances of many classical route strategies are introduced and analyzed. In order to test the performance of LEACH and LEACH-C algorithm, the NS2 (Network Simulator Version 2) is used to realize the simulation of the LEACH and LEACH-C algorithm in three different scenarios and analyze the results from three angles. The result shows that, in condition that the SINK node is not far from the deployed sensors, no matter the nodes are randomly deployed or deployed in linear, LEACH-C performs much better than LEACH. In the opposite condition, LEACH will perform better than LEACH-C except in the data throughput.

During the process of experiment teaching in high schools, the matters of large amount of experimental data or information data bring the storage management difficulties by using traditional way, and also bring the problems of Data Synchronization. For the purpose of solving these problems, we propose a solution of using the Sea file technology to construct the private cloud storage platform in colleges and University which has been achieved in Information Technology Engineering Experiment Center of Shenyang Institute of Engineering. It shows that the private cloud storage platform has the advantages of large capacity, high security, easy management, the practice shows that it is good to meet the needs of experimental activities in colleges and university, and can improve the quality of experimental learning.

An analytic solution to the axisymmetric problem of a long, radially polarized, functionally graded piezoelectric material (FGPM) hollow cylinder rotating about its axis at a constant angular velocity is presented. For the case that electric, thermal and mechanical properties of the material obey different power laws in the thickness direction, distributions for radial displacement, stresses and electric potential in the FGPM hollow cylinder are determined by using the theory of electro-thermoelasticity Some useful discussions and numerical examples are presented to show the significant influence of material nonhomogeneity, and adopting suitable graded indexes and applying suitable geometric size and rotating velocity ω may optimize the rotating FGPM hollow cylindrical structures.

To solve the problem the lower efficiency of task-performing caused by the mobility and failure-prone of ad hoc nodes, a multi-object optimization task scheduling algorithm (MOTA) is proposed for wireless ad hoc networks. This algorithm tries its best to make less Makespan, but meanwhile, it also pay much more attention to the failure probability and the energy-consuming of nodes. MOTA avoids the task assigned to the failure-prone node, which effectively reducing the effect of failed nodes on task-performing. Simulation results show that the proposed algorithm can trade off these three objectives well. Compared with the traditional task scheduling algorithms, simulation experiments obtain better results.

The ammonia microsensor is fabricated by the 0.35 μm complementary metal oxide semiconductor (CMOS) process. The sensor is composed of a sensitive film and polysilicon electrodes. Area of the ammonia microsensor is about 1 mm2. The sensitive film of the ammonia microsensor is zinc oxide prepared by hydrothermal method. The sensor requires a wet etching process to remove the sacrificial oxide layer and coats the zinc oxide sensitive film on the polysilicon electrodes after the CMOS process. The ammonia microsensor is resistive type. When the sensitive film absorbs or desorbs ammonia gas at room temperature, the sensitive film generates a change in resistance. Experimental results present that the sensitivity of the ammonia microsensor is about 12.6 Ω/ppm at room temperature.

This study investigates the fabrication and characterization of an energy harvesting device using the commercial 0.18 μm CMOS (complementary metal oxide semiconductor) process. Based on the thermoelectric method, the energy harvesting device converts thermal energy into electrical power. The energy harvesting device is constructed by 408 thermocouples in series, and each thermocouple is composed of p-type and n-type polysilicon strips. In order to increase the temperature difference in the hot and cold parts of the thermocouples, the hot part of the thermocouples is suspended to reduce heat sink. The experimental results showed that the energy harvesting device had an output voltage of 0.4 mV at the temperature difference of 15 K and an output power of 125 nW at the temperature difference of 15 K.

This paper proposes and studies an online algorithm (‘NORTKNAR’) for controlling a monotone Wiener system to a given level. Such systems consist of a FIR model, followed by a monotonically in- or decreasing nonlinear static function. We consider phenomena which obey such system up to stochastic perturbations. We study almost sure convergence under weak regularity assumptions. Theoretical results are complemented by empirical results on the control of a PharmacoKinetics-PharmacoDynamic (PK-PD) system regulating concentrations of levuodopa in the bloodstream. Finally, we indicate how those ideas find application to regulating the rate of events in pulsatile systems.

This paper studies a recursive identification method (i.e. an adaptive filter, or online learning algorithm) - termed the RANKTRON - for learning a Monotone Wiener model from observed input-output pairs. Such a model consists of a sequence of an unknown Linear Time-Invariant (LTI) dynamic model, followed by an unknown monotone (in- or decreasing) static nonlinear function. The main contribution is the introduction of a technical argument which establish worst-case performance of the proposed algorithm. The same tool is then used to derive properties in case the Monotone Wiener assumption only holds approximatively, and to the case where the output nonlinearity is a quantization function. An application of the RANKTRON is reported for the identification of a 20e order LTI based on quantized observations, using a mere O(1000) samples.

