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The present study was conducted at department of Agrometeorology, IGKV Raipur (C.G.) during 2019-2021, to assess the district level vulnerability in different districts of Chhattisgarh with reference to climate change. The data on various components was collected from the Census department of Chhattisgarh (2001 and 2011), department of Agrometeorology, Raipur and the report of Directorate of Economics & Statistics Raipur, C.G. for the period 2000 to 2018 and divided into three different periods 2000-2005, 2006-2010 and 2011-2018 as districts increased. We have used the Hiremath and Shiyani methodology to prepare vulnerability index. The outcome of study indicates that the agricultural sector played major role and contributing significantly to quantify the vulnerability followed by climatic and demographic indicators during all most three periods which was considered for the study. During the period 2000-2005, the results indicates that district Dantewada ranked 1st followed by korba and Mahasamund district. While, district Surguja falls under least vulnerable followed by Durg and Raipur district. During the study period 2006-2010, district Bijapur observed in 1st position followed by Dantewada and korba districts. Whereas, district Durg was supposed to be least vulnerable followed by Surguja and Bilaspur district. During the period 2011-2018, district Sukma ranked 1st rank followed by Dantewada and Narayanpur districts. While, district Dhamtari belongs to least vulnerable followed by Balrampur and Janjgir-champa. On the basis of degree of vulnerability during year 2000-2005, out of 16 districts the 2 and 12 districts were falls under very highly vulnerable and highly vulnerable category, respectively. While, only 2 districts were belong to vulnerable category. During year 2006-2010, out of 18 districts the 5, 11 and 2 districts were supposed to be very highly vulnerable, highly vulnerable category and vulnerable category, respectively. During year 2011-18, out of 27 districts the 9 and 18 districts were belongs to very highly vulnerable and highly vulnerable category. We have not found less and moderately vulnerable districts during the period 2000-2005 and 2006-2010, while only two viz., highly vulnerable and very highly vulnerable districts found during the period 2011-2018.

Objective: Till date, a very few prospective studies have examined the association between serum homocysteine levels and the risk of stroke and stroke subtypes in Indian populations. Methods: A prospective, case - control study of Indian subjects 10–90 years of age was conducted using frozen serum samples from 103 participants in cardiovascular risk surveys collected from December 2017 to November 2018. By the end of 103, we identified 55 incidents of severe strokes, one control subject per case was selected by matching for sex, age, community, year of serum storage, and fasting status. Serum total homocysteine levels were measured by Cobas c-311. Results: Compared with control subjects, total (n_206), hemorrhagic (n_106), and ischemic (n_87) strokes had higher geometric mean values of total homocysteine and higher proportions of homocysteine −25.0 μ mol/L. Homocysteine was estimated after adjustment for body mass index, smoking, alcohol intake, hypertension, and other cardiovascular risk factors. The excess risk of total and ischemic strokes did not vary significantly according to sex, age, smoking status, or hypertensive status. Conclusion: High total homocysteine concentrations were associated with the increased risk of total stroke, more specifically ischemic stroke) Capsuloganglionic and frontoparietal infarct (8 each)., among Indian men and women.

While mobile apps often need to transmit sensitive information out to support various functionalities, they may also abuse the privilege by leaking the data to unauthorized third parties. This makes us question: Is the given transmission required to fulfill the app functionality ? In this paper, we make the first attempt to automatically identify suspicious transmissions from app visual interfaces, including app names, descriptions, and user interfaces. We design and implement a novel framework called FlowIntent to detect nonfunctional transmissions at both software and network levels. During the exercising of the given apps, FlowIntent automatically detects privacy-sharing transmissions and determines their purposes by utilizing the fact that mobile users rely on visible app interface to perceive the functionality of the app at certain context. The characterizations of nonfunctional network traffic are then summarized to provide network level protection. FlowIntent not only reduces the false alarms caused by traditional taint analysis, but also captures the sensitive transmissions missed by widely-used taint analysis system TaintDroid . Evaluation using 2125 sharing flows collected from more than a thousand running instances shows that our approach achieves about 94 percent accuracy in detecting nonfunctional transmissions.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses, etc. Such communication has a unique property that a user can visually identify a transmitter (i.e., LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement, etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low to support any practical application. In this paper, we present $\mathsf {ColorBars}$ ColorBars , an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. $\mathsf {ColorBars}$ ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement $\mathsf {ColorBars}$ ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that $\mathsf {ColorBars}$ ColorBars can achieve a data rate of 7.7 Kbps on Nexus 5, 3.7 Kbps on iPhone 5S, and 2.9 Kbps on Samsung Note8. It is also shown that lower CSK modulations (e.g., four and eight CSK) provide extremely low symbol error rates ( $ 10 - 3 ), making them a desirable choice for reliable LED-to-camera communication.

Home assistant devices such as Amazon Echo and Google Home have become tremendously popular in the last couple of years. However, due to their voice-controlled functionality, these devices are not accessible to Deaf and Hard-of-Hearing (DHH) people. Given that over half a million people in the United States communicate using American Sign Language (ASL), there is a need of a home assistant system that can recognize ASL. The objective of this work is to design a home assistant system for DHH users (referred to as mmASL) that can perform ASL recognition using 60 GHz millimeter-wave wireless signals. mmASL has two important components. First, it can perform reliable wake-word detection using spatial spectrograms. Second, using a scalable and extensible multi-task deep learning model, mmASL can learn the phonological properties of ASL signs and use them to accurately recognize the ASL signs. We implement mmASL on 60 GHz software radio platform with phased array, and evaluate it using a large-scale data collection from 15 signers, 50 ASL signs and over 12K sign instances. We show that mmASL is tolerant to the presence of other interfering users and their activities, change of environment and different user positions. We compare mmASL with a well-studied Kinect and RGB camera based ASL recognition systems, and find that it can achieve a comparable performance (87% average accuracy of sign recognition), validating the feasibility of using 60 GHz mmWave system for ASL sign recognition.

