Parent Aid Mobile Application

Every day thousands of children are reported missing. Of which, many are never found. According to National Human Rights Commission on an average 44000 children are reported missing every year. Of these, as many as 11,000 remain untraced. The safety of children is the major concern for every parent. This project deals with the development and implementation of an Andr oid application that traces the location of a child. It provides the parent with real time positioning of their child with finest accuracy. Application uses OpenGTS (Open Source GPS Tracking System) which is an open source project designed specifically to provide web - based GPS tracking services. It takes data input from a GPS device and performs evaluation and provides comprehensive reporting. The application is developed using Hibernate; it provide s a framework for mapping java classes to database tables. Hibernate makes work with relational databases easy, it helps our project to work independent of database used at back - end

Comparison of Speech Enhancement Algorithms

The simplest and very familiar method to take out stationary background noise is spectral subtraction. In this algorithm, a spectral noise bias is calculated from segments of speech inactivity and is subtracted from noisy speech spectral amplitude, retaining the phase as it is. Secondary procedures follow spectral subtraction to reduce the unpleasant auditory effects due to spectral error. The drawback of spectral subtraction is that it is applicable to speech corrupted by stationary noise. The research in this topic aims at studying the spectral subtraction & Wiener filter technique when the speech is degraded by non-stationary noise. We have studied both algorithms assuming stationary noise scenario. In this we want to study these two algorithms in the context of non-stationary noise. Next, decision directed (DD) approach, is used to estimate the time varying noise spectrum which resulted in better performance in terms of intelligibility and reduced musical noise. However, the a priori SNR estimator of the current frame relies on the estimated speech spectrum from the earlier frame. The undesirable consequence is that the gain function doesn’t match the current frame, resulting in a bias which causes annoying echoing effect. A method called Two-step noise reduction (TSNR) algorithm was used to solve the problem which tracks instantaneously the non-stationarity of the signal but, not by losing the advantage of the DD approach. The a priori SNR estimation was modified and made better by an additional step for removing the bias, thus eliminating reverberation effect. The output obtained even with TSNR still suffers from harmonic distortions which are inherent to all short time noise suppression techniques, the main reason being the inaccuracy in estimating PSD in single channel systems. To outdo this problem, a concept called, Harmonic Regeneration Noise Reduction (HRNR) is used wherein a non-linearity is made use of for regenerating the distorted/missing harmonics. All the above discussed algorithms have been implemented and their performance evaluated using both subjective and objective criteria. The performance is significantly improved by using HRNR combined with TSNR, as compared to TSNR, DD alone, as HRNR ensures restoration of harmonics. The spectral subtraction performance stands much below the above discussed methods for obvious reasons

CloudHealth: A Model-Driven Approach to Watch the Health of Cloud Services

Cloud systems are complex and large systems where services provided by different operators must coexist and eventually cooperate. In such a complex environment, controlling the health of both the whole environment and the individual services is extremely important to timely and effectively react to misbehaviours, unexpected events, and failures. Although there are solutions to monitor cloud systems at different granularity levels, how to relate the many KPIs that can be collected about the health of the system and how health information can be properly reported to operators are open questions. This paper reports the early results we achieved in the challenge of monitoring the health of cloud systems. In particular we present CloudHealth, a model-based health monitoring approach that can be used by operators to watch specific quality attributes. The CloudHealth Monitoring Model describes how to operationalize high level monitoring goals by dividing them into subgoals, deriving metrics for the subgoals, and using probes to collect the metrics. We use the CloudHealth Monitoring Model to control the probes that must be deployed on the target system, the KPIs that are dynamically collected, and the visualization of the data in dashboards.Comment: 8 pages, 2 figures, 1 tabl

Comparison of Speech Enhancement Algorithms

AbstractThe simplest and very familiar method to take out stationary background noise is spectral subtraction. In this algorithm, a spectral noise bias is calculated from segments of speech inactivity and is subtracted from noisy speech spectral amplitude, retaining the phase as it is. Secondary procedures follow spectral subtraction to reduce the unpleasant auditory effects due to spectral error. The drawback of spectral subtraction is that it is applicable to speech corrupted by stationary noise. The research in this topic aims at studying the spectral subtraction & Wiener filter technique when the speech is degraded by non-stationary noise. We have studied both algorithms assuming stationary noise scenario. In this we want to study these two algorithms in the context of non-stationary noise. Next, decision directed (DD) approach, is used to estimate the time varying noise spectrum which resulted in better performance in terms of intelligibility and reduced musical noise. However, the a priori SNR estimator of the current frame relies on the estimated speech spectrum from the earlier frame. The undesirable consequence is that the gain function doesn’t match the current frame, resulting in a bias which causes annoying echoing effect. A method called Two-step noise reduction (TSNR) algorithm was used to solve the problem which tracks instantaneously the non-stationarity of the signal but, not by losing the advantage of the DD approach. The a priori SNR estimation was modified and made better by an additional step for removing the bias, thus eliminating reverberation effect. The output obtained even with TSNR still suffers from harmonic distortions which are inherent to all short time noise suppression techniques, the main reason being the inaccuracy in estimating PSD in single channel systems. To outdo this problem, a concept called, Harmonic Regeneration Noise Reduction (HRNR) is used wherein a non-linearity is made use of for regenerating the distorted/missing harmonics. All the above discussed algorithms have been implemented and their performance evaluated using both subjective and objective criteria. The performance is significantly improved by using HRNR combined with TSNR, as compared to TSNR, DD alone, as HRNR ensures restoration of harmonics. The spectral subtraction performance stands much below the above discussed methods for obvious reasons

HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words

Streaming keyword spotting is a widely used solution for activating voice assistants. Deep Neural Networks with Hidden Markov Model (DNN-HMM) based methods have proven to be efficient and widely adopted in this space, primarily because of the ability to detect and identify the start and end of the wake-up word at low compute cost. However, such hybrid systems suffer from loss metric mismatch when the DNN and HMM are trained independently. Sequence discriminative training cannot fully mitigate the loss-metric mismatch due to the inherent Markovian style of the operation. We propose an low footprint CNN model, called HEiMDaL, to detect and localize keywords in streaming conditions. We introduce an alignment-based classification loss to detect the occurrence of the keyword along with an offset loss to predict the start of the keyword. HEiMDaL shows 73% reduction in detection metrics along with equivalent localization accuracy and with the same memory footprint as existing DNN-HMM style models for a given wake-word

LSTM Based Lip Reading Approach for Devanagiri Script

Speech Communication in a noisy environment is a difficult and challenging task. Many professionals work in noisy environments like aviation, constructions, or manufacturing, and find it difficult to communicate orally. Such noisy environments need an automated lip-reading system that could be helpful in communicating some instructions and commands. This paper proposes a novel lip-reading solution, which extracts the geometrical shape of lip movement from the video and predicts the words/sentences spoken. An Indian specific language data set is developed which consists of lip movement information captured from 50 persons. This includes students in the age group of 18 to 20 years and faculty in the age group of 25 to 40 years . All have spoken a paragraph of 58 words within 10 sentences in Hindi (Devanagari, spoken in India) language which was recorded under various conditions. The implementation consists of facial parts detection, along with Long short term memory’s. The proposed solution is able to predict the words spoken with 77% and 35% accuracy for data set of 3 and 10 words respectively. The sentences are predicted with 20% accuracy, which is encouraging

Prevalence of subclinical keratoconus and impact on adults undergoing routine, uncomplicated age-related cataract extraction

AimTo determine the prevalence of subclinical keratoconus (SKCN) among individuals undergoing routine, uncomplicated age-related cataract surgery and its impact on visual and refractive outcomes.Patient and MethodsAt a major academic ophthalmology department in the United States, we reviewed records of patients aged 50 years and older who underwent surgery from January 2011 to June 2022. We excluded patients who had poor-quality or unreliable tomographic data, previous corneal surgery, keratorefractive procedures, and significant vision-limiting ocular pathology. We defined SKCN if an eye had a Belin-Ambrósio enhanced ectasia index (BAD-D) ≥1.7, which was based on the results of a meta-analysis of large studies. In addition to the BAD-D cutoff, the eye had to deviate significantly on at least one of seven additional parameters: 1) posterior elevation at thinnest point, 2) index of vertical asymmetry, 3) index of surface variation, 4) total front higher order aberrations, 5) front vertical coma, 6) front secondary vertical coma, 7) back vertical coma. An individual had SKCN if at least one eye met the tomography-based classification and did not have manifest KCN in either eye. Visual and refractive outcomes data were acquired from patients of one experienced cataract surgeon with cases done from July 2021 to June 2022. Statistical significance was set at p < 0.05.ResultsAmong 5592 eyes from 3828 individuals, the prevalence of SKCN was 24.7% (95% CI, 23.4 – 26.1, 945 individuals), and the prevalence of KCN was 1.9% (95% CI, 1.6 – 2.4, 87 individuals). The prevalence of SKCN did not increase with age and was more prevalent among females and non-white races. Median post-operative month one distance-corrected visual acuity (DCVA) and proportion of eyes with improvement in DCVA were similar between normal and SKCN eyes. The proportion of eyes reaching ±0.5 and ±1.0 diopter within the refractive target were similar between normal and SKCN eyes.ConclusionSKCN is highly prevalent and should be detected but is unlikely to have a significant deleterious effect on outcomes in routine, uncomplicated cataract surgery

Clinical Study Crevicular Fluid and Serum Concentrations of Progranulin and High Sensitivity CRP in Chronic Periodontitis and Type 2 Diabetes

Introduction. This study was designed to correlate the serum and gingival crevicular fluid (GCF) levels of progranulin (PGRN) and high sensitivity C-reactive protein (hs CRP) in chronic periodontitis and type 2 diabetes mellitus (DM). Design. PGRN and hs CRP levels were estimated in 3 groups: healthy, chronic periodontitis, and type 2 DM with chronic periodontitis. Results. The mean PGRN and hs CRP concentrations in serum and GCF were the highest for group 3 followed by group 2 and the least in group 1. Conclusion. PGRN and hs CRP may be biomarkers of the inflammatory response in type 2 DM and chronic periodontitis