Dual-Band Multi-Channel Airborne Radar for Mapping the Internal and Basal Layers of Polar Ice Sheets

Rapid thinning of the Jakobshavn and a few other outlet glaciers in Greenland and the Antarctic has been observed in the past few years. The key to understanding these dramatic changes is basal conditions. None of the spaceborne radars, that have been providing a wealth of information about the ice surface, is capable of measuring ice thickness or mapping bed conditions. At the Center for Remote Sensing of Ice Sheets (CReSIS), we have developed an airborne radar system to map the internal and basal layers to obtain a 3-dimensional representation of the ice sheets in Polar Regions. We have also devised advanced signal processing techniques to overcome the effects of surface clutter. We have developed a radar for measuring ice thickness up to a 5000 m depth from low-altitude (500 m) and high-altitude (7000 m) aircraft. This airborne radar system can operate at two bands: very high frequency band (VHF-band) (140 MHz to 160 MHz) with a peak power of 800 W and P-band (435 MHz to 465 MHz) with a peak power of 1.6 kW for collecting data to develop effective ice sheet models. The pulse signal has a duration of 3 us or 10 us. The radar has 1 transmitter and 6 receivers inside the aircraft and an 8 element dipole antenna array mounted beneath the wings of the aircraft. This system is designed to have 32 coherent integrations and pulse compression due to which a high loop sensitivity of at least 208 dB was obtained. This system was tested and data were collected in the recent September 2007 field experiment over various parts of Greenland. From the initial observations of the collected data it can be deduced that the signal losses at 450 MHz are more than predicted by existing models and clutter masked the weak bed echoes when the data were collected at higher altitudes both at 150 MHz and 450 MHz

A molecular phylogeny of the cicadas (Hemiptera: Cicadidae) with a review of tribe and subfamily classification:

A molecular phylogeny and a review of family-group classification are presented for 137 species (ca. 125 genera) of the insect family Cicadidae, the true cicadas, plus two species of hairy cicadas (Tettigarctidae) and two outgroup species from Cercopidae. Five genes, two of them mitochondrial, comprise the 4992 base-pair molecular dataset. Maximum-likelihood and Bayesian phylogenetic results are shown, including analyses to address potential base composition bias. Tettigarcta is confirmed as the sister-clade of the Cicadidae and support is found for three subfamilies identified in an earlier morphological cladistic analysis. A set of paraphyletic deep-level clades formed by African genera are together named as Tettigomyiinae n. stat. Taxonomic reassignments of genera and tribes are made where morphological examination confirms incorrect placements suggested by the molecular tree, and 11 new tribes are defined (Arenopsaltriini n. tribe, Durangonini n. tribe, Katoini n. tribe, Lacetasini n. tribe, Macrotristriini n. tribe, Malagasiini n. tribe, Nelcyndanini n. tribe, Pagiphorini n. tribe, Pictilini n. tribe, Psaltodini n. tribe, and Selymbriini n. tribe). Tribe Tacuini n. syn. is synonymized with Cryptotympanini, and Tryellina n. syn. is synonymized with an expanded Tribe Lamotialnini. Tribe Hyantiini n. syn. is synonymized with Fidicinini. Tribe Sinosenini is transferred to Cicadinae from Cicadettinae, Cicadatrini is moved to Cicadettinae from Cicadinae, and Ydiellini and Tettigomyiini are transferred to Tettigomyiinae n. stat from Cicadettinae. While the subfamily Cicadinae, historically defined by the presence of timbal covers, is weakly supported in the molecular tree, high taxonomic rank is not supported for several earlier clades based on unique morphology associated with sound production

