Seminars by CECS

PhD Defense: Robust Data Hiding in Multimedia for Authentication and Ownership Protection

Name: Farhan A. Alenizi

Date: May 26, 2017

Time: 10:00 AM

Location: Engineering Hall 3206

Committee: Professor Fadi Kurdahi (Chair), Professor Ahmed Eltawil and Professor Rainer Doemer

Abstract:

Establishing robust and blind data hiding techniques in multimedia is very important for authentication, ownership protection and security. The multimedia being used may include images, videos and 3D mesh objects. A hybrid pyramid Discrete-Wavelet-Transform (DWT) Singular-Value-Decomposition (SVD) data hiding scheme for video authentication and ownership protection is proposed. The data being hidden will be in the shape of a main  color logo image watermark and another secondary Black and White (B&W) logo image.   The color watermark will be decomposed to Bit-Slices.   A pyramid transform is performed on the Y-frames of a video stream resulting in error images;  then, a Discrete Wavelet Transform (DWT) process is implemented using orthonormal filter banks on these  error images, and the Bit-Slices watermarks are inserted in one or more of the resulting subbands in a way that is fully controlled by the owner; then, the watermarked video is reconstructed. SVD will be performed on the color watermark Bit-Slices. A secondary B&W watermark will be inserted in the main color watermark using another SVD process.     The reconstruction was perfect without attacks, while the average Bit-Error-Rates (BER’s) achieved under attacks are in the limits of  2% for the color watermark and 5% for the secondary watermark; meanwhile,  the mean Peak Signal-to-Noise Ratio (PSNR) is 57 dB. Furthermore, a selective denoising filter to eliminate the noise in video frames is proposed; and the performance with data hiding is evaluated.

Moreover, a 3D mesh blind  optimized watermarking   technique is proposed in this research. The technique relies on  the displacement process of  the vertices locations depending on the modification of the  variances of the vertices’s norms. Statistical analysis were performed to establish the proper distributions that best fit each mesh, and hence establishing the bins sizes.   Experimental results showed that the approach is robust in terms of both the perceptual  and the quantitative  qualities.

In conclusion,  the degree of robustness and security  of the  proposed techniques are shown. Also the schemes that can be adopted to further enhance the performance,  and the future work that can be done in the field are introduced.

 

 
PhD Defense: Millimeter-wave and Sub-THz Signal Generation and Detection in Silicon Technologies

Name: Peyman Nazari

Date: May 15, 2017

Time: 10:00 AM

Committee: Prof. Payam Heydari (Chair). Prof. Michael Green, Prof. Ahmed Eltawil

Abstract: 

MM-wave/sub-Terahertz (THz) signal generation, radiation, and detection have become increasingly attractive due to its fast-growing applications in spectroscopy, radar, biomedical and security imaging as well as high-speed wireless communication.
Silicon technology, in one hand, offering high-density signal processing capabilities due to aggressive scaling of its feature size, and on the other hand, allowing integration of mm-wave/THz antenna elements owing to their shrunk footprint at these bands, is well-suited for implementation of fully-integrated multi-antenna mm-wave/THz wireless System-on-Chips (SoC’s).
Performance of such system is dominantly governed by the quality and efficiency of signal generation, transmission, and detection. Passive and active components as means of realizing these functionalities must be optimized for operation at these frequency ranges. However, excessive loss of on-chip passive components and limited gain and output power of transistors at such high frequencies demand novel passive and active structures. Furthermore, high level of integration implies that the co-design of front-end components leads to a better end-to-end performance, thus a holistic design methodology must be employed. Radiation characteristics of the wireless signal must also be engineered to improve its transmission quality. For example, circularly polarized radiation is found to be a viable choice for many imaging and communication applications by exhibiting excellent robustness against de-polarization effects.
In this dissertation, silicon realization of on-chip waveguides, as low loss mediums for high-frequency wave propagation, is explored and implementations of low-loss cavity-backed passives are discussed. Furthermore, a silicon-integrated IMPATT diode, together with its fabrication and modeling is introduced as a solution for obtaining active behavior beyond fmax of transistors. Next, a high-power/efficiency mm-wave circularly-polarized cavity-backed radiator, employing a multi-port multi-function passive network as a resonator, power combiner, and antenna, is introduced. Necessary conditions for robust operation of such multi-port oscillators/radiators are also derived. Fabricated in a 0.13µm SiGe BiCMOS process, the prototype chip achieves 14.2dBm EIRP, -99.3dBc/Hz phase noise at 1MHz offset, and 5.2% DC-to-EIRP conversion efficiency which is the highest reported value among silicon-based radiators not using silicon lens or substrate processing.
Finally, a 210GHz low noise amplifier (LNA) is presented to address the detection challenges. This LNA, achieves 18dB of gain, with less than 12dB noise-figure and 3dB bandwidth of more than 15GHz, thereby showing best performance metrics among prior work. This is achieved by incorporating circuit and EM techniques enabling simultaneous optimization of stable gain-, noise- and bandwidth-performance parameters at this frequency range.

