AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G

Based on the system architecture and software structure of GMLC （Gateway Mobile Location Center） in 3G （third generation）, a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB （Dynamic FeedBack） algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.     

Performance of a recursive algorithm for polynomial predistorter design

In this article, based on least square estimation, a recursive algorithm for indirect learning structure predistorter is introduced. Simulation results show that of all polynomial predistorter nonlinear terms, higher-order （higher than 7th-order） nonlinear terms are so minor that they can be omitted in practical predistorter design. So, it is unnecessary to construct predistorter with higher-order polynomials, and the algorithm will always be stable. Further results show that even when 15th-order polynomial model is used, the algorithm is convergent after 10 iterations, and it can improve out-band spectrum of 20 MHz bandwidth signal by 64 dB, with a 1.2×10^11 matrix condition number.     

Joint Scheduling and Resource Allocation for Federated Learning in SWIPT-Enabled Micro UAV Swarm Networks

Micro-UAV swarms usually generate massive data when performing tasks. These data can be harnessed with various machine learning(ML) algorithms to improve the swarm’s intelligence. To achieve this goal while protecting swarm data privacy, federated learning(FL) has been proposed as a promising enabling technology. During the model training process of FL, the UAV may face an energy scarcity issue due to the limited battery capacity. Fortunately, this issue is potential to be tackled via simultaneous wireless information and power transfer(SWIPT). However, the integration of SWIPT and FL brings new challenges to the system design that have yet to be addressed, which motivates our work. Specifically,in this paper, we consider a micro-UAV swarm network consisting of one base station(BS) and multiple UAVs, where the BS uses FL to train an ML model over the data collected by the swarm. During training, the BS broadcasts the model and energy simultaneously to the UAVs via SWIPT, and each UAV relies on its harvested and battery-stored energy to train the received model and then upload it to the BS for model aggregation. To improve the learning performance, we formulate a problem of maximizing the percentage of scheduled UAVs by jointly optimizing UAV scheduling and wireless resource allocation. The problem is a challenging mixed integer nonlinear programming problem and is NP-hard in general. By exploiting its special structure property, we develop two algorithms to achieve the optimal and suboptimal solutions, respectively. Numerical results show that the suboptimal algorithm achieves a near-optimal performance under various network setups, and significantly outperforms the existing representative baselines. considered.     

Rayleigh-distribution based minimum error thresholding for SAR images

This paper presents a minimum error thresholding (MET) algorithm under the hypothesis that the gray level histogram of SAR image fits to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other MET algorithms based on various models of histogram. The hypothesis of using Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the comparison results obtained show that the proposed new algorithm has good performance in thresholding SAR images.     

A Topology-Cognitive Algorithm Framework for Virtual Network Embedding Problem

The virtual network embedding/ mapping problem is an important issue in network virtualization in Software-Defined Networking （SDN）. It is mainly concerned with mapping virtual network requests, which could be a set of SDN flows, onto a shared substrate network automatically and efficiently. Previous researches mainly focus on developing heuristic algorithms for general topology virtual network. In practice however, the virtual network is usually generated with specific topology for specific purpose. Thus, it is a challenge to optimize the heuristic algorithms with these topology information. In order to deal with this problem, we propose a topology-cognitive algorithm framework, which is composed of a guiding principle for topology algorithm developing and a compound algorithm. The compound algorithm is composed of several sub- algorithms, which are optimized for specific topologies. We develop star, tree, and ring topology algorithms as examples, other sub- algorithms can be easily achieved following the same framework. The simulation results show that the topology-cognitive algorithm framework is effective in developing new topology algorithms, and the developed compound algorithm greatly enhances the performance of the Revenue/Cost （R/C） ratio and the Runtime than traditional heuristic algorithms for multi-topology virtual network embedding problem.     

Low-Complexity Adaptive Moving Target Tracking Algorithm over Multipath Fading Channels

The time-varying multipath channel and angular spreads caused by multipath and mobility of mobile stations degrade the performance of the conventional Direction-of-arrival （DOA） tracking algorithms. Moreover, although the DOA estimation methods based on the maximum likelihood have higher resolution than the beamforming and the subspace based methods, prohibitively heavy computation limits their practical applications. Hence, In this paper we propose a low-complexity adaptive moving target tracking algorithm, which includes a suboptimal DOA estimation algorithm that combines the advantages of the lower complexity of subspace algorithm and the high accuracy of ML based algorithms, and a Kalman filtering based adaptive tracking algorithm that models the dynamic property of directional changes for mobile terminals. Simulation results show that this proposed algorithm has better performance of DOA estimation and tracking of MS than the conventional ML or subspace based algorithms in terms of accuracy and robustness over multipath fading channels.     

