Filters








16 Hits in 0.96 sec

Visible Light Communications Based Indoor Positioning via Compressed Sensing [article]

Kristina Gligoric, Manisha Ajmani, Dejan Vukobratovic, Sinan Sinanovic
2018 arXiv   pre-print
Manisha Ajmani and Sinan Sinanović are with the School of Engineering and Built Environment, Glasgow Caledonian University, Glasgow, G4 0BA, UK, (e-mail: manisha.ajmani@gcu.ac.uk, sinan.sinanovic@gcu.ac.uk  ... 
arXiv:1805.01001v1 fatcat:ufkp74owrvgwjpuhl72sxs3l3q

The TerraByte Client: providing access to terabytes of plant data [article]

Michael A. Beck, Christopher P. Bidinosti, Christopher J. Henry, Manisha Ajmani
2022 arXiv   pre-print
In this paper we demonstrate the TerraByte Client, a software to download user-defined plant datasets from a data portal hosted at Compute Canada. To that end the client offers two key functionalities: (1) It allows the user to get an overview on what data is available and a quick way to visually check samples of that data. For this the client receives the results of queries to a database and displays the number of images that fulfill the search criteria. Furthermore, a sample can be downloaded
more » ... within seconds to confirm that the data suits the user's needs. (2) The user can then download the specified data to their own drive. This data is prepared into chunks server-side and sent to the user's end-system, where it is automatically extracted into individual files. The first chunks of data are available for inspection after a brief waiting period of a minute or less depending on available bandwidth and type of data. The TerraByte Client has a full graphical user interface for easy usage and uses end-to-end encryption. The user interface is built on top of a low-level client. This architecture in combination of offering the client program open-source makes it possible for the user to develop their own user interface or use the client's functionality directly. An example for direct usage could be to download specific data on demand within a larger application, such as training machine learning models.
arXiv:2203.13691v1 fatcat:dsarhlk7sfanfbmldpldnzfxu4

Optical Wireless Communication Based Indoor Positioning Algorithms: Performance Optimisation and Mathematical Modelling

Manisha Ajmani, Sinan Sinanović, Tuleen Boutaleb
2018 Computation  
In this paper, the performance of the optimal beam radius indoor positioning (OBRIP) and two-receiver indoor positioning (TRIP) algorithms are analysed by varying system parameters in the presence of an indoor optical wireless channel modelled in line of sight configuration. From all the conducted simulations, the minimum average error value obtained for TRIP is 0.61 m against 0.81 m obtained for OBRIP for room dimensions of 10 m × 10 m × 3 m. In addition, for each simulated condition, TRIP,
more » ... ch uses two receivers, outperforms OBRIP and reduces position estimation error up to 30%. To get a better understanding of error in position estimation for different combinations of beam radius and separation between light emitting diodes, the 90th percentile error is determined using a cumulative distribution frequency (CDF) plot, which gives an error value of 0.94 m for TRIP as compared to 1.20 m obtained for OBRIP. Both algorithms also prove to be robust towards change in receiver tilting angle, thus providing flexibility in the selection of the parameters to adapt to any indoor environment. In addition, in this paper, a mathematical model based on the concept of raw moments is used to confirm the findings of the simulation results for the proposed algorithms. Using this mathematical model, closed-form expressions are derived for standard deviation of uniformly distributed points in an optical wireless communication based indoor positioning system with circular and rectangular beam shapes.
doi:10.3390/computation7010001 fatcat:iareii5mtnh35f246zsdl5dfsa

Optimal beam radius for LED-based indoor positioning algorithm

Manisha Ajmani, Sinan Sinanovic, Tuleen Boutaleb
2016 2016 International Conference for Students on Applied Engineering (ISCAE)  
Indoor positioning systems have the potential to replicate the success of outdoor positioning systems, but owing to the expensive and less accurate technology currently available for indoor positioning, they have not been able to take-off. LEDbased Visible Light Communication (VLC) systems can solve this problem, but owing to complex algorithms and unoptimized parameter values the desired accuracy has not yet been achieved. This research addresses the problem of overlapping light radiation
more » ... ns, which leads to lower accuracy in a VLC system, and proposes an algorithm to accurately determine the position of a device with respect to pre-positioned LEDs when it is receiving signal from multiple transmitting LEDs. To check the accuracy of this Optimal Beam Radius Indoor Positioning (OBRIP) algorithm numerous possible positions of a device uniformly distributed in a room with an indoor positioning system have been simulated to calculate the error in position estimation. Also, from the simulations, optimal values for beam radius, for a given number of LEDs in an array, separation between adjacent LEDs in an array for different room shapes has been calculated.
doi:10.1109/icsae.2016.7810217 fatcat:xinsbz44ozhznextor7hvmjpee

