Investigation of performance variations due to the amplitude of group-delay ripple in chirped fiber Bragg gratings

We measure the system impacts due to the amplitude of group-delay (GD) ripple in single and cascaded chirped fiber Bragg gratings (CFBGs). Signals with smaller pulse width result in smaller performance variation at the same data rate. A 65-ps peak-to-peak GD ripple induces 0.9, 1.7, and 2.7 dB maximum penalties for 10, 20, and 40-Gb/s, respectively. We also find that cascading gratings with random ripple causes much less degradation than cascading gratings with the same ripple profile.     

基于深度强化学习的无人机间通信链路智能决策

由于无人机组网灵活、快速、低成本的特性,空中基站被视为在未来无线通信中有前景的技术。无人机集群可以通过相互协调和合作,完成的复杂任务,具有重大的研究和实用价值,而无人机间的高效通信是当下面临的重大挑战。为了在满足无人机间通信速率的前提下,尽可能节省发射功率,本文提出基于深度强化学习的集群方案和功率控制的智能决策算法。首先,本文设计了三种无人机集群方案,以对地面用户提供无缝的无线覆盖;然后,本文提出了基于深度Q网络（Deep Q-network）算法的集群方案和功率控制决策算法,用深度神经网络输出不同条件下联合决策的无人机集群方案和发射功率,并研究了重要性采样技术,提高训练效率。仿真结果表明,本文提出的深度强化学习算法能够正确决策无人机集群方案和发射功率,与不带强化学习的深度学习（Deep Learning Without Reinforcement Learning, DL-WO-RL）算法相比,用更低的发射功率满足无人机之间的通信速率要求,并且重要性采样技术能够缩短DQN算法的收敛时间。      

Trends in highly scalable crossbar-based packet switch architecture

A combined input and crosspoint queued (CICQ) switch is receiving significant attention to be the next generation high speed packet switch for its scalability; however, a multi-cabinet implementation of a combined input and crosspoint queued (CICQ) switch unavoidably introduces a large round-trip time (RTT) latency between the line cards and switch fabric, resulting a large crosspoint (CP) buffer requirement. In this paper, virtual crosspoint queues (VCQs) that significantly reduces the CP buffer requirement of the CICQ switch is investigated. The VCQs unit resides inside the switch fabric, is dynamically shared among virtual output queues (VOQ) from the same source port, and is operated at the line rate, making the implementation practical. A threshold-based exhaustive round-robin (T-ERR) arbitration is employed to reduce buffer hogging at VCQ. The T-ERR at VCQ and CP arbiters serves packets residing in a longer queue more frequently than packet residing in a shorter queue. Consequently, the T-ERR, drastically increases the throughput of the CICQ switch with small CP buffers. A multi-cabinet implementation of CICQ switch do not support multicasting traffic well since a combination of small CP buffer in the switch fabric and a large RTT latency between the line cards and switch fabric results in non-work conservation of the intra-switch link. Deployment of multicast FIFO buffer between the input buffer and CP buffer shows a promise. With its ability to achieve high throughput independent of RTT and switch port size, the integration of the VCQ architecture and T-ERR scheduler to the CICQ switch is ideal for supporting ever-increasing Internet traffic that requires higher data rate, larger switch size, and efficient multicasting.     

Opportunistic channel selection approach under collision probability constraint in cognitive radio systems

In this work, we consider a cognitive radio system with multiple primary channels and one secondary user, and then we introduce a channel-usage pattern model and some basic concepts in this system. Based on this system model and the basic concepts, we propose two opportunistic channel selection algorithms to optimize the throughput of the secondary user: minimum collision rate channel selection algorithm and minimum handoff rate channel selection algorithm. According to the two algorithms, we, respectively, present the channel selection scheme based on minimum collision rate algorithm (CSS-MCRA) and the channel selection scheme based on minimum handoff rate algorithm (CSS-MHRA) under the constraint that the collision probability is bounded below collision tolerable level. Theoretical analysis and simulation results both show that, on one hand, both CSS-MCRA scheme and CSS-MHRA can follow the constraint of collision tolerable level; on the other hand, the performance of CSS-MCRA scheme is better than that of CSS-MHRA scheme if handoff latency is zero or very low, while the performance of CSS-MHRA scheme is better than that of CSS-MCRA scheme if handoff latency is long enough.     

An extension of fuzzy TOPSIS for a group decision making with an application to tehran stock exchange

In financial markets, investors attempt to maximize their profits within a constructed portfolio with the aim of optimizing the tradeoffs between risk and return across the many stocks. This requires proper handling of conflicting factors, which can benefit from the domain of multiple criteria decision making (MCDM). However, the indexes and factors representing the stock performance are often imprecise or vague and this should be represented by linguistic terms characterized by fuzzy numbers. The aim of this research is to first develop three group MCDM methods, then use them for selecting undervalued stocks by dint of financial ratios and subjective judgments of experts. This study proposes three versions of fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution): conventional TOPSIS (C-TOPSIS), adjusted TOPSIS (A-TOPSIS) and modified TOPSIS (M-TOPSIS) where a new fuzzy distance measure, derived from the confidence level of the experts and fuzzy performance ratings have been included in the proposed methods. The practical aspects of the proposed methods are demonstrated through a case study in the Tehran stock exchange (TSE), which is timely given the need for investors to select undervalued stocks in untapped markets in the anticipation of easing economic sanctions from a change in recent government leadership.     

