Concurrent online tracking of mobile computing

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Purchasing a time and attendance system may not be the first thing small business owners think of when considering ways to achieve their expected revenue growth. When the medical practitioners of North and South Carolina need laboratory diagnostics, they often turn to the blood and urine analysis services of Select Labs.

Barcodes play a crucial role in the effective and efficient operation of our economy, from small businesses to large multinational conglomerates.

Wasp listens to the needs of small business customers because of its passion to help each of those customers succeed. Applications by Use Case:. Applications by Industry:. Frequent Support Options:.

Other Resources:. Search Quote Cart Account. Eliminate Payroll Errors With one of our cost-saving, easy-to-implement time and attendance tracking systems. Increase Time Card Accuracy. Reduce Costly Data Entry Errors.

Rules for Rounding and Overtime. Minimize Payroll Processing Time. Over 30 Management Reports. Exports to Current Payroll Systems. Eliminate Costly Errors Stop worrying about payroll mistakes or timesheet fraud, WaspTime tracks employee time based on the rules you set. Simplify Payroll WaspTime integrates seamlessly with many current payroll systems, removing the need for manual data entry and reducing payroll error. Knowledge is Power! More Accurate Pay Checks No more worry about losing your time sheet or forgetting to write down when you came back from lunch.

Accurate Ensures Time Cards are Accurate. Cost Effective More bang for your buck. Efficient Reduces time spent on payroll and eliminates errors. Reliable Always on top of its game when you need it to be. Precise Eliminate human errors. Manual data entry time in, time out and breaks is a thing of the past. Time and Attendance Software Training Options Customized learning plans ensure each user becomes proficient in using the software or system.

Live and prerecorded 1-hour web training covers basic installation and setup. Learn at your own pace and watch on-demand videos of detailed product tutorials. Instructor-led, one-on-one, web-based training to help you master key features. Our system is designed for smart speakers that can monitor an infant's sleep using white noise. The key enabler underlying our system is a set of novel algorithms that can extract the minute infant breathing motion as well as position information from white noise which is random in both the time and frequency domain.

We describe the design and implementation of our system, and present experiments with a life-like infant simulator as well as a clinical study at the neonatal intensive care unit with five new-born infants. Our study demonstrates that the respiratory rate computed by our system is highly correlated with the ground truth with a correlation coefficient of 0. Parkinson's disease PD is a chronic neurodegenerative disorder resulting from the progressive loss of dopaminergic nerve cells.

Early detection of PD plays an important role in symptom relief and improvement in the performance of activities in daily life ADL , which eventually reduces societal and economic burden. However, conventional PD detection methods are inconvenient in daily life e. To overcome this challenge, we propose and identify the non-speech body sounds as the new PD biomarker, and utilize the data in smartphone usage to realize the passive PD detection in daily life without interrupting the user.

Specifically, we present PDVocal, an end-to-end smartphone-based privacy-preserving system towards early PD detection. PDVocal can passively recognize the PD digital biomarkers in the voice data during daily phone conversation. At the user end, PDVocal filters the audio stream and only extracts the non-speech body sounds e. At the cloud end, PDVocal analyzes the body sounds of interest and assesses the health condition using a customized residual network.

For the sake of reliability in real-world PD detection, we investigate the method of the performance optimizer including an opportunistic learning knob and a long-term tracking protocol. We evaluate our proposed PDVocal on a collected dataset from participants and real-life conversations from publicly available data sources.

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Results indicate that non-speech body sounds are a promising digital biomarker for privacy-preserving PD detection in daily life. With the rise of ever-more sophisticated wearables and sensing technologies, mobile health continues to be an active area of research. However, from a clinical researcher point of view, testing novel use of the mobile health innovations remains a major hurdle, as composing a clinical trial using a combination of technologies still remains in the realm of computer scientists.

We take a software-inspired viewpoint of clinical trial designs to design, develop and validate HealthSense to enable expressibility of complex ideas, composability with diverse devices and services while maximally maintaining simplicity for a clinical research user. A key innovation in HealthSense is the concept of a study state manager SSM that modifies parameters of the study over time as data accumulates and can trigger external events that affect the participant; this design allows us to implement nearly arbitrary clinical trial designs.

The SSM can funnel data streams to custom or third-party cloud processing pipelines and the result can be used to give interventions and modify parameters of the study. HealthSense supports both Android and iOS platforms and is secure, scalable and fully operational. We outline three trials two with clinical populations to highlight simplicity, composability, and expressibility of HealthSense.

Wrist-worn devices hold great potential as a platform for mobile health mHealth applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? In the past five years the team conducted 11 studies in the lab and in the field, involving participants and collecting over 77, hours of sensor data.

We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms. Despite the pervasive use of real-time video telephony services, the users' quality of experience QoE remains unsatisfactory, especially over the mobile Internet.

Previous work studied the problem via controlled experiments, while a systematic and in-depth investigation in the wild is still missing. To bridge the gap, we conduct a large-scale measurement campaign on Taobao-Live, an operational mobile video telephony service. Our measurement logs fine-grained performance metrics over 1 million video call sessions. Our analysis shows that the application-layer video codec and transport-layer protocols remain highly uncoordinated, which represents one major reason for the low QoE.

We thus propose Concerto, a machine learning based framework to resolve the issue. To attain the ability, we train Concerto with the aforementioned massive data traces using a custom-designed imitation learning algorithm, which enables Concerto to learn from past experience. We have implemented and incorporated Concerto into Taobao-Live. Our experiments show that Concerto outperforms state-of-the-art solutions, improving video quality while reducing stalling time by multi-folds under various practical scenarios. The popularity of 4K videos has grown significantly in the past few years.

Yet coding and streaming live 4K videos incurs prohibitive cost to the network and end system. Motivated by this observation, we explore the feasibility of supporting live 4K video streaming over wireless networks using commodity devices.

Given the high data rate requirement of 4K videos, 60 GHz is appealing, but its large and unpredictable throughput fluctuation makes it hard to provide desirable user experience.