This paper puts forward the concept of cellular network location with less information which can overcome the weaknesses of the cellular location technology in practical applications. After a systematic introduction of less-information location model, this paper presents a location algorithm based on AGA (Adaptive Genetic Algorithm) and an optimized RBF (Radical Basis Function) neural network. The virtues of this algorithm are that it has high location accuracy, reduces the location measurement parameters and effectively enhances the robustness. The simulation results show that under the condition of less information, the optimized location algorithm can effectively solve the fuzzy points in the location model and satisfy the FCC's (Federal Communications Commission) requirements on location accuracy.

The 3-D channel routing is a fundamental problem on the physical design of 3-D integrated circuits. The 3-D channel is a 3-D grid G and the terminals are vertices of G located in the top and bottom layers. A net is a set of terminals to be connected. The objective of the 3-D channel routing problem is to connect the terminals in each net with a Steiner tree (wire) in G using as few layers as possible and as short wires as possible in such a way that wires for distinct nets are disjoint. This paper shows that the problem is intractable. We also show that a sparse set of ν 2-terminal nets can be routed in a 3-D channel with O(√ν) layers using wires of length O(√ν).

This paper presents a non-crossover and multi-mutation based genetic algorithm (NMGA) for the Flexible Job-shop Scheduling problem (FJSP) with the criterion to minimize the maximum completion time (makespan). Aiming at the characteristics of FJSP, three mutation operators based on operation sequence coding and machine assignment coding are proposed: flip, slide, and swap. Meanwhile, the NMGA framework, coding scheme, as well as the decoding algorithm are also specially designed for the FJSP. In the framework, recombination operator crossover is not included and a special selection strategy is employed. Computational results based on a set of representative benchmark problems were provided. The evidence indicates that the proposed algorithm is superior to several recently published genetic algorithms in terms of solution quality and convergence ability.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

NAND flash memory has been widely used in storage systems. Aiming to design an efficient buffer policy for NAND flash memory, a life-aware buffer management algorithm named LAB-LRU is proposed, which manages the buffer by three LRU lists. A life value is defined for every page and the active pages with higher life value can stay longer in the buffer. The definition of life value considers the effect of access frequency, recency and the cost of flash read and write operations. A series of trace-driven simulations are carried out and the experimental results show that the proposed LAB-LRU algorithm outperforms the previous best-known algorithms significantly in terms of the buffer hit ratio, the numbers of flash write and read operations and overall runtime.

Channel state estimation-based backoff algorithms for channel access are being widely studied to solve wireless channel accessing and sharing problem especially in super dense wireless networks. In such algorithms, the precision of the channel state estimation determines the performance. How to make the estimation accurate in an efficient way to meet the system requirements is essential in designing the new channel access algorithms. In this paper, we first study the distribution and properties of inaccurate estimations using a novel biased estimation analysis model. We then propose an efficient backoff algorithm based on the theory of confidence interval estimation (BA-CIE), in which the minimum sample size is deduced to improve the contention window tuning efficiency, while a fault-tolerance interval structure is applied to reduce the inaccurate estimations so as to improve the contention window tuning accuracy. Our simulation results show that the throughput of our proposed BA-CIE algorithm can achieve 99% the theoretical maximum throughput of IEEE 802.11 networks, thanks to the improved contention window tuning performance.

Given a graph G=(V,E) where V and E are a vertex and an edge set, respectively, specified with a subset VNT of vertices called a non-terminal set, the spanning tree with non-terminal set VNT is a connected and acyclic spanning subgraph of G that contains all the vertices of V where each vertex in a non-terminal set is not a leaf. In the case where each edge has the weight of a nonnegative integer, the problem of finding a minimum spanning tree with a non-terminal set VNT of G was known to be NP-hard. However, the complexity of finding a spanning tree on general graphs where each edge has the weight of one was unknown. In this paper, we consider this problem and first show that it is NP-hard even if each edge has the weight of one on general graphs. We also show that if G is a cograph then finding a spanning tree with a non-terminal set VNT of G is linearly solvable when each edge has the weight of one.

