2,311 research outputs found

    Imaging-based Parametric Resonance in an Optical Dipole Atom Trap

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    We report sensitive detection of parametric resonances in a high-density sample of ultracold 87Rb^{87}Rb atoms confined to a far-off-resonance optical dipole trap. Fluorescence imaging of the expanded ultracold atom cloud after a period of parametric excitation shows significant modification of the atomic spatial distribution and has high sensitivity compared with traditional measurements of parametrically-driven trap loss. Using this approach, a significant shift of the parametric resonance frequency is observed, and attributed to the anharmonic shape of the dipole trap potential

    Human Rights Protection for Indonesian Migrant Workers: Challenges for ASEAN

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    The AEC is good news for Indonesian migrant workers wanting to work overseas. Unfortunately, many Indonesian migrant workers have been deported from ASEAN countries because of having problems. This study adopts the normative legal research method. It argues that AICHR may be slow in resolving the problems of human rights. It is also argued that the ASEAN Committee on Migrant Workers works in the absence of the political commitment of ASEAN leaders to implement the Cebu Declaration. Therefore, the best solution is public participation in the ASEAN countries to protect migrant workers.IntisariKomunitas Masyarakat Ekonomi ASEAN adalah berita baik untuk Tenaga Kerja Indonesia (TKI) untuk bekerja di luar negeri. Namun, banyak TKI yang kembali dari negara-negara ASEAN dikarenakan mendapatkan berbagai permasalahan. Penelitian ini mengadopsi jenis penelitian hukum normatif. Penelitian ini menyimpulkan bahwa AICHR lamban dalam menyelesaikan permaslahan tentang hak asasi manusia. Penelitian ini juga menyimpulkan bahwa komunitas ASEAN tentang Pekerja Migran bekerja dengan tidak adanya komitmen politik dari para pemimpim ASEAN dalam menerapkan Deklarasi Cebu. Oleh sebab itu, dibutuhkan partisipasi ASEAN dalam melindungi TKI

    Group-blind detection with very large antenna arrays in the presence of pilot contamination

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    Massive MIMO is, in general, severely affected by pilot contamination. As opposed to traditional detectors, we propose a group-blind detector that takes into account the presence of pilot contamination. While sticking to the traditional structure of the training phase, where orthogonal pilot sequences are reused, we use the excess antennas at each base station to partially remove interference during the uplink data transmission phase. We analytically derive the asymptotic SINR achievable with group-blind detection, and confirm our findings by simulations. We show, in particular, that in an interference-limited scenario with one dominant interfering cell, the SINR can be doubled compared to non-group-blind detection.Comment: 5 pages, 4 figure

    Structured Near-Optimal Channel-Adapted Quantum Error Correction

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    We present a class of numerical algorithms which adapt a quantum error correction scheme to a channel model. Given an encoding and a channel model, it was previously shown that the quantum operation that maximizes the average entanglement fidelity may be calculated by a semidefinite program (SDP), which is a convex optimization. While optimal, this recovery operation is computationally difficult for long codes. Furthermore, the optimal recovery operation has no structure beyond the completely positive trace preserving (CPTP) constraint. We derive methods to generate structured channel-adapted error recovery operations. Specifically, each recovery operation begins with a projective error syndrome measurement. The algorithms to compute the structured recovery operations are more scalable than the SDP and yield recovery operations with an intuitive physical form. Using Lagrange duality, we derive performance bounds to certify near-optimality.Comment: 18 pages, 13 figures Update: typos corrected in Appendi

    Spatiotemporal information coupling in network navigation

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    Network navigation, encompassing both spatial and temporal cooperation to locate mobile agents, is a key enabler for numerous emerging location-based applications. In such cooperative networks, the positional information obtained by each agent is a complex compound due to the interaction among its neighbors. This information coupling may result in poor performance: algorithms that discard information coupling are often inaccurate, and algorithms that keep track of all the neighbors’ interactions are often inefficient. In this paper, we develop a principled framework to characterize the information coupling present in network navigation. Specifically, we derive the equivalent Fisher information matrix for individual agents as the sum of effective information from each neighbor and the coupled information induced by the neighbors’ interaction. We further characterize how coupled information decays with the network distance in representative case studies. The results of this work can offer guidelines for the development of distributed techniques that adequately account for information coupling, and hence enable accurate and efficient network navigation.RYC-2016-1938

    Soft information for localization-of-things

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    Location awareness is vital for emerging Internetof- Things applications and opens a new era for Localizationof- Things. This paper first reviews the classical localization techniques based on single-value metrics, such as range and angle estimates, and on fixed measurement models, such as Gaussian distributions with mean equal to the true value of the metric. Then, it presents a new localization approach based on soft information (SI) extracted from intra- and inter-node measurements, as well as from contextual data. In particular, efficient techniques for learning and fusing different kinds of SI are described. Case studies are presented for two scenarios in which sensing measurements are based on: 1) noisy features and non-line-of-sight detector outputs and 2) IEEE 802.15.4a standard. The results show that SI-based localization is highly efficient, can significantly outperform classical techniques, and provides robustness to harsh propagation conditions.RYC-2016-1938

    Crowd-Centric Counting via Unsupervised Learning

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    Counting targets (people or things) within a moni-tored area is an important task in emerging wireless applications,including those for smart environments, safety, and security.Conventional device-free radio-based systems for counting targetsrely on localization and data association (i.e., individual-centric information) to infer the number of targets present in an area(i.e., crowd-centric information). However, many applications(e.g., affluence analytics) require only crowd-centric rather than individual-centric information. Moreover, individual-centric approaches may be inadequate due to the complexity of data association. This paper proposes a new technique for crowd-centric counting of device-free targets based on unsupervised learning, where the number of targets is inferred directly from a low-dimensional representation of the received waveforms. The proposed technique is validated via experimentation using an ultra-wideband sensor radar in an indoor environment.RYC-2016-1938
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