Adaptive Power and Bit Allocation in Multicarrier Systems

We present two adaptive power and bit allocation algorithms for multicarrier systems in a frequency selective fading environment. One algorithm allocstes bit based on maximizing the channel capacity, another allocates bit based on minimizing the bit-error-rate （BER）. Two algorithms allocate power based on minimizing the BER. Results show that the proposed algorithms are more effective than Fischer＇s algorithm at low average signal-to-noise ration （SNR）. This indicates that our algorithms can achieve high spectral efficiency and high communication reliability during bad channel state. Results also denote the bit and power allocation of each algorithm and effects of the number of subcarriers on the BER performance.     

Automatic Region-Based Image Annotation Using an Improved Multiple-Instance Learning Algorithm

Many existing image annotation algorithms work under probabilistic modeling mechanism. In this paper, we formulate the problem as a variation of supervised learning task and propose an Improved Citation kNN （ICKNN） Multiple-instance learning （MIL） algorithm for automatic image annotation. In contrast with the existing MIL based image annotation algorithm which intends to learn an explicit correspondence between image regions and keywords, here we annotate the keywords on the entire image instead of its regions. Concretely, we first explore the concept of Confidence weight （CW） for every training bag （image） to reflect the relevance extent between a bag and a semantic keyword. It can be treated as a stage of re-ranking on training set before an- notation starts. Moreover, a modified hausdoriT distance is adopted for the ICKNN algorithm to solve the automatic annotation problem. The proposed annotation approach demonstrates a promising performance over 5,000 images from COREL dataset, as compared with some current algorithms in the literature.     

Learning Bayesian Networks with the Combination of Evolutionary Algorithm and EMI Method

Most Bayesian network （BN） learning algorithms use EMI algorithm to deal with incomplete data. But EMI algorithm is of low efficiency due to its iterative parameter refinement, and the problem will become even worse when multiple runs of EMI algorithm are needed. Besides, EMI algorithm usually converges to local maxima, which also degrades the accuracy of EMI based BN learning algorithms. In this paper, we replace EMI algorithm used in BN learning tasks with EMI method to deal with incomplete data. EMI is a very efficient method, which estimates probability distributions directly from incomplete data rather than performs iterative refinement of parameters. Base on EMI method, we propose an effec- tive algorithm, namely EMI-EA. EMI-EA algorithm uses EMI method to estimate probability distribution over local structures in BNs, and evaluates BN structures with a variant of MDL scoring function. To avoid getting into local maxima of the search process, EMI-EA evolves BN struc- tures with an Evolutionary algorithm （EA）. The experi- mental results on Alarm, Asia and an examplar network show that EMI-EA algorithm outperforms EMI-EA for all samples and E-TPDA algorithms for small and middle size of samples in terms of accuracy. In terms of efficiency, EMI-EA is comparable with E-TPDA algorithm and much more efficient than EMI-EA algorithm. EMI-EA also out- performs EMI-EA and M-V algorithm when learning BNs with hidden variables.     

Modeling and Global Conflict Analysis of Firewall Policy

The global view of firewall policy conflict is important for administrators to optimize the policy.It has been lack of appropriate firewall policy global conflict analysis,existing methods focus on local conflict detection.We research the global conflict detection algorithm in this paper.We presented a semantic model that captures more complete classifications of the policy using knowledge concept in rough set.Based on this model,we presented the global conflict formal model,and represent it with OBDD（Ordered Binary Decision Diagram）.Then we developed GFPCDA（Global Firewall Policy Conflict Detection Algorithm） algorithm to detect global conflict.In experiment,we evaluated the usability of our semantic model by eliminating the false positives and false negatives caused by incomplete policy semantic model,of a classical algorithm.We compared this algorithm with GFPCDA algorithm.The results show that GFPCDA detects conflicts more precisely and independently,and has better performance.     

