Array signal processing for source localization and enhancement

“A common approach to the wide-band microphone array problem is to assume a certain array geometry and then design optimal weights (often in subbands) to meet a set of desired criteria. In addition to weights, we consider the geometry of the microphone arrangement to be part of the optimization problem. Our approach is to use particle swarm optimization (PSO) to search for the optimal geometry while using an optimal weight design to design the weights for each particle’s geometry. The resulting directivity indices (DI’s) and white noise SNR gains (WNG’s) form the basis of the PSO’s fitness function. Another important consideration in the optimal weight design are several regularization parameters. By including those parameters in the particles, we optimize their values as well in the operation of the PSO. The proposed method allows the user great flexibility in specifying desired DI’s and WNG’s over frequency by virtue of the PSO fitness function. Although the above method discusses beam and nulls steering for fixed locations, in real time scenarios, it requires us to estimate the source positions to steer the beam position adaptively. We also investigate source localization of sound and RF sources using machine learning techniques. As for the RF source localization, we consider radio frequency identification (RFID) antenna tags. Using a planar RFID antenna array with beam steering capability and using received signal strength indicator (RSSI) value captured for each beam position, the position of each RFID antenna tag is estimated. The proposed approach is also shown to perform well under various challenging scenarios”--Abstract, page iv

Passive RFID Tags for Metallic Environments using Phased Array Reader Antennas

In this study, we consider the operation of radio frequency identification (RFID) tag antennas in metallic environments. Phased array is built using 2 RFID reader antennas for electronic beam steering and improving overall read range of the RFID antenna tags. The performance of RFID tag antenna is known to degrade in metallic environments. RFID tag antenna with ground plane is designed to improve the performance in harsh metallic environments. In this study, we use double slit antennas with ground plane for tags radiating in challenging metallic environments to achieve a 30 feet read range

3D Localization of RFID Antenna Tags using Convolutional Neural Networks

With the Internet of Things becoming widespread, there has been a rising demand for indoor location-based services. In recent trends, radio frequency identification has become an integral part of the production of IoT. Conventional methods use prior knowledge of antenna and tag positioning along with high-precision equipment capable of collecting phase or time-of-arrival data for robust estimation of three-dimensional location. In this work, we propose a three-dimensional localization method based on deep learning that relies on the phase and received signal strength indicator (RSSI) captured by steering beams to various locations using a phased array antenna. We evaluate the efficiency of this system by estimating three-dimensional location of 7 RFID tags mounted on metallic surfaces placed in a naturalistic environment. To evaluate the generalization of the proposed approach we crossvalidate the localization performance in different environments. The localization performance of the proposed approach is also tested on different formfactor of the RFID tag. With no prior information of either the tags or environment, the proposed system was able to achieve an average localization error as low as 1.33 cm with better system stability

DNN-Based RFID Antenna Tags Localization

Radio frequency identification technology (RFID) is increasingly becoming an integral part of the Internet-of-Things (IoT). It offers different advantages including battery-free operation, small form-factor, and low cost. This makes the RFID an enticing technology for an indoor localization-based application and services. Geometry based localization approaches often achieve low accuracy due to errors introduced by a multipath propagation and interference in indoor environments. Many range-based algorithms assume that reader position is known in advance and there are carefully placed reference tags. In contrast, this paper presents a data driven localization methodology for direction-of-arrival (DOA) estimation using a deep neural network processing of signal captured with a reader antenna array. The proposed approach learns the complex mapping of the radio waves interactions in adverse metal environments based on received signal strength indicator (RSSI) values. The RSSI is captured while electrically steering a planar phased array through the area of interest. The proposed methodology is evaluated with multiple tags placed on metallic surfaces. Using readily available measurements, the proposed approach is able to achieve an average DOA error of 5.93 degrees

