484 research outputs found
Geographic data Visualisation and Map Generation
No full textThis project is one of the academic projects given to us in the Geographic Information System (GIS) Course.
Created by: Pranav Pandya (Me) and
Kartikey Hadiya
We sampled information for pollution emission in Delhi, India.
Pollution data was obtained from https://data.gov.in/resources/real-time-air-quality-index-various-locations
Pollution index data can be obtained from https://cpcb.nic.in/RealTimeAirQualityData.php
Pollution data only had address of Indian Meteorological Department, so each station was located in Google Earth and pin points were added at each station.
Then in the sidebar containing those pins on right-click, a new folder was added and all the pins were added in that new folder in google earth. Then that folder was saved as kml file.
This kml file was uploaded to Mygeodata: https://mygeodata.cloud/converter/kml-to-csv and was converted into csv.
Then the csv file was opened and coordinates were copied in the pollution data file. That file was later saved as CSV and imported in ArcGIS and xy data was displayed.
Shapefile was obtained from web search, which is attached as well. That shapefile was imported in ArcGIS and the final view was generated which is shown in the picture
FIGURE 1c in Report of Caridina babaulti Bouvier, 1918 (Crustacea: Decapoda: Caridea: Atyidae) and description of a new species Caridina kutchi sp. nov. from Gujarat, India
No full textFIGURE 1c. Sampling locations, Khari River. Cardina kutchi sp. nov.Published as part of Pandya, Pranav J. & Richard, Jasmine, 2019, Report of Caridina babaulti Bouvier, 1918 (Crustacea: Decapoda: Caridea: Atyidae) and description of a new species Caridina kutchi sp. nov. from Gujarat, India, pp. 470-482 in Zootaxa 4568 (3) on page 472, DOI: 10.11646/zootaxa.4568.3.3, http://zenodo.org/record/260166
Caridina kutchi Pandya & Richard 2019, sp. nov.
No full textCaridina kutchi sp. nov. (Figs. 4, 5) Material examined. Gujarat, India. Types: Caridina kutchi sp. nov. Holotype. Jagadiya Dam, Khari River, Kutch, coll. Pandya, 7.9.2014, det. Pandya & Richard, 2015, RRLC /BIO-SH/02, ♂; Paratypes. Jagadiya Dam, Khari River, Kutch, coll. Pandya, 7.9.2014, det. Pandya & Richard, 2015, RRLC /BIO-SH/02, ♂; Bhadra, Khari River, Kutch, coll. Pandya, 7.9.2014, det. Pandya & Richard, 2015, RRLC /BIO-SH/01, 2♀; Ker-vandh, Khari River, coll. Pandya, 7.9.2014, det. Pandya & Richard, 2015, RRLC /BIO-SH/03, 4♂, 2juv.; Khari river catchment, Bhojraj vandh, Gadhshisha, Kutch. coll. Pandya, 7.9.2014, det. Pandya & Richard, 2015, RRLC / Bio-Gadh /07, 2♂, 2♀. Other material examined. Sri Lanka ( Ceylon). Types: Caridina simoni Bouvier, 1904, coll. E. Simon, 1904, Lectotype, designated by Richard & Clark 2014, MNHN Na 856, ♂; Paralectotype MNHN Na 856 ♂; coll. E. Simon, 1904, exch. Paris Museum, 117-97, NHM reg. 1907.1.7.33, 1♀. Nontypes: Sri Lanka. Caridina simoni Bouvier, 1904, irrigation streams, Peradeniya, pres. R. Gurney, NHM reg.1920.2.5.11-13, 4♀; stream running in to Mahawallagunga River, Peradeniya, pres. R. Gurney, NHM reg. 1920.2.5.14-16, 1♂, 1♀ ovig., 1♀, 1 damaged specimen; Keani River, Kekirawa, Colombo, pres. D. R. R. Burt, NHM reg. 1935.5.30.26-27, 4♂, 3♀; Kalaweva, April 1932, pres. D. R. R. Burt, Department of Zoology, University College, NHM reg. 1935.5.30.15-19, 1♂ (abnormal), 4♀ ovig., 2♀; from streams running into Mahawallagunga River, pres. Dr. R. Gurney, det. W.T. Calman, NHM reg. 1947.3.18, 1♀ ovig; pres. Dr. R. Gurney, NHM reg. 1950.1.2.148, dissected parts; irrigation streams, Peradeniya, pres. Dr. R. Gurney, NHM reg. 1951.2. 17.1792/3, 1♂, 1♀; fresh water pond, Botanical Gardens, Perademiya, 17.6.1954, coll. & pres. E.S. Brown, NHM reg. 1954.10.27.1-10, 20♂, 5♀ ovig., 7♀; Ambanganga Anoiont, nr. Polonarraw, 1962, coll. & pres. C. H. Fernandes, NHM reg. 1962.8.24.104, 3♀ ovig., 1♀. India. Hindupur, S. India. coll. P. K. Sartory, pres. Mr. Scourfield, det. J. Richard & P. Cark 2009, NHM reg. 1945.vii.27.5-12, 3♂, 4♀; Madras (Chennai) area, coll. and pres. Dr. Sanjeevaraj, det. I. Gordon, 0 5. 1965. NHM reg. 1965.5.7.1-10, 31♀ ovig. Description. Adult size 15–28 mm. Carapace length 2.2–3.5 mm. Rostrum (Fig. 4a, b, c): Slender, 1.4–1.7×long as carapace, distinctly longer than antennal scale; 12–22 teeth proximally leaving 0.5–0.65 of dorsal margin unarmed distally which is interrupted by a single tooth at distal end; tip pointed and setose dorsally. 