A Non-Cyclic Prefixed SFBC-SCFDE System Based on a Recursive Algorithm of Joint Channel Estimation and Data Detection

We propose a non-cyclic prefixed Single carrier frequency-domain equalization （SCFDE） system with Space-frequency block codes （SFBC）. The transmitted signals in proposed system are composed of Singlecarrier （SC） information sequences derived from SFBC and training sequences, both of which have been placed in an alternate order without any Cyclic prefix （CP） before each SC information sequence. Furthermore, a recursire algorithm of Joint channel estimation and data detection （recursive-JCEDD） is proposed for system receiver. Simulation results show that the proposed non-cyclic prefixed SFBC-SCFDE system based on the recursive-JCEDD algorithm has better Bit error rate （BER） performance than traditional cyclic prefixed SFBC-SCFDE, Space-time block-coded SCFDE （STBC-SCFDE） and STBC-OFDM systems with some common channel estimation algorithms.     

A Robust Novel Technique for SPICE Simulation of ESD Snap-back Characteristics

This paper presents a robust and novel technique for the circuit simulation of ESD （ElectroStatic discharge） snap-back characteristics. A new linearization scheme by introducing current as independent variable for the avalanche current model in ESD evaluation shows a good convergence behavior during ESD stress simulation. This technique is compatible with the traditional circuit simulator based on the Modified nodal analysis （MNA） like SPICE. We have implemented the well known Amerasekera＇s ESD MOSFET model in SPICE3fS. The commonly used ESD protection configurations such as GGNMOS （Gate-grounded NMOS） and GCNMOS （Gatecoupled NMOS） are simulated and the simulation results demonstrated the good convergence behavior of this new technique.     

Unified Maneuvering Model for Target Tracking by Using Range-rate Measurement

A novel Unified maneuvering model （UMM） for tracking the target performed the complex curvilinear motion in two dimensions is presented, where the model is constructed by time-varying parameters and driven by the along-track and cross-track acceleration in- put, and the range rate plays a key role to update vari- able parameters online. In Cartesian coordinates, by incorporating the cross-track acceleration component which is estimated online by using the range-rate, and the along- track acceleration component which is determined under the assumption of zero-mean first order Markovian process, the proposed UMM exhibits highly self-adjustment capability to compensate the mismatch between the actual motion and the mathematic model adaptively, espe-cially, shows a well capability to approximate the standard Interacting multiple model （IMM） under the situation of complex curvilinear motion and a higher level of the measurement noise. Simulation results validate our theory and show that UMM-based filtering is superior to that using the Current statistic model （CSM）, and has a well approx- imation to IMM, meanwhile, has a low computational load substantially.     

Multi-Feature Based Bayesian Segmentation of Cerebrovascular from Time-of-flight Magnetic Resonance Angiography

Vessel analysis in medical images is important both for diagnostic and intervention planning purposes, especially, a three-dimensional representation of vasculature can be extremely important in image-guided neurosurgery and pre-surgical planning. In this paper, a Bayesian approach is proposed to aggregating geometric shape and intensity features for whole cerebrovascular tree extraction from Time-of-flight Magnetic resonance angiography （TOF-MRA）, while most of the current segmentation methods solely deal with the latter. In this method, we first utilige scale space analysis to get shape feature of blood vessels, then both shape and speed features are incorporated into a Bayesian segmentation framework. Maximum a posterior （MAP） method is used to estimate the posterior probabilities of vessel and background for classi- fication. The experimental results show that the proposed method can obtain a better quality of segmentation than those sole feature utilized methods.     

Exploring Semantic Association Rules Between Keyword and Visual Feature Clusters for Web Image Retrieval

The semantic gap is a big challenge in image retrieval area. Previous studies in web image retrieval have mainly focused on Relevance feedback （RF） and Latent semantic indexing （LSI） to alleviate the gap. This paper proposes an approach base on Frequent itemset mining （FIM） and Association rule （AR） techniques, which explores the semantic association rule between the two modalities that are represented by keyword and visual feature clusters. The rules are obtained oftline based on the inverted file, and utilized in query process online to realize the integration of the two modalities of web im- ages. Our approach improves the retrieval performance and is scalable well, as well as satisfies the requirement of the web users with no additional interactions. The exper- iments are carried out in our web image retrieval system named VAST （VisuAl ＆ SemanTic image search）, and the results show the effectiveness of the proposed approach.     