This paper presents a humanoid eye robot with five degrees of freedom, including independent horizontal rotation for each eyeball, one synchronous vertical rotation, and two vertical motions of the eyelids. Two cameras were implemented for target tracking. The color difference of the images received by the cameras was compared for precise image registration. The horizontal rotation of each eyeball was monitored by an angle gauge, and the control accuracy was investigated. Actual geometric measurement was conducted for calibrating the angle gauge. The gauge value was then compared with the image registration. The comparison result indicated that the image registration algorithm effectively promoted the horizontal angle accuracy by decreasing rotation error from more than 2 degrees to less than 0.5 degrees.

This paper determines the shortest period of time for picking out a bad one from n(random quantity) coins and measuring its weight. The paper also offers a universal method to find out which one is bad among n coins and make sure of its weight with a balance in the shortest time, and the method is realized and tested with C++. The testing results show that the algorithm only requires as small space as that of simulating coins sequence, and that it needs one more comparison when the number of coins triples. The time and space needed minimize theoretically. The difference of before algorithm is it can judge if a bad coin in n coins.

In publish/subscribe systems, XML message filtering performed at application layer is an important operation for XML message multicast. As a specific case of content-based multicast in application layer, XML message multicast depends on the data filtering and matching processes and the forwarding and routing schemes. As the XML data emerges in transition, XML message filtering and matching becomes more and more desirable. BFilter, proposed in this paper, conducts the XML message filtering and matching by leveraging branch points in both the XML document and user query. It evaluates user queries that use backward matching branch points to delay further matching processes until branch points match in the XML document and user query. In this way, XML message filtering can be performed more efficiently as the probability of mismatching is reduced. A number of experiments have been conducted and the results demonstrate that BFilter has better performance than the well-known YFilter for complex queries.

In this paper, we employ to provide a method which combines Turbo codes and Low-Density Parity-Check (LDPC) codes based on Orthogonal Frequency Division Multiplexing (OFDM) system by adaptive encoding. The technique of channel estimation is used. The code rate and encoding scheme are all adaptive selection in the light of different channel conditions, so that we can make good use of these two types of codes' respective advantages. This new method can guarantee the quality of service and pursuit of maximum useful bit rate. Through analysis and simulation, we show the better bit error rate (BER) performance compared to conventional method.

One key characteristic of mobile wireless communication is the typically rapid and significant variations in the instantaneous channel conditions. So, precise channel estimation and signal detection algorithm with low computational complexity at the receiver is very important and necessary. In this paper, linear Minimum Mean Square Error (MMSE) and Maximum Likelihood (ML) detection methods in MIMO-OFDM systems are analyzed, and then we propose a novel Selective Signal Detection (SSD) approach based on Iterative Joint Channel Estimation and Signal Detection (IJCESD) algorithm. Simulation results show that the new approach can improve the system BER performance at low SNR, but requires less complexity.

Wireless sensor networks with co-processor refer to each node can measure and report data simultaneously. To minimize the execution time of a given task, a task scheduling algorithm based on divisible load theory(DTSWC) was proposed under clustering structure in wireless sensor networks with co-processor. Using this algorithm, the tasks can be reasonably allocated to each node in wireless sensor networks, such that the entire application can be finished in the shortest possible time. The optimal solution for the load allocation ratio on each node, and the closed-form solution for the shortest time required by finishing the application are obtained. Simulation results have shown the correctness of our theoretical analysis.

Mobility of nodes is considered as the potential solution for improving Ad Hoc network performance, such as maintaining a specified network topology, improving the power efficiency, and enhancing signal-to-noise ratio at the receivers, etc. However, node movement consumes certain amount of energy, which is limited at nodes, and should be counted when optimizing the system performance. In this paper, a distributed adaptive movement control algorithm to optimize the performance and overall power of Ad Hoc networks is proposed. In this algorithm, the decision of node movement is adaptively made accordance with the receiving signal strength. The results show that the mobility control algorithm can be effective to improve the network connectivity and also the performance.

This study investigates the fabrication of a micro humidity sensor integrated with circuitry using the complimentary metal oxide semiconductor (CMOS) process. The area of the humidity sensor chip is about 1 mm2. The advantages of the humidity sensor include small volume, high sensitivity, and integrated with circuitry on a chip. The sensor, which is a capacitive type, comprises the improved interdigital electrodes that can enhance its sensitivity. The sensitive film of the humidity sensor is polypyrrole, which is prepared by the chemical polymerization method. The humidity sensor needs a post-CMOS process to coat the sensitive film. The post-CMOS process uses a wet etching to etch the sacrificial layers, and then the polypyrrole is coated on the humidity sensor. The sensor produces a change in capacitance when the sensitive film absorbs or desorbs vapor. The humidity sensor consists of a ring oscillator circuit that is utilized to convert the capacitance variation into the oscillation frequency output.

A novel affine projection blind equalization CMA algorithm is presented in this paper. Based on quantization estimation errors and variable step-size, the algorithm can enhance convergence speed and decrease steady-state errors. Meanwhile, the algorithm has lower hardware implementation complexity because of the use of quantization estimation errors. Simulation results show that the performance of the novel algorithm has obvious advantages in convergence speed and steady-state errors.

