Improving Energy Efficiency Through Multimode Transmission in the Downlink MIMO Systems

Adaptively adjusting system parameters including bandwidth, transmit power and mode to maximize the "Bits per-Joule" energy efficiency (BPJ-EE) in the downlink MIMO systems with imperfect channel state information at the transmitter (CSIT) is considered in this paper. By mode we refer to choice of transmission schemes i.e. singular value decomposition (SVD) or block diagonalization (BD), active transmit/receive antenna number and active user number. We derive optimal bandwidth and transmit power for each dedicated mode at first. During the derivation, accurate capacity estimation strategies are proposed to cope with the imperfect CSIT caused capacity prediction problem. Then, an ergodic capacity based mode switching strategy is proposed to further improve the BPJ-EE, which provides insights on the preferred mode under given scenarios. Mode switching compromises different power parts, exploits the tradeoff between the multiplexing gain and the imperfect CSIT caused inter-user interference, improves the BPJ-EE significantly.Comment: 19 pages, 10 figures, EURASIP Journal on Wireless Communications and Networking; EURASIP Journal on Wireless Communications and Networking (2011) 2011:20

Unified Joint Matrix-Monotonic Optimization of MIMO Training Sequences and Transceivers

Channel estimation and transmission constitute the most fundamental functional modules of multiple-input multiple-output (MIMO) communication systems. The underlying key tasks corresponding to these modules are training sequence optimization and transceiver optimization. Hence, we jointly optimize the linear transmit precoder and the training sequence of MIMO systems using the metrics of their effective mutual information (MI), effective mean squared error (MSE), effective weighted MI, effective weighted MSE, as well as their effective generic Schur-convex and Schur-concave functions. Both statistical channel state information (CSI) and estimated CSI are considered at the transmitter in the joint optimization. A unified framework termed as joint matrix-monotonic optimization is proposed. Based on this, the optimal precoder matrix and training matrix structures can be derived for both CSI scenarios. Then, based on the optimal matrix structures, our linear transceivers and their training sequences can be jointly optimized. Compared to state-of-the-art benchmark algorithms, the proposed algorithms visualize the bold explicit relationships between the attainable system performance of our linear transceivers conceived and their training sequences, leading to implementation ready recipes. Finally, several numerical results are provided, which corroborate our theoretical results and demonstrate the compelling benefits of our proposed pilot-aided MIMO solutions.Comment: 29 pages, 7 figure

A new assessment model for tumor heterogeneity analysis with [18]F-FDG PET images

It has been shown that the intratumor heterogeneity can be characterized with quantitative analysis of the [18]F-FDG PET image data. The existing models employ multiple parameters for feature extraction which makes it difficult to implement in clinical settings for the quantitative characterization. This article reports an easy-to-use and differential SUV based model for quantitative assessment of the intratumor heterogeneity from 3D [18]F-FDG PET image data. An H index is defined to assess tumor heterogeneity by summing voxel-wise distribution of differential SUV from the [18]F-FDG PET image data. The summation is weighted by the distance of SUV difference among neighboring voxels from the center of the tumor and can thus yield increased values for tumors with peripheral sub-regions of high SUV that often serves as an indicator of augmented malignancy. Furthermore, the sign of H index is used to differentiate the rate of change for volume averaged SUV from its center to periphery. The new model with the H index has been compared with a widely-used model of gray level co-occurrence matrix (GLCM) for image texture characterization with phantoms of different configurations and the [18]F-FDG PET image data of 6 lung cancer patients to evaluate its effectiveness and feasibility for clinical uses. The comparison of the H index and GLCM parameters with the phantoms demonstrate that the H index can characterize the SUV heterogeneity in all of 6 2D phantoms while only 1 GLCM parameter can do for 1 and fail to differentiate for other 2D phantoms. For the 8 3D phantoms, the H index can clearly differentiate all of them while the 4 GLCM parameters provide complicated patterns in the characterization. Feasibility study with the PET image data from 6 lung cancer patients show that the H index provides an effective single-parameter metric to characterize tumor heterogeneity in terms of the local SUV variation, and it has higher correlation with tumor volume change after radiotherapy (R2 = 0.83) than the 4 GLCM parameters (R2 = 0.63, 0.73, 0.59 and 0.75 for Energy, Contrast, Local Homogeneity and Entropy respectively). The new model of the H index has the capacity to characterize the intratumor heterogeneity feature from 3D [18]F-FDG PET image data. As a single parameter with an intuitive definition, the H index offers potential for clinical applications

Development of Patient-specific AAV Vectors After Neutralizing Antibody Selection for Enhanced Muscle Gene Transfer

A major hindrance in gene therapy trials with adeno-associated virus (AAV) vectors is the presence of neutralizing antibodies (NAbs) that inhibit AAV transduction. In this study, we used directed evolution techniques in vitro and in mouse muscle to select novel NAb escape AAV chimeric capsid mutants in the presence of individual patient serum. AAV mutants isolated in vitro escaped broad patient-specific NAb activity but had poor transduction ability in vivo. AAV mutants isolated in vivo had enhanced NAb evasion from cognate serum and had high muscle transduction ability. More importantly, structural modeling identified a 100 amino acid motif from AAV6 in variable region (VR) III that confers this enhanced muscle tropism. In addition, a predominantly AAV8 capsid beta barrel template with a specific preference for AAV1/AAV9 in VR VII located at threefold symmetry axis facilitates NAb escape. Our data strongly support that chimeric AAV capsids composed of modular and nonoverlapping domains from various serotypes are capable of evading patient-specific NAbs and have enhanced muscle transduction

