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Interference Management for Concurrent Transmission in Downlink Wireless Communications

It is well known that the communication capacity of wireless networks is limited by interference. Depending on the strength of the interference, there are three conventional approaches to this problem. If the interference is very strong, then the receiver can decode the interfering signal and subtract from the desired signal using successive interference cancelation. If the interference signal is very weak compared to the desired signal, it can be treated as noise. The third and most common possibility is when the interference is comparable with the desired signal. In this case the interference can be avoided by orthogonalizing it with the desired signal using techniques such as time division multiple access (TDMA) or frequency division multiple access (FDMA). In addition to interference, wireless networks also experience channel fading. Conventional approaches to wireless networking attempt to combat fading. Depending on the coherence time of the fading, various approaches have been used. For example, fast fading may be mitigated by the use of diversity techniques, interleaving, and error-correcting codes. Certain diversity techniques, such as the use of multiple antennas, has been shown to help combat fading as well as increase multiplexing gain and system capacity. Multiuser diversity scheme is a technique to increase the capacity of wireless networks using multiple antennas at the base station. In this approach the base station selects a mobile device that has the best channel condition, maximizing the signal-to-noise ratio (SNR). According to some implementations of this approach, K random beams are constructed and information is transmitted to the users with the highest signal-to-noise plus interference ratio (SINR). Searching for the best SINR in the network, however, requires feedback from the mobile devices that scales linearly with the number of users. These implementations also use beamforming, which is complex to implement. In addition, the cooperation requirement is substantial.

Advanced Human Pose Recognition Technology

This technology revolutionizes human pose recognition by overcoming dataset and environmental limitations.

Rapid and Low-cost Sensor for Measuring Volatile Compounds in Nuts and Oils

Researchers at the University of California, Davis have developed a sensor for measuring food spoilage of nuts, seeds, and oils. It measures volatile organic compounds as a biomarker of food spoilage through a simple device in only three minutes.

Microbial-Induced Barriers To Striga Parasitism

Researchers at the University of California, Davis have discovered an Arthrobacter bacterial strain that promotes suberization of the endodermis in sorghum roots. Suberin, a poly-fatty acid polymer, acts as a physical barrier in sorghum roots, helping to prevent infection by the parasitic plant Striga hermonthica, a significant threat to sorghum production. These microbial-based solutions offer a cost-effective and easily deployable strategy to manage Striga infection in the predominantly smallholder farmer-driven sorghum cultivation of sub-Saharan Africa.

(SD2021-430) Deep learning volumetric deformable registration: CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT

Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0in 5.4pt 0in 5.4pt; mso-para-margin-top:0in; mso-para-margin-right:0in; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0in; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-font-kerning:1.0pt; mso-ligatures:standardcontextual;} Researchers from UC San Diego developed a patent-pending convolutional neural network (CNN)-based deformable registration algorithm to reduce computation time for analysis of medical images such as CT and MRI. These fast, fully-automated CNN-based lung deformable registration algorithms can facilitate translation of measurements into clinical practice, potentially improving the diagnosis and severity assessment of small airway diseases.

Simplified Workflow For Hybridoma Antibody Sequencing

Before recombinant antibody expression plasmids can be designed, sequncing of the antibody light and heavy chain variable regions is necessary. Several other methods of sequencing antibody variable regions are available. Some involve high throughput RNA sequencing. These techniques are unavailable to many labs; they require the preparation of RNA-seq libraries, and computational analysis. As a result, the cost of performing such techniques is substantial and with sequencing cores being oversubscribed, turnaround can be as long as weeks to months. Other methods involve PCR and Sanger sequencing. However PCR amplification of variable regions results from difficulties in generating universal primers that can amplify any given variable region - particularly given the inherent low sequence identity in the 5' leader sequence of antibody light chains and heavy chains upstream of the variable regions. Sometimes degenerate primers can be used, but amplification success rate is only 80-90% due to non-specific priming and/or failure to prime at all. In addition, there is a significant risk that the variable regions of the parental myeloma line can amplify using the degenerate primers. 5' RACE (rapid amplification of 5' cDNA ends) can also be used, but mRNA degradation, cDNA purification and poly-A addition between reverse transcription and PCR, makes the technique long and difficult to perform. Non degenerate primers can be used, but each variable region requires multiple amplification attempts with different primer sets as well as sequence validation using mass spectrometry. And with both of these methods, primer derived mutations can be introduced. Mass spectrometry can be used to determine antibody variable regions, but these can result in ambiguous sequences because of isobaric residues such as isoleucine and leucine. But this method is time consuming, requires huge amounts of purified monoclonal antibody, is expensive and is inaccessible to most researchers. This technology involves a template switch reverse transcription of hybridoma RNA with at least three chain specific RT primers - one for the kappa chain, one for the lambda chain, and at least one for the heavy chain (for efficiency, this can be limited to IgG in a first pass). These are amplified in three separate PCR reactions and sequenced using Sanger sequencing.

Methods and Systems for Rapid Antimicrobial Susceptibility Tests

Rapid antimicrobial susceptibility testing (AST) is a method for quickly determining the most effective antibiotic therapy for patients with bacterial infections. These techniques enable the detection and quantification of antibiotic-resistant and susceptible bacteria metabolites at concentrations near or below ng/mL in complex media. Employing bacterial metabolites as a sensing platform, the system integrates machine learning data analysis processes to differentiate between antibiotic susceptibility and resistance in clinical infections within an hour. With the results, a clinician can prescribe appropriate medicine for the patient's bacterial infection.