Optical Wireless Communication based two receiver Indoor Positioning algorithm

Manisha Ajmani, Tuleen Boutaleb, Sinan Sinanovic
2017 2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)  
This research focuses on developing a positioning algorithm for Optical Wireless Communication (OWC) based indoor positioning systems. The proposed Two Receiver Indoor Positioning (TRIP) algorithm has the potential to overcome the light beam overlapping problem and locate the position of an object in an indoor environment precisely. The current research builds upon a previously proposed positioning algorithm named Optimal Beam Radius Indoor Positioning (OBRIP) algorithm and proposes the use of
more » ... wo receivers to detect the optical signal to further reduce the error in position estimation while also investigating the best values for important parameters like beam radius of transmitting Light-Emitting Diodes (LED), grid size of LED array and number of LEDs in an array for multiple room shapes. The performance analysis of TRIP algorithm and subsequent comparison with the performance of OBRIP algorithm is also carried out in this research using MATLAB simulations. From the simulation results, this algorithm proves to be effective for a room with floor dimensions of 10 m x 10 m yielding an average error in position estimation whose value is 0.69 m and surpasses the performance of the OBRIP with 30% lesser average error values. Using this technology for indoor positioning and tracking in the medical sector, and extending help to elderly dementia affected patients is a noble and intriguing idea.
doi:10.1109/iwcmc.2017.7986362 dblp:conf/iwcmc/AjmaniBS17 fatcat:c26y3ms34rbovcf2gynm62daki

Role of vitamin D supplementation in burka clad pregnant women and its correlation with feto-maternal outcome-----a study in 450 bedded maternity hospital of walled city of Delhi

Sangita Nangia Ajmani, Manisha Uddey, Leela Pant, Poonam Chauhan, Ajmani AK
2018 Obstetrics & Gynecology International Journal  
Citation: Ajmani SN, Uddey M, Pant L, et al.  ...  Citation: Ajmani SN, Uddey M, Pant L, et al.  ... 
doi:10.15406/ogij.2018.09.00317 fatcat:7cvonu73njbabp5radvlhq462m

Comparative Analysis of DCF and OPC as Means to Minimize FWM in WDM System

Manisha Ajmani, Preeti Singh
2015 Indian Journal of Science and Technology  
Wavelength Division Multiplexing (WDM) has made possible bidirectional communication while simultaneously multiplying the capacity of Optical Communication System. Apart from dispersion, WDM system is prone to non-linear effects like Four-Wave Mixing (FWM). In this paper, FWM effects have been discussed in detail with important techniques to mitigate FWM effects. Dispersion Compensating components like, Dispersion Compensating Fiber (DCF) and Optical Phase Conjugator (OPC), used for FWM
more » ... n, have been compared on the basis of certain parameters. The comparison is carried out in the OptiSystem simulator, varying different parameters and analyzing the results. Based on these results and observations, the best component at various values of important parameters can be determined. This comparison will help determine the feasibility of using any one component in practical applications. Also, this research opens the door to check the possibility of utilizing hybrid configurations of dispersion compensating components for FWM reduction.
doi:10.17485/ijst/2015/v8i27/70577 fatcat:gzi2uk4yivgvjhg5puaoxdwave

Presenting an extensive lab- and field-image dataset of crops and weeds for computer vision tasks in agriculture [article]

Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti, Christopher J. Henry, Cara M. Godee, Manisha Ajmani
2021 arXiv   pre-print
We present two large datasets of labelled plant-images that are suited towards the training of machine learning and computer vision models. The first dataset encompasses as the day of writing over 1.2 million images of indoor-grown crops and weeds common to the Canadian Prairies and many US states. The second dataset consists of over 540,000 images of plants imaged in farmland. All indoor plant images are labelled by species and we provide rich etadata on the level of individual images. This
more » ... prehensive database allows to filter the datasets under user-defined specifications such as for example the crop-type or the age of the plant. Furthermore, the indoor dataset contains images of plants taken from a wide variety of angles, including profile shots, top-down shots, and angled perspectives. The images taken from plants in fields are all from a top-down perspective and contain usually multiple plants per image. For these images metadata is also available. In this paper we describe both datasets' characteristics with respect to plant variety, plant age, and number of images. We further introduce an open-access sample of the indoor-dataset that contains 1,000 images of each species covered in our dataset. These, in total 14,000 images, had been selected, such that they form a representative sample with respect to plant age and ndividual plants per species. This sample serves as a quick entry point for new users to the dataset, allowing them to explore the data on a small scale and find the parameters of data most useful for their application without having to deal with hundreds of thousands of individual images.
arXiv:2108.05789v1 fatcat:r2tiibwjnrf5dpyu2cg3jwyu7m

Multiple cerebral aneurysms: a rare presentation in systemic lupus erythematosus

Avtar Singh Dhanju, Deepshikha Singla, K. Thiyagu, Manisha Khubber, Pranjali Batra, Namit Gupta, Praneet Manekar, Upasna Ajmani
2020 International Journal of Advances in Medicine  
Systemic lupus erythematosus is a multisystem autoimmune disorder and its complications include cerebral vasculitis and vasculopathy which can be the first manifestation of SLE. Subarachnoid haemorrhage due to rapid aneurysm growth and rupture is a major neurosurgical emergency associated with significant morbidity and mortality. There is need for more rapid diagnosis and aggressive treatment of SLE patients with unruptured aneurysms. Authors report a case of 23 years old female, a newly
more » ... ed case of SLE complicated by rupture of cerebral aneurysms.
doi:10.18203/2349-3933.ijam20204533 fatcat:oku5uinmtje33phdhflvtc72bi

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture

Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti, Christopher J. Henry, Cara M. Godee, Manisha Ajmani, Jeonghwan Gwak
2020 PLoS ONE  
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need
more » ... for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and image segementation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.
doi:10.1371/journal.pone.0243923 pmid:33332382 fatcat:usndjk5lozdbhmeplz2ktzuqva

Prevalence of Overt and Subclinical Thyroid Dysfunction Among Pregnant Women and Its Effect on Maternal and Fetal Outcome

Sangita Nangia Ajmani, Deepa Aggarwal, Pushpa Bhatia, Manisha Sharma, Vinita Sarabhai, Mohini Paul
2013 Journal of Obstetrics and Gynecology of India  
Aim To determine the current prevalence of thyroid dysfunction in normal pregnant women and to study the impact of thyroid dysfunction on maternal and fetal outcome. Methods 400 pregnant women between 13 and 26 weeks of gestation were registered for the study. Apart from routine obstetrical investigations, TSH tests were done. Free T4 and anti-TPO antibody tests were done in patients with deranged TSH. Patients were followed up till delivery. Their obstetrical and perinatal outcomes were noted.
more » ... Results The prevalence of hypothyroidism and hyperthyroidism was 12 and 1.25 %, respectively. Adverse maternal effects in overt hypothyroidism included preeclampsia (16.6 vs. 7.8 %) and placental abruption (16.6 vs. 0.8 %). Subclinical hypothyroidism was associated with preeclampsia (22.3 vs. 7.8 %) as compared to the euthyroid patients. Adverse fetal outcomes in overt hypothyroidism included spontaneous abortion (16.6 vs. 2.39 %), preterm birth (33.3 vs. 5.8 %), low birth weight (50 vs. 12.11 %), intrauterine growth retardation (25 vs. 4.9 %), and fetal death (16.6 vs. 1.7 %) as compared to the euthyroid women. Adverse fetal outcomes in subclinical hypothyroidism included spontaneous abortion (5.5 vs. 2.39 %), preterm delivery (11.2 vs. 5.8 %), low birth weight (25 vs. 12.11 %), and intrauterine growth retardation (8.4 vs. 4.9 %) as compared to the euthyroid women. Conclusions The prevalence of thyroid disorders was high in our study with associated adverse maternal and fetal outcomes. Routine screening of thyroid dysfunction is recommended to prevent adverse fetal and maternal outcome.
doi:10.1007/s13224-013-0487-y pmid:24757337 pmcid:PMC3984645 fatcat:wdd6v4o7d5ad5glopvomsd22oa