 
PhD Defense: Optimizing Many-Threads-to-Many-Cores Mapping in Parallel Electronic System Level Simulation

Name: Guantao Liu

Date: March 2, 2017

Time: 4:00 PM

Location: Engineering Hall 3206

Committee: Rainer Doemer (Chair), Kwei-Jay Lin, Mohammad Al Faruque

Abstract:

In hardware/software co-design, Discrete Event Simulation (DES) has been in use for decades to verify and validate the functionality of Electronic System Level (ESL) models. Since the parallel computing platforms are readily available today, many Parallel Discrete Event Simulation (PDES) approaches are proposed to improve the simulation performance. However, as the thread parallelism increases in ESL designs and core count multiplies on multi-core and many-core platforms, thread-to-core mapping becomes critical in PDES.

In this dissertation, we propose a computation- and communication-aware approach to optimize thread mapping for parallel ESL simulation, with the aims of load balancing and communication minimization. As we identify that the order of dispatching parallel threads has a significant influence on the total simulation time, and Longest Job First (LJF) shows better performance than the Linux default thread dispatch policy, we first propose a segment- aware LJF scheduler for PDES. Our segment-aware scheduler can accurately predict the run time of the thread segments ahead, and thus make better dispatching decisions. Next, we define the concept of core distance for multi-core and many-core architectures, which quantifies core-to-core communication latency and characterizes processor hierarchies. For many-core architectures using directory-based cache coherence protocols, we observe that core-to-core transfers are not always significantly faster than main memory accesses, and the core-to-core communication latency depends not only on the physical placement on the chip, but also on the location of the distributed cache tag directory. Thus, using a memory ping-pong benchmark, we quantify the core distance on a ring-network many-core platform and propose an algorithm to optimize thread-to-core mapping in order to minimize on-chip communication overhead. Altogether, based on a static analysis of communication patterns and core distance and a dynamic profiling of computation load, our proposed framework utilizes a heuristic graph partitioning algorithm and automatically generates an optimized thread mapping, which minimizes inter-chip communication overhead. In our systematic evaluation, our approach consistently shows a significant performance gain on top of the order-of-magnitude speedup of PDES.

The contributions of this dissertation include a segment-aware multi-core scheduler, core distance profiling, a communication-aware thread mapping framework, together with an open-source software package for Out-of-Order PDES.

 
PhD Defense: Low Power Reliable Design using Pulsed Latch Circuits

Name: Wael Mahmoud Elsharkasy

Date: February 15, 2017

Time: 11:00 AM

Location: Engineering Hall 3206

Committee: Prof. Fadi J. Kurdahi, Prof. Ahmed Eltawil, Rainer Doemer

Abstract:

