A Reliability-Aware Methodology to Isolate Timing-Critical Paths under Aging

Temporal unreliability due to aging, such as Negative-Bias Temperature Instability (NBTI) and Hot Carrier Injection (HCI) effects etc., in the CMOS circuits may not appear just after the chip production, instead it becomes apparent when it is used under certain workload and environmental conditions over time. Identifying aged paths that may become critical to circuit performance, is a real challenge for many researchers and reliability engineers. In this work, firstly we identify a set of parameters that impact the circuit performance under aging and use them in the proposed algorithm which is substantially faster than commercially available SPICE simulator with an approx 94% accuracy in estimating path delays. Secondly, we explore the possibility of using the proposed methodology, instead of using time expensive SPICE and pessimistic static timing analysis (STA), to identify a set of speed-limiting paths under aging. Experimental results demonstrate the effectiveness of the proposed algorithm and the associated methodology in comparison to SPICE simulated results.     

Machine learning based optimized live virtual machine migration over WAN links

Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.     

Centralized dynamic frequency allocation for cell-edge demand satisfaction in fractional frequency reuse networks

Fractional frequency reuse (FFR) has emerged as a well-suited remedy for inter-cell interference reduction in the next-generation networks by allocating frequency reuse factor (FRF) of unity for the cell-center (CC) and higher FRF for the cell-edge (CE) users. However, this strict FFR comes at a cost of equal partitioning of frequency resources to the CE which most likely has varying demands in current networks. In order to mitigate this, we propose a centralized dynamic resource allocation scheme which allocates demand-dependent resources to CE users. The proposed scheme therefore outperforms the fixed allocation scheme of strict FFR for both CC and CE users. Complexity analysis provides a fair means of analyzing the suitability of proposed algorithm. We have also compared the proposed methodology with a reference dynamic fractional frequency reuse (DFFR) scheme. Results show maximum performance gain of up to 30% for 3 reference cells employing Rayleigh fading—through normalized area spectral efficiency (ASE) analysis for both fixed allocation and DFFR. Spectral efficiency analysis also indicates per-cell performance gain for both CC and CE users. Further, detailed three-dimensional ASE plots give insights into the affects to other cells. Due to dynamic nature of traffic loads, the proposed scheme is a candidate solution for satisfying the demands of individual cells.     

Fair QoS multi-resource allocation for uplink traffic in WLAN

In wireless local area network (WLAN), improving the quality of service (QoS) of users is often at odd with striking fairness among users. In this work, we suggest that in WLAN, multiple types of network resources should be jointly allocated to users to achieve “QoS fairness”, which is a new fairness concept targeting at balancing QoS and fairness in WLAN by allocating multiple types of network resources to users. To this end, we first transform user QoS requirements to multi-resource demands and apply the dominant resource fairness scheme to allocate network resources for each user. We prove several salient QoS-based fairness properties based on a model mapping between QoS and resources. We further discuss about more general conditions for diverse mapping models where QoS fairness properties can be satisfied. We find that the QoS fairness properties can be guaranteed as long as the mapping model meets a few practical requirements, indicating the wide applicability of our scheme. To consolidate our multi-resource allocation scheme, we design a practical protocol for WLAN. The simulation results validate that the QoS fairness can be guaranteed in practical WLAN scenario.     

Path Loss Models for Low-Height Mobiles in Forest and Urban

This paper presents path loss measurements at 2.1 GHz in forest and urban areas. Empirical path loss models have been presented for low-height dual-mobility channels. Three test scenarios are considered for the transmitter (Tx) and receiver (Rx) placed inside the test vehicle or on a test cart pushed at walking speed. Based on measurements, the in-leaf and single-slope path loss models are presented. The path loss exponents for the dual-mobility channels are found to be between 2.1 and 3.4 in urban and 8.0 in forest, with higher reference when antennas are placed inside the vehicle.     

Network Coding Based Converge-Cast Scheme in Wireless Sensor Networks

The converge-cast in wireless sensor networks (WSNs) is widely applied in many fields such as medical applications and the environmental monitoring. WSNs expect not only providing routing with high throughput but also achieving efficient energy saving. Network coding is one of the most promising techniques to reduce the energy consumption. By maximizing the encoding number, the message capacity per package can be extended to the most efficient condition. Thus, many researchers have focused their work on this field. Nevertheless, the packages sent by the outer nodes need to be temporary stored and delayed in order to maximize the encoding number. To find out the balance between inserting the delay time and maximizing the encoding number, a Converge-cast Scheme based on data collection rate prediction (CSRP) is proposed in this paper. To avoid producing the outdated information, a prediction method based on Modifying Index Curve Model is presented to deal with the dynamic data collection rate of every sensor in WSNs. Furthermore, a novel coding conditions based on CDS is proposed to increase the coding opportunity and to solve the collision problems. The corresponding analysis and experimental results indicate that the feasibility and efficiency of the CSRP is better than normal conditions without the prediction.     

