Dengue is a disease transmitted primarily through mosquito bites. Innovative solutions have been developed to combat outbreaks. However, in developing countries these dengue detection solutions are often not affordable and easily accessible. Additionally, these traditional approaches are slow to diagnose and treat dengue. We present a dengue detection solution that uses vision sensors in cellular phones, a lightweight object identification algorithm, and a web server that provides spatial information to healthcare providers. Our systems leverages a novel paper based technology developed by researchers at the Harvard University Department of Chemistry (Martinez et al. Angew Chem Int Ed 46:1318–1320, 2007 ). Our dengue detection algorithm rapidly diagnoses dengue, transmits the results to the Center for Disease Control (CDC) for further analysis, and presents healthcare providers with spatial information on outbreaks. This novel approach can improve the quality of life in developing countries by accurately and economically detecting dengue and providing data to the CDC for monitoring of dengue epidemics.

Social system networks with high data rates and limited storage will discard data if the system cannot connect and upload the data to a central server. We address the challenge of limited storage capacity in mobile health systems during network partitions with a heuristic that achieves efficiency in storage capacity by modifying the granularity of the medical data during long intercontact periods. Patterns in the connectivity, reception rate, distance, and location are extracted from the social system network and leveraged in the global algorithm and online heuristic. In the global algorithm, the stochastic nature of the data is modeled with maximum likelihood estimation based on the distribution of the reception rates. In the online heuristic, the correlation between system position and the reception rate is combined with patterns in human mobility to estimate the intracontact and intercontact time. The online heuristic performs well with a low data loss of 2.1%-6.1%.

Depression is one of the leading causes of disability. Methods are needed to quantitatively classify emotions in order to better understand and treat mood disorders. This research proposes techniques to improve communication in body sensor network (BSN) that gathers data on the affective states of the patient. These BSNs can continuously monitor, discretely quantify, and classify a patient's depressive states. In addition, data on the patient's lifestyle can be correlated with his/her physiological conditions to identify how various stimuli trigger symptoms. This continuous stream of data is an improvement over a snapshot of localized symptoms that a doctor often collects during a medical examination. Our research first quantifies how the body interferes with communication in a BSN and detects a pattern between the line of sight of an embedded device and its reception rate. Then, a mathematical model of the data using linear programming techniques determines the optimal placement and number of sensors in a BSN to improve communication. Experimental results show that the optimal placement of embedded devices can reduce power cost up to 27% and reduce hardware costs up to 47%. This research brings researchers a step closer to continuous, real-time systemic monitoring that will allow one to analyze the dynamic human physiology and understand, diagnosis, and treat mood disorders.

Hardware Trojan horses (HTHs) are the malicious altering of hardware specification or implementation in such a way that its functionality is altered under a set of conditions defined by the attacker. There are numerous HTHs sources including untrusted foundries, synthesis tools and libraries, testing and verification tools, and configuration scripts. HTH attacks can greatly comprise security and privacy of hardware users either directly or through interaction with pertinent systems and application software or with data. However, while there has been a huge research and development effort for detecting software Trojan horses, surprisingly, HTHs are rarely addressed. HTH detection is a particularly difficult task in modern and pending deep submicron technologies due to intrinsic manufacturing variability.

The improvement in processor performance through continuous breakthroughs in transistor technology has resulted in the proliferation of lightweight embedded systems. Advances in wireless technology and embedded systems have enabled remote healthcare and telemedicine. While medical examinations could previously extract only localized symptoms through snapshots, now continuous monitoring can discretely analyze how a patient's lifestyle affects his/her physiological conditions and if additional symptoms occur under various stimuli. We demonstrate how medical applications in particular benefit from a hierarchical networking scheme that will improve the quantity and quality of ubiquitous data collection. Our Telehealth networking infrastructure provides flexibility in terms of functionality and the type of applications that it supports. We specifically present a case study that demonstrates the effectiveness of our networked embedded infrastructure in an in vivo pressure application. Experimental results of the in vivo system demonstrate how it can wirelessly transmit pressure readings measuring from 0 to 1.5 lbf/in (2) with an accuracy of 0.02 lbf/in (2). The challenges in biocompatible packaging, transducer drift, power management, and in vivo signal transmission are also discussed. This research brings researchers a step closer to continuous, real-time systemic monitoring that will allow one to analyze the dynamic human physiology.