The optimization of spoken dialog management policies is a non-trivial task due to the erroneous inputs from speech recognition and language understanding modules. The dialog manager needs to ground uncertain semantic information at times to fully understand the need of human users and successfully complete the required dialog tasks. Approaches based on reinforcement learning are currently mainstream in academia and have been proved to be effective, especially when operating in noisy environments. However, in reinforcement learning the dialog strategy is often represented by complex numeric model and thus is incomprehensible to humans. The trained policies are very difficult for dialog system designers to verify or modify, which largely limits the deployment for commercial applications. In this paper we propose a novel framework for optimizing dialog policies specified in human-readable domain language using genetic algorithm. We present learning algorithms using user simulator and real human-machine dialog corpora. Empirical experimental results show that the proposed approach can achieve competitive performance on par with some state-of-the-art reinforcement learning algorithms, while maintaining a comprehensible policy structure.

This paper presents non-iterative cooperative/parallel Kalman filtering algorithms for decentralized network navigation, in which mobile nodes cooperate in both spatial and temporal domains to infer their positions. We begin by presenting an augmented minimum-mean-square error (MMSE) estimator for centralized navigation network, and then decouple it into a set of local sub-ones each corresponding to a mobile node; all these sub-estimators work in parallel and cooperatively — with the state estimates exchanging between neighbors — to provide results similar to those obtained by the augmented one. After that, we employ the approximation methods that adopted in the conventional nonlinear Kalman filters to calculate the second-order terms involved in these sub-estimators, and propose a decentralized cooperative/parallel Kalman filtering based network navigation framework. Finally, upon the framework, we present two cooperative/parallel Kalman filtering algorithms corresponding to the extended and unscented Kalman filters respectively, and compare them with conventional decentralized methods by simulations to show the superiority.

For an efficient processing of large data in a distributed system, Hadoop MapReduce performs task scheduling such that tasks are distributed with consideration of the data locality. The data locality, however, is limitedly exploited, since it is pursued one node at a time basis without considering the global optimality. In this paper, we propose a novel task scheduling algorithm that globally considers the data locality. Through experiments, we show our algorithm improves the performance of MapReduce in various situations.

A self-adaptive scaled min-sum algorithm for LDPC decoding based on the difference between the first two minima of the check node messages (Δmin) is proposed. Δmin is utilized for adjusting the scaling factor of the check node messages, and simulation results show that the proposed algorithm improves the error correcting performance compared to existing algorithms.

In data sharing privacy has become one of the main concerns particularly when sharing datasets involving individuals contain private sensitive information. A model that is widely used to protect the privacy of individuals in publishing micro-data is k-anonymity. It reduces the linking confidence between private sensitive information and specific individual by generalizing the identifier attributes of each individual into at least k-1 others in dataset. K-anonymity can also be defined as clustering with constrain of minimum k tuples in each group. However, the accuracy of the data in k-anonymous dataset decreases due to huge information loss through generalization and suppression. Also most of the current approaches are designed for numerical continuous attributes and for categorical attributes they do not perform efficiently and depend on attributes hierarchical taxonomies, which often do not exist. In this paper we propose a new model for k-anonymization, which is called Similarity-Based Clustering (SBC). It is based on clustering and it measures similarity and calculates distances between tuples containing numerical and categorical attributes without hierarchical taxonomies. Based on this model a bottom up greedy algorithm is proposed. Our extensive study on two real datasets shows that the proposed algorithm in comparison with existing well-known algorithms offers much higher data utility and reduces the information loss significantly. Data utility is maintained above 80% in a wide range of k values.

Bistatic multi-input multi-output (MIMO) radar has the capability of measuring the transmit angle from the receiving array, which means the existence of information redundancy and benefits data association. In this paper, a data association decision for bistatic MIMO radar is proposed and the performance advantages of bistatic MIMO radar in data association is analyzed and evaluated. First, the parameters obtained by receiving array are sent to the association center via coordinate conversion. Second, referencing the nearest neighbor association (NN) algorithm, an improved association decision is proposed with the transmit angle and target range as association statistics. This method can evade the adverse effects of the angle system errors to data association. Finally, data association probability in the presence of array directional error is derived and the correctness of derivation result is testified via Monte Carlo simulation experiments. Besides that performance comparison with the conventional phased array radar verifies the excellent performance of bistatic MIMO Radar in data association.

