The BIIP committee members have been performing scientific reviews for the 2018 IEEE International Symposium for Biomedical Imaging (IEEE-ISBI) over the past few months. IEEE-ISBI is a leading scientific conference dedicated to mathematical, algorithmic, and computational aspects of biological and biomedical imaging, and EMBS has been a technical co-sponsor of ISBI since 2002. 

We created the Opportunity to learn one of the popular and applicable  VHDL language for Medical applications and Network with fellow programmers and coding enthusiasts. The research on Internet of Things (IoT) is being promoted in the field of Medical Application. This workshop is to intend to impart knowledge in the basics of IoT in the field Medical application. The hands on training is provided in the different sensors with FPGA board. Advanced applications included in the workshop to provide knowledge on current industrial trends. 

More than 15 million people in the United States suffer from depression. According to a 2015 U.S. Centers for Disease Control (CDC) study, half of the 120 Americans who take their lives every day have been diagnosed with the disease. Despite the urgency of these numbers, science has yet to provide an adequate solution: A third of all people have no response to antidepressants whatsoever, and for all existing treatments-antidepressants, ECT, TMS, and DBS-there's only a general understanding of how and why they work. 

This reflects a larger problem in the field of neuropsychology: what causes depression in the first place? Some researchers have argued that depression symptoms are a direct response to the interactions among neurotransmitters, neurocircuits, and the immune system. Recently, by using functional magnetic resonance imaging (fMRI) on patients diagnosed with depression to observe brain activity in real time, researchers were able to find some commonalities in the degree and location of abnormal circuitry. 

Lower urinary tract dysfunctions, such as urinary incontinence and overactive bladder, are conditions that greatly affect the quality of life for millions of individuals worldwide. For those with more complex pathophysiologies, diagnosis of these conditions often requires a urodynamics study, providing physicians with a snapshot view of bladder mechanics. Recent advancements in implantable bladder pressure monitors and advanced data analysis techniques have made diagnosis through chronic monitoring a promising prospect. However, implants targeted at treatment must remain in the bladder for long periods of time, making minimizing power consumption a primary design objective. Currently, much of the typical implant's power draw is due to data transmission. Previous work has demonstrated an adaptive rate transmission technique to reduce power consumption. 

However, the ultimate reduction in power consumption can only be attained when the device does not transmit bladder pressure samples, but rather bladder events. In this paper, we present an algorithm and circuit level implementation for on-chip bladder pressure data compression and event detection. It is designed to be a complete, tunable, and lightweight diagnosis and treatment framework for bladder pressure monitoring implants, capable of selectively transmitting compressed bladder pressure data with tunable quality, "snapshots" of significant bladder events, or simply indicate events occurred for the highest energy efficiency. The design aims to minimize area through resource reuse, leading to a total area of 1.75 mm2, and employs advanced VLSI techniques for power reduction. With compression and event detection enabled, the design consumes roughly 2.6 nW in TSMC 0.18-μm technology. With only event detection, this reduces to 2.1 nW, making this approach ideal for long-life implantable bladder pressure monitoring devices. .

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