On the Design of Optimal Linear Microphone Array Geometries

In this paper, we present a simultaneous optimization model for aperiodic linear microphone array geometry and weights. Desirable properties for Broadband arrays include robust superdirective frequency invariant beampatterns. Our approach is to employ particle swarm optimization (PSO) to search for the optimal geometry while selecting optimal weights for each particle\u27s geometry. The resulting directivity factor (DFs) and white noise gains (WNGs) are used to define the PSO fitness function. The proposed approach also optimizes the trade-off between WNG and DF, to find a geometry within a given aperture, thus, maximizing both these parameters. The proposed method allows the user great flexibility in specifying desired DFs and WNGs over frequency by virtue of the PSO fitness function. The resultant array geometry is smaller and yields greater WNG and DF than conventional approach

Apolipoprotein A-II Plus Lipid Emulsion Enhance Cell Growth via SR-B1 and Target Pancreatic Cancer <i>In Vitro</i> and <i>In Vivo</i>

<div><p>Background</p><p>Apolipoprotein A-II (ApoA-II) is down regulated in the sera of pancreatic ductal adenocarcinoma (PDAC) patients, which may be due to increase utilization of high density lipoprotein (HDL) lipid by pancreatic cancer tissue. This study examined the influence of exogenous ApoA-II on lipid uptake and cell growth in pancreatic cancer (PC) both <i>in vitro</i> and <i>in vivo</i>.</p><p>Methods</p><p>Cryo transmission electron microscopy (TEM) examined ApoA-II’s influence on morphology of SMOFLipid emulsion. The influence of ApoA-II on proliferation of cancer cell lines was determined by incubating them with lipid+/-ApoA-II and anti-SR-B1 antibody. Lipid was labeled with the fluorophore, DiD, to trace lipid uptake by cancer cells <i>in vitro</i> by confocal microscopy and <i>in vivo</i> in PDAC patient derived xenograft tumours (PDXT) by fluorescence imaging. Scavenger receptor class B type-1(SR-B1) expression in PDAC cell lines and in PDAC PDXT was measured by western blotting and immunohistochemistry, respectively.</p><p>Results</p><p>ApoA-II spontaneously converted lipid emulsion into very small unilamellar rHDL like vesicles (rHDL/A-II) and enhanced lipid uptake in PANC-1, CFPAC-1 and primary tumour cells as shown by confocal microscopy. SR-B1 expression was 13.2, 10.6, 3.1 and 2.3 fold higher in PANC-1, MIAPaCa-2, CFPAC-1 and BxPC3 cell lines than the normal pancreatic cell line (HPDE6) and 3.7 fold greater in PDAC tissue than in normal pancreas. ApoA-II plus lipid significantly increased the uptake of labeled lipid and promoted cell growth in PANC-1, MIAPaCa-2, CFPAC-1 and BxPC3 cells which was inhibited by anti SR-B1 antibody. Further, ApoA-II increased the uptake of lipid in xenografts by 3.4 fold.</p><p>Conclusion</p><p>Our data suggest that ApoA-II enhance targeting potential of lipid in pancreatic cancer which may have imaging and drug delivery potentialities.</p></div

Confocal micrographs of CFPAC-1 cells demonstrating the influence of ApoA-II on lipid uptake.

<p>SMOFlipid was labelled with DiD (red), ApoA-II was labelled green with a secondary antibody and DAPI stained the nuclei blue. (A1) cells treated with ApoA-II alone, (A2) cells treated with lipid alone, (A3) cells treated with reconstituted lipoproteins after adding ApoA-II to DiD labeled SMOFlipid (SMOF/A-II) demonstrating increased uptake and (A4) cells treated with (SMOF/A-II) after pretreating with anti-SR-B1antibodies which reduced the lipid uptake (scale bar, 20 μm). (B) Cells were pretreated with unlabeled lipid at 1:10 dilution and (B2) after 2 h SMOF/A-II was added, it appeared to be endocytosed in cytoplasmic vesicles as shown in (B3) and (B4). ApoA-II (green) is attached with the cell membrane (yellow arrow head) and as well in the cytoplasm in part associated with the lipid (red arrow head) in Differential Interference Contrast (DIC) overlay image (scale bar 25 μm). (C) Live cell imaging experiment demonstrated greater uptake of lipid in cells when reconstituted lipoproteins after adding ApoA-II to DiD labeled SMOFlipid was added to the media for lipid concentrations of 1:20 and 1:10 (scale bar 100 μm). (D) The intensity of the DiD staining was greater when 4 samples were examined with eight to twelve areas of interest for each study where ApoA2 increased the uptake of lipid 25.6±2.2, <i>P</i> = 0.02 and 54.8±2.2, <i>P</i> = 0.004 for dilutions of 1:20 and 1:10 respectively.</p