System-on-Chip (SoC) faced lots of challenges over the past decade. With nowadays applications centered around Internet-of-Everything (IoE), these challenges are expected to be more critical. Among these challenges are the reduction of power consumption for better energy efficiency, the overcoming of different sources of variations to ensure reliable operation and the reduction of design area to reduce the cost and increase the integration. As a result, chip designers find themselves facing lots of problems, trying to build reliable systems that integrate complex level of functionality, on a minimum die size and with a limited power budgets. Among different circuit components in every chip, memory components are of great concern. They consume the majority of the chip area and power, in addition to affecting the entire chip performance and reliability. These include large memory arrays, caches, register files and different sequential elements in the logic paths. Sequential elements play an important and critical role in modern synchronous CMOS circuits. Indeed, they can represent up to 50% of the standard cells used in a chip. In addition, the power consumption of the clock tree, including these elements can be more than half of the total chip power. In addition, they come in the second place after memory to be affected by different sources of variation. Hence, efficient implementation of these elements is of great importance for the design of energy efficient and reliable integrated circuits. Pulsed latches have been proposed as efficient replacement of flip-flops in the implementation of sequential element. They can achieve higher performance when compared to traditional flip-flop, and can be designed to be smaller in area and more power efficient. However, the operation of pulsed latch is more sensitive to process, voltage and temperature (PVT) variations. In this thesis, we are proposing a methodology to study the reliability of pulsed latches and we have used it to evaluate the effect of PVT variations on their behavior. In addition, novel approaches to enhance the reliability of pulsed latches without significant degradation in performance, area or power are presented. Also, since sequential elements can be used to build small size register files, pulsed latch implementation of register files are discussed and compared to other traditional implementations, including SRAM and flip-flops. In addition, since multiport register files are very beneficial for quite few applications, novel implementations of multiport register files are also presented. The proposed implementation is proved to highly reduce the significant overhead in area, power and latency associated with the traditional way of designing multiport register files.

 
PhD Defense: Runtime Memory Management in Many-core Systems

Name: Hossein Tajik

Date: November 15, 2016

Time: 3:00PM – 4:00PM

Location: DBH 3011 Conference Room

Committee: Nikil Dutt (Chair), Tony Givargis, Alex Nicolau

Abstract:

With the increasing number of cores on a chip, we are moving towards an era where many-core platforms will soon be ubiquitous. Efficient use of tens to hundreds of cores on a chip and their memory resources comes with unique challenges.

In this dissertation, we propose SPMPool: a scalable platform for sharing Software Programmable Memories (SPMs). The SPMPool approach exploits underutilized memory resources by dynamically sharing SPM resources between applications running on different cores and adapts to the overall memory requirements of multiple applications that are concurrently executing on the many-core platform. We propose both central and distributed management schemes for SPMPool and study the efficiency of auction-based mechanisms in solving the memory mapping problem. We also propose offline and online memory phase detection methods in order to increase the adaptivity of memory management to temporal changes in memory requirements of a single application. The runtime memory management schemes proposed in this dissertation enable better performance and power for many-core systems.

 
PhD Defense: On Optimizing the Performance of Interference-Limited Wireless Systems

Name: Rana A. Abdelaal

Date: January 30, 2017

Time: 1:00 PM

Location: Engineering Hall 3106

Committee: Professor Ahmed Eltawil

Abstract:

Multi Input Multi Output (MIMO) technology has seen prolific use to achieve higher data rates and an improved communication experience for cellular systems. However, one of the challenging problems in MIMO systems is interference. Interference limits the system performance in terms of rate and reliability. In this thesis, we analyze methods that provide high performance over interference-limited wireless networks such as Long Term Evolution (LTE) and WiFi. In this thesis, we tackle different sources of interference. One of the interference sources is the neighboring interference, we propose methods that include an optimized solution that models the interference as correlated noise, and uses its statistical information to jointly optimize the base station precoding and user receiver design of LTE systems. We study the benefits of exploiting interference in terms of both probability of error and signal-to-noise ratio (SNR). In addition, we compare the proposed method with the conventional beamforming and maximum ratio combining (MRC). One of the key challenges to enable high data rates in the downlink of LTE is the precoding and receiver design. We focus primarily on the UE and the base station (BS) processing, particularly on estimating and using the interference resulting from neighboring stations. In this thesis, we propose a receiver design that performs well in the presence of interference. Furthermore, we present a precoding scheme that the BS can use to maximize the signal-to-interference plus noise-ratio (SINR). An interference free scenario is used as a benchmark to evaluate the proposed system performance. In this thesis, we optimize the performance of LTE by tackling practical considerations that affects the system performance. We present a suboptimal practical way of estimating the interference and utilizing this information on the processing techniques used at both the UE and the eNodeB sides. We focus on managing both MU-MIMO interference and other cell interference. The proposed study improves system performance even under non-perfect channel knowledge, enabling the throughput gains promised by MU-MIMO.

Other types of interference exist in In-band full-duplex (IBFD) communication systems. IBFD is very promising in enhancing wireless LANs, where full-duplex access points (APs) can support simultaneous uplink (UL) and downlink (DL) flows over the same frequency channel. One of the key challenges limiting IBFD benefits is interference. In this thesis, we also propose a scheduling technique to manage interference in wireless LANs with full-duplex capability. We focus primarily on scheduling UL and DL stations (STAs) that can be efficiently served simultaneously.