Let G be a probabilistic graph, in which the vertices fail independently with known probabilities. Let K represent a specified subset of vertices. The K-terminal reliability of G is defined as the probability that all vertices in K are connected. When |K|=2, the K-terminal reliability is called the 2-terminal reliability, which is the probability that the source vertex is connected to the destination vertex. The problems of computing K-terminal reliability and 2-terminal reliability have been proven to be #P-complete in general. This work demonstrates that on multi-tolerance graphs, the 2-terminal reliability problem can be solved in polynomial-time and the results can be extended to the K-terminal reliability problem on bounded multi-tolerance graphs.

Performance evaluation of an improved multiband impulse radio ultra-wideband (MIR UWB) system based on sub-band selection is proposed in this paper. In the improved scheme, a data mapping algorithm is introduced to a conventional MIR UWB system, and out of all the sub-bands, only partial ones are selected to transmit information data, which can improve the flexibility of sub-bands/spectrum allocation, avoid interference and provide a variety of data rates. Given diagrams of a transmitter and receiver, the exact bit error rate (BER) of the improved system is derived. A comparison of system performance between the improved MIR UWB system and the conventional MIR UWB system is presented in different channels. Simulation results show that the improved system can achieve the same data rate and better BER performance than the conventional MIR UWB system under additive white Gaussian noise (AWGN), multipath fading and interference coexistence channels. In addition, different data transmission rates and BER performances can be easily achieved by an appropriate choice of system parameters.

SM3 is a hash function standard defined by China. Unlike SHA-1 and SHA-2, it is hard for SM3 to speed up the throughput because it has more complicated compression function than other hash algorithm. In this paper, we propose a 4-round-in-1 structure to reduce the number of rounds, and a logical simplifying to move 3 adders and 3 XOR gates from critical path to the non-critical path. Based in SMIC 65nm CMOS technology, the throughput of SM3 can achieve 6.54Gbps which is higher than that of the reported designs.

A parallel Aho-Corasick (AC) approach, named PAC-k, is proposed for string matching in deep packet inspection (DPI). The proposed approach adopts graphic processing units (GPUs) to perform the string matching in parallel for high throughput. In parallel string matching, the boundary detection problem happens when a pattern is matched across chunks. The PAC-k approach solves the boundary detection problem because the number of characters to be scanned by a thread can reach the longest pattern length. An input string is divided into multiple sub-chunks with k characters. By adopting the new starting position in each sub-chunk for the failure transition, the required number of threads is reduced by a factor of k. Therefore, the overhead of terminating and reassigning threads is also decreased. In order to avoid the unnecessary overlapped scanning with multiple threads, a checking procedure is proposed that decides whether a new starting position is in the sub-chunk. In the experiments with target patterns from Snort and realistic input strings from DEFCON, throughputs are enhanced greatly compared to those of previous AC-based string matching approaches.

Because wavelet transforms have the characteristic of decomposing signals that are similar to the human acoustic system, speech enhancement algorithms that are based on wavelet shrinkage are widely used. In this paper, we propose a new speech enhancement algorithm of hearing aids based on wavelet shrinkage. The algorithm has multi-band threshold value and a new wavelet shrinkage function for recursive noise reduction. We performed experiments using various types of authorized speech and noise signals, and our results show that the proposed algorithm achieves significantly better performances compared with other recently proposed speech enhancement algorithms using wavelet shrinkage.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

Optimizating the deployment of wireless sensor networks, which is one of the key issues in wireless sensor networks research, helps improve the coverage of the networks and the system reliability. In this paper, we propose an evolutionary algorithm based on modified t-distribution for the wireless sensor by introducing a deployment optimization operator and an intelligent allocation operator. A directed perturbation operator is applied to the algorithm to guide the evolution of the node deployment and to speed up the convergence. In addition, with a new geometric sensor detection model instead of the old probability model, the computing speed is increased by 20 times. The simulation results show that when this algorithm is utilized in the actual scene, it can get the minimum number of nodes and the optimal deployment quickly and effectively.Compared with the existing mainstream swarm intelligence algorithms, this method has satisfied the need for convergence speed and better coverage, which is closer to the theoretical coverage value.