Temporal Topic-Based Multi-Dimensional Social Influence Evaluation in Online Social Networks

Analytical applications in online social networks can be generalized as the influence evaluation problem, which targets at finding most influential users. Nowadays social influence evaluation is still an open and challenging issue. Most influence evaluation models focus on the single dimensional evaluation factor but fail to research on the multi-dimensional factors. In this paper, we propose a novel influence evaluation model: the temporal topic influence (TTI) evaluation model, which is a time-aware, content-aware and structure-aware evaluation model. For the aim of multi-dimensional evaluation, we incorporate multi-dimensional measure factors into our model, including the time factor, the topological information and the topic distribution information, etc. We propose a novel concept of user gravitational ability which is inspired by Newton’s law of universal gravitation. It can integrate multi-dimensional factors in an appropriate way. Our experiments are conducted on the Sina Weibo data set. Through the experimental analysis, we prove TTI model can calculate users’ influences effectively and efficiently. The TTI model can distinguish the value of users’ influences. And the TTI model identifies the top-k influential users with higher quality. We also validate the effect of time and topic measure factors in the influence evaluation process.     

WDTF: A Technique for Wireless Device Type Fingerprinting

In this Work, a technique for wireless device type fingerprinting has been introduced. The technique utilizes the information that revealed as a result of the homogeneity in devices of the same make and the heterogeneity in devices of a different make. The diversity in devices of different make is due to different device hardware compositions and the variations in their management capabilities. We apply the statistical technique on network traffic to create unique, reproducible device signatures. We demonstrate the efficacy of our technique on network traffic captured in different scenarios. We have used a total of 300 devices types representing a wide range of device classes. In the experiment, we have used more than 1.5 GB of filtered traffic for analysis and performance evaluation. We measure the performance of the technique by considering the accuracy of device type detection. The results obtained are promising with a higher detection rate than its counterparts.     

Thermoelectric and Magnetic Properties of Pt-Substituted $${BaFe_{4-{x}}Pt_{{x}}Sb_{12}}$$ Compounds

\({BaFe_{4-{x}}Pt_{{x}}Sb_{12}}\) (x = 0, 0.1, 0.2) compounds were prepared by melting and annealing, followed by a spark plasma sintering method. Low-temperature thermoelectric and magnetic properties were investigated based on Seebeck coefficient, electrical and thermal conductivity and magnetization measurements. The structural properties of \({BaFe_{4-{x}}Pt_{{x}}Sb_{12}}\) (x = 0, 0.1, 0.2) compounds were ascertained by powder x-ray diffraction analysis, confirming that all samples have a main phase of a skutterudite structure with the space group Im\({\mathrm {\bar{3}}}\). The lattice parameters obtained, 9.202(5), 9.199(5) and 9.202(1) Å for x = 0, 0.1 and 0.2, respectively, were found consistent with literature. The Seebeck coefficient sign shows that holes are dominant carriers in all compounds. The local maximum Seebeck coefficient was observed around 50 K which may be a trace of paramagnon-drag effect of charge carriers. Thermal conductivity and electrical resistivity measurements were carried out between 4.2 and 300 K. Temperature dependence of electrical resistivity reflects that all samples show semi-metallic behavior in our temperature range of 4.2–300 K. Samples for x = 0.1 and x = 0.2 show Kondo-like behavior. In magnetization measurement, we observe that there are two successive magnetic transitions in Pt-substituted compounds; however, there is only one (transition from a paramagnetic state to long-range magnetic ordering) in Pt-free compounds. In Pt-substituted compounds, the first transition appears at \( T _{ {\rm c}}\) = 48 K. In addition, the second transition is observed at \( T _{ {\rm irr}}\) = 30 K where an intermediate state is observed before the magnetic ordering transforms to an irreversible ferromagnetic state. We concluded that Pt substitution on the Fe side effectual on the thermoelectric and magnetic properties of \({BaFe_{4-{x}}Pt_{{x}}Sb_{12}}\) (x = 0, 0.1, 0.2) compounds.     

Fabrication and Electromagnetic Properties of Conjugated NH<Subscript>2</Subscript>-CuPc@Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>

Conjugated amino-phthalocyanine copper containing carboxyl groups/magnetite (NH₂-CuPc@Fe₃O₄) has been fabricated from FeCl₃·6H₂O and NH₂-CuPc via a simple solvothermal method and its electromagnetic properties investigated. Scanning electron microscopy and transmission electron microscopy revealed that the NH₂-CuPc@Fe₃O₄ was a waxberry-like nanomaterial with NH₂-CuPc molecules effectively embedded in the interior of Fe₃O₄ particles in the form of beads. Introduction of NH₂-CuPc effectively improved the complementarity between the dielectric and magnetic losses of the system, resulting in excellent electromagnetic performance. The minimum reflection loss of the as-prepared composite reached ?33.4 dB at 7.0 GHz for coating layer thickness of 4.0 mm and bandwidth below ?10.0 dB (90% absorption) of up to 3.8 GHz. These results indicate that introduction of NH₂-CuPc results in a composite with potential for use as an electromagnetic microwave absorption material.