CDMA Uplink Power Control as a Noncooperative Game

We present a game-theoretic treatment of distributed power control in CDMA wireless systems. We make use of the conceptual framework of noncooperative game theory to obtain a distributed and market-based control mechanism. Thus, we address not only the power control problem, but also pricing and allocation of a single resource among several users. A cost function is introduced as the difference between the pricing and utility functions, and the existence of a unique Nash equilibrium is established. In addition, two update algorithms, namely, parallel update and random update, are shown to be globally stable under specific conditions. Convergence properties and robustness of each algorithm are also studied through extensive simulations.     

A 50-ns 256 K CMOS split-gate EPROM

A high-speed 32 K×8 CMOS EPROM has been designed and implemented in a polycide 1.2-μm n-well epi CMOS technology. A high-read-current split-gate EPROM cell combined with address transition detection-based SRAM-like precharge, equalization, and clocked differential sensing schemes has resulted in a typical address access time of less than 50 ns. The typical power dissipation at 18.2 MHz is 60 mW. Row redundancy is used to enhance the yield and the part has been designed to be compatible with plastic packaging     

Flexible Developable Surfaces

We introduce a discrete paradigm for developable surface modeling. Unlike previous attempts at interactive developable surface modeling, our system is able to enforce exact developability at every step, ensuring that users do not inadvertently suggest configurations that leave the manifold of admissible folds of a flat two‐dimensional sheet. With methods for navigation of this highly nonlinear constraint space in place, we show how to formulate a discrete mean curvature bending energy measuring how far a given discrete developable surface is from being flat. This energy enables relaxation of user‐generated configurations and suggests a straightforward subdivision scheme that produces admissible smoothed versions of bent regions of our discrete developable surfaces.     

Real‐Time Major Events Monitoring and Alert System through Social Networks

The volume of information generated by social and cellular networks has significantly increased in recent years. Automated collection of these data and its rapid analyses allow for better and faster detection of major (in terms of National impact) ‘real life’ events. This study uses data obtained from social networks such as Twitter and Google+. It proposes a mechanism for detecting major events and a system to alert on their manifestation. The article describes the considerations and needed algorithms required to develop and establish such a system. The methodology presented here is based on linking major events that occurred in Israel during the years 2011–2014, with information extracted from social networks. Results indicate that alerts were received shortly after the event occurred for most of major events. Such are large fires, earthquakes and terror attacks. However, attempts to achieve alerts for ‘local’ secondary events failed. This as their impact on the social network is low. Furthermore, it was found that the volume of false alerts depends on the type of domain and keywords.     

Troika - An improved stacking schema for classification tasks

Stacking is a general ensemble method in which a number of base classifiers are combined using one meta-classifier which learns their outputs. Such an approach provides certain advantages: simplicity; performance that is similar to the best classifier; and the capability of combining classifiers induced by different inducers. The disadvantage of stacking is that on multiclass problems, stacking seems to perform worse than other meta-learning approaches. In this paper we present Troika, a new stacking method for improving ensemble classifiers. The new scheme is built from three layers of combining classifiers. The new method was tested on various datasets and the results indicate the superiority of the proposed method to other legacy ensemble schemes, Stacking and StackingC, especially when the classification task consists of more than two classes.     

Improving malware detection by applying multi-inducer ensemble

Detection of malicious software (malware) using machine learning methods has been explored extensively to enable fast detection of new released malware. The performance of these classifiers depends on the induction algorithms being used. In order to benefit from multiple different classifiers, and exploit their strengths we suggest using an ensemble method that will combine the results of the individual classifiers into one final result to achieve overall higher detection accuracy. In this paper we evaluate several combining methods using five different base inducers (C4.5 Decision Tree, Naïve Bayes, KNN, VFI and OneR) on five malware datasets. The main goal is to find the best combining method for the task of detecting malicious files in terms of accuracy, AUC and Execution time.     

The two-bit NROM reliability

Saifun NROM/spl trade/ is a novel localized charge-trapping-based nonvolatile memory technology that employs inherent two-bits-per-cell operation. NROM technology is able to provide code flash, data flash, embedded flash, and true EEPROM functionality with a single fabrication process and minor architectural adjustments. Reliability topics of NROM technology are discussed, focusing on the ability to achieve 10-year data retention after 10/sup 5/ program and erase cycles. The accumulated knowledge of NROM physics allows this technology to successfully compete with the industry standard floating-gate memory technology and to gain the acceptance of the memory market.     

Complementary Surface-Enhanced Raman Scattering (SERS) and IR Absorption Spectroscopy (SEIRAS) with Nanorods-on-a-Mirror

The surface-enhanced counterparts of Raman scattering (SERS) and infrared (IR) absorption (SEIRAS) are commonly used to probe and identify nanoscale matter and small populations of molecules. The contrasting selection rules offer complementary vibrational information of bulk solids or solutions. In this study, a complementary surface-enhanced vibrational spectroscopy approach is presented to probe the vibrational signature of metal-bound molecular monolayers. Nanocavities are designed and produced with sharp and tunable visible (VIS) and mid-IR gap resonances by placing nanorods on a mirror that is coated with a thin dielectric spacer. Their VIS resonances are tuned to match a 1.61 eV (770 nm) resonant excitation for SERS, while their mid-IR resonances span the 1500–2800 cm⁻¹ range (6.5–3.5 µm) in high resolution for SEIRAS, targeting CN bond vibrations at 2220 cm⁻¹. Both the VIS and mid-IR gap modes support spatially overlapping and highly enhanced near-fields ensuring strong SERS and SEIRAS signals from the same monolayer molecular population. The differences in the vibrational information obtained with the two surface-enhanced spectroscopies when probing coupled molecular vibrations are highlighted and the advantages of using such a platform for investigating cavity-modified chemical reactions are discussed.