Access to metro systems plays a critical role in shaping urban housing markets by enhancing neighborhood accessibility and driving property demand. We present RailEstate, a novel web-based system that integrates spatial analytics, natural language interfaces, and interactive forecasting to analyze how proximity to metro stations influences residential property prices in the Washington metropolitan area. Unlike static mapping tools or generic listing platforms, RailEstate combines 25 years of historical housing data with transit infrastructure to support low-latency geospatial queries, time-series visualizations, and predictive modeling. Users can interactively explore ZIP-code-level price patterns, investigate long-term trends, and forecast future housing values around any metro station. A key innovation is our natural language chatbot, which translates plain-English questions (e.g., "What is the highest price in Falls Church in the year 2000?") into executable SQL over a spatial database. This unified and interactive platform empowers urban planners, investors, and residents to derive actionable insights from metro-linked housing data—without requiring technical expertise. A demonstration video of the system is available at https://www.youtube.com/watch?v=ZLiz8S1UXsc.

Packet classification is an enabling technology for various network services. Fast single-match packet classification can be achieved by using ternary content addressable memory (TCAM) because of the superior speed performance. TCAM has some drawbacks including incapability to store arbitrary ranges, confined TCAM capacity and limited choices of entry lengths. Moreover, TCAM only reports the first matching entry to impose a limitation on supporting multi-match packet classification, which requires all matching rules. The existing algorithms deal with the issues of TCAM-based multi-match packet classification by burdening TCAM with extra entries and/or accesses. In this work, we offload the overhead of TCAM to static random access memory (SRAM) to achieve efficient multi-match packet classification. Our scheme synthesizes TCAM compatible entries by using binary decision trees and employs SRAM for further comparisons. Each synthesized entry can be stored in one TCAM entry to significantly reduce TCAM consumption and fulfill low power consumption. The experimental results show that our scheme can lower the demand of TCAM to improve both search latency and energy efficiency. The scalability of TCAM-based multi-match packet classification can thus be improved drastically.

Binary search on levels (BSOL) is a decision-tree algorithm for packet classification with superior speed performance. However, the most decision-tree-based algorithms, like BSOL, may suffer from a memory explosion problem caused by filter replications. In this work, we improve the storage performance of BSOL by employing a scheme, replication control. Our scheme dynamically generates multiple decision trees to eliminate filter replications in BSOL. The experimental results show that the new scheme achieves better performance than the existing decision-tree-based algorithms.

The paper illustrates that multiple access interference (MAI) can be reduced by arranging the spreading codes in multi-carrier code division multiple access (MC-CDMA) systems. An effective spreading code allocation procedure using simple least square (LS) channel estimation is presented to reduce the MAI in downlink MC-CDMA systems. Moreover, the spreading codes chosen by the method are in a consistent sequence. Using this code selection method, the system capacity of MC-CDMA can be effectively increased without increasing too much system complexity. Simulation results show that the presented method can work well for both time-invariant and fading channels.

The paper illustrates that multiple access interference (MAI) can be reduced by re-ordering the spreading codes in multi-carrier code division multiple access (MC-CDMA) systems. An effective spreading code allocation procedure using simple least square (LS) channel estimation is presented to reduce the MAI in downlink MC-CDMA systems. Moreover, the spreading codes chosen by the method are in a consistent sequence. Using this code selection method, the system capacity of MC-CDMA can be effectively increased without increasing the system complexity. Simulation results show that the presented method can work well for both time-invariant and fading channels.