Sparse variational regularization for visual motion estimation

The computation of visual motion is a key component in numerous computer vision tasks such as object detection, visual object tracking and activity recognition. Despite exten- sive research effort, efficient handling of motion discontinuities, occlusions and illumina- tion changes still remains elusive in visual motion estimation. The work presented in this thesis utilizes variational methods to handle the aforementioned problems because these methods allow the integration of various mathematical concepts into a single en- ergy minimization framework. This thesis applies the concepts from signal sparsity to the variational regularization for visual motion estimation. The regularization is designed in such a way that it handles motion discontinuities and can detect object occlusions

Molecular Analyses of Phenylketonuria in The Intellectually Disabled Children from Faisalabad, Punjab, Pakistan

ABSTRACT Background and Objective: Phenylketonuria (PKU) is a rare inherited metabolic disorder, caused by mutations in the phenylalanine hydroxylase (PAH). It is a treatable disorder if diagnosed earlier in life. The objective was to identify PKU patient(s) amongst the intellectually disabled children. Methods: Blood samples (n=100) were collected from intellectually disabled children from Faisalabad, Pakistan. Screening was performed on plasma samples through High Performance Liquid Chromatography (HPLC), and DNA samples were examined for mutation analysis of PAH through direct PCR and SSCP analyses. Results: In the current study, 85% consanguinity rate was observed, with the average BMI (16.15 kg/m2) and head circumference(50.21 cm) was observed and the age range of the patients were 8-14 years. Moreover, through biochemical and genetic analyses, not a single PKU patient was identified. Conclusion: Based on just one previous report and our small dataset it is concluded that either mutations are not common in the hotspot regions or chances of occurrence of PKU might be rare in Pakistan. Moreover, there is a need of more research on large scale to find the incidence of PKU in Pakistan

Dense Optical Flow Estimation Using Sparse Regularizers from Reduced Measurements

Optical flow is the pattern of apparent motion of objects in a scene. The computation of optical flow is a critical component in numerous computer vision tasks such as object detection, visual object tracking, and activity recognition. Despite a lot of research, efficiently managing abrupt changes in motion remains a challenge in motion estimation. This paper proposes novel variational regularization methods to address this problem since they allow combining different mathematical concepts into a joint energy minimization framework. In this work, we incorporate concepts from signal sparsity into variational regularization for motion estimation. The proposed regularization uses a robust l1 norm, which promotes sparsity and handles motion discontinuities. By using this regularization, we promote the sparsity of the optical flow gradient. This sparsity helps recover a signal even with just a few measurements. We explore recovering optical flow from a limited set of linear measurements using this regularizer. Our findings show that leveraging the sparsity of the derivatives of optical flow reduces computational complexity and memory needs.Comment: 12 pages, 9 figures, and 3 table

Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1\%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method

Regular post dinner walk; can be a useful lifestyle modification for gastroesophageal reflux

OBJECTIVES: To evaluate the correlation of gastroesophageal reflux disease (GERD) symptoms with routine post dinner physical activity and time interval before going to bed, in multiethnic South Asian population. METHODS: Prospective, cross sectional analytical, multicenter study was conducted from February 2009 to March 2010. Patient\u27s relative sitting in outpatient clinics with no comorbids, nonsmoker and non alcoholic were included. They were asked to fill a validated GERD questionnaire and were also inquired about routine post dinner physical activity (lying, sitting, walking) and dinner-bed time interval. Odds Ratios (OR) and their 95% Confidence Intervals (CI) were estimated using Logistic Regression, with gastroesophageal reflux (GER) symptoms as an outcome. RESULTS: Subjects analyzed were 1875. Mean age was 35.37 +/- 12.69 years of which 689 (36.74%) had GERD symptoms. GERD symptoms were 42.08% in routine post dinner recumbency position. While 35.17% and 30.52% had the symptoms in post dinner sitting and walking before going to bed [OR for walking 0.66 (95% CI 0.5-0.88) when compared with lying posture]. GERD symptoms were 45.86% among those with dinner-bed time of one hour, progressively decreasing to 41.68%, 31.45% and 29.88% in the second, third and forth hour respectively. Odds ratio was significant only at 3rd [0.55 (0.41-0.74)] and \u3e or = 4th hr [0.51 (0.37-0.71)] when compared with first hour. CONCLUSION: Regular post dinner walk and \u3e 3 hour dinner-bed time interval were less associated with GERD symptoms

Energy Disaggregation & Appliance Identification in a Smart Home: Transfer Learning enables Edge Computing

Non-intrusive load monitoring (NILM) or energy disaggregation aims to extract the load profiles of individual consumer electronic appliances, given an aggregate load profile of the mains of a smart home. This work proposes a novel deep-learning and edge computing approach to solve the NILM problem and a few related problems as follows. 1) We build upon the reputed seq2-point convolutional neural network (CNN) model to come up with the proposed seq2-[3]-point CNN model to solve the (home) NILM problem and site-NILM problem (basically, NILM at a smaller scale). 2) We solve the related problem of appliance identification by building upon the state-of-the-art (pre-trained) 2D-CNN models, i.e., AlexNet, ResNet-18, and DenseNet-121, which are fine-tuned two custom datasets that consist of Wavelets and short-time Fourier transform (STFT)-based 2D electrical signatures of the appliances. 3) Finally, we do some basic qualitative inference about an individual appliance's health by comparing the power consumption of the same appliance across multiple homes. Low-frequency REDD dataset is used for all problems, except site-NILM where REFIT dataset has been used. As for the results, we achieve a maximum accuracy of 94.6\% for home-NILM, 81\% for site-NILM, and 88.9\% for appliance identification (with Resnet-based model).Comment: 10 pages, 4 figures, 3 tables, under review with a journa

Clinical laboratory : journal for laboratory medicine, transfusion medicine and cell therapy

Modern variational motion estimation techniques use total variation regularization along with the L1 norm in constant brightness data term. An algorithm based on such homogeneous regularization is unable to preserve sharp edges and leads to increased estimation errors. A better solution is to modify regularizer along strong intensity variations and occluded areas. In addition, using neighborhood information with data constraint can better identify correspondence between image pairs than using only a pointwise data constraint. In this work, we present a novel motion estimation method that uses neighborhood dependent data constraint to better characterize local image structure. The method also uses structure adaptive regularization to handle occlusions. The proposed algorithm has been evaluated on Middlebury’s benchmark image sequence dataset and compared to state-of-the-art algorithms. Experiments show that proposed method can give better performance under noisy conditions