Thin films of Se90Cd10–xSbx (2 ≤ x ≤ 8) of thickness 0.4 microns were prepared on ultra-clean glass substrate by thermal evaporation technique. The vacuum level was 10–6 torr. This paper intends to investigate the impact of Sb concentration on the optical characterization. XRD measurement has been done to investigate the Structural characterization of the prepared thin films. XRD result indicates the prepared thin have amorphous nature. To analyze the optical characterization of the thin films the absorption spectra were recorded over 400–1100 nm wavelength range. In the present study the optical absorption follows direct allowed transition. An increase in photon energy causes an increase in absorption coefficient while extinction coefficient has been found to increase with an increase in frequency of the photons i.e., deceases with increase of wavelength. Optical bandgap (Eg) of thin films have been studied and an increase in it has been recorded with increasing Sb concentration.

The inclusion of infants in the SARS-CoV-2 vaccine rollout is important to prevent severe complications of pediatric SARS-CoV-2 infections and to limit transmission and could possibly be implemented via the global pediatric vaccine schedule. However, age-dependent differences in immune function require careful evaluation of novel vaccines in the pediatric population. Toward this goal, we assessed the safety and immunogenicity of two SARS-CoV-2 vaccines. Two groups of eight infant rhesus macaques (RMs) were immunized intramuscularly at weeks 0 and 4 with stabilized prefusion SARS-CoV-2 S-2P spike (S) protein encoded by mRNA encapsulated in lipid nanoparticles (mRNA-LNP) or the purified S protein mixed with 3M-052, a synthetic TLR7/8 agonist in a squalene emulsion (Protein+3M-052-SE). Neither vaccine induced adverse effects. Both vaccines elicited high-magnitude IgG binding to RBD, amino-terminal domain, S1 and S2, ACE2 blocking activity, and high neutralizing antibody titers, all peaking at week 6. S-specific memory B cells were detected by week 4, and S-specific T cell responses were dominated by the production of IL-17, IFN-γ, or TNF-α. Antibody and cellular responses were stable through week 22. The immune responses for the mRNA-LNP vaccine were of a similar magnitude to those elicited by the Moderna mRNA-1273 vaccine in adults. The S-2P mRNA-LNP and Protein–3M-052-SE vaccines were well tolerated and highly immunogenic in infant RMs, providing proof of concept for a pediatric SARS-CoV-2 vaccine with the potential for durable immunity that might decrease the transmission of SARS-CoV-2 and mitigate the ongoing health and socioeconomic impacts of COVID-19.

The insect problem is accentuated in intensive rice cropping where the insects occur throughout the year in overlapping generations. Over 800 insect species damaging rice in one way or another, although the majority of them do very little damage. In India, about a dozen of insect species are of major importance but the economic damage caused by these species varies greatly from field to field and from year to year. Insect pests cause about 10-15 per cent yield losses. Farmers lose an estimated average of 37% of their rice crop to insect pests and diseases every year. This review focuses on precision farming tools being used in rice pest and diseases management viz., forecasting model for real-time pest-advisory services, hyper-spectral remote sensing in pest damage assessment, computer-based decision support system, disruptive technologies (mobile apps).

Gestures in American Sign Language (ASL) are characterized by fast, highly articulate motion of upper body, including arm movements with complex hand shapes and facial expressions. In this work, we propose a new method for word-level sign recognition from American Sign Language (ASL) using video. Our method uses both motion and hand shape cues while being robust to variations of execution. We exploit the knowledge of the body pose, estimated from an off-the-shelf pose estimator. Using the pose as a guide, we pool spatio-temporal feature maps from different layers of a 3D convolutional neural network. We train separate classifiers using pose guided pooled features from different resolutions and fuse their prediction scores during test time. This leads to a significant improvement in performance on the WLASL benchmark dataset [25]. The proposed approach achieves 10%, 12%, 9.5% and 6.5% performance gain on WLASL100, WLASL300, WLASL1000, WLASL2000 subsets respectively. To demonstrate the robustness of the pose guided pooling and proposed fusion mechanism, we also evaluate our method by fine tuning the model on another dataset. This yields 10% performance improvement for the proposed method using only 0.4% training data during fine tuning stage.

This work is centered on IMC (Internal Model Control) control system design and its robustness analysis for controlling the basis weight of the paper produced by the paper milll. The headbox has a significant role in a paper mill. It maintains the pressure inside the box and discharges stock at an optimum required speed. In this paper, the robustness of the IMC based controllers will be investigated for the two parameters, setpoint tracking and disturbance rejection. The MATLAB software is used for carrying out all simulations done in this paper.

Backscattering RFIDs have emerged as a versatile platform for low-power wireless networking and sensing in the era of Internet-of-Things (IoT). At the same time, millimeter-wave (mmWave) wireless has gained substantial attraction with high-speed WLAN and 5G cellular networks. Although the majority of RFID backscatter research focuses on lower frequencies, mmWave backscatter provides unique opportunities due to its directionality and wide bandwidth. Existing mmWave backscatter systems rely on expensive, custom-designed, dedicated readers which limit their widespread use. In this work, we present an empirical characterization of the first-of-its-kind mmWave backscatter system using 60 GHz commercial off-the-shelf (COTS) 802.11ad radios. Our presented system only relies on Channel State Information (CSI) and requires no modification to hardware or protocol. Through experimentation, we demonstrate that mmWave backscatter can be detected at a distance of up to 6m at different angles and locations. We also present how the backscatter can be leveraged in estimating the blockages in a WLAN which can reduce the beamsearching overhead. Our work opens a new direction of research where mmWave backscatter can be used to realize an integrated mmWave networking, sensing, and applications.