It is very important to apply system knowledge to reduce power and/or improve performance. Thus, we also aim at exploring energy and power management techniques for practical wireless communication systems. An important topic for practical communication systems is handling the interference due to the power amplifier nonlinearities. Managing this type of interference is of very high importance, especially in Orthogonal Frequency-Division Multiple Access (OFDMA) based communication systems. Although, OFDMA is the modulation of choice due to its robustness to time-dispersive radio channels, low-complexity receivers, and simple combining of

signals from multiple transmitters in broadcast networks. The transmitter design for OFDMA is more costly, as the Peak-to-Average Power Ratio (PAPR) of an OFDMA signal is relatively high, resulting in the need for highly linear RF power amplifiers (PA). This problem becomes more compounded when a large number of PAs is required, as in Massive MIMO for example. In this thesis, we discuss the impact of PAs on cellular systems. We show the constraints that PAs introduce, and we take these constraints into consideration while searching for the optimum set of transmitter and receiver filters. Moreover, we highlight how Massive MIMO cellular networks can relax PAs constraints resulting in low cost PAs, while maintaining high performance. The performance is evaluated by showing the probability of error curves and signal-to-noise-ratio curves for different transmit powers and different number of transmit antennas. Another promising topic for efficient practical communication systems is Associative processors (APs). AP is a good candidate for in-memory computation, however it has been deemed too costly and energy hungry in the past. The advent of ultra-dense resistive memories is changing this paradigm, allowing for efficient in-memory associative processors. However, with high levels of integration, issues related to power density become the bottleneck. In this thesis, we show the potential use of the approximate computing in wireless communication systems, specifically, we present Fast Fourier Transform (FFT) implemented by associative processor. Results confirm that approximate computing for in-memory associative processors is a viable approach to reduce power consumption while maintaining good performance. A promising approach to save energy is through reducing the bit width, however reducing the bit width introduces errors that may affect the performance. In this thesis, our goal is to adjust the bit width based on the channel SNR, aiming at achieving good performance at reduced energy consumption. The mathematical approach that analytically describes the system performance under the reduced bit width noise is presented. Based on this model, an adaptive bit width adjustment algorithm is presented that utilizes the received SNR estimates to find the optimal bit width that achieves performance goals at reduced energy consumption. Simulation results show that the proposed algorithms can achieve up to 45% energy savings as compared to wireless communication systems with conventional FFT.

 

 
PhD Defense: Scalable Runtime support for Edge-To-Cloud integration of Distributed Sensing Systems

Name: Brett Chien

Date: November 29, 2016

Time: 10:00AM

Location: EH 2210

Committee: Pai H. Chou (Chair)

Abstract: 

While Internet-Of-Things (IoT) has drawn more attention to researchers and the public, to build a complete system from the edge sensing units to the cloud services requires massive amount of efforts. Researchers with strong interests in collected information are often lost in various technologies, including distributed sensing embedded systems, bridge devices between Internet and local network, and data backend services.

This work takes a cross-system, script-based, and semantic-enhanced approach to address the problem of lacking suitable runtime supports. We proposed a threaded code runtime support for edge sensing systems, a script based wrapper on Physical-to-Cyber bridges, and scalable middleware into the backend services.

With proposed runtime supports, we are able to apply distributed sensing systems into real world applications quickly and explorer insights from collected information. As a result, a building structure monitoring system is installed and allow civil researchers to develop algorithms to prevent disaster events. Body area sensing systems such as ECG monitoring, CO2 detection, and body movement are developed. This enables baby screening and detect potential heart problems. The results have shown that with proposed runtime supports applications can be realized quickly and scalable.

 
PhD Defense: Resource Aggregation for Collaborative Projected Video from Multiple Mobile Devices

Name: Hung Nguyen

Date: November 17, 2016

Time: 1:30 P.M.

Location: EH 3206

Committee: Fadi Kurdahi (Chair), Aditi Majumder (Co-Chair)

Abstract: 

We explore and develop an embedded real time system and associated algorithms that enable an aggregation of limited resource, low-quality, projection-enabled mobile devices to collaboratively produce a higher quality video stream for a superior viewing experience. Such a resource aggregation across multiple projector enabled devices can lead to a per unit resource savings while moving the cost to the aggregate.

