This paper proposed a cascaded harmonic enhanced injection-locked frequency tripler fabricated in a 40-nm CMOS process. In order to suppress the fundamental signal and the second harmonic signal, an injection-locked doubler is explored, and then the obtained ×2 frequency signal is mixed with the fundamental signal to realize the third harmonic signal and then the third harmonic signal is injected into the tank of the injection-locked frequency selection network to improve the output power. The tripler achieves a fundamental suppression over -69 dBc and a second harmonic suppression over -66 dBc. The output power over the entire locking range from 71.5 to 86.7 GHz is larger than 5.5 dBm. The chip occupies a die area of 0.25 × 0.35mm2and dissipates 20 mW of power.

In this paper, we present the implementation of Velocity Controlled (or tuned) Oscillators (VCO) to model spatial coding and navigation in the mammalian hippocampus. Specifically, we demonstrate these spatial cells by representing a spatial firing map of grid, place, and border cells. Since the VCO is the basis for Oscillatory Interference (OI) models based on the Spatial Envelope Synthesis (SES) approach of hippocampal and entorhinal navigation, we use these models in our hardware implementation to construct more complex spatial cells from simple interference between VCOs. We develop the design of a VCO ASIC chip containing up to 128 independently tuned VCOs.

In this paper we describe WEST (Web entity search technologies) system that we have developed to improve people search over the Internet. Recently the problem of Web people search (WePS) has attracted significant attention from both the industry and academia. In the classic formulation of WePS problem the user issues a query to a Web search engine that consists of a name of a person of interest. For such a query, a traditional search engine such as Yahoo or Google would return Web pages that are related to any people who happened to have the queried name. The goal of WePS, instead, is to output a set of clusters of Web pages, one cluster per each distinct person, containing all of the Web pages related to that person. The user then can locate the desired cluster and explore the Web pages it contains.

Entity Resolution (ER) is an important real world problem that has attracted significant research interest over the past few years. It deals with determining which object descriptions co-refer in a dataset. Due to its practical significance for data mining and data analysis tasks many different ER approaches has been developed to address the ER challenge. This paper proposes a new ER Ensemble framework. The task of ER Ensemble is to combine the results of multiple base-level ER systems into a single solution with the goal of increasing the quality of ER. The framework proposed in this paper leverages the observation that often no single ER method always performs the best, consistently outperforming other ER techniques in terms of quality. Instead, different ER solutions perform better in different contexts. The framework employs two novel combining approaches, which are based on supervised learning. The two approaches learn a mapping of the clustering decisions of the base-level ER systems, together with the local context, into a combined clustering decision. The paper empirically studies the framework by applying it to different domains. The experiments demonstrate that the proposed framework achieves significantly higher disambiguation quality compared to the current state of the art solutions.

Nowadays, searches for webpages of a person with a given name constitute a notable fraction of queries to web search engines. Such a query would normally return webpages related to several namesakes, who happened to have the queried name, leaving the burden of disambiguating and collecting pages relevant to a particular person (from among the namesakes) on the user. In this article we develop a Web People Search approach that clusters webpages based on their association to different people. Our method exploits a variety of semantic information extracted from Web pages, such as named entities and hyperlinks, to disambiguate among namesakes referred to on the Web pages. We demonstrate the effectiveness of our approach by testing the efficacy of the disambiguation algorithms and its impact on person search.

In this paper we develop a disambiguation algorithm and then study its impact on People Search. The proposed algorithm first uses extraction techniques to automatically extract `significant' entities such as the names of other persons, organizations, and locations on each Web page. In addition, it extracts and parses HTML and Web related data on each Web page, such as hyperlinks and email addresses. The algorithm then views all this information in a unified way: as an entity-relationship graph where entities (e.g., people, organizations, locations, Web pages) are interconnected via relationships (e.g., `Web page-mentions-person', relationships derived from hyperlinks, etc). The algorithm gains its power by being able to analyze several types of information: attributes associated with the entities (e.g., TF/IDF for Web pages) and, most importantly, direct and indirect interconnections that exist among entities in the ER graph. We next outline our approach in Section 2 and then compare it with the state of the art solutions in Section 3.

In this paper we address the problem of reference disambiguation. Specifically, we consider a situation where entities in the database are referred to using descriptions (e.g., a set of instantiated attributes). The objective of reference disambiguation is to identify the unique entity to which each description corresponds. The key difference between the approach we propose (called RelDC) and the traditional techniques is that RelDC analyzes not only object features but also inter-object relationships to improve the disambiguation quality. Our extensive experiments over two real datasets and also over synthetic datasets show that analysis of relationships significantly improves quality of the result.