Over half a million people in the United States use American Sign Language (ASL) as their primary mode of communication. Automatic ASL recognition would enable Deaf and Hard of Hearing (DHH) users to interact with others who are not familiar with ASL as well as voice-controlled digital assistants (e.g., Alexa, Siri, etc.). While ASL recognition has been extensively studied, there is a little attention given to recognition of ASL non-manual body markers. The non-manual markers are typically expressed through head, torso and shoulder movements, and add essential meaning and context to the signed sentences. In this work, we present ExASL, a sentence-level ASL recognition system using millimeter-wave radars. ExASL can recognize manual markers (hand gestures) and non-manual markers (head and torso movements). It utilizes multi-distance clustering to recognize body parts and cluster mmWave point clouds. We then present a multi-view deep learning algorithm that can learn from clustered body part representation for an expressive sentence-level recognition. Our evaluation shows that ExASL can recognize ASL sentences with a word error rate of 0.79%, sentence error rate of 1.25%, and non-manual markers with an accuracy of 83.5%.

American Sign Language recognition is a difficult gesture recognition problem, characterized by fast, highly articulate gestures. These are comprised of arm movements with different hand shapes, facial expression and head movements. Among these components, hand shape is the vital, often the most discriminative part of a gesture. In this work, we present an approach for effective learning of hand shape embeddings, which are discriminative for ASL gestures. For hand shape recognition our method uses a mix of manually labelled hand shapes and high confidence predictions to train deep convolutional neural network (CNN). The sequential gesture component is captured by recursive neural network (RNN) trained on the embeddings learned in the first stage. We will demonstrate that higher quality hand shape models can significantly improve the accuracy of final video gesture classification in challenging conditions with variety of speakers, different illumination and significant motion blurr. We compare our model to alternative approaches exploiting different modalities and representations of the data and show improved video gesture recognition accuracy on GMU-ASL51 benchmark dataset.

The 3rd ACM Workshop on Millimeter-Wave Wireless Networks and Sensing Systems (mmNets'19) is focused on the design and implementation of new millimeter-wave (mmWave) protocols and systems that can enable multi-Gbps wireless connectivity for 5G-and-beyond cellular systems and wireless LANs, as well as new wireless sensing and imaging systems. The goal of the mmNets'19 workshop is to bring together researchers from mmWave hardware, communication and signal processing, wireless networking, and mobile applications to set the future research agenda of mmWave systems, and present innovative ideas that will help realize the vision of extremely high data rate wireless networks and novel advanced sensing applications. The workshop will serve as a platform for both academia and industry to identify key challenges, present solutions and advance the field of mmWave technology.

Introduction : Neonatal sepsis continues to be a major cause of morbidity and mortality in neonates. It is 30 per 1000 live births in India. The prevalence is more in preterm neonates. Objectives To describe the clinical and bacteriological spectrum in early onset sepsis and late onset sepsis. To describe the antibiotic resistance profile of bacteria causing early and late onset sepsis. To compare the risk factors and bacteriological spectrum in early onset and late onset sepsis. Setting: NICU of civil hospital Ahmedabad Study period: June 2016 to December 2016 Design: retrospective cohort study Methods: all neonate admitted in NICU of civil hospital Ahmedabad during the abovementioned period were assessed. Inclusion criteria: presence of one or more of established clinical features like , (fever/hypothermia, poor feeding, poor activity, respiratory distress/apneic spells, hepatospleenomegaly, vomitting, abdominal distension, seizures, signs of either respiratory or circulatory dysfunction evidenced by tachycardia/bradycardia, capillary refill time>3 seconds,, respiratory rate>60/minute, chest indrawing and/or grunting)along with >_2 of the following laboratory criteria(total blood leukocyte count15000, absolute neutrophil count(ANC)1500 cells/mm3,C reactive protein (CRP)>0.6microgram/ml, positive blood culture). Information assessed in a proforma. Cases were divided into early onset sepsis(EOS) (presenting in the first 72 hours) and late onset sepsis(LOS) (presenting after 72 hours). All cases were started on antibiotics and upgraded or stopped based on culture and sensitivity. Cases were followed to discharge/death.

This paper presents a general technique for an efficient and accurate near-field (NF) to far-field (FF) transformation based on the complex source beam (CSB) method. This CSB-based NF–FF transformation is applicable to all three conventional NF measurement techniques, namely, to the planar, cylindrical, and spherical as well as to an arbitrary measurement surface. In this technique, a set of CSBs launched radially out from the antenna under test (AUT) is used to represent its NF. The weights of these CSB radiation basis functions are found by matching them to the measured AUT NF on the measurement surface. Once the CSB weights are found, the same CSBs remain continuously valid everywhere from NF to FF. It is noted that due to the occurrence of a natural Gaussian-like field taper away from each beam axis, only a relatively few CSBs are typically needed to represent the AUT fields at any point in its far zone in comparison to the conventional plane wave or vector wave function expansions for a given size of AUT. The remaining CSBs can be discarded with negligible error. This property among a few others leads to some advantages over conventional NF–FF methods.

