A Practical Cross-Layer Mechanism For Fairness in 802.11 Networks

Many companies, organizations and communities are providing wireless hotspots that provide networking access using 802.11b wireless networks. Since wireless networks are more sensitive to variations in bandwidth and environmental interference than wired networks, most networks support a number of transmission rates that have different error and bandwidth properties. Access points can communicate with multiple clients running at different rates, but this leads to unfair bandwidth allocation. If an access point communicates with a mix of clients using both 1 Mb/s and 11 Mb/s transmission rates, the faster clients are effectively throttled to 1 Mb/s as well. This happens because the 802.11 MAC protocol approximate “station fairness”, with each station given an equal chance to access the media. We provide a solution to provide “rate proportional fairness”, where the 11 Mb/s stations receive more bandwidth than the 1 Mb/s stations. Unlike previous solutions to this problem, our mechanism is easy to implement, works with common operating systems and requires no change to the MAC protocol or the stations. Joseph Dunn received an M.S. in computer science from the University of Colorado at Boulder in 2003, and B. S. in coputer science and mathematics from the University of Arizona in 2001. His research interests are in the general area of computer systems, primarily focusing on security and scalability in distributed systems. He is currently working on his Ph.D. in computer science from the University of Colorado at Boulder. Michael Neufeld received a Ph.D. in Computer Science from the University of Colorado at Boulder in December of 2004, having previously received an M.S. in Computer Science from the University of Colorado at Boulder in 2000 and an A.B. in Computer Science from Princeton University in 1993. His research interests are in the general area of computer system, specifically concentrating on wireless networking, software defind/cognitive radio, and streerable antennas. He is currently a postdoc in the Computer Science department at the University of Calorado at Boulder pursuing research related to software defined radio and new MAC protocols for steerable phase array antennas. Anmol Sheth is a Ph.D. student in Computer Science at the University of Colorado at Boulder. He received his B.S. in Computer Science from the University of Pune, India in 2001. He has been co-leading the development of the MANTIS operating system. He has co-authored three papers include MAC layer protocol design, energy-efficient wireless communication, and adapting communications to mobility. Dirk Grunwald received his Ph.D. from the University of Illinois in 1989 and joined the University of Colorado the same year. His work addresses research and teaching in the broad area of “computer systems”, which includes computer architecture, operating systems, networks, and storage systems. His interests also include issues in pervasive computing, novel computing models, and enjoying the mountains. He is currently an Associate Professor in the Department of Computer Science and in Electrical and Computer Engineering and is also the Director of the Colorado Center for Information Storage. John Bennett is a Professor of Computer Science with a joint appointment in Electrical and Computer Engineering at the University of Colorado at Boulder. He also serves as Associate Dean for Education in the College of Engineering and Applied Science. He joined the CU-Boulder faculty in 2000, after serving on the faculty of Rice University for 11 years. While at Rice, Bennett pioneered a course in engineering design for both engineering and non-engineering students that has been emulated at several universities and high schools. In addition to other teaching awards, Bennett received the Keck Foundation National Award for Engineering Teaching Excellence for his work on this course. Bennett received his Ph.D. in 1988 from the University of Washington. Prior to completing his doctoral studies, he was a U.S. Naval Officer for several years and founded and served as President of Pacific Mountain Research, Inc., where he supervised the design and development of a number of commercial computing systems. Bennett's primary research interests are broadly focused in the area of distributed systems, and more narrowly in distributed information management and distributed robotic macrosensors.     

采用级联原子滤光器的新型卫星激光链路系统研究

提出级联佛克脱型原子滤光器(Linked VADOF)的新构想。从理论上分析并计算了级联佛克脱型原子滤光器的透射谱。结果表明，级联佛克脱型原子滤光器将佛克脱型原子滤光器的多峰改造为单峰结构，具有比佛克脱型原子滤光器更窄的线宽和更高的噪声抑制比。将这种新型结构应用于激光信标锁频。该锁频方案提高了信标光频率的稳定性。在此基础上，给出了发射端采用级联佛克脱型原子滤光器锁频，接收端采用法拉第型原子滤光器(FADOF)滤光的新型卫星激光链路系统。外场初步联调实验表明了该系统的可行性。     

An Estimate of Inequality of Signature and Nullity for Links

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Simulation of aerated lagoon using artificial neural networks and multivariate regression techniques

The aim of this study was to develop an empirical model that provides accurate predictions of the biochemical oxygen demand of the output stream from the aerated lagoon at International Paper of Brazil, one of the major pulp and paper plants in Brazil. Predictive models were calculated from functional link neural networks (FLNNs), multiple linear regression, principal components regression, and partial least-squares regression (PLSR). Improvement in FLNN modeling capability was observed when the data were preprocessed using the PLSR technique. PLSR also proved to be a powerful linear regression technique for this problem, which presents operational data limitations.     