We present the first approach for post-silicon leakage power reduction through input vector control (IVC) that takes into account the impact of the manufacturing variability (MV). Because of the MV, the integrated circuits (ICs) implementing one design require different input vectors to achieve their lowest leakage states. We address two major challenges. The first is the extraction of the gatelevel characteristics of an IC by measuring only the overall leakage power for different inputs. The second problem is the rapid generation of input vectors that result in a low leakage for a large number of unique ICs that implement a given design, but are different in the post-manufacturing phase. Experimental results on a large set of benchmark instances demonstrate the efficiency of the proposed methods. For example, the leakage power consumption could be reduced in average by more than 10.4%, when compared to the previously published IVC techniques that did not consider MV.

Remote medical monitoring with medical sensors and wireless communications has recently gained attention for the potential savings and improved quality in patient health-care. Currently, many mobile wireless medical infrastructures connect to the Internet in a point-to-point fashion, such as the cellular network. However, our research investigates new models for remote patient care that exploit P2P networking among patients and healthcare providers (nurses, doctors, emergency personnel). In this paper, we identify several medical applications based on P2P Health Networking focusing on two specific scenarios where nurses and patients are both equipped with Bluetooth devices. Nurses opportunistically collect, share and upload data in a P2P fashion. During an emergency, the nearest nurse is alerted via Bluetooth Enhanced Inquiry Response mechanism. The simulation and test-bed experimentation demonstrate that Bluetooth P2P networking is both feasible and cost-effective in remote medical monitoring.

Power consumption has emerged as the premier and most constraining aspect in modern microprocessor and application-specific designs. Gate sizing has been shown to be one of the most effective methods for power (and area) reduction in CMOS digital circuits. Recently, as the feature size of logic gates (and transistors) is becoming smaller and smaller, the effect of soft-error rates caused by single-event upsets (SEUs) is becoming exponentially greater. As a consequence of technology feature size reduction, the SEU rate for typical microprocessor logic at sea level will go from one in hundred years to one every minute. Unfortunately, the gate sizing requirements of power reduction and resiliency against SEU can be contradictory. 1) We consider the effects of gate sizing on SEU and incorporate the relationship between power reduction and SEU resiliency to develop a new method for power optimization under SEU constraints. 2) Although a nonlinear programming approach is a more obvious solution, we propose a convex programming formulation that can be solved efficiently. 3) Many of the optimal existing techniques for gate sizing deal with an exponential number of paths in the circuit. We prove that it is sufficient to consider a linear number of constraints. 4) We generalize our methodology to include nonlinear delay models and leakage power as well. As an important preprocessing step, we apply statistical modeling and validation techniques to quantify the impact of fault masking on the SEU rate. Furthermore, we adapt our method to incorporate process variation and evaluate our gate sizing technique under uncertainty. We evaluate the effectiveness of our methodology on ISCAS benchmarks and show that error rates can be reduced by a factor of 100%-200% while, on average, the power reduction is simultaneously decreased by less than 6%-10%, respectively, compared to the optimal power saving with no error rate constraints.

In wireless sensor networks, the data collected from numerous low end sensors can be equivalent or superior to a few high fidelity sensors. In this paper, we evaluate the validity of this concept with low-resolution optical sensors. We implemented passive localization using visual object identification techniques on a field programmable gate array (FPGA) sensor platform. The complexity, memory utilization, and accuracy of our algorithms were analyzed for performance on a resource constrained environment. We show that low-resolution optical sensors can accurately localize objects on a global two dimensional plane. We also present a model that quantifies fidelity versus scale for optical sensors. Using our model and application, we demonstrate that low-fidelity optical sensors are an effective tool in sensor networks and at a certain scale can provide superior coverage of an area over a high-resolution camera. This analysis lays the groundwork for future advances in optical sensors.

Light-weight embedded systems are now gaining more popularity due to the recent technological advances in fabrication that have resulted in more powerful tiny processors with greater communication capabilities that pose various scientific challenges for researchers. Perhaps the most significant challenge is the energy consumption concern and reliability, mainly due to the small size of batteries. In this tutorial, we portray a brief description of low-power, light-weight embedded systems, depict several power profiling studies previously conducted, and present several research challenges that require low-power consumption in embedded systems. For each challenge, we highlight how low-power designs may enhance the overall performance of the system. Finally, we present a several techniques that minimize the power consumption in such systems.