Background: Currently, limited reports analyze pediatric patients infected with SARS-Covid-19. We conducted this study with the purpose of assessing the disease profile and percentage of COVID positivity among pediatric patients presenting to our triage area.Methods: A retrospective quantitative observational study was conducted in the pediatric triage area, department of pediatrics, Civil hospital campus, Ahmedabad, India. Patients aged 0-12 years were included with the study period from 1stApril to 30th June, 2020. Patients fulfilling the ICMR testing criteria were considered as suspected COVID patients and tested for COVID. Remaining patients were triaged and managed either on OPD basis or admitted in PICU, NICU or pediatric ward. Also, data of OPD attendance rate and immunization rates of 3 months (April, May and June) of 2019 and 2020 were collected from pediatric OPD and vaccination center.Results: A total of 709 patients presented in the triage out of which 35.5% were neonates, 28.6% were between 1-12 month, 23.2%were between 1-5 years and 12.6% were between 5-12 years. 12 out of 97 suspected patients tested positive for COVID 19. Among COVID positive patients, 50% had uncomplicated illness or mild pneumonia, while 50 % had critical illness and were admitted in COVID ICU. Mortality among non COVID patients was 22.7%, and among COVID patients was 16.6%. A sharp fall was noted in the OPD attendance (10 times) and immunization rates (3 times) this year compared to last year.Conclusions: Incidence of COVID positivity was comparable to the studies done in other countries. Even during the pandemic peak, the major chunk of pediatric admissions still belonged to the non COVID conditions. The drastic fall in immunization rates can have implications in the form of re-emergence of vaccine preventable diseases in the coming times.

© 2017 Elsevier B.V. The rise of location-based services has enabled many opportunities for content service providers to optimize the content delivery to user's wireless devices based on her location. Since the sharing precise location remains a major privacy concern among the users, certain location-based services rely on contextual location (e.g. residence, work, etc.) as opposed to acquiring user's exact physical location. In this paper, we present PACL (Privacy-Aware Contextual Localizer) model, which can learn user's contextual location just by passively monitoring user's network traffic. PACL can discern a set of vital attributes (statistical and application-based) from user's network traffic, and predict user's contextual location with a very high accuracy. We design and evaluate PACL using real-world network traces of over 1700 users with over 100GB of total data. Our results show that PACL, when built using the Bayesian Network machine learning algorithm, can predict user's contextual location with the accuracy of around 89%.

Continuous monitoring of human’s breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This article presents mmVital, a system that uses 60GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human’s body and the Received Signal Strength (RSS) of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital also provides sleep monitoring with sleeping posture identification and detection of central apnea and hypopnea events. It relies on a novel human finding procedure where a human can be located within a room by reflection loss-based object/human classification. We evaluate mmVital using a 60GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 breaths per minute (Bpm; breathing rate) and 2.15 beats per minute (bpm; heart rate). Also, it can locate the human subject with 98.4% accuracy within 100ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind a wall.

In today's digital world, rapid technological advancements continue to lessen the burden of tasks for individuals. Among these tasks is communication across perceived language barriers. Indeed, increased attention has been drawn to American Sign Language (ASL) recognition in recent years. Camera-based and motion detection-based methods have been researched extensively; however, there remains a divide in communication between ASL users and non-users. Therefore, this research team proposes the use of a novel wireless sensor (Frequency-Modulated Continuous-Wave Radar) to help bridge the gap in communication. In short, this device sends out signals that detect the user's body positioning in space. These signals then reflect off the body and back to the sensor, developing thousands of cloud points per second, indicating where the body is positioned in space. These cloud points can then be examined for movement over multiple consecutive time frames using a cell division algorithm, ultimately showing how the body moves through space as it completes a single gesture or sentence. At the end of the project, 95% accuracy was achieved in one-object prediction as well as 80% accuracy on cross-object prediction with 30% other objects' data introduced on 19 commonly used gestures. There are 30 samples for each gesture per person from three persons.

This paper presents a measurement study that analyzes large-scale traffic data gathered from two different wireless scenarios: cellular and Wi-Fi networks. We first analyze packet traces and security event logs generated by over 2 million devices in a major US-based cellular network, and show that 0.17% of mobile devices are affected by security threats. We then analyze the aggregate network footprint of malicious and benign traffic in the cellular network, and demonstrate that statistical network features (e.g., uplink data transfer volume, IP entropy) can be effectively used to distinguish such malicious and benign traffic. We next investigate over 2.4 TB of Wi-Fi traffic data, which are generated by 27 K distinct users, in a university campus network. Based on the lessons learned from a comprehensive exploration of a large feature space consisting of over 500 statistical attributes derived from network traffic to/from malicious and benign domains, we propose a novel, in-house traffic screening method, which has the capability of effectively identifying potential malicious domains. Our method achieves over 90% accuracy with only using a small set of simple statistical network features, without using any additional specialized datasets (e.g., geo-location database) or resource-intensive solutions (e.g., DPI boxes to collect HTTP traffic.).

Voice-controlled personal and home assistants (such as the Amazon Echo and Apple Siri) are becoming increasingly popular for a variety of applications. However, the benefits of these technologies are not readily accessible to Deaf or Hard-ofHearing (DHH) users. The objective of this study is to develop and evaluate a sign recognition system using multiple modalities that can be used by DHH signers to interact with voice-controlled devices. With the advancement of depth sensors, skeletal data is used for applications like video analysis and activity recognition. Despite having similarity with the well-studied human activity recognition, the use of 3D skeleton data in sign language recognition is rare. This is because unlike activity recognition, sign language is mostly dependent on hand shape pattern. In this work, we investigate the feasibility of using skeletal and RGB video data for sign language recognition using a combination of different deep learning architectures. We validate our results on a large-scale American Sign Language (ASL) dataset of 12 users and 13107 samples across 51 signs. It is named as GMUASL51. We collected the dataset over 6 months and it will be publicly released in the hope of spurring further machine learning research towards providing improved accessibility for digital assistants., conference : IEEE DSAA, 2019, Washington DC