How can the Cross-Link Adducts Formed by Novel Trans Platinum Drug be Influenced by Hydrogen Bond

A systematic quantum chemical characterization of intrinsic structure, energies and spectral properties of all the studied cross-link adducts formed by the novel trans platinum with thiazole ligand has been carried out at B3LYP/6-31G^＊ level of theory with the Lanl2dz pseudo potential basis set for the Pt atom. Special attention has been paid to the relative stability of these complexes and the factors that probably alter the order of the relative stability. The important influence of hydrogen bond on the structures, the energies and the spectral property was revealed. Other factors that contribute to relative stability including solvation effect, entropy and electronic delocalization energy were taken into account. The stability energy of the whole complex, and the interaction energy between two purine bases and the [Pt-（NH3）thiazole]^2＋ group were adopted to study the interplay among subsystems and their contribution to relative stability of all the studied cross-link model. Finally, basic spectral properties of these complexes including H（8） chemical shifts of all the studied complexes and the VCD （vibrational circular dichroism） spectra of two pairs of GG chelate enantiomers, were provided in order to define the structure of the most possible duplex bearing novel trans platinum drug lesions.     

高铁场景下联合天线与IOS单元选择的分布式IOS-SM传输方案

针对高铁场景下封闭车厢带来的高信号穿透损耗导致传输速率下降问题,提出一种分布式智能全表面（intelligent omni-surface,IOS）辅助空间调制（spatial modulation,SM）的传输方案。首先,采用SM技术保证索引信息部分不受穿透损耗的影响;然后,为了减小远距离IOS车窗的高路径损耗和功率损耗,根据最大化接收信噪比原则对分布式IOS遍历选择,激活最佳IOS车窗传输SM调制信息。最后,为了减小缺失直视路径增益对系统容量性能的影响,基于功率损耗模型,采用天线移除原则和排序选择方式,对接收天线和IOS单元进行动态选择激活。仿真结果表明,相比于传统中继转发和可重构智能超表面-空间调制（reconfigurable intelligence surface-SM,RIS-SM）方案,所提方案更适合高铁通信场景。并且在功率损耗模型下,联合天线与IOS单元选择的分布式IOS-SM方案仍能够保证系统高性能传输。     

PLC-RF无线传输网络中的能效最优设计方法

针对电力线通信（PLC）和射频（RF）无线通信混合传输的室内通信场景,提出了一种基于角度信息的信道状态信息（AI-CSI）的能效优先传输方案。首先,Wi-Fi无线网络和PLC网络分别作为主网络和次级网络,并且采用认知无线电技术来提高频谱效率的情况下,建立次级网络总能效最大化为目标函数的优化问题。其次,为了求解该问题,通过基于AI-CSI的迫零波束成形方法,获得波束成形权矢量,并进一步提出Dinkelbach与拉格朗日乘子法相结合的优化方法,进行最优的功率分配。最后,计算机仿真结果不仅验证了所提方案的有效性和优越性,而且分析了中继天线数和用户个数等典型参数对系统能效带来的影响,从而为实际系统设计提供了参考和依据。     

融合课程思政的“自动化综合实验”教学探索

为将思政教育融入课程教学中,培养德能兼备的自动化专业优秀人才。在《自动化综合实验》课程中,首先明确提出了教学的专业目标和思政目标,然后将教学环节分为课程介绍、选题汇报、学生实践以及项目展示和答辩环节,挖掘出丰富的思政元素并融入到课程教学的每个环节,并选取典型案例,通过思维导图法剖析了课程思政的融入策略,最后介绍了课程思政考核模式和取得的初步成效。该课程思政教学模式可以为“专业实验课程-思政教育体系”的打造提供参考。     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.