Finite sample size effect on minimum variance beamformers: optimum diagonal loading factor for large arrays

Minimum variance beamformers are usually complemented with diagonal loading techniques in order to provide robustness against several impairments such as imprecise knowledge of the steering vector or finite sample size effects. This paper concentrates on this last application of diagonal loading techniques, i.e., it is assumed that the steering vector is perfectly known and that diagonal loading is used to alleviate the finite sample size impairments. The analysis herein is asymptotic in the sense that it is assumed that both the number of antennas and the number of samples are high but have the same order of magnitude. Borrowing some results of random matrix theory, the authors first derive a deterministic expression that describes the asymptotic signal-to-noise-plus-interference ratio (SINR) at the output of the diagonally loaded beamformer. Then, making use of the statistical theory of large observations (also known as general statistical analysis or G-analysis), the authors derive an estimator of the optimum loading factor that is consistent when both the number of antennas and the sample size increase without bound at the same rate. Because of that, the estimator has an excellent performance even in situations where the quotient between the number of observations is low relative to the number of elements of the array.     

Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

Power-aware network swapping for wireless palmtop PCs

Virtual memory is considered to be an unlimited resource in desktop or notebook computers with high storage capabilities. However, in wireless mobile devices, like palmtops and personal digital assistants (PDAs), storage memory is limited or absent due to weight, size, and power constraints, so that swapping over remote memory devices can be considered as a viable alternative. However, power-hungry wireless network interface cards (WNICs) may limit the battery lifetime and application performance if not efficiently exploited. In this paper, we study performance and energy of network swapping in comparison with swapping on local microdrives and flash memories. We report the results of extensive experiments conducted on different WNICs and local swapping devices, using both synthetic and natural benchmarks. Our study points out that remote swapping over power-manageable WNICs can be more efficient than local swapping, especially in bursty workload conditions. Such conditions can be forced where possible by reshaping swapping requests to increase energy efficiency and performance.     

An Integrated Approach to Thermal Management in High-Level Synthesis

Thermal effects are becoming an important factor in the design of integrated circuits due to the adverse impact of temperature on performance, reliability, leakage, and chip packaging costs. Making all phases of the design flow aware of this physical phenomenon helps in reaching faster design closure. In this paper, we present an integrated approach to thermal management in architectural synthesis. Our synthesis flow combines temperature-aware scheduling and binding based on feedback from thermal simulation. We show that our flow is effective in preventing hotspot formation and creating an even thermal profile of the resources. Our integrated thermal management technique on average reduces the peak temperature of the resources by 7.34 degC when compared to a thermal unaware flow without increasing the number of resources across our set of benchmarks     

Operating Penalties in Single-Fiber Operation 10-Gb/s, 1024-Way Split, 110-km Long-Reach Optical Access Networks

We report for the first time optical signal-to-noise penalties which lead to performance degradations in single-fiber long-reach optical access networks when compared to identical dual-fiber systems. A simplified architecture, with reduced optical amplifier count compared to previous work, for single-fiber operation of a symmetrical 10-Gb/s, 1024-way split, 110-km long-reach optical access network is presented and demonstrated. In addition, a possible solution to remove the optical signal-to-noise penalty is suggested     

Multi-level attention model for tracking and segmentation of objects under complex occlusion

A multi-level attention framework for tracking and segmentation of humans and objects under complex occlusions is investigated, featuring an effective probabilistic appearance-based technique for pixel reclassification during object grouping and splitting. A novel ’spatial-depth affinity metric’ is introduced in the conventional likelihood function, utilising information of both spatial locations of pixels and dynamic depth ordering of the component objects in grouping. Depth ordering estimation is achieved through a combination of top-down and bottom-up approach. Experiments on some realworld difficult scenarios of low quality and highly compressed videos demonstrate the very promising results achieved.     

Organic Nanoparticles: Preparation,Self‐Assembly,and Properties

The strong tendency of organic nanoparticles to rapidly self‐assemble into highly aligned superlattices at room temperature when solution‐cast from dispersions or spray‐coated directly onto various substrates is described. The nanoparticle dispersions are stable for years. The novel precipitation process used is believed to result in molecular distances and alignments in the nanoparticles that are not normally possible. Functional organic light‐emitting diodes (OLEDs)—which have the same host–dopant emissive‐material composition—with process‐tunable electroluminescence have been built with these nanoparticles, indicating the presence of novel nanostructures. For example, only changing the conditions of the precipitation process changes the OLED emission from green light to yellow.     

Controlled elements for designing ciphers suitable to efficient VLSI implementation

This work considers the problem of increasing the performance of the ciphers based on Data-Dependent (DD) operations (DDO) for VLSI implementations. New minimum size primitives are proposed to design DDOs. Using advanced DDOs instead of DD permutations (DDP) in the DDP-based iterative ciphers Cobra-H64 and Cobra-H128 the number of rounds has been significantly reduced yielding enhancement of the “performance per cost” value and retaining security at the level of indistinguishability from a random transformation. To obtain further enhancement of this parameter a new crypto-scheme based on the advanced DDOs is proposed. The FPGA implementation of the proposed crypto-scheme achieves higher throughput value and minimizes the allocated resources than the conventional designs. Design of the DDO boxes of different orders is considered and their ASIC implementation is estimated.     

Mean vs. variance trade-off analysis in multi-antenna multi-user channels

In multi-user communications, the access point (AP) has several alternatives for distributing the scarce resources among users. Since there exists a trade-off between the global performance and the individual needs, an analytical framework to study fairness is derived, which completes the scope given by the existing fairness indexes in the literature. The framework proposed in this paper is a way to interpret fairness that has been inspired by portfolio selection; basically, it analyzes the mean vs. standard deviation trade-off. In this work, the target application is a multi-antenna AP transmitting simultaneously to several single-antenna terminals, although this framework is valid to analyze other procedures in multi-user communications.