An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture [article]

Michael A. Beck, Chen-Yi Liu, Christopher P. Bidinosti, Christopher J. Henry, Cara M. Godee, Manisha Ajmani
2020 arXiv   pre-print
A lack of sufficient training data, both in terms of variety and quantity, is often the bottleneck in the development of machine learning (ML) applications in any domain. For agricultural applications, ML-based models designed to perform tasks such as autonomous plant classification will typically be coupled to just one or perhaps a few plant species. As a consequence, each crop-specific task is very likely to require its own specialized training data, and the question of how to serve this need
more » ... for data now often overshadows the more routine exercise of actually training such models. To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. The system can image plants from virtually any angle, thereby ensuring a wide variety of data; and with an imaging rate of up to one image per second, it can produce lableled datasets on the scale of thousands to tens of thousands of images per day. As such, this system offers an important alternative to time- and cost-intensive methods of manual generation and labeling. Furthermore, the use of a uniform background made of blue keying fabric enables additional image processing techniques such as background replacement and plant segmentation. It also helps in the training process, essentially forcing the model to focus on the plant features and eliminating random correlations. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources. We now plan to generate much larger datasets of Canadian crop plants and weeds that will be made publicly available in the hope of further enabling ML applications in the agriculture sector.
arXiv:2006.01228v1 fatcat:ywvj6jqkzjgepfwhxkjlb6clna

Comparison of Clinical, Metabolic and Hormonal Effects of Metformin Versus Combined Therapy of Metformin With Myoinositol Plus D-Chiro-Inositol in Women With Polycystic Ovary Syndrome (PCOS): A Randomized Controlled Trial

Anupama Bahadur, Hitanshi Arora, Anoosha K Ravi, Manisha Naithani, Yogesh Bahurupi, Jaya Chaturvedi, Megha Ajmani, Rajlaxmi Mundhra
2021 Cureus  
doi:10.7759/cureus.15510 fatcat:eggxfvvhgfad5mpczvrfcxu73m

CORRELATION OF MENSTRUAL DISORDERS AND THYROID DISEASES AMONG REPRODUCTIVE AGE GROUP WOMEN IN A TERTIARY CARE CENTRE, KIMSDU, KARAD
English

Manisha Laddad M, Khirsagar N S, Sanjaykumar Patil, Gauri Shinde
2016 Journal of Evolution of Medical and Dental Sciences  
In the study by Padmaleela and Ajmani, [14, 16] among the hyperthyroid patients, 42.8% had menorrhagia, 28.6% had polymenorrhoea, and 14.3% had hypo/ oligomenorrhoea.  ...  Similar results in Ajmani et al study. In our study, of total 36 hypothyroid patients, most of the patients had menorrhagia followed by polymenorrhoea, hypo/oligomenorrhoea, and metrorrhagia.  ... 
doi:10.14260/jemds/2016/1277 fatcat:2ikyrhepareupnhlx2jhho6uvq

PREVALENCE OF ANATOMICAL VARIATIONS OF THE POSITION OF APPENDIX IN ACUTE APPENDICITIS BY CT SCAN

Azhagiri R, Assistant Professor Department of Anatomy, ESIC Medical College & PGIMSR, KK Nagar, Chennai, Tamil Nadu 600078 Dr. MGR Medical University, Chennai, India, Anitha M, Hemapriya J, Lecturer, Department of Microbiology, Shri Sathya Sai Medical College & Research Institute, Ammapettai, Tamil Nadu 603108, India, Tutor, Department of Anatomy, ESIC Medical College & PGIMSR, KK Nagar, Chennai, Tamil Nadu 600078 Dr. MGR Medical University, Chennai, India
2019 International Journal of Anatomy and Research  
This finding is in agreement with findings by other authors Chaudhari Manisha et al. (2013) [19], Mian Azhar et al., (2017) [20] in which the subcecal position was greater in males than in females.  ...  In this study the incidence of retrocecal position of appendix was highest accounting for 43%.This result was similar to other studies by Bakheit and Warille (1999) [8] , Ajmani (1983) [9] , Solanke  ... 
doi:10.16965/ijar.2019.304 fatcat:jft47qj3fjf43jqd3ugcqpr5ku
« Previous Showing results 1 — 15 out of 16 results