Analysis and Design of Multi-aspect SAR System for Compressive Sensing-Based 3D Imaging

This paper focuses on the analysis and design of Multl-aspect SAR （MuSAR） system for Compressive sensing-based （CS-based） 3D imaging. For this purpose,the Point ambiguous function （PAF） is proposed to analyze the factors that dominate the Mutual coherence （MC） of MuSAR sensing matrix. The PAF contacts with the parameters and configuration of MuSAR system directly and is easy to manipulate. With PAF, the present study analyzes the factors that dominate the performance of CS-based MuSAR 3D imaging. First of all, the stochastic waveform is an excellent selection. Second, the angular-frequency-diversity can improve the robustness of 3D imaging. Finally, the finer sampling of received data could improve the robustness of MuSAR 3D imaging. Simulation experiments show the validity of conclusion.     

Parameterized Integrated Power and Performance （PIPP） Model for Ultra High-Performance of TOPS level DSP

Amdahl＇s law is a simple and fundamen- tal tool for understanding the evolution of performance as a function of parallelism. Following a recent trend on timing and power analysis of general purpose many-core chip using this law, we develop a novel PIPP analytical model for evaluating the performance and power of hier- archical on-chip large-scale parallel architectures with the core number, super-node size, processing element number, and function unit number taken into consideration. We thereby investigate the influence of workload characteris- tics （Thread-level parallel TLP, Instruction-level parallel ILP and Data-level parallel DLP） on resource allocation with the restriction of performance and power. The re- sults provide some feasible options to design TOPS level DSP architecture as well as a theoretical basis for making the design more scalable.     

Joint Antenna Selection and Robust Beamforming Design in Multi-cell Distributed Antenna System

Most of studies on Distributed Antenna System （DAS） focus on maximizing the sum capacity and perfect channel state information at transmitter （CSIT）. However, CSI is inevitable imperfect in practical wireless networks. Based on the sources of error, there are two models. One assumes error lies in a bounded region, the other assumes random error. Accordingly, we propose two joint antenna selection （AS） and robust- beamforming schemes aiming to minimize the total transmit power at antenna nodes subject to quality of service （QoS） guarantee for all the mobile users （MUs） in multicell DAS. This problem is mathematically intractable. For the bounded error model, we cast it into a semidefinite program （SDP） using semidefinite relaxation （SDR） and S-procedure. For the second, we first design outage constrained robust beamforming and then formulate it as an SDP based on the Bernstein-type inequality, which we generalize it to the multi-cell DAS. Simulation results verify the effectiveness of the proposed methods.     

Channel Mismatches Correction in Time-Interleaved ADCs Based on Hybrid Filter Bank Reconstruction

Time-interleaved analog-to-digital con- verter （TIADC） is an efficient way to achieve higher sam- pling rate for medium-to-high resolution applications. The performance of a TIADC suffers from the mismatch errors among the sub-channels. This paper presents a method to estimate the channel mismatches using the sub-channels output data. The proposed method introduces an equiv- alent transfer function for each channel to model and es- timate the mismatch errors. A Hybrid filter bank （HFB） structure is used to both model the TIADC and recon- struct the desired uniformly sampled sequence based on the perfect reconstruction conditions of the HFB system. A four-channel 12-bit 400MHz TIADC has been imple- mented in hardware to verify the proposed calibration method. The measured results show that the Spurious- free dynamic range （SFDR） can be improved up to 74dB after being corrected with 64-tap Finite-impulse response （FIR） filters.     

The Price of Anarchy of Network Coding and Routing Based on an ACS Pricing Mechanism

To emphasize the decisions of all users, and the total number of users sharing the same technique, we propose a novel Average cost sharing （ACS） pricing mechanism to study the game between Network coding （NC） and routing flows sharing a single link when users are price anticipating. We characterize the worst-case efficiency bounds of the game compared with the optimal （i.e., the Price of anarchy （PoA））, which can be as low as 4/9 when the number of routing users becomes sufficiently large. NC cannot improve the PoA significantly under ACS. However, to achieve a more efficient use of limited resources, this approach indicates the sharing users have a greater tendency to choose NC. However, the users will follow the majority users＇ choice of data transmission technique.