With the development of novel sensing techniques, continuous monitoring, data-driven inferences, precision irrigation control, and intelligent Internet-of-Things (IoT) systems, agriculture sector is witnessing a revolution. Specialized devices based on infrared and laser are developed to assist farmers in assessing the produce quality, especially its sugar content. However, such devices are expensive and not readily available to consumers. In this paper, we investigate the feasibility of using 60 GHz millimeter-wave (mmWave) signal as a ubiquitous and non-invasive way to estimate the Soluble Sugar Content (SSC) in fruits. With the rapid development in the mmWave technology, 60 GHz WiFi is likely to become pervasive in future mobile devices. Our study shows that when 60 GHz WiFi signals reflect from a fruit, the reflection can be used to infer the fruit's sugar content. We identify the underlying reasons of variations in reflection signals with varying SSC and study the impact of size, shape and density of fruits on reflections. We then develop statistical features based on received signal strength and amplitude, and use them to design regression-based estimation models. With an extensive evaluation with 300 fruit samples, we find that our proposed technique can estimate SSC in three different type of fruits with an average correlation coefficient of 85%. Our prediction errors are within the range of user's taste perception.

Land use and land cover (LULC) change have considerable influence on ecosystem services. Assessing change in ecosystem services due to LULC change at different spatial and temporal scales will help to identify suitable management practices for sustaining ecosystem productivity and maintaining the ecological balance. The objective of this study was to investigate variations in ecosystem services in response to LULC change over 27 years in four agro-climatic zones (ACZ) of eastern India using satellite imagery for the year 1989, 1996, 2005, 2011 (Landsat TM) and 2016 (Landsat 8 OLI). The satellite images were classified into six LULC classes, agriculture land, forest, waterbody, wasteland, built-up, and mining area. During the study period (1989 to 2016), forest cover reduced by 5.2%, 13.7%, and 3.6% in Sambalpur, Keonjhar, and Kandhamal districts of Odisha, respectively. In Balasore, agricultural land reduced by 17.2% due to its conversion to built-up land. The value of ecosystem services per unit area followed the order of waterbodies > agricultural land > forests. A different set of indicators, e.g., by explicitly including diversity, could change the rank between these land uses, so the temporal trends within a land use are more important than the absolute values. Total ecosystem services increased by US$ 1296.4 × 105 (50.74%), US$ 1100.7 × 105 (98.52%), US$ 1867 × 105 (61.64%), and US$ 1242.6 × 105 (46.13%) for Sambalpur, Balasore, Kandhamal, and Keonjhar, respectively.

60 GHz millimeter-wave networks have emerged as a potential candidate for designing the next generation of multi-gigabit WLANs. Since the 60 GHz links suffer from frequent outages due to blockages caused by human mobility, deploying 60 GHz WLANs that can provide robust coverage in presence of blockages is a challenging problem. In this paper, we study blockage-aware coverage and deployment of 60 GHz WLANs. We first show that the reflection profile of an indoor environment can be sensed using a few measurements. A novel coverage metric (angular spread coverage) which captures the number of available paths and their spatial diversity is proposed. Additionally, it is shown that using relays can extend the coverage of the AP at a lower cost and provide added spatial diversity in the available paths. We propose a heuristic algorithm that determines the AP and relay locations while maximizing the angular spread coverage metric for the clients. Our testbed-based evaluation shows that for five different rooms, our proposed deployment can guarantee an average connectivity of 91.7%, 83.9%, and 74.1% of client locations in the presence of 1, 3 and 5 concurrent human blockages respectively, substantially increasing the robustness of 60 GHz links against blockages.

Background: There is a popular belief that cardiac murmurs are common in neonate and most are innocent or physiological. However, a cardiac murmur may be the first sign of a serious structural cardiac disease in neonate. Objective of study was conducted to determine the clinical significance of cardiac murmur as a sole clinical sign in neonate.Methods: 100 neonates having cardiac murmur were selected from neonatal intensive care unit of department of pediatrics BJMC Ahmedabad from June to November 2019. Murmurs were clinically classified as innocent or significant murmur. Echocardiography was done at UN Mehta institute of cardiology for confirmation of the diagnosis and then patients were reclassified as innocent murmur and structural heart defect. structural heart defects were further subdivided into physiological variant or significant heart defect.Results: Amongst 100 neonates studied, sixty two percent cases with murmur were found to have structural heart defect and 38% had innocent murmur. Among the cases with structural heart defect. 65.3% were found to have significant heart defect and 34.7% were physiological variant. Clinical suspicion was able to differentiate innocent murmur or structural heart defect (p

Background: Infantile tremor syndrome (ITS) is a condition reported from many areas of Indian subcontinent due to lack of proper nutrition, improper weaning or delayed introduction of complementary feeding. There is no proven macro or micro nutrient deficiency responsible for it but relation with vitamin B12 deficiency has been identified in some studies. The aim of this study was to study the clinical profile of patients with ITS.Methods: It is a prospective observational study including total 30 patients, from 6 months to 3-year age group, admitted to pediatric wards and nutritional rehabilitation centre, civil hospital, Ahmedabad, from June 2018 to December 2019.Results: Out of 30 patients studied, 69% had pre-ITS and rest had ITS, 61.5% were males and 38.4% were females, 69.2% had severe acute malnutrition, 84.6% had severe anemia and 23% of them presented with CCF, 15.4% patients had microcephaly and 23% had hypotonia. 23% had motor and speech delay. 61.5% belonged to lower socio-economic group. There was delayed introduction of complementary feeding in 63% of patients. 69% patients had severe vitamin B12 deficiency and 21% had moderate vitamin B12 deficiency.Conclusion: ITS, is commonly seen among male children belonging to lower socio-economic group among age group of 6 months to 24 months, presents with coarse tremors with moderate to severe anemia with moderate to severe acute malnutrition and is associated with deficiency of vitamin B12. Early diagnosis and treatment including nutritional rehabilitation grossly improved the outcome.