The pico-projectors that are embedded in mobile devices such as cell phones have a much lower resolution and brightness than standard projectors. Tiling (putting the projection area of multiple projectors in a rectangular array overlapping them slightly around the boundary) and superimposing (putting the projection area of multiple projectors right on top of each other) multiples of such projectors, registered via automated registration through the cameras residing within those mobile devices, result in different ways of aggregating resources across these multiple devices. Evaluation of our proof-of-concept system shows significant improvement for each mobile device in two primary factors of bandwidth usage and power consumption when using a collaborative federation of projection-embedded mobile devices.

The portable, low-power, light weight, small size pico-projectors are key components of projection-enabled mobile devices for the future. Due to the reduction of weight and dimension and the portability nature of the projector-enabled mobile devices, the calibrated integrated systems are prone to physical un-stabilizing of the projected image during the presentation. Thus the auto re-calibration and projected video stabilization features during the presentation time becomes essential requirements to enhance user experience. The design, algorithm, and implementation methods for these features will be presented in the second part of the dissertation.

 
PhD Defense: Specification and Runtime Verification of Distributed Multiprocessor Systems: Languages, Tools and Architectures

Name: Ahmed Nassar

Date: September 2, 2016

Time: 12:00 P.M.

Location: EH 3403

Committee: Fadi Kurdahi (Chair), Rainer Doemer, Ahmed Eltawil

Abstract: Post-Deployment runtime verification (RV) has recently emerged as a complementary technology to extend coverage of conventional software verification and testing methods. This thesis is an attempt to tackle three major barriers that need to be surmounted before RV technologies become in widespread use:
Barrier-1: Lack of an expressive, yet efficiently monitorable, specification language. Distributed software behavior is projected onto an observation interface consisting of data-carrying (or parameterized) events, such as Linux system calls including argument values, and self-replicating deterministic finite automata (SR-DFAs) are introduced for RV purposes as well as anomaly-based intrusion detection in embedded and general-purpose software systems based on these parametric traces.
Barrier-2: The substantial performance and power overhead of pure software RV frameworks. NUVA, which stands for nonuniform verification architecture, a distributed automata-based RV architecture for software specifications in the form of SR-DFAs. NUVA has been implemented over a cache-coherent nonuniform-memory-access (ccNUMA) multiprocessor and can be deployed on the FPGA fabric that will reside on all next-generation processor chips. The core of NUVA is a coherent distributed automata transactional memory (ATM) that efficiently maintains states of a dynamic population of automata checkers organized into a rooted dynamic directed acyclic graph (DAG) concurrently shared among all processor nodes.
Barrier-3: Formal specifications are hard to formulate and maintain for evolving complex embedded and general-purpose software systems. Therefore, specification mining has long ago been envisioned to play a key role in software verification, modification and documentation. However, in order to scale beyond simple, library/API-level properties having short temporal spans, specification mining tools need to support more expressive specification languages that can capture complex, application-level properties. This thesis introduces a bio-inspired complete specification mining methodology for SR-DFAs using an iterative and interactive mining tool, called ParaMiner. ParaMiner relies on novel mining algorithms invoking multiple-sequence alignment (MSA) techniques to enable learning specifications from temporal slices of software behavior while overcoming the initial-state uncertainty problem.
SR-DFAs and ParaMiner have been leveraged in a new specification-based intrusion detection (ID) framework that protects distributed, reactive computing systems against cyberattacks having very sparse signatures, arbitrarily long time spans and wide attack fronts. Such attacks lie outside the scope of conventional anomaly-based ID methods which typically work with short event windows and ignore manipulated data objects, such as files and sockets. We demonstrate the effectiveness of the constructed SR-DFAs at classifying as well as resolving subtle behaviors typical of cyberattacks with varying evasion parameter values.