1–3 post orbital teeth present. 9–15 teeth proximally leaving 0.1–0.2 of ventral margin unarmed distally. Formula (1–3) 12–22+1/9–15. Carapace (Fig. 4a, c): Antennal spine well developed. Pterygostomian angle rounded without a spine. Mouth parts: Mandibles asymmetrical without palp. Incisor process of mandibles ending in irregular teeth, molar process truncated. Maxillula with broadly truncated lower lacinia and elongated upper lacinia bearing distinct teeth on inner margin; palp slender. Upper endites of maxilla subdivided, palp elongated, scaphognathite with long narrow posterior lobe bearing tuft of setae at truncated tip. Palp of first maxilliped rounded ending in a finger like projection. Endopod of second maxilliped with ultimate segment fused to penultimate segment; exopod longer than endopod. Third maxilliped reaching the end of second segment of antennular peduncle. Exopod reaching 2 nd segment of endopod. Epipod present. Antennular peduncle (Fig. 4a, b, c): 0.8–0.9×carapace. Stylocerite 0.6–0.75×length of basal segment. Anterolateral teeth of basal segment 0.19–0.23×second segment. 10–25 segments bearing aesthetascs. First pereiopod (Fig. 5a): Dactylus 1.3–1.4×palm of propodus. Chela 3.2–3.7×long as broad. Carpus 1.7– 2.3×long as broad, with anterior excavation. Second pereiopod (Fig. 5b): Dactylus 1.5–1.9×long as palm of propodus. Chela 2.7–3.7×long as broad. Carpus 4.9–6.4×long as broad. Third pereiopod (Fig. 5c, d): Dactylus 3.0–3.7×long as broad. 7–12 marginal spines on dactylus. Propodus 4.1–5.0×long as dactylus and 10.0–12.5×long as broad with 10–14 spines along inner margin. Carpus 0.45– 0.55×long as propodus, with 1 large spine and 3–5minute spines on inner margin. Merus 1.6–2.0×carpus length. Merus with 3 large spines on posterior margin. Ischium with a spine. Fifth pereiopod (Fig. 5e, f): Dactylus3.9–5.0×long as broad with 40–50 marginal spines. Propodus 12–16×long as broad and 3.7–4.2×long as dactylus and with 10–15 spines along posterior margin. Carpus 0.4 5–0.6×propodus length and with 4–5 minute spines along inner margin. Merus 1.5–1.9×carpus length, with 2 large spines at posterior margin. Ischium with a spine. Epipod: present on 1–4 pereiopods; absent on fifth pereiopod. Setobranchs: 1 seta on all pereiopods. First male pleopod (Fig. 5g, h): Endopod 0.25–0.35×exopod, appendix interna absent. First female pleopod: Endopod 0.5–65×exopod. Second male pleopod (Fig. 5i, j): Appendix masculina 1.4–1.7×appendix interna and 0.25–0.3×endopod. 6th abdominal somite (Fig. 4a): 0.57–0.86×long as carapace. Telson (Fig. 4a, 5k, l): Narrow and tapering, 1.0–1.1×long as 6th abdominal somite. Dorsal spines 4–6 pairs (including subterminal spine). Posterior margin narrow and triangular, with a median projection, bearing 1 pair of long lateral spines and 2–3 pairs of sparsely plumose spines of equal length and shorter than laterals. Uropod (Fig. 5m): 8–12 diaeresis spinules. Preanal carina (Fig. 5n): armed with a spine. Colouration. Freshly collected specimens were light greenish transparent in colour. Type locality. Jagadiya Dam, River Khari, Kutch District (also spelt as Kachchh) Gujarat, India. Etymology. The species is named for Kutch District, Gujarat, from where the specimens were collected. Remarks. Caridina kutchi sp. nov. is distinguished by long, slender rostrum that is distinctly longer than antennal scale, the unarmed dorsal margin interrupted by a single tooth distally; pointed tip of rostrum with fine setae on the dorsal margin; telson posterior margin narrow and triangular with a median projection bearing intermediate spines of equal length that are distinctly shorter than the laterals. Caridina kutchi sp. nov. is similar to Caridina simoni Bouvier, 1904 which was described from Sri Lanka and now reported from South India (Richard and Clark 2014) in the structure of rostrum with pointed tip and the distal unarmed rostral margin interrupted by a single tooth distally. However, C. kutchi sp. nov. distinctly differs from C. simoni in telson structure. C. kutchi sp. nov. could be distinguished from C. simoni in having rostrum that is distinctly longer than antennal scale (vs. equal to or slightly longer than antennal scale in C. simoni); unarmed dorsal rostral margin interrupted by a single tooth distally (vs. unarmed dorsal rostral margin interrupted by 0–4 teeth in C. simoni); posterior margin of telson narrow and triangular with