Differential spatial modulation for high-rate transmission systems

This paper introduces a new differential spatial modulation (DSM) scheme which subsumes both the previously introduced DSM and high-rate spatial modulation (HR-SM) for wireless multiple input multiple output (MIMO) transmission. By combining the codeword design method of the HR-SM scheme with the encoding method of the DSM scheme, we develop a high-rate differential spatial modulation (HR-DSM) scheme equipped with an arbitrary number of transmit antennas that requires channel state information (CSI) neither at the transmitter nor at the receiver. The proposed approach can be applied to any equal energy signal constellations. The bit error rate (BER) performance of the proposed HR-DSM schemes is evaluated by using both theoretical upper bound and computer simulations. It is shown that for the same spectral efficiency and antenna configuration, the proposed HR-DSM outperforms the DSM in terms of bit error rate (BER) performance

Automated and model-free bridge damage indicators with simultaneous multi-parameter modal anomaly detection

This paper pursues a simultaneous modal parameter anomaly detection paradigm to structural damage identification inferred from vibration-based structural health monitoring (SHM) sensors, e.g., accelerometers. System Realization Using Information Matrix (SRIM) method is performed in short duration sweeping time windows for identification of state matrices, and then, modal parameters with enhanced automation. Stable modal poles collected from stability diagrams are clustered and fed into the Gaussian distribution-based anomaly detection platform. Different anomaly thresholds are examined both on frequency and damping ratio terms taking two testbed bridge structures as application means, and simplistic Boolean Operators are performed to merge univariate anomalies. The first bridge is a reinforced concrete bridge subjected to incremental damage through a series of seismic shake table experiments conducted at the University of Nevada, Reno. The second bridge is a steel arch structure at Columbia University Morningside Campus, which reflects no damage throughout the measurements, unlike the first one. Two large-scale implementations indicate the realistic performance of automated modal analysis and anomaly recognition with minimal human intervention in terms of parameter extraction and learning supervision. Anomaly detection performance, presented in this paper, shows variation according to the designated thresholds, and hence, the information retrieval metrics being considered. The methodology is well-fitted to SHM problems which require sole data-driven, scalable, and fully autonomous perspectives

War-Gaming Applications for Achieving Optimum Acquisition of Future Space Systems

This chapter describes an innovative modeling and simulation approach using newly proposed Advanced Game-based Mathematical Framework (AGMF), Unified Game-based Acquisition Framework (UGAF) and a set of War-Gaming Engines (WGEs) to address future space systems acquisition challenges. Its objective is to assist the DoD Acquisition Authority (DAA) to understand the contractor’s perspective and to seek optimum Program-and-Technical-Baseline (PTB) solution and corresponding acquisition strategy under both the perspectives of the government and the contractors. The proposed approach calls for an interdisciplinary research that involves game theory, probability and statistics, and non-linear programming. The goal of this chapter is to apply the proposed war-gaming frameworks to develop and evaluate PTB solutions and associated acquisition strategies in the context of acquisition of future space systems. Our simulation results suggest that our optimization problem for the acquisition of future space systems meets the affordability and innovative requirements with minimum acquisition risk

Early Detection of Human Decision-Making in Concealed Object Visual Searching Tasks: An EEG-BiLSTM Study

Detecting concealed objects presents a significant challenge for human and artificial intelligent systems Detecting concealed objects task necessitates a high level of human attention and cognitive effort to complete the task successfully Thus in this study we use concealed objects as stimuli for our decision making experimental paradigms to quantify participants decision making performance We applied a deep learning model Bi directional Long Short Term Memory BiLSTM to predict the participant s decision accuracy by using their electroencephalogram EEG signals as input The classifier model demonstrated high accuracy reaching 96 1 with an epoching time range of 500 ms following the stimulus event onset The results revealed that the parietal occipital brain region provides highly informative information for the classifier in the concealed visual searching tasks Furthermore the neural mechanism underlying the concealed visual searching and decision making process was explained by analyzing serial EEG components The findings of this study could contribute to the development of a fault alert system which has the potential to improve human decision making performanc