一种在LTE-Advanced载波聚合下降低CM的方法

3GPP LTE采用OFDMA作为物理层多址接入技术。而峰值引起的功率回退是OFDM技术的主要问题,因此在LTE中,采用CM(立方度量)来度量功率回退。LTE-Advanced中为了提升峰值速率引入了载波聚合技术,多个成员载波的引入可能会带来CM的提升。主要介绍一种载波聚合下降低由于重复导频引起的CM过高的方法。该方法不仅能够在多个成员载波情况下有效地降低CM,并且可以将复杂度和处理时延控制在较小范围内。     

Perceptual encryption with compression for secure vector map data processing

With the rapidly rising interest in geographic information system (GIS) contents, a large volume of valuable map data has been unlawfully distributed by pirates. Therefore, the secure storage and transmission of classified national digital map datasets have been increasingly threatened. As the importance of secure, large-volume map datasets has increased, vector map security techniques that focus on secure network and data encryption have been studied. These techniques are required to ensure access control and prevent illegal copying of digital maps. This paper presents perceptual encryption on the vector compression domain for copy protection and access control of vector maps. Our algorithm compresses all vector data of polylines and polygons by lossless minimum coding object (MCO) units and perceptually encrypts using two processes using the mean points and directions of MCOs. The first process changes the position of vector data by randomly permuting the mean points of MCOs, the so-called position encryption. The second process changes the geographic shape by circularly encrypting the directions of vertices in MCOs by the XOR operator. Experimental results have verified that our algorithm can encrypt GIS digital maps effectively and simply and can also improve the compression ratio, unlike general data encryption techniques, and thus, our algorithm is very effective for a large volume of GIS datasets.     

A reconfigurable,regular-topology cluster/datacenter network using commodity optical switches

Hybrid optical/electrical interconnects using commercial optical circuit switches have been previously proposed as an attractive alternative to fully-connected electronically-switched networks. Among other advantages, such a design offers increased port density, bandwidth/port, cabling and energy efficiency, compared to conventional packet-switched counterparts. Recent proposals for such system designs have looked at small and/or medium scale networks employing hybrid interconnects. In our previous work, we presented a hybrid optical/electrical interconnect architecture targeting large-scale deployments in high-performance computing and datacenter environments. To reduce complexity, our architecture employs a regular shuffle network topology that allows for simple management and cabling. Thanks to using a single-stage core interconnect and multiple optical planes, our design can be both incrementally scaled up (in capacity) and scaled out (in the number of racks) without requiring major re-cabling and network re-configuration. In this paper, we extend the fundamentals of our existing work towards quantifying and understanding the performance of these type of systems against more diverse workload communication patterns and system design parameters. In this context, we evaluate–among other characteristics–the overhead of the reconfiguration (decomposition and routing) scheme proposed and extend our simulations to highly adversarial flow generation rate/duration values that challenge the reconfiguration latency of the system.     

Human action recognition using multi-layer codebooks of key poses and atomic motions

Taking fully into consideration the fact that one human action can be intuitively considered as a sequence of key poses and atomic motions in a particular order, a human action recognition method using multi-layer codebooks of key poses and atomic motions is proposed in this paper. Inspired by the dynamics models of human joints, normalized relative orientations are computed as features for each limb of human body. In order to extract key poses and atomic motions precisely, feature sequences are segmented into pose feature segments and motion feature segments dynamically, based on the potential differences of feature sequences. Multi-layer codebooks of each human action are constructed with the key poses extracted from pose feature segments and the atomic motions extracted from motion feature segments associated with each two key poses. The multi-layer codebooks represent action patterns of each human action, which can be used to recognize human actions with the proposed pattern-matching method. Three classification methods are employed for action recognition based on the multi-layer codebooks. Two public action datasets, i.e., CAD-60 and MSRC-12 datasets, are used to demonstrate the advantages of the proposed method. The experimental results show that the proposed method can obtain a comparable or better performance compared with the state-of-the-art methods.     

Switch cost and packet delay tradeoff in data center networks with switch reconfiguration overhead

Cost minimization is a major concern in data center networks (DCNs). Existing DCNs generally adopt Clos network with crossbar middle switches to achieve non-blocking data switching among the servers, and the number of middle switches is proportional to the number of ports of the aggregation switches in a fixed manner. Besides, reconfiguration overhead of the switches is generally ignored, which may contradict the engineering practice. In this paper, we consider batch scheduling based packet switching in DCNs with reconfiguration overhead at each middle switch, which inevitably leads to packet delay. With existing state-of-the-art traffic matrix decomposition algorithms, we can generate a set of permutations, each of which stands for the configuration of a middle switch. By reconfiguring each middle switch to fulfill multiple configurations in parallel with others, we reveal that a tradeoff exists between packet delay and switch cost (denoted by the number of middle switches), while performance guaranteed switching with bounded packet delay can be achieved without any packet loss. Based on the tradeoff, we can minimize the number of middle switches (under a given packet delay bound) and an overall cost metric (by translating delay into a comparable cost factor), as well as formulating criteria for choosing a proper matrix decomposition algorithm. This provides a flexible way to reduce the number of middle switches by slightly enlarging the packet delay bound.