Understanding and Predicting Data Hotspots in Cellular Networks

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today’s cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We show that using standard machine learning methods, future hotspots can be accurately predicted from past observations. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to empirically characterize traffic hotspots in today’s cellular networks.     

Advanced receiver design for satellite‐based automatic identification system signal detection

This paper describes an innovative receiver architecture for the satellite‐based automatic identification system. The receiver performance has been fully validated in the presence of the typical satellite channel characteristics. In particular, it is shown that the devised receiver provides an excellent performance against the noise, as well as a large resilience against message collisions, Doppler shift, and delay spread. Copyright © 2012 John Wiley & Sons, Ltd.     

Hierarchically Structured Magnetic Nanoconstructs with Enhanced Relaxivity and Cooperative Tumor Accumulation

Iron oxide nanoparticles are formidable multifunctional systems capable of contrast enhancement in magnetic resonance imaging, guidance under remote fields, heat generation, and biodegradation. Yet, this potential is underutilized in that each function manifests at different nanoparticle sizes. Here, sub‐micrometer discoidal magnetic nanoconstructs are realized by confining 5 nm ultra‐small super‐paramagnetic iron oxide nanoparticles (USPIOs) within two different mesoporous structures, made out of silicon and polymers. These nanoconstructs exhibit transversal relaxivities up to ≈10 times (r ₂ ≈ 835 mm ^?1 s^?1) higher than conventional USPIOs and, under external magnetic fields, collectively cooperate to amplify tumor accumulation. The boost in r ₂ relaxivity arises from the formation of mesoscopic USPIO clusters within the porous matrix, inducing a local reduction in water molecule mobility as demonstrated via molecular dynamics simulations. The cooperative accumulation under static magnetic field derives from the large amount of iron that can be loaded per nanoconstuct (up to ≈65 fg) and the consequential generation of significant inter‐particle magnetic dipole interactions. In tumor bearing mice, the silicon‐based nanoconstructs provide MRI contrast enhancement at much smaller doses of iron (≈0.5 mg of Fe kg^?1 animal) as compared to current practice.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.     

An Integrated CAD Methodology for Yield Enhancement of VLSI CMOS Circuits Including Statistical Device Variations

In this paper a novel CAD methodology for yield enhancement of VLSI CMOS circuits including random device variations is presented. The methodology is based on a preliminary characterization of the technological process by means of specific test chips for accurate mismatch modeling. To this purpose, a very accurate position-dependent parameter mismatch model has been formulated and extracted. Finally a CAD tool implementing this model has been developed. The tool is fully integrated in an environment of existing commercial tools and it has been experimented in the STMicroelectronics Flash Memory CAD Group.As an example of application, a bandgap reference circuit has been considered and the results obtained from simulations have been compared with experimental data. Furthermore, the methodology has been applied to the read path of a complex Flash Memory produced by STMicroelectronics, consisting of about 16,000 MOSFETs. Measurements of electrical performances have confirmed the validity of the methodology, and the accuracy of both the mismatch model and the simulation flow.     

Radiolabeled Polymeric Nanoconstructs Loaded with Docetaxel and Curcumin for Cancer Combinatorial Therapy and Nuclear Imaging

Growing evidence suggests that multifaceted diseases as cancer can be effectively tackled by hitting simultaneously different biological targets and monitoring patient‐specific responses. Combinatorial therapies, relying on the administration of two or more molecules with different cytotoxic mechanisms, are rapidly progressing in the clinic. Here, 100 nm spherical polymeric nanoconstructs (SPNs) are proposed for the combinatorial treatment of tumors by codelivering a potent antimitotic drug—docetaxel (DTXL)—and a broad spectrum anti‐inflammatory molecule—curcumin (CURC). In vitro, SPNs loaded with DTXL and CURC induce a threefold decrease in IC₅₀ as compared to DTXL‐loaded SPNs. This synergic antitumor effect is also significant in mouse models of glioblastoma multiforme, where, after 22 d of treatment, the combinatorial approach leads to complete disease regression. At 90 d post‐treatment initiation, mice injected with DTXL + CURC SPNs have a 100% survival, whereas only 50% of the DTXL SPN treated mice survive. SPNs are also labeled with radioactive ⁶⁴Cu(DOTA) molecules to document, via PET imaging, the progressive tumor mass shrinkage. Sensitization of DTXL by CURC is associated with NF‐κB downregulation and increased apoptosis. These theranostic nanoconstructs could be used for combinatorial treatment and assessment of therapeutic efficacy in other malignancies.     

TCP Performance in Optical Packet-switched Networks

Optical packet assembly is a key function to support inter-working between TCP/IP networks and optical packet-switched networks. It is characterized by the assembly delay and by the segment aggregation needed to form an optical packet. These counter-balancing aspects depend on several environment variables, such as the TCP parameters, the access link speed, the optical packet size whose effects are studied in this paper. Performance evaluations are obtained by extensive simulations in terms of send rate of TCP flows, fairness, efficiency, and assembly delay. Some guidelines in the design of optical packets that take into account the results presented are given.     

A new method for the analysis of integrated transmission lines

A flexible and efficient technique for the analysis of transmission lines is described. The transmission line is considered as the union of « elementary waveguides », where the medium is uniformly stratified. The modes are determined by matching the field at the interfaces using a procedure that leads to a linear eigenvalue problem involving a limited number of variables.     

Bioinspired Security Analysis of Wireless Protocols

Fraglets represent an execution model for communication protocols that resembles the chemical reactions in living organisms. The strong connection between their way of transforming and reacting and formal rewriting systems makes a fraglet program amenable to automatic verification. Grounded on past work, this paper investigates feasibility of adopting fraglets as model for specifying security protocols and analysing their properties. In particular, we give concrete sample analyses over a secure RFID protocol, showing evolution of the protocol run as chemical dynamics and simulating an adversary trying to circumvent the intended steps. The results of our analysis confirm the effectiveness of the cryptofraglets framework for the model and analysis of security properties and eventually show its potential to identify and uncover protocol flaws.