Bülent Yener

Abstract: Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.

Abstract: Time synchronization is an important issue in multihop ad hoc wireless networks such as sensor networks. Many applications of sensor networks need local clocks of sensor nodes to be synchronized, requiring various degrees of precision. Some intrinsic properties of sensor networks, such as limited resources of energy, storage, computation, and bandwidth, combined with potentially high density of nodes make traditional synchronization methods unsuitable for these networks. Hence, there has been an increasing research focus on designing synchronization algorithms specifically for sensor networks. This article reviews the time synchronization problem and the need for synchronization in sensor networks, then presents in detail the basic synchronization methods explicitly designed and proposed for sensor networks.

Abstract: Advances in technology introduce new application areas for sensor networks. Foreseeable wide deployment of mission critical sensor networks creates concerns on security issues. Security of large scale densely deployed and infrastructure less wireless networks of resource limited sensor nodes requires efficient key distribution and management mechanisms. We consider distributed and hierarchical wireless sensor networks where unicast, multicast and broadcast type of communications can take place. We evaluate deterministic, probabilistic and hybrid type of key pre-distribution and dynamic key generation algorithms for distributing pair-wise, group-wise and network-wise keys.

Abstract: Two-way arrays or matrices are often not enough to represent all the information in the data and standard two-way analysis techniques commonly applied on matrices may fail to find the underlying structures in multi-modal datasets. Multiway data analysis has recently become popular as an exploratory analysis tool in discovering the structures in higher-order datasets, where data have more than two modes. We provide a review of significant contributions in the literature on multiway models, algorithms as well as their applications in diverse disciplines including chemometrics, neuroscience, social network analysis, text mining and computer vision.

Abstract: The broadcast nature of a wireless link provides a natural eavesdropping and intervention capability to an adversary. Thus, securing a wireless link is essential to the security of a wireless network, and key generation algorithms are necessary for securing wireless links. However, traditional key agreement algorithms can be very costly in many settings, e.g. in wireless ad-hoc networks, since they consume scarce resources such as bandwidth and battery power.Traditional key agreement algorithms are not suitable for wireless ad-hoc networks since they consume scarce resources such as bandwidth and battery power.This paper presents a novel approach that couples the physical layer characteristics of wireless networks with key generation algorithms. It is based on the wireless communication phenomenon known as the principle of reciprocity which states that in the absence of interference both transmitter and receiver experience the same signal envelope. The key-observation here is that the signal envelope information can provide to the two transceivers two correlated random sources that provide sufficient amounts of entropy which can be used to extract a cryptographic key. In contrast, it is virtually impossible for a third party, which is not located at one of the transceiver's position, to obtain or predict the exact envelope; thus retrieve the key. Since in the presence of interference strict reciprocity property can not be maintained; our methodology is based on detecting deep fades to extract correlated bitstrings. In particular, we show how a pair of transceivers can reconcile such bitstrings and finally flatten their distribution to reach key agreement. In our constructions we use cryptographic tools related to randomness extraction and information reconciliation. We introduce "secure fuzzy information reconciliators" a tool that enables us to describe robust key generation systems in our setting. Finally we provide a computational study that presents a simulation of a wireless channel that demonstrates the feasibility of our approach and justifies the assumptions made in our analysis.

Abstract: This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Abstract: Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Abstract: Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.