A hybrid optimization approach for chip placement of multi-chip module packaging

The aim of this article is to provide a systematic method to perform optimization design for chip placement of multi-chip module in electronic packaging. Based on the investigation of the structural and thermal characteristics of multi-chip module, the key performance indexes of multi-chip module that include the lowest internal temperature objective, thermal-transfer accuracy, chip placement are analyzed. A hybrid model is presented by using genetic algorithm and response surface methodology for optimization. Furthermore, some design processes for improving the performance are induced. Finally, an example is discussed to apply the method.     

Energy‐efficient multihop routing in WSN using the hybrid optimization algorithm

The technical growth in the field of the wireless sensor networks (WSNs) has resulted in the process of collecting and forwarding the massive data between the nodes, which was a major challenge to the WSNs as it is associated with greater energy loss and delay. This resulted in the establishment of a routing protocol for the optimal selection of the multipath to progress the routing in WSNs. This paper proposes an energy‐efficient routing in WSNs using the hybrid optimization algorithm, cat–salp swarm algorithm (C‐SSA), which chooses the optimal hops in progressing the routing. Initially, the cluster heads (CHs) are selected using the low‐energy adaptive clustering hierarchy (LEACH) protocol that minimizes the traffic in the network. The CHs are engaged in the multihop routing, and the selection of the optimal paths is based on the proposed hybrid optimization, which chooses the optimal hops based on the energy constraints, such as energy, delay, intercluster distance, intracluster distance, link lifetime, delay, and distance. The simulation results prove that the proposed routing protocol acquired minimal delay of 0.3165 with 50 nodes and two hops, maximal energy of 0.1521 with 50 nodes and three hops, maximal number of the alive nodes as 39 with 100 nodes and two hops, and average throughput of 0.9379 with 100 nodes and three hops.     

Provisioning for interdomain quality of service: the MESCAL approach

This article presents an architecture for supporting interdomain QoS across the multi-provider global Internet. While most research to date has focused on supporting QoS within a single administrative domain, mature solutions are not yet available for the provision of QoS across multiple domains administered by different organizations. The architecture described in this article encompasses the full set of functions required in the management (service and resource), control and data planes for the provision of end-to-end QoS-based IP connectivity services. We use the concept of QoS classes and show how these can be cascaded using service level specifications (SLSs) agreed between BGP peer domains to construct a defined end-to-end QoS. We illustrate the architecture by describing a typical operational scenario.     

WSN中一种混合型拓扑控制算法

拓扑控制对于延长网络寿命,减小通信干扰,提高路由协议效率等具有重要的意义。睡眠调度和功率控制是现在研究的主流方向。全文首先阐述了无线传感器拓扑控制算法的研究进展,接着提出了一个混合型拓扑控制算法HTC,叙述了HTC算法的实现过程,它融合了睡眠调度和功率控制算法的优点,克服了它们的缺点,在提高网络性能和延长网络寿命方面起到很好的作用。     

A hybrid beaconless geographic routing for different packets in WSN

The localized operation and stateless features of geographic routing make it become an attractive routing scheme for wireless sensor network (WSN). In this paper, we proposed a novel routing protocol, hybrid beaconless geographic routing (HBGR), which provides different mechanisms for different packets. Based on the requirement of application on latency, we divide the packets of WSN into delay sensitive packets and normal packets. HBGR uses two kinds of Request-To-Send/Clear-To-Send handshaking mechanisms for delay sensitive packets and normal packets, and assigns them different priority to obtain the channel. The simplified analysis is given, which proves that delay sensitive packets have lower latency and higher priority to obtain the channel than normal packets. Moreover, forwarding area division scheme is proposed to optimize the forwarder selection. Simulation results show that HBGR achieves higher packet delivery ratio, lower End-to-End latency and lower energy consumption than existing protocols under different packet generation rates in stationary and mobility scenario. Besides, compared with normal packets, delay sensitive packets have at least 10 % (9 %) improvement in terms of End-to-End latency. The improvement increases with the increasing of packet generation rate, and achieves 58 % (73 %) when the packet generation rate is 24 packets per second in stationary (mobility) scenario.     

A closer look at service level metrics

Defined service level metrics that define acceptable frame relay network performance are increasingly offered and requested. The work of the Frame Relay Forum in providing a common vocabulary to describe frame relay service delivery is examined in this article. Copyright © 1999 John Wiley & Sons, Ltd.     