Background: Indicators showing levels of nutritional status in children are often regarded as representative of the health and general well-being of a society at large. Malnutrition stands as a consequence of several key social and economic factors such as lack of education, inadequate health care services and ill-informed cultural behaviors. In order to holistically address the issues surrounding malnutrition, a comprehensive understanding of the multi-dimensional complexities at play in society is crucial. Objective was to identify determinants of severe acute malnutrition among children with severe acute malnutrition under 5 years (between 6 months to 60 months) of age. Methods: A cross sectional study design involving 64 patients with severe acute malnutrition between 6 to 60 month of age was employed to identify the risk factors of severe acute malnutrition among children admitted in pediatric wards and nutritional rehabilitation centers, Civil hospital Ahmedabad, from April 2018 to march 2019. A detailed history of all the patients were taken and data collected using structured interviewer-administered questionnaire.Results: Thus, from the above study, it is clear that age of the child

60 GHz millimeter-wave WLANs are gaining traction with their ability to provide multi-gigabit per second data rates. In spite of their potential, link outages due to human body blockage remain a challenging outstanding problem. In this work, we propose mmChoir, a novel proactive blockage mitigation technique that utilizes joint transmissions from multiple Access Points (APs) to provide blockage resilience to clients. We derive a new reliability metric based on angular spread of incoming paths to a client and their blockage probabilities. The metric can be used to intelligently select joint transmissions that can provide higher reliability. The reliability metric along with a novel interference estimation model, is used by mmChoir's scheduler to judiciously schedule joint transmissions, and increase network capacity and reliability. Our testbed and trace-driven simulations show that mmChoir can outperform existing beamswitching based blockage mitigation scheme with on an average 58% higher network throughput.

Estimation of building occupancy has emerged as an important research problem with applications ranging from building energy efficiency, control and automation, safety, communication network resource allocation, etc. In this research work, we propose the estimation of occupancy using non-intrusive information that is already available from existing sensing modes, namely, number of WiFi devices, electrical energy demand and water consumption rate. Using data collected from 76 buildings in a university campus, we study the feasibility of multi-modal fusion between the three data sources for estimating fine-grained occupancy. In order to make the estimation model scalable, we propose three different clustering schemes to identify similarity in building characteristics and training per-cluster occupancy estimation models. The presented multi-modal fusion estimation framework achieves a mean absolute percentage error of 13.22% and we find that leveraging all three modalities provide an improvement of 48% in accuracy as compared to WiFi-only occupancy estimation. Our evaluation also shows that clustering buildings greatly increases the scalability of the proposed approach through significant reduction in training overhead, while providing an accuracy comparable to exhaustive, per-building estimation models.

A uniform geometrical theory of diffraction (UTD) ray solution has been developed for describing the radiation fields produced by a linear antenna array which is conformal to a cylindrical conducting structure. The fields are described collectively in terms of just a few rays which are launched from a specific critical point in the interior and from the two end points of the linear array. Numerical results are presented to demonstrate the validity and efficiency of the present collective UTD ray solution.

A uniform geometrical theory of diffraction (UTD) ray analysis is developed for analyzing the problem of electromagnetic (EM) scattering by vertices at the tip of a pyramid formed by curved surfaces with curvilinear edges when illuminated by an arbitrarily polarized astigmatic wavefront. The UTD vertex diffraction coefficient involves various geometrical parameters such as the local radii of curvature of the faces of the pyramid, of its edges, and of the incident ray wavefront, and it is able to compensate for those discontinuities of the field predicted by the UTD for edges (i.e., geometrical optics (GO) combined with the UTD edge diffracted rays) occurring when an edge diffraction point lies at the tip or vertex. This provides an effective engineering tool able to describe the field scattered by truncated edges in curved surfaces within a UTD framework, as required in modern ray-based codes. Some numerical examples highlight the accuracy and the effectiveness of the proposed UTD ray solution for vertex diffraction.

It is understood from past decade of research that a wireless multi-hop network can achieve maximum network throughput only when its nodes operate at a minimum common transmission power level that ensures network connectivity (availability). This point of optimality where maximum availability and throughput is guaranteed in an interference-optimal network has been the basis of numerous design problems in wireless networks. In this paper, we claim that when performability (availability weighted performance) is considered as opposed to average case throughput performance, there does not exist a transmission power (or node density) that can maximize both availability and performability. Since the current mesh networks are expected to deliver carrier-grade services to its users, the availability-performability tradeoff presented in this paper holds a special importance. While availability metric is a necessary one for any networking system intended to provide continuous service, past research [1] has shown a strong correlation between performability and quality of user experience in case of wireless networks. The contributions of the paper are as follows: (1) We first define availability and performability in the context of wireless mesh networks, and then develop efficient algorithms on the basis of intelligent state sampling that can calculate both the quantities with reasonable accuracy. (2) We apply the evaluation methods to two existing mesh networks (GoogleWiFi [2] and PoncaCityMesh [3] ) to demonstrate that their current design can not guarantee a reasonable level of availability or performability. (3) Using hundreds of hours of simulations, we analyze the impact of two basic deployment factors (node density and transmission power) on availability and performability. We outline numerous novel results that emerge due to joint availability-performability analysis including the observation about availability-performability tradeoff.

