Browsing by Author "Li, Yingying"

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    Compressive Sensing Based High Resolution Channel Estimation for OFDM System
    (2011-08) Meng, Jia (Jasmine); Yin, Wotao; Li, Yingying; Nguyen, Nam T.; Han, Zhu
    Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next generation wireless communication. Channel estimation is one of the key challenges in OFDM, since high-resolution channel estimation can significantly improve the equalization at the receiver and consequently enhance the communication performances. In this paper, we propose a system with an asymmetric DAC/ADC pair and formulate OFDM channel estimation as a compressive sensing problem. By skillfully designing pilots and taking advantages of the sparsity of the channel impulse response, the proposed system realizes high resolution channel estimation at a low cost. The pilot design, the use of a high-speed DAC and a regular-speed ADC, and the estimation algorithm tailored for channel estimation distinguish the proposed approach from the existing estimation approaches. We theoretically show that in the proposed system, an N-resolution channel can be faithfully obtained with an ADC speed at M=O(S^2 log(N/S)), where N is also the DAC speed and S is the channel impulse response sparsity. Since S is small and increasing the DAC speed to N>M is relatively cheap, we obtain a high-resolution channel at a low cost. We also present a novel estimator that is both faster and more accurate than the typical L1 minimization. In the numerical experiments, we simulated various numbers of multipaths and different SNRs and let the transmitter DAC run at 16 times the speed of the receiver ADC for estimating channels at the 16x resolution. While there is no similar approaches (for asymmetric DAC/ADC pairs) to compare with, we derive the Cramer-Rao lower bound.
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    High Resolution OFDM Channel Estimation with Low Speed ADC using Compressive Sensing
    (2010-11) Meng, Jia (Jasmine); Li, Yingying; Nguyen, Nam; Yin, Wotao; Han, Zhu
    Orthogonal frequency division multiplexing (OFDM) is a technique that will prevail in the next generation wireless communication. Channel estimation is one of the key challenges in an OFDM system. In this paper, we formulate OFDM channel estimation as a compressive sensing problem, which takes advantage of the sparsity of the channel impulse response and reduces the number of probing measurements, which in turn reduces the ADC speed needed for channel estimation. Specifically, we propose sending out pilots with random phases in order to "spread out" the sparse taps in the impulse response over the uniformly downsampled measurements at the low speed receiver ADC, so that the impulse response can still be recovered by sparse optimization. This contribution leads to high resolution channel estimation with low speed ADCs, distinguishing this paper from the existing attempts of OFDM channel estimation. We also propose a novel estimator that performs better than the commonly used L1 minimization. Specifically, it significantly reduces estimation error by combing L1 minimization with iterative support detection and limited-support least-squares. While letting the receiver ADC run at a speed as low as 1/16 of the speed of the transmitter DAC, we simulated various numbers of multipaths and different measurement SNRs. The proposed system has channel estimation resolution as high as the system equipped with the high speed ADCs, and the proposed algorithm provides additional 6 dB gain for signal to noise ratio.
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    Nanotechnology-enhanced immunotherapy for metastatic cancer
    (Elsevier, 2021) Zhang, Peisen; Meng, Junli; Li, Yingying; Yang, Chen; Hou, Yi; Tang, Wen; McHugh, Kevin J.; Jing, Lihong
    A vast majority of cancer deaths occur as a result of metastasis. Unfortunately, effective treatments for metastases are currently lacking due to the difficulty of selectively targeting these small, delocalized tumors distributed across a variety of organs. However, nanotechnology holds tremendous promise for improving immunotherapeutic outcomes in patients with metastatic cancer. In contrast to conventional cancer immunotherapies, rationally designed nanomaterials can trigger specific tumoricidal effects, thereby improving immune cell access to major sites of metastasis such as bone, lungs, and lymph nodes, optimizing antigen presentation, and inducing a persistent immune response. This paper reviews the cutting-edge trends in nano-immunoengineering for metastatic cancers with an emphasis on different nano-immunotherapeutic strategies. Specifically, it discusses directly reversing the immunological status of the primary tumor, harnessing the potential of peripheral immune cells, preventing the formation of a pre-metastatic niche, and inhibiting the tumor recurrence through postoperative immunotherapy. Finally, we describe the challenges facing the integration of nanoscale immunomodulators and provide a forward-looking perspective on the innovative nanotechnology-based tools that may ultimately prove effective at eradicating metastatic diseases.
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    Oil Spill Sensor using Multispectral Infrared Imaging via L1 Minimization
    (2010-11) Li, Yingying; Shih, Wei-Chuan; Han, Zhu; Yin, Wotao
    Early detection of oil spill events is the key to environmental protection and disaster management. Current technology lacks the sensitivity and specificity in detecting the early onset of a small-scale oil spill event. Based on an infrared oil-water contrast model recently developed, we propose a novel nonscanning computational infrared sensor that has the potential to achieve unprecedented detection sensitivity. Such a system can be very low-cost and robust for automated outdoor operations, leading to massive offshore deployment. Taking advantage of the characteristic oil thickness multispectral signatures, we have streamlined an algorithm that incorporates 3D image reconstruction and classification in a single inversion step capitalizing on the benefits of L1 minimization.
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    Theranostic nanoparticles with disease-specific administration strategies
    (Elsevier, 2022) Zhang, Peisen; Li, Yingying; Tang, Wen; Zhao, Jie; Jing, Lihong; McHugh, Kevin J.
    Recent advances in the synthesis of nanomaterials with diagnostic and therapeutic capabilities have been rapidly reshaping the landscape of precision medicine. Impressive progress has been made toward the design and production of innovative theranostic nanomaterials to treat a variety of diseases, yet their potential is currently limited by low bioavailability, biocompatibility, or undesirable pharmacokinetics, hindering their widespread clinical implementation. Here, we summarize the state of the art for theranostic nanoparticles and discuss the diverse administration routes being used in the diagnosis and treatment of different diseases. In addition to the most commonly used intravenous (IV) administration, newly emerging nanomaterial administration routes are described in depth to explore the potential benefits of these routes that can bypass biological barriers and thereby facilitate the delivery of nanoparticles to boost imaging sensitivity and therapeutic efficacy in specific use cases. Some of the biggest challenges facing nanoparticle delivery systems are site-specific targeting, controlled nanoparticle accumulation, and safe metabolic processing. By providing examples of their in vivo applications for various diseases, we highlight the benefits, challenges, and opportunities of theranostic nanoprobes and routes of administration to inform future nanoparticle design.