a median projection (vs. posterior margin of telson broad and rounded without a median projection in C. simoni); telson posterior margin bearing 2–3pairs of sparsely plumose intermediate spines of equal length and distinctly shorter than laterals spine (vs. 3–4 pairs of sparsely plumose intermediate spines either equal in length and slightly shorter than the laterals or the median pair longer and equal to laterals in C. simoni); preanal carina armed with a spine (vs. preanal carina unarmed in C. simoni). Caridina kutchi sp. nov. differs from C. babaulti, which is now reported from Gujarat, in possessing rostrum that is distinctly longer than antennal scale (vs. rostrum equal to antennular peduncle or shorter reaching middle of 3 rd antennular peduncle segment in C. babaulti); 12–22 teeth proximally leaving 0.5–0.65 of dorsal margin unarmed distally which is interrupted by a single tooth at distal end (vs. 14–25teeth proximally leaving 0.1–0.23 of dorsal margin unarmed distally in C. babaulti); 1–3 post orbital teeth present (vs. 3–7 postorbital teeth present in C. babaulti); 9–15 teeth proximally leaving 0.1–0.2 of ventral margin unarmed distally (vs. 3–8 teeth proximally leaving 0.1–0.45 of ventral margin unarmed distally in C. babaulti); carpus of first pereiopod with anterior excavation (vs. carpus of first pereiopod with deep anterior excavation in C. babaulti); telson posterior margin narrow and triangular, with a median projection (vs. telson posterior margin broad and rounded, with or without median protrusion in C. babaulti); 2–3 pairs of sparsely plumose intermediate spines of equal length and distinctly shorter than laterals (vs. 2–4 pairs or 5 sparsely plumose intermediate spines of varying length; fractionally longer or shorter than the lateral spines in C. babaulti); 8–12 uropod diaeresis spinules (vs. 12–21 uropod diaeresis spinules in C. babaulti); preanal carina armed with a spine (vs. preanal carina unarmed in C. babaulti). Caridina kutchi sp. nov. is the first Caridna species to be described from Kutch district, Gujarat state, which is known for its complex geological set up.Published as part of Pandya, Pranav J. & Richard, Jasmine, 2019, Report of Caridina babaulti Bouvier, 1918 (Crustacea: Decapoda: Caridea: Atyidae) and description of a new species Caridina kutchi sp. nov. from Gujarat, India, pp. 470-482 in Zootaxa 4568 (3) on pages 477-480, DOI: 10.11646/zootaxa.4568.3.3, http://zenodo.org/record/260166
Panoply.io for Database Warehousing and Post Analysis using Sequal Language (SQL)
No full textIt has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.
I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.
The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder
QUERY 1
SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Deaths>0
Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.
Query 2
SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll"
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Confirmed>0
GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries
Query 3
SELECT country as "Countries where Coronavirus has reached"
FROM "public"."coronavirus_updated"
WHERE confirmed>0 GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.
Query 4
SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
AND confirmed=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.
Query 5
SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss",
100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list
Coronavirus Panoply.io for Database Warehousing and Post Analysis using Sequal Language (SQL)
No full textIt has never been easier to solve any database related problem using any sequel language and the following gives an opportunity for you guys to understand how I was able to figure out some of the interline relationships between databases using Panoply.io tool.
I was able to insert coronavirus dataset and create a submittable, reusable result. I hope it helps you work in Data Warehouse environment.
The following is list of SQL commands performed on dataset attached below with the final output as stored in Exports Folder
QUERY 1
SELECT "Province/State" As "Region", Deaths, Recovered, Confirmed
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Deaths>0
Description: How will we estimate where Coronavirus has infiltrated, but there is effective recovery amongst patients? We can view those places by having Recovery twice more than the Death Toll.