A zero-watermarking scheme for audio based on the steady sign of certain DWT-DCT coefficients with maximal absolute value, is proposed. Compared with traditional watermarking schemes, the proposed scheme can solve the contradiction between robustness and imperceptibility perfectly without introducing audio quality degradation as occurred usually in traditional watermarking schemes. The multiresolution characteristic of discrete wavelet transform (DWT), the energy compression characteristic of discrete cosine transform (DCT) and the steady sign of certain DWT-DCT coefficients are combined to extract important features from the host audio signal, which are used to construct the secret key with the watermark image. Simulation results show that the proposed scheme has stronger robustness as well as security, as compared to the schemes in [5] and [8].

An improved intraframe rate-quantization(R-Q) model for H.264/AVC video coding is proposed in this paper. Based on this R-Q model, a frame layer bit number prediction method is derived. The proposed method can be applied in rate control to select accurate quantization parameters (QP) for intraframe according to the target bitrate, especially when multi-pass coding is permitted to get a refined bit allocation. Experimental results show that when employing our proposed bit number prediction method, the intraframe target bits mismatch ratio is much smaller than quadratic model which is adopted in H.264/AVC reference encoder. Hence, this method is extremely useful for H.264/AVC rate control applications.

A new algorithm SGD-CMA for QAM signals in high-speed equalizer is presented in this paper. SGD-CMA integrates conventional CMA and DD-LMS under stop-and-go principle. The SGD-CMA equalizer performs six times faster, with 3~5 dB better in rudimental MSE, more rapidly on capturing the channel variety than conventional CMA and almost the same error rate characteristic, while decreasing 82% of operation complexity and increasing 5% of hardware consumption. MATLAB simulations support SGD-CMA on all performance and characteristics, validating its feasibility and advantages. Finally using CMOS 0.18 mum library to synthesis, the new equalizer is embedded into DVB-C demodulation chip, and chip test result shows that the performance of the new equalizer is better.

In its current art, peer-to-peer streaming solution has been mainly employed in the domain of live event broadcasting. In such a paradigm, users are required to simultaneously participate the streaming, which yields tremendous bandwidth pool to alleviate the server load. However, little effort has been paid to study the performance gain when peer-to-peer solution is deployed into the domain of Video-on-Demand (VoD) applications, when users have the freedom to access a large pool of media files at their preferred times. Users of VoD applications exhibit file-dependent and time-varying access patterns, which are hard to simulate without realistic guidance from the operational system observation. In this paper, we present an empirical study on the traces collected by the Vanderbilt University media streaming service over a period of 8 months. We pay special attention to peer aggregation around one media file, in which peer-to-peer streaming is able to play an essential role. With this regard, we investigate three key factors: file popularity, request inter-arrival time, and user online duration. Our analysis proves the existence of skewed file popularity, concentrated user requests, and long enough online duration. Furthermore, through replaying the trace via simulation, we show that peer-to-peer streaming could reduce the server load by as high as 90% over popular files.

Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution in practice, one must take into account the dynamic and uncertain nature of wireless traffic demand. There are two basic approaches to address the traffic uncertainty in network routing: (1) predictive routing which infers the traffic demand with maximum possibility based in its history and optimizes the routing strategy based on the predicted traffic demand and (2) oblivious routing which considers all the possible traffic demands and selects the routing strategy where the worst-case network performance could be optimized. This paper conducts a systematic comparison study of these two approaches based on the extensive simulation study over a variety of network and traffic scenarios. It identifies the key factors of the network topology and traffic profile that affect the performance of each routing strategy and provides guidelines towards the strategy selection in mesh network routing under uncertain traffic demands.

Traffic routing plays a critical role in determining the performance of a wireless mesh network. To investigate the best solution, existing work proposes to formulate the mesh network routing problem as an optimization problem. In this problem formulation, traffic demand is usually implicitly assumed as static and known a priori. Contradictorily, recent studies of wireless network traces show that the traffic demand, even being aggregated at access points, is highly dynamic and hard to estimate. Thus, in order to apply the optimization-based routing solution into practice, one must take into account the dynamic and unpredictable nature of wireless traffic demand. This paper presents an integrated framework for network routing in multi-radio multi-channel wireless mesh networks under dynamic traffic demand. This framework consists of two important components: traffic estimation and routing optimization. By analyzing the traces collected at wireless access points, the traffic estimation component predicts future traffic demand based on its historical value using time-series analysis, and represents the prediction result in two forms - mean value and statistical distribution. The optimal mesh network routing strategies then take these two forms of traffic demand estimations as inputs. In particular, two routing algorithms are proposed based on linear programming which consider the mean value and the statistical distribution of the predicted traffic demands, respectively. The trace-driven simulation study demonstrates that our integrated traffic estimation and routing optimization framework can effectively incorporate traffic dynamics in mesh network routing, where both algorithms outperform the shortest path algorithm in about 80% of the test cases.