InaudibleKey: Generic Inaudible Acoustic Signal based Key Agreement Protocol for Mobile Devices

Secure Device-to-Device (D2D) communication is becoming increasingly important with the ever-growing number of Internetof-Things (IoT) devices in our daily life. To achieve secure D2D communication, the key agreement between different IoT devices without any prior knowledge is becoming desirable. Although various approaches have been proposed in the literature, they suffer from a number of limitations, such as low key generation rate and short pairing distance. In this paper, we present InaudibleKey, an inaudible acoustic signal based key generation protocol for mobile devices. Based on acoustic channel reciprocity, InaudibleKey exploits the acoustic channel frequency response of two legitimate devices as a common secret to generating keys. InaudibleKey employs several novel technologies to significantly improve its performance. We conduct extensive experiments to evaluate the proposed system in different real environments. Compared to state-of-the-art works, InaudibleKey improves key generation rate by 3-145 times, extends pairing distance by 3.2-44 times, and reduces information reconciliation counts by 2.5 16 times. Security analysis demonstrates that InaudibleKey is resilient to a number of malicious attacks. We also implement InaudibleKey on modern smartphones and resourcelimited IoT devices. Results show that it is energy- efficient and can run on both powerful and resource-limited IoT devices without incurring excessive resource consumption

Loss of ARHGEF6 Causes Hair Cell Stereocilia Deficits and Hearing Loss in Mice

ARHGEF6 belongs to the family of guanine nucleotide exchange factors (GEFs) for Rho GTPases, and it specifically activates Rho GTPases CDC42 and RAC1. Arhgef6 is the X-linked intellectual disability gene also known as XLID46, and clinical features of patients carrying Arhgef6 mutations include intellectual disability and, in some cases, sensorineural hearing loss. Rho GTPases act as molecular switches in many cellular processes. Their activities are regulated by binding or hydrolysis of GTP, which is facilitated by GEFs and GTPase-activating proteins, respectively. RAC1 and CDC42 have been shown to play important roles in hair cell (HC) stereocilia development. However, the role of ARHGEF6 in inner ear development and hearing function has not yet been investigated. Here, we found that ARHGEF6 is expressed in mouse cochlear HCs, including the HC stereocilia. We established Arhgef6 knockdown mice using the clustered regularly interspaced short palindromic repeat-associated Cas9 nuclease (CRISPR-Cas9) genome editing technique. We showed that ARHGEF6 was indispensable for the maintenance of outer hair cell (OHC) stereocilia, and loss of ARHGEF6 in mice caused HC stereocilia deficits that eventually led to progressive HC loss and hearing loss. However, the loss of ARHGEF6 did not affect the synapse density and did not affect the mechanoelectrical transduction currents in OHCs at postnatal day 3. At the molecular level, the levels of active CDC42 and RAC1 were dramatically decreased in the Arhgef6 knockdown mice, suggesting that ARHGEF6 regulates stereocilia maintenance through RAC1/CDC42

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Large-scale power systems exhibit more complex dynamics due to the increasing integration of inverter-based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide-Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large-scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra-high density synchrophasor and point-on-wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)-based advanced applications are introduced, including AI-based inertia estimation, AI-based disturbance size and location estimation, AI-based system stability assessment, and AI-based data authentication

Fully automatic reconstruction of personalized 3D volumes of the proximal femur from 2D X-ray images

PURPOSE: Accurate preoperative planning is crucial for the outcome of total hip arthroplasty. Recently, 2D pelvic X-ray radiographs have been replaced by 3D CT. However, CT suffers from relatively high radiation dosage and cost. An alternative is to reconstruct a 3D patient-specific volume data from 2D X-ray images. METHODS: In this paper, based on a fully automatic image segmentation algorithm, we propose a new control point-based 2D-3D registration approach for a deformable registration of a 3D volumetric template to a limited number of 2D calibrated X-ray images and show its application to personalized reconstruction of 3D volumes of the proximal femur. The 2D-3D registration is done with a hierarchical two-stage strategy: the scaled-rigid 2D-3D registration stage followed by a regularized deformable B-spline 2D-3D registration stage. In both stages, a set of control points with uniform spacing are placed over the domain of the 3D volumetric template first. The registration is then driven by computing updated positions of these control points with intensity-based 2D-2D image registrations of the input X-ray images with the associated digitally reconstructed radiographs, which allows computing the associated registration transformation at each stage. RESULTS: Evaluated on datasets of 44 patients, our method achieved an overall surface reconstruction accuracy of [Formula: see text] and an average Dice coefficient of [Formula: see text]. We further investigated the cortical bone region reconstruction accuracy, which is important for planning cementless total hip arthroplasty. An average cortical bone region Dice coefficient of [Formula: see text] and an inner cortical bone surface reconstruction accuracy of [Formula: see text] were found. CONCLUSIONS: In summary, we developed a new approach for reconstruction of 3D personalized volumes of the proximal femur from 2D X-ray images. Comprehensive experiments demonstrated the efficacy of the present approach