 
PhD Defense:

Name:  Aras Pirbadian

Date: August 29, 2016

Time: 3:30pm

Location:  EH 3403

Committee: Ahmed Eltawil (Chair), Fadi Kurdahi, Rainer Doemer

With the continued scaling of chip manufacturing technologies, the significance of process variation in performance of the systems is increasing. Specifically, process variation results in growing voltage and frequency overhead margins required to ensure error free operation of circuits. However, the traditional practice of overdesigning the systems to cover process variation is no longer an efficient design methodology in an age with high demands for processing power and limited energy supplies. In this dissertation, a novel analytical model is proposed to predict the required margin accurately in the early stages of design space exploration. The model can be used to optimize the system overhead in error free calculations or to release the bound by full correctness in error tolerant parts of systems and optimize the energy vs. performance trade-off. Additionally, this model also considers the statistics of the inputs of the circuit as compared to other existing efforts enabling it to achieve close predictions of full circuit simulation results in a short time. This model is finally used in an adaptive carry select/ripple carry adder configuration to demonstrate the potential achievable power savings.

Growing variation in newer technology nodes is not always a negative side effect. The increased inherent randomness in the process manufacturing technology can be utilized to develop unique physically unclonable functions (PUFs). These functions are irreproducible hardware-based authenticating systems, which do not require memory-based storage. A low overhead delay-based PUF using the variation of the silicon manufacturing is also proposed in the second part of this work. The proposed PUF uses a simple and efficient structure to convert the randomness of the manufacturing process into random responses to fixed challenges in identically designed circuits.

 
PhD Defense: Progression and Edge Intelligence Framework for IoT Systems

Name:  Zhenqiu Huang

Date: July 29, 2016

Time: 10:00am

Location:  EH 4106

Committee: Kwei-Jay Lin (Chair), Fadi Kurdahi, Mohammad Al Faruque

Abstract: This thesis studies the issues on building and managing future Internet of Things (IoT)
systems. IoT systems consist of distributed components with services for sensing, processing,
and controlling through devices deployed in our living environment as part of the global
cyber-physical ecosystem.

Systems with perpetually running IoT devices may use a lot of energy. One challenge is to
implement good management policies for energy saving. In addition, a large scale of devices
may be deployed in wide geographical areas through low bandwidth wireless communication
networks. This brings the challenge of con

 
PhD Defense: Frameworks and Algorithms for Wearable Medical Applications

Name:  SeungJae Lee

Date:  April, 5, 2016

Time:  3:00p.m.

Location:  EH 4106

Committee:  Pai H. Chou (Chair), Fadi Kurdahi, Tony Givargis

Abstract:

Wearable embedded systems with sensing, communication, and computing capabilities have given rise to innovations in e-health and telemedicine in general. The scope of such systems ranges from devices and mobile apps to cloud backend and analysis algorithms, all of which must be well integrated. To manage the development, operation, and evolution of such complex systems, a framework systematic framework is needed. This dissertation makes contributions in two parts. First is a framework for defining the structure of a wide range of wearable medical applications with modern cloud support. The second part includes several algorithms that can be plugged into this framework for making these systems more efficient in terms of processing performance and data size. We propose a novel QT analysis algorithm that can take advantage of GPU as well as in a server-client environment, and we show competitive results in terms of both performance and energy consumption with or without parallelization. We also propose ECG compression techniques using trained overcomplete dictionary. After constructing the dictionary through learning process with a given dataset, the signal can be compressed by sparse estimation using the trained dictionary. We propose reconstructing ECG signal from undersampled data based on compressive sensing framework that can reconstruct the ECG signals precisely from fewer samples so long as the signal is sparse or compressible. Together, these algorithms operating in the context of our proposed framework validate the effectiveness of our structured approach to the framework for wearable medical application.

 

 
PhD Defense: Power Optimization for Medical Sensing Systems

Name:  Jun Luan

Location:  EH 4106

Date: February 24, 2016

Time: 1:00pm

Committee: Pai Chou (Chair), Mohammad Al Faruque, Fadi Kurdahi

Abstract: 

Medical sensing systems collect and analyze the patients’ physiological data for monitoring, aid or diagnostic purposes. System designers are faced with stringent requirements on not only correctness and safety but also power. Reference designs and multi-purpose platforms help to significantly shorten the development cycle.

This work takes a cross-layer, system-level, platform-based approach to addressing the problem of saving power in a class of portable medical system. We propose a low-power medical sensing system that can be used to monitor Electrocardiography (ECG), Photo- plethysmogram (PPG), and muscle tension. It also includes a hand gesture recognition system to aid mobility-impaired patients.