Indoor in-network asset localization using Crownstone network

This thesis project has been done with Crownstone, a subsidiary company of Almende. One of their project goals was to develop indoor localization algorithms to determine room-level location of an asset within the Crownstone network for smart-building, home automation and healthcare applications. An asset is a wireless device transmitting Bluetooth messages that are heard by Crownstones(sensors) and measure the strength of the received signal(RSSI). The terms position and location are used to denote two different concepts in this thesis. Position refers to estimating the specific coordinates, whereas a location refers to a much wider space like a room. In this thesis, we are interested in getting a location estimate. There are two most widely used and researched localization techniques to determine the location of the asset. First, a model based(MB) method (e.g. Trilateration algorithm) which uses a mathematical model based on distance and second is a data-driven(DD) method (e.g. Fingerprinting algorithm) that relies on existing data, like RSSI to directly get the location. Algorithms are tested on real data collected by the Crownstones at the Almende office(test environment) divided into a finite number of locations or rooms. Metrics are defined based on the requirements of Almende to compare the MB algorithms with the DD algorithms. In this thesis, firstly, a centralized multilateration(MB-C) algorithm is implemented taking into account distances from N Crownstones at the office. Since one of the requirements was to perform in-network localization, a simple averaging consensus based distributed(MB-D) algorithm was selected and compared against the MB-C algorithm. Results show that the MB-D algorithm is faster, scalable and robust against single-point of failure than the MB-C but is less accurate and does not converge to the centralized solution for a noise variance greater than 10dB.The MB algorithms have limitations in terms of selecting a model, learning themodel parameters and an additional step of mapping the position output of the implemented MB algorithms to a location is also required. To deal with these challenges, a Machine-learning(ML) based data-driven algorithm is proposed. In this, training datasets were iteratively improved with different features. Then, an Ensemble based centralized ML algorithm (DD-C) is implemented, giving a classification accuracy of 65%. Algorithm is further improved by distributed data handling leading to a classification accuracy of 77%. There has been very little to no study on finding the room-level location of an asset in an indoor setting using a distributed ML based data-driven algorithm. A consensus based distributed ML algorithm (DD-D) is proposed that performs local predictions within the Crownstone network using the same globally trained model giving a classification accuracy of 73%.The results show that the proposed DD algorithms perform better than the MBalgorithms in terms of accuracy and are comparable in terms of prediction time. Results also indicate the proposed DD algorithms are more scalable, robust against noise but are computationally expensive.Electrical Engineering | Signals and System

Rhizobium pusense-Mediated Selenium Nanoparticles–Antibiotics Combinations against Acanthamoeba sp.

Severe ocular infections by Acanthamoeba sp. lead to keratitis, resulting in irreversible vision loss in immune-compromised individuals. When a protozoal infection spreads to neural tissues, it causes granulomatous encephalitis, which can be fatal. Treatment often takes longer due to the transition of amoeba from trophozoites to cyst stages, cyst being the dormant form of Acanthamoeba. A prolonged use of therapeutic agents, such as ciprofloxacin (Cipro), results in severe side effects; thus, it is critical to improve the therapeutic efficacy of these widely used antibiotics, possibly by limiting the drug-sensitive protozoal-phase transition to cyst formation. Owing to the biomedical potential of selenium nanoparticles (SeNPs), we evaluated the synergistic effects of ciprofloxacin and Rhizobium pusense–biogenic SeNPs combination. SeNPs synthesized using Rhizobium pusense isolated from root nodules were characterized using UV–Visible spectrophotometer, FT-IR, SEM with EDX, particle size analysis, and Zeta potential. The combination was observed to reduce the sub-lethal dose of Cipro, which may help reduce its side effects. The selenium and ciprofloxacin (SeNPs–Cipro) combination reduced the LC50 by 33.43%. The anti-protozoal efficacy of SeNPs–Cipro was found to transduce through decreased protozoal-cyst formations and the inhibition of the galactosidase and protease enzymes of trophozoites. Furthermore, high leakage of sugar, proteins, and amino acids during the SeNPs–Cipro treatment was one primary reason for killing the trophozoites. These experimental results may be helpful in the further pre-clinical evaluation of SeNPs–Cipro to combat protozoal infections. Future studies for combinations of SeNPs with other antibiotics need to be conducted to know the potential of SeNPs against antibiotic resistance in Acanthamoeba