SMOFlipid ApoA-II targets pancreatic ductal adenocarcinoma (PDAC).

<p>(A) Spectral reflectance fluorescence images of 8 week old xenografts taken 48h after tail vein injection of PBS, DiD labeled SMOFlipid without or with ApoA-II using Carestream molecular imaging. Note the widespread distribution of DiD is better localized to the tumours when ApoA-II is included with lipid. Arrows indicate the site of tumours. (B) Spectral fluorescence images of explanted tumours and organs taken 48h after injection from mice after a tail vein injection of either PBS, lipid/DiD (n = 2) or lipid/DiD plus ApoA-II (n = 2) (right panel) along with the H&E photo micrograph of the tumour from two mice (left panel), retained the morphological characteristics with the original patient’s PC tumours in lipid and lipid plus ApoA-II treated mice. Note the uptake of fluorescence by the tumours, liver and spleen. (C) Graph represents the uptake by the tumour relative to the uptake of the liver as a fraction of area. These results were then normalised to the value for mice receiving lipid only (defined as 1) when the tumour uptake for the mice receiving lipid and ApoA-II had 3.4 fold increased uptake. Values are mean ± SD; <i>n</i> = 3.</p

ApoA-II plus lipid increased lipid uptake and cell growth may be via SR-B1 in pancreatic cancer.

<p>(A) HPDE6, PANC-1, MIAPaCa-2, CFPAC-1 and BxPC3 cells were cultured and SR-B1 expression was measured by western blotting. The band intensity of the proteins was normalized with β-actin and HPDE6 was defined as 1. Values are the mean of two separate experiments. (B) Expression of SR-B1 by IHC on normal pancreas (NP) and human PDAC tissues. Inset, IgG represents the isotyped matched primary antibody to anti SR-B1. Graph shows the quantification of SR-B1 expression by the percentage of stained cells X intensity from 3 separate patients and NP was defined as 1. (C) Expression of SR-B1 and ApoA-II by IHC in corresponding xenografted PDAC tumours at 48 h after tail vein injection of lipid with or without ApoA-II. Inset, IgG represents the isotyped matched primary antibody to anti-ApoA-II. (D) Effects of anti SR-B1 alone in HPDE6, PANC-1, MIAPaCa-2, CFPAC-1 and BxPC3 cell growth. (E) Cells were pretreated with or without the SR-B1 antibody (2.5 μg/mL) for 2 hours and then treated with ApoA-II, lipid and lipid plus ApoA-II for 48 hours. Proliferation was measured by crystal violet assay. Values are mean ± SD; <i>n</i> = 4. *^{, ‡, #} and ± p< 0.05 vs. control, ApoA-II, lipid and lipid+ApoA-II respectively, with the use of analysis of variance.</p

ApoA-II reduces the size of lipid.

<p>Cryo-TEM micrograph picture of the SMOFlipid emulsion without ApoA-II (A) and after addition of ApoA-II (B). Note the bi-layer structure of the lipid surface of the nanoparticle like structures in presence of ApoA-II (black arrows). (C) Spectral flourescence color photograph of a 0.5% agarose gel run for 45 min at 90 V in 1 × TBE buffer. SMOFlipid labeled with DiD did not migrate through the well but reconstituted labeled SMOFlipid with ApoA-II migrated as a band.</p