Ensuring quality of service for image transmission: hybrid loss protection

We present hybrid loss protection as a new channel coding and packetization scheme for image transmission over nonprioritized lossy packet networks. The scheme employs an interleaver-based structure, and attempts to maximize the expected peak signal-to-noise ratio (PSNR) at the receiver given the constraint that the probability of failure, i.e., the probability that the PSNR of the decoded image is below a given threshold, is upper-bounded by a user-defined value. A new code-allocation algorithm is proposed, which employs Gilbert-Elliot modeling of the network statistics. Experimental results are provided in the case of transmission of images encoded by SPIHT and JPEG 2000 over a wireline, as well as a wireless UMTS-based Internet connection.     

基于位置的无线传感器网络可靠性区分服务机制

如何根据不同任务的不同可靠性需求来提供相应的服务是无线传感器网络所面临的最重要问题之一.结合基于位置的路由机制,给出衡量可靠性的量化度量模型,该模型无需全局网络拓扑信息.通过引入虚拟参考点,将数据的转发限制在特定区域从而减少无关传感器节点的能耗.分析了该区域与可靠性的函数关系,得到了可靠性随着参考点与源节点距离的增长而平方级增长的结论.在此基础上给出区分可靠性的服务机制.实验结果表明所提机制有效.     

A preamble-based approach for providing quality of service support in wireless sensor networks

Medium Access Control (MAC) protocols for Wireless Sensor Networks (WSN) are usually designed as random access protocols that apply different kinds of backoff strategies since Time Division Multiple Access (TDMA) based protocols with admission control are very complex and require additional mechanisms for synchronization. Without such mechanisms, fair or priority based medium access with Quality of Service (QoS) guarantees can hardly be achieved by existing protocols. Therefore, we developed a random access protocol which uses a new preamble-based medium access strategy that enables collision-free priority based access without the need of synchronization. In this paper we introduce different QoS strategies and their use cases. All strategies can be easily integrated in our protocol to meet the requirements of different target applications. Furthermore, we compare the performance of the strategies with a typical carrier-sense based protocol.     

CH selection and compressive sensing-based data aggregation in WSN using hybrid Golden circle-inspired optimization

The arbitrary distribution of sensor nodes and irregularity of the routing path led to unordered data, which is complex to handle in a wireless sensor network (WSN). To increase WSN lifetime, data aggregation models are developed to minimize energy consumption or ease the computational burden of nodes. The compressive sensing (CS) provides a new technique for prolonging the WSN lifetime. A hybrid optimized model is devised for cluster head (CH) selection and CS-based data aggregation in WSN. The method aids to balance the energy amidst different nodes and elevated the lifetime of the network. The hybrid golden circle inspired optimization (HGCIO) is considered for cluster head (CH) selection, which aids in selecting the CH. The CH selection is done based on fitness functions like distance, energy, link quality, and delay. The routing is implemented with HGCIO to transmit the data projections using the CH to sink and evenly disperse the energy amidst various nodes. After that, compressive sensing is implemented with the Bayesian linear model. The convolutional neural network-long short term memory (CNN-LSTM) is employed for the data aggregation process. The proposed HGCIO-based CNN-LSTM provided the finest efficiency with a delay of 0.156 s, an energy of 0.353 J, a prediction error of 0.044, and a packet delivery ratio (PDR) of 76.309%.     

Perceptual visual quality metrics: A survey

Visual quality evaluation has numerous uses in practice, and also plays a central role in shaping many visual processing algorithms and systems, as well as their implementation, optimization and testing. In this paper, we give a systematic, comprehensive and up-to-date review of perceptual visual quality metrics (PVQMs) to predict picture quality according to human perception. Several frequently used computational modules (building blocks of PVQMs) are discussed. These include signal decomposition, just-noticeable distortion, visual attention, and common feature and artifact detection. Afterwards, different types of existing PVQMs are presented, and further discussion is given toward feature pooling, viewing condition, computer-generated signal and visual attention. Six often-used image metrics (namely SSIM, VSNR, IFC, VIF, MSVD and PSNR) are also compared with seven public image databases (totally 3832 test images). We highlight the most significant research work for each topic and provide the links to the extensive relevant literature.     

A new algorithm for optimization of quality of service in peer to peer wireless mesh networks

Wireless Networks - Nowadays, wireless mesh networks are known as important parts of different commercial, scientific, and industrial processes. Their prevalence increases day-by-day and the future...     