The ITER vacuum vessel (VV) is under manufacturing by four domestic agencies after completion of engineering designs that have been approved by the Agreed Notified Body (ANB). Manufacturing designs of the VV have been being completed, component by component, by accommodating requirements of the RCC-MR 2007 edition. Manufacturing of the VV first sector has been started in February 2012 in Korea and in-wall shielding in May 2013 in India. EU will start manufacturing of its first sector from September 2013 and Russia the upper port by the end of 2013. All DAs have manufactured several mock-ups including real-size ones to justify/qualify and establish manufacturing techniques and procedures.

User's personal portable devices such as smartphone, tablet and laptop require continuous authentication of the user to prevent against illegitimate access to the device and personal data. Current authentication techniques require users to enter password or scan fingerprint, making frequent access to the devices inconvenient. In this work, we propose to exploit user's on-body wearable devices to detect their proximity from her portable devices, and use the proximity for continuous authentication of the portable devices. We present \ksystem which utilizes acoustic communication for secure proximity estimation with sub-meter level accuracy. \ksystem uses Differential Pulse Position Modulation scheme that modulates data through varying the silence period between acoustic pulses to ensure energy efficiency even when authentication operation is being performed once every second. It yields an secure and accurate distance estimation even when user is mobile by utilizing Doppler effect for mobility speed estimation. We evaluate \ksystem using smartphone and smartwatches, and show that it supports up to 34 hours of continuous authentication with a fully charged battery.

A collective Uniform Geometrical Theory of Diffraction (UTD) solution is developed for describing, in a simple fashion, the radiation from a periodic, finite but long, linear array of elemental antennas mounted conformally on a larger canonical circular cylinder geometry which is assumed to be a perfect electric conductor (PEC). This work is relevant to the prediction of the performance of large and conformal electronically scanned phased arrays in modern applications. The array elements can be placed along either the axial or the circumferential directions, respectively, on this canonical cylindrical platform. The elements are excited with constant magnitude and linear phase delay along the elements for scanning a beam within a conical region about the linear array distribution. The collective UTD radiation field of the entire array in the presence of the cylinder, when observed at any point which is not too close to the array, is vividly described here in terms of just three propagating rays; one such ray arrives to an external observation point from an appropriate point within the array interior, while the remaining arrive from each of the two ends of the linear array, respectively. The latter “collective” UTD ray description is in stark contrast to the conventional, inefficient, array “element by element” UTD field summation approach which lacks physical insight into the array radiation mechanisms. The future extensions of this work to treat quasi periodic arrays with realistic antenna elements having typical tapered array excitations, and also more general convex surfaces, will be briefly discussed in this paper.

The number of wireless-enabled devices owned by a user has had huge growth over the past few years. Over one third of adults in the United States currently own three wireless devices: a smartphone, laptop, and tablet. This article provides a study of the network usage behavior of today’s multidevice users. Using data collected from a large university campus, we provide a detailed multidevice user (MDU) measurement study of more than 30,000 users. The major objective of this work is to study how the presence of multiple wireless devices affects the network usage behavior of users. Specifically, we characterize the usage pattern of the different device types in terms of total and intermittent usage, how the usage of different devices overlap over time, and uncarried device usage statistics. We also study user preferences of accessing sensitive content and device-specific factors that govern the choice of WiFi encryption type. The study reveals several interesting findings about MDUs. We see how the use of tablets and laptops are interchangeable and how the overall multidevice usage is additive instead of being shared among the devices. We also observe how current DHCP configurations are oblivious to multiple devices, which results in inefficient utilization of available IP address space. All findings about multidevice usage patterns have the potential to be utilized by different entities, such as app developers, network providers, security researchers, and analytics and advertisement systems, to provide more intelligent and informed services to users who have at least two devices among a smartphone, tablet, and laptop.

Individuals with the knowledge of electrical energy consumed by them can take steps to reduce their energy footprint, which can lead to energy conservation. Current energy apportionment schemes either require prohibitively large amounts of user-appliance training or performs poorly in detecting user-appliance interaction when there are multiple users and appliances in close proximity to each other. In this paper, we build MotionSync, a privacy-aware, scalable and robust personal energy analytics system. The system exploits the similarity between the motions of user's arm/hand (captured through wrist-worn wearable) and appliance interface (captured through a motion tag) to determine user-appliance interaction. We show that commonly used plugload devices can be classified in five categories based on their interfaces - button, door, free-floating, knob and switch. Based on this, it is possible to train a generic machine learning model for each category to detect user-appliance interaction with a significant lower training overhead. MotionSync is privacy-aware and allows users to measure their own energy consumption without sharing any private information with building infrastructure. MotionSync is also robust to crowded scenarios since it does not depend on user's location. We implement and evaluate our system on a real testbed, and find that it can determine user-appliance interaction with an average accuracy of 92.5% and has low average false positive rate of 8.6%. We also show that pre-trained models of five interface types provide very high accuracy even for new and untrained appliances and users, eliminating per-appliance and per-user training overhead.

With advancements in solid-state lighting, Visible Light Communication (VLC) provides novel opportunities for high-speed networking. Properties such as feasibility of multi-gbps data rates, lower interference, better communication privacy, and dual use of LEDs for illumination and communication make VLC an attractive choice for indoor Internet access networking. However, three major challenges - absence of low-cost solution for LED-Internet connectivity, unavailability of uplink communication, and performance degradation due to device mobility - need to be addressed for realizing VLC's full potential as Internet access network technology. In this paper, we propose PLiFi, a hybrid WiFi-VLC network architecture through the use of Power Line Communication (PLC). PLiFi creates a high-speed interconnection between LEDs themselves and to Internet at low cost, and also seamlessly integrates WiFi and VLC networks. We show how PLiFi can address the above mentioned challenges, and can create a framework for implementing VLC CoMP hybrid WiFi-VLC MAC protocols. Our preliminary results prove that today's PLC technology provides sufficient data rates and coverage. We demonstrate that RSS variations due to mobility and SINR degradation due to co-channel interference can be alleviated using coordination between LEDs which is feasible through PLiFi design.

