Human Re-identification with Global and Local Siamese Convolution Neural Network

Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification task in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches

Object's shadow removal with removal validation

We introduce in this paper, a shadow detection and removal method for moving objects especially for humans and vehicles. An effective method is presented for detecting and removing shadows from foreground figures. We assume that the foreground figures have been extracted from the input image by some background subtraction method. A figure may contain only one moving object with or without shadow. The homogeneity property of shadows is explored in a novel way for shadow detection and image division technique is used. The process is followed by filtering, removal, boundary removal and removal validation

An adapted point based tracking for vehicle speed estimation in linear spacing

Vehicle velocity estimation is an important aspect of intelligent transportation systems. Normally velocity is estimated using dedicated laser speed traps and Doppler radars. Recently, the use of cameras is becoming more common for the purpose of traffic surveillance and smart surveillance system. It is thus the aim of this paper to propose a method for vehicle speed estimation using these existing video cameras. In this paper, we propose a vehicle speed estimation method from video analysis. The method proposed contains several steps; image preprocessing, centroid extraction and tracking. The proposed method transforms the 2D image points into a 3D virtual world to obtain actual vehicle position in 3D space. This is to account for perspective distortion commonly seen in images. Using these 3D points and measuring the time for displacement, the vehicle speed is obtained. Experimental results have shown that the proposed method gives accurate velocity estimation

H1-antihistamines for primary mast cell activation syndromes: a systematic review

Background Primary mast cell activation syndromes (MCAS) are a group of disorders presenting with symptoms of mast cell mediator release. Objectives To assess the effectiveness and safety of orally administered H1-antihistamines in the treatment of primary MCAS compared with placebo and other pharmacologic treatments. Methods We systematically searched five databases and three trial repositories and contacted an international panel of experts to identify published and unpublished trials. Results A total of 36 potentially relevant studies were identified. Of these, five crossover trials, enrolling a total of 71 patients (63 adults), met the eligibility criteria. All five of these studies were judged to be at moderate or high risk of bias. Two studies compared an H1-antihistamine with placebo, two compared two different H1-antihistamines, and one study compared H1- and H2-antihistamines with oral cromolyn sodium. Four of the five randomized controlled trials were historic (reported from 1983–1993), small (enrolling 8–15 patients), and used agents and/or dosing regimens that are now less commonly used in clinical practice (i.e. azelastine, chlorpheniramine, hydroxyzine, and ketotifen). The fifth trial, which enrolled 33 adults with cutaneous and systemic mastocytosis found 4 weeks of treatment with the second-generation H1-antihistamine rupatadine, compared with placebo, resulted in significant improvements in quality of life, symptom control (itching, wheals and flares, flushing, tachycardia, and headache, but not gastrointestinal symptoms), and reduction in itching and whealing after standardized skin provocation to elicit Darier's sign. Conclusions There is an urgent need for large, well-designed, double-blind, placebo-controlled randomized trials investigating the effectiveness, cost-effectiveness, and safety of second-generation H1-antihistamines in treatment of primary MCAS

Overseeing Automobiles through CAN bus with intact WiFi

In my paper I have presented to avoid calamities on boulevard areas. In this I can track through sensors for monitoring speed, Engine temperature and fuel consumption status. Adding up with the driving behavior like excessive breakings, quick accelerations etc. For all the monitoring purpose CAN bus is used as a communication in a distributed control network. This paper is mainly introduces the ARM based design of hardware and then analyzed through Dashboards reports of software

Toward an mHealth Intervention for Smoking Cessation

The prevalence of tobacco dependence in the United States (US) remains alarming. Invariably, smoke-related health problems are the leading preventable causes of death in the US. Research has shown that a culturally tailored cessation counseling program can help reduce smoking and other tobacco usage. In this paper, we present a mobile health (mHealth) solution that leverages the Short Message Service (SMS) or text messaging feature of mobile devices to motivate behavior change among tobacco users. Our approach implements the Theory of Planned Behavior (TPB) and a phase-based framework. We make contributions to improving previous mHealth intervention approaches by delivering personalized and evidence-based motivational SMS messages to participants. Our proposed solution implements machine learning algorithms that take the participant\u27s demographic profile and previous smoking behavior into account. We discuss our preliminary evaluation of the system against a couple of pseudo-scenarios and our observation of the system\u27s performance

Smartphone-Based Prenatal Education for Parents with Preterm Birth Risk Factors

Objective To develop an educational mobile application (app) for expectant parents diagnosed with risk factors for premature birth. Methods Parent and medical advisory panels delineated the vision for the app. The app helps prepare for preterm birth. For pilot testing, obstetricians offered the app between 18–22 weeks gestational age to English speaking parents with risk factors for preterm birth. After 4 weeks of use, each participant completed a questionnaire. The software tracked topics accessed and duration of use. Results For pilot testing, 31 participants were recruited and 28 completed the questionnaire. After app utilization, participants reported heightened awareness of preterm birth (93%), more discussion of pregnancy or prematurity issues with partner (86%), increased questions at clinic visits (43%), and increased anxiety (21%). Participants reported receiving more prematurity information from the app than from their healthcare providers. The 15 participants for whom tracking data was available accessed the app for an average of 8 h. Conclusion Parents with increased risk for preterm birth may benefit from this mobile app educational program. Practice implications If the pregnancy results in preterm birth hospitalization, parents would have built a foundation of knowledge to make informed medical care choices