Query 2
SELECT country, sum(confirmed) as "Confirmed Count", sum(Recovered) as "Recovered Count", sum(Deaths) as "Death Toll"
FROM "public"."coronavirus_updated"
WHERE Recovered>(Deaths/2)
AND Confirmed>0
GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries
Query 3
SELECT country as "Countries where Coronavirus has reached"
FROM "public"."coronavirus_updated"
WHERE confirmed>0 GROUP BY country
Description: Coronavirus Epidemic has infiltrated multiple countries, and the only way to be safe is by knowing the countries which have confirmed Coronavirus Cases. So here is a list of those countries.
Query 4
SELECT country, sum(suspected) as "Suspected Cases under potential CoronaVirus outbreak"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
AND confirmed=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is spreading at alarming rate. In order to know which countries are newly getting the virus is important because in these countries if timely measures are taken, it could prevent any causalities. Here is a list of suspected cases with no virus resulted deaths.
Query 5
SELECT country, sum(suspected) as "Coronavirus uncontrolled spread count and human life loss",
100*sum(suspected)/(SELECT sum((suspected)) FROM "public"."coronavirus_updated") as "Global suspected Exposure of Coronavirus in percentage"
FROM "public"."coronavirus_updated"
WHERE suspected>0
AND deaths=0
GROUP BY country
ORDER BY sum(suspected)
DESC
Description: Coronavirus is getting stronger in particular countries, but how will we measure that? We can measure it by knowing the percentage of suspected patients amongst countries which still doesn’t have any Coronavirus related deaths. The following is a list.
Data Provided by:
SRK, Data Scientist at H2O.ai, Chennai, Indi
Geotagging Images using Geosetter in Google Earth Environment
No full textAttachment: Methodology flow diagram & Final Outputs in kmz
Coordinates were required to be assigned to a set of 50 images which were to be taken manually by the user. So, Images had to be procured, their locations were to be known and the location was required to be inscribed in the Image Metadata.
Step 1: Obtaining Images + Location
Google Earth has Google Photos which are uploaded by Individual users. These photos were enabled in Google Earth and each image was copied and pasted in MS Paint and then saved with an appropriate name
At each image, the location pins were placed to mark the coordinates for each image
Step 2: Extracting the coordiantes
The marked pin locations in Google Earth were saved as: Save My Places -> .kml
This kml file was uploaded to https://mygeodata.cloud/converter/kml-to-csv to convert it into csv file to read the lat & long for each pinned markings
Step 3: Embedding Coordinates in Image Metadata
Geosetter Software enables us to embed coordinated in each Image file.
Images were loaded in Geosetter and the subsequent coordinates were added with the Edit Data dialogue. The location name of each image was added in Object Name.
The Images were then exported with thumbnail size=2000 and thumbnail={Object_Name
Web Based Resource Mapping of Model Colony, Pune, India
No full textResource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given.
Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages.
Source link of those webpages are determined and were added in a iframe in src link.
In web html design a table was made and all three iframe are added in table.
The final html was added as html element in sites.google.com to create a custom website.
The website link:
www.sites.google.com/site/pranavrsmap
Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location researc
Geographic data Visualisation and Map Generation
No full textThis project is one of the academic projects given to us in the Geographic Information System (GIS) Course.
Created by: Pranav Pandya (Me) and
Kartikey Hadiya
We sampled information for pollution emission in Delhi, India.
Pollution data was obtained from https://data.gov.in/resources/real-time-air-quality-index-various-locations
Pollution index data can be obtained from https://cpcb.nic.in/RealTimeAirQualityData.php
Pollution data only had address of Indian Meteorological Department, so each station was located in Google Earth and pin points were added at each station.
Then in the sidebar containing those pins on right-click, a new folder was added and all the pins were added in that new folder in google earth. Then that folder was saved as kml file.
This kml file was uploaded to Mygeodata: https://mygeodata.cloud/converter/kml-to-csv and was converted into csv.
Then the csv file was opened and coordinates were copied in the pollution data file. That file was later saved as CSV and imported in ArcGIS and xy data was displayed.
Shapefile was obtained from web search, which is attached as well. That shapefile was imported in ArcGIS and the final view was generated which is shown in the picture
Broadband mm-wave signal generation and amplification in CMOS using synthetic impedance
No full textThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 75-76).This thesis explores the concept of synthesizing tunable impedances by establishing the appropriate phase relationship between the drain voltage and drain current of a MOS transistor. A high frequency, wide tuning range 105-121GHz oscillator and a small-footprint 20-40GHz oscillator using synthetic resonance are presented. The concept of impedance synthesis is also used to generate a novel frequency-adaptive loss compensation scheme for distributed amplifiers which is shown to improve the bandwidth by 30%. The performance of these circuits was analyzed and simulated on a TSMC 65nm bulk CMOS process.by Pranav R Kaundinya.M. Eng
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