VLSP SHARED TASK: SENTIMENT ANALYSIS

Sentiment analysis is a natural language processing (NLP) task of identifying orextracting the sentiment content of a text unit. This task has become an active research topic since the early 2000s. During the two last editions of the VLSP workshop series, the shared task on Sentiment Analysis (SA) for Vietnamese has been organized in order to provide an objective evaluation measurement about the performance (quality) of sentiment analysis tools, and encouragethe development of Vietnamese sentiment analysis systems, as well as to provide benchmark datasets for this task. The rst campaign in 2016 only focused on the sentiment polarity classication, with a dataset containing reviews of electronic products. The second campaign in 2018 addressed the problem of Aspect Based Sentiment Analysis (ABSA) for Vietnamese, by providing two datasets containing reviews in restaurant and hotel domains. These data are accessible for research purpose via the VLSP website vlsp.org.vn/resources. This paper describes the built datasets as well as the evaluation results of the systems participating to these campaigns

Gerasimov-Drell-Hearn Sum Rule and the Discrepancy between the New CLAS and SAPHIR Data

Contribution of the K^+\Lambda channel to the Gerasimov-Drell-Hearn (GDH) sum rule has been calculated by using the models that fit the recent SAPHIR or CLAS differential cross section data. It is shown that the two data sets yield quite different contributions. Contribution of this channel to the forward spin polarizability of the proton has been also calculated. It is also shown that the inclusion of the recent CLAS C_x and C_z data in the fitting data base does not significantly change the result of the present calculation. Results of the fit, however, reveal the role of the S_{11}(1650), P_{11}(1710), P_{13}(1720), and P_{13}(1900) resonances for the description of the C_x and C_z data. A brief discussion on the importance of these resonances is given. Measurements of the polarized total cross section \sigma_{TT'} by the CLAS, LEPS, and MAMI collaborations are expected to verify this finding.Comment: 15 pages, 8 figure

Status of water use and potential of rainwater harvesting for replacing centralized supply system in remote mountainous areas: a case study.

The failure of the centralized water supply system forced XY community to become more dependent on uncertain and unstable water sources. The results of surveying 50 households showed that 89.18% of total households depended on water collected from rivers, which contributed 58.3% of the total water volume used for the domestic demands. The average water volume consumed was 19.5 liters/person/day (l/p/d), and 86.5% of households used more than one source; 13.5% of households collected water only from rivers, and 45.94% of families had rainwater harvesting (RWH) for their activities (domestic water demand); however, RWH only provided 9.9% of total water consumption. In this study, basic methods were applied to calculate the storage tanks necessary to balance the water deficit created by drought months. Three levels of water demand (14, 20, and 30 l/p/d) can be the best choices for RWH; for a higher demand (40 and 60 l/p/d), small roof area (30-40 m2), and many people (six to seven) per family, RWH might be impractical because of unsuitable rainfall or excessively large storage tanks

Enhancement of Friction between Carbon Nanotubes: An Efficient Strategy to Strengthen Fibers

Interfacial friction plays a crucial role in the mechanical properties of carbon nanotube based fibers, composites, and devices. Here we use molecular dynamics simulation to investigate the pressure effect on the friction within carbon nanotube bundles. It reveals that the intertube frictional force can be increased by a factor of 1.5 ~ 4, depending on tube chirality and radius, when all tubes collapse above a critical pressure and when the bundle remains collapsed with unloading down to atmospheric pressure. Furthermore, the overall cross-sectional area also decreases significantly for the collapsed structure, making the bundle stronger. Our study suggests a new and efficient way to reinforce nanotube fibers, possibly stronger than carbon fibers, for usage at ambient conditions.Comment: revtex, 5 pages, accepted by ACS Nano 10 Dec 200