Chunsheng Zhu CKN based Sleep Scheduling in Duty-cycled Wireless Sensor Network
In this poster, focusing on designing a comprehensive sleep scheduling scheme in wireless sensor network (WSN), we review the recently proposed four novel types of connected-k neighborhood (CKN) based sleep scheduling schemes: GSS (geographic routing oriented sleep scheduling), GCKN (geographic distance based CKN), SECKN (secured energy-aware CKN), EC-CKN (energy-consumption based CKN). Based on the analytical reviews, we further propose a data content oriented sleep scheduling (DSS) scheme and summarize CKN based sleep scheduling schemes.
Javad Hajipour DDRA : Dynamic Distributed Resource Allocation in Relay Assisted OFDMA Networks
Relay assisted OFDMA networks are promising solutions for provision of high-data-rate services in wide coverage areas. However, the deployment of relays makes the resource allocation a more challenging and complex task. In this paper we study dynamic allocation of power and subchannels in an OFDMA downlink system with regenerative relays which have the capability of buffering the users' data to transmit in a suitable time. We model the network as a multicell scenario with small serving areas and provide a novel framework for resource allocation, in which each of the relays and BS allocate resources based on the queue and channel state information of their own users. We propose Dynamic Distributed Resource Allocation (DDRA) algorithm for this purpose, where BS and relays decide about the allocation of the power and subchannels by passing messages among themselves and based on the local queue and channel state information. Simulation results show significant improvement in terms of system throughput and users queue stability
Existing video interaction systems do not provide the kind of personalized experience possible with a simple record of personal video history. Navigating through a video space creates new experiences for the user, even if revisiting previously viewed content, and the record of viewed content can be used effectively to enhance the user experience. We present a combination of a new history representation specifically for video and a set of interaction mechanisms to effectively view, browse, find, edit and share video viewing histories. We provide the ability to record personal video navigation so that users may re-view the video space exactly, re-use portions of their history or share it with others. We have designed a new video interface based on the history representation we proposed, which provides simple mechanisms for a variety of applications, such as: finding interesting intervals in unwatched video; quickly finding intervals which the user found interesting in viewed video; video summarization; and consumer-level video editing. Demonstrations of these applications are provided to prove the utility of the history representation for video navigation.
Wei Cai Next Generation Mobile Cloud Gaming
With the proliferation of smart mobile devices and broadband wireless networks, the mobile gaming market is rapidly expanding among younger generations due to its ubiquitous entertainment features. Cloud-based mobile computing is a promising technology to address the inherent restrictions of mobile devices, such as limited battery lifetime and computational capacity. In this work, we categorize the current approaches to cloud-based mobile gaming and explicitly define Mobile Cloud Gaming (MCG) 1. Based on its unique features, we propose a framework for next generation MCG systems. Open research issues for MCG are also discussed.
Since the discovery of carbon nanotubes, various devices have been made in different fields of science and engineering. The mechanical and electrical properties that carbon nanotubes offer make them a great candidate for use in the structure of artificial muscles. In this thesis, for the first time, we have demonstrated that metallic nanowires can be engineered to become strong and comparable to the CNT yarns in mechanical and electrical properties. The niobium yarns offer conductivity of up to 3 10 S/m, tensile strength of up to 1.1 GPa and Young's modulus of 19 GPa. The niobium nanowire fibres are fabricated by extracting the niobium nanowires from copper-niobium nano-composite matrix, which was made by using a severe plastic deformation process. As a practical application, torsional artificial muscles were made out of the niobium yarns by twisting and impregnating them with paraffin wax. Upon applying voltage to the twisted yarn the wax melts and expands due to the heat generated by the current. Thermal expansion of wax untwists the yarn, which translated to torsional actuation. Torsional speeds of 7,200 RPM (in a destructive test) and 1,800 RPM (continuous) were achieved. In addition to torsional actuation, niobium yarns also can provide up to 0.24% of isobaric tensile actuation along the yarn's axis at 20 MPa load. Due to the high conductivity of the niobium yarns, the actuator can be made to actuate by even one single 1.5 V battery (for a 1 cm of niobium yarn). The electrochemical capacitance of niobium yarns was measured to be 1.3 10 F/m at a scan rate of 25 mV/s in 0.2 M TBAPF salt dissolved in acetonitrile. This value is comparable to the electrochemical capacitance of the carbon multi-walled nanotube yarns.
Burak Yoldemir Multimodal Assessment of the Functional Organization in the Human Brain
Although it is now well established that brain fiber pathways serve as the physical substrate for functional interactions, this information is rarely exploited in current functional magnetic resonance imaging (fMRI) studies. In this project, we investigate the implications of fusing information regarding brain structure and brain activity in the presence and absence of explicit input. This multimodal approach enables us to analyze how the brain responds to stimuli around its baseline and to regularize the analyses of functional brain dynamics using structural connectivity information. The methods we develop ultimately serve to enhance our general