We explore the theory and application of a compressive sensing framework to medical signal processing. A novel compressive sensing-based ECG compression algorithm and a dominant frequency extraction-based PPG heart-rate calculation algorithm are proposed to reduce the system power. The unique combination of hardware structure and software signal-processing algorithms makes low-power design possible. The system test results show that the proposed system is superior to existing works in terms of power consumption and system size.

 
PhD Defense: A Centralized IoT Middleware System for Devices Working Across Application Domains Using Self-descriptive Capability Profile

Name: Chengjia Huo

Location: EH 4404

Date: September 30, 2015

Time: 1pm

Committee: Pai H. Chou (chair), Phillip Sheu, Rainer Doemer

Abstract:

The Internet of Things (IoT) has been receiving growing attention in recent years as the next wave of computing revolution made possible by all types of networks of things (NoTs), where devices powered with low-cost, miniature low-power systems-on-chip (SoC) with computing and communication capabilities, and are bridged to the Internet with the assistance of gateways. More and more NoT device are designed to provide more than one functionalities to fulfill different requirements from the application domains. We believe that the true power of IoT is that functionalities of devices can work across application domains. In order to reveal the potential of IoT, the description of a device’s capability needs to represent the functionalities that the device can provide. We discover the previous solutions on describing a device’s capability focus mainly on hiding the vendor-specific interfaces made by different manufacturers, but they do not reflect different functionalities that a device provides. In this thesis, the concept of device capability profile is proposed. Different from the previous solutions, the device capability profile specified in the firmware of a device allows the device to work across different application domains. Together with device capability profile, a centralized IoT middleware framework, called rimware, is proposed. Rimware tracks every device’s capability and state in a centralized manner and provides different ways for application domains to query against the device’s functionalities. In addition, rimware utilizes the device capability profile to carry out the enforcement of the security and privacy throughout the communication with the devices. Moreover, tasks can be scheduled through the rimware which enables functionalities from multiple devices to work together to fulfill the requirements from application domains. Optimization is applied on cases that one device working for multiple task simultaneously. An implementation of rimware that is specifically designed for BLE devices, called BlueRim, which takes advantages of BLE’s very long battery life on the device side and the cloud functionality on the centralized side is provided. The fundamental features of rimware have been validated in several real-world applications from different different domains while incurring minimal code size and communication overhead on BLE devices. We believe that our approach represents an important technology in taking IoT closer to realizing the full potentials.

 
PhD Defense: CARL-SJR: A Socially Assistive Neurorobot for Autism Therapy and Research

Name: Ting-Shuo Chou

Date: May 21, 2015

Time: 3:00pm – 5:00pm

Location: Social & Behavioral Sciences Gateway 2200 Conference Room

Committee Chair: Jeffrey Krichmar (Chair), Nikil Dutt, Alexandru Nicolau

Abstract:

Neurodevelopmental disorders, such as Attention-Deficit–Hyperactivity
Disorder (ADHD) and Autism Spectrum Disorder (ASD), have core clinical
symptoms of inattention, hyperactivity, and impulsivity (often hyper-
and hypo- responsiveness. These symptoms are often accompanied by
reduced motor coordination and impaired sensory processing. We introduce
a Socially Assistive Robot (SAR) with the goal of automating therapy for
children with neurodevelopmental disorders. The novel robot, which is
called Cognitive Anteater Robotics Laboratory – Spiking Judgment Robot
(CARL-SJR), is designed for therapy and diagnosis. CARL-SJR is
autonomous and capable of tactile sensing and interaction. A spiking
neural network model and neurally inspired algorithms controls
CARL-SJR’s behavior. By providing a large tactile sensing surface that
encourages touching with hand movements, CARL-SJR especially addresses
impairments in tactile sensitivity and social interaction observed in
children with neurodevelopmental disorders. Using CARL-SJR, we conducted
a pilot study where children with different neurodevelopment disorders
show different behavioral metrics and tactile movements. The results
suggest CARL-SJR might serve as a diagnose tool for developmental
disorders. Second, we showed that the information carried by temporal
coding is higher than the traditional rate coding when decoding spike
trains in response to tactile movements. Third, we implemented online
learning capabilities on CARL-SJR, where the robot could associate a
user’s preferred color pattern displayed on the robot with the user’s
hand sweep across the robot’s body. The emerged behaviors and neural
activities in the SNN are consistent with biological recordings. The
underlying neural mechanism in the SNN also serves as an alternative
explanation of how brains encode timing and associate (or learning) two
temporal separated events.