Stevens Johnson syndrome is an acute self limited disease presenting with severe mucosal erosions with widespread erythematous, cutaneous macules or atypical targets .The majority of cases are drug induced affecting oral and perioral region. A 3 year old male child presented with chief complaints of fever, cough, cold , breathlessness and extensive rashes on the skin of face , oral mucosa, neck, abdomen, erythema of conjunctiva , ulceration of eyelid and oral cavity. The reaction was evoked after consumption of tab oxcarbamezapine since 10 days . He was treated with corticosteroids , antimicrobial drugs and oral topical anesthetic agents. Health care providers must be careful regarding the adverse effects of drugs especially the allergic manifestations like anaphylaxis and Steven Johnson syndrome which are potentially fatal conditions. The most commonly and widely prescribed drug regimens should also be used judiciously and continuously monitored to prevent such a fatal adverse drug reaction.

This paper presents a uniform geometrical theory of diffraction (UTD) for describing the electromagnetic (EM) scattering from finite, quasi periodic, planar arrays illuminated by incident spherical waves. It is applied here to analyze the scattering by a finite frequency selective surface (FSS), and by a finite reflectarray, respectively when they are excited by a nearby antenna. The UTD yields not only a relatively simple solution to these problems, but it also lends physical insights into the scattering mechanisms via Floquet modal rays arising from the interior points, and diffracted rays arising from the truncation boundaries, respectively, of such finite structures. Numerical examples are shown to validate the UTD analysis.

Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.

We propose human mobility tracking and activity monitoring using 60 GHz millimeter wave (mmWave). We discuss the benefits of using mmWave signals for the purpose over existing 2.4/5 GHz based techniques. We also identify related challenges of determining human's initial location and tracking, and demonstrate the feasibility of activity monitoring using an example of walking activity.

This paper presents a fast analysis of the transient radiation from large reflector antennas which are excited by a pulsed feed. This development employs a superposition of a set of time domain (TD) complex source beams (CSBs), or TD-CSBs, as basis function to represent a pulsed feed which excites a reflector antenna. Each TD-CSB undergoes reflection and diffraction in a manner analogous to rays from a pulsed point source, at an external position in real space, when they are incident on the reflector. This reflection and diffraction of real rays can be found via the previously available TD version of the uniform geometrical theory of diffraction (UTD), or TD-UTD. That TD-UTD is analytically continued here to allow the point source to reside in complex space because the latter generates a CSB. The resulting CSB-TD -UTD overcomes the field singularities of the conventional TD-UTD at ray caustics, while retaining all their advantages of the UTD. The total field of the reflector is given by a superposition of the fields of each CSB-TD-UTD. The speed and validity of the proposed method is demonstrated via examples.

When shortest path routing is employed in large scale multi-hop wireless networks such as sensor networks, nodes located near the center of the network have to perform disproportionate amount of relaying for others. Nodes in such traffic hot-spots deplete their batteries faster than others due to their high relay load. These traffic hot-spots also adversely affect the network capacity due to increased congestion in the regions. To solve the problem, various divergent routing schemes are used which route the data on center-avoiding divergent routing paths. Though they achieve better load balancing, overall relaying is increased significantly due to their longer routing paths. In this paper, we propose power control as a way for balancing relay load and mitigating hot-spots in wireless sensor networks. Using a heuristic based on the concept of centrality, we show that if we increase the power levels of only the nodes which are expected to relay more packets, significant relay load balancing can be achieved even with shortest path routing. Different from divergent routing schemes, such load balancing strategy is applicable to any arbitrary topology and traffic pattern. With extensive simulations, we show that centrality based power control can drastically increase the network lifetime of sensor networks. We compare its performance with other divergent routing schemes and multiple battery level assignment strategy. Also, it is shown that centrality based power control results into better throughput capacity in many different topologies.

LED-to-camera communication allows LEDs deployed for illumination purposes to modulate and transmit data which can be received by camera sensors available in mobile devices like smartphones, wearable smart-glasses etc. Such communication has a unique property that a user can visually identify a transmitter (i.e. LED) and specifically receive information from the transmitter. It can support a variety of novel applications such as augmented reality through mobile devices, navigation using smart signs, fine-grained location specific advertisement etc. However, the achievable data rate in current LED-to-camera communication techniques remains very low (a 12 bytes per second) to support any practical application. In this paper, we present ColorBars, an LED-to-camera communication system that utilizes Color Shift Keying (CSK) to modulate data using different colors transmitted by the LED. It exploits the increasing popularity of Tri-LEDs (RGB) that can emit a wide range of colors. We show that commodity cameras can efficiently and accurately demodulate the color symbols. ColorBars ensures flicker-free and reliable communication even in the presence of inter-frame loss and diversity of rolling shutter cameras. We implement ColorBars on embedded platform and evaluate it with Android and iOS smartphones as receivers. Our evaluation shows that ColorBars can achieve a data rate of 5.2 Kbps on Nexus 5 and 2.5 Kbps on iPhone 5S, which is significantly higher than previous approaches. It is also shown that lower CSK modulations (e.g. 4 and 8 CSK) provide extremely low symbol error rates (