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

On the performance of objective quality metrics for lightfields

Lightfield (LF) technology has attained significant attention in recent years due to its capability to capture much richer textural and geometric information in the scene compared to the classical 2D representation. The resampling and compression operations on LFs often lead to visual quality degradation, thus, sophisticated visual quality assessment methods play a crucial role to ensure a pleasant viewing experience. To this end, it is necessary to examine the performance of quality assessment methods for LF contents. The paper provides a comprehensive study on the reliability of various objective algorithms for LF quality prediction. Three subjectively-annotated LF data sets were selected and an extensive quality estimation analysis has been conducted using several objective quality assessment methods. In total, 250 LFs (more than 48000 perspective images) were evaluated. The results were compared against human opinion scores using various correlation indices and their statistical significance. Next, a decision-making strategy was adopted to choose the most reliable quality metrics for evaluation of LFs and finally, a metric fusion framework was proposed to further improve the quality prediction accuracy. To best of our knowledge, the benchmark and the analytical methodologies used in this paper are the most comprehensive study on the objective quality assessment methods for LF application.     

A quality of service aware cross-layer approach for wireless ad hoc networks with smart antennas

Recent technological advances have led to a growing interest in multi-antenna wireless ad hoc networks in which each node has more than one antenna. This paper proposes a cross-layer approach called QoS-aware smart antenna protocol (QSAP) for such networks that assures quality of service (QoS) needs of applications with reduced energy consumption. The proposed scheme adaptively allocates the degrees of freedom present in the multi-antenna system at each node to reduce the transmit energy while meeting the QoS needs of the application. The effectiveness of the proposed approach is demonstrated through simulations. The simulations show that the cross-layer aspects of the proposed approach result in considerable energy savings compared to schemes which meet the QoS needs without the cross-layer interaction.     

A hybrid evolutionary programming method for circuit optimization

A hybrid evolutionary programming (EP) method is presented for global optimization of complex circuits. The conventional EP is integrated with a clustering algorithm to improve the robustness of the algorithm for complex multimodal circuit optimization problems. The EP generates populations around the regions of the search space which can potentially contain a minimum but may be overlooked. The clustering algorithm is used to identify these regions dynamically. In order to improve the speed of optimization, the EP is combined with a gradient-based search method in an efficient fashion. The local search is performed from the center of each identified cluster in order to find the minimum in the region very fast. The hybrid algorithm can also reduce the search space by avoiding the search in the areas that were previously investigated. This feature greatly improves the speed of optimization and prevents the premature convergence as well. The algorithm performed very well in several benchmark problems including a test function minimization and global optimization of a complex RF diplexer circuit.     

A service‐agnostic method for predicting service metrics in real time

We predict performance metrics of cloud services using statistical learning, whereby the behaviour of a system is learned from observations. Specifically, we collect device and network statistics from a cloud testbed and apply regression methods to predict, in real‐time, client‐side service metrics for video streaming and key‐value store services. Results from intensive evaluation on our testbed indicate that our method accurately predicts service metrics in real time (mean absolute error below 16% for video frame rate and read latency, for instance). Further, our method is service agnostic in the sense that it takes as input operating systems and network statistics instead of service‐specific metrics. We show that feature set reduction significantly improves the prediction accuracy in our case, while simultaneously reducing model computation time. We find that the prediction accuracy decreases when, instead of a single service, both services run on the same testbed simultaneously or when the network quality on the path between the server cluster and the client deteriorates. Finally, we discuss the design and implementation of a real‐time analytics engine, which processes streams of device statistics and service metrics from testbed sensors and produces model predictions through online learning.     

A hybrid approach for the sensitivity analysis of integrated inductors

This paper proposes a hybrid methodology for the evaluation of integrated inductors sensitivity against technological/geometrical parameters variation. The obtained results are used in an optimization-based design environment for integrated inductors, as a way of guaranteeing that obtained solutions are robust against parameter variation. For the inductor characterization, a lumped element model is used, where each element value is evaluated through physics based equations. The sensitivity of the inductor characterization to parameter variations is evaluated at two levels. At the physical level, the sensitivity of the model element values to technological/geometrical parameters variations is computed through an equation-based strategy. Then, the sensitivity of the inductor characterization to the model parameter variations is obtained through a simulation-based approach, where the Richardson extrapolation technique is used for the calculation of the partial derivatives. Several examples considering the evaluation of sensitivity of both inductance and quality factor of two inductors in UMC130 technology are presented. Obtained results are compared against Monte-Carlo simulations.     

城市宽带网络服务质量优化方法研究

随着网络承载业务的不断增加,对网络服务质量进行优化很有必要。文中简述了城市宽带网络服务质量的概念与衡量指标,以中小城市宽带网络优化为背景,分析网络服务中存在的问题,研究各种网络优化技术,探讨网络服务质量模型,为实现更好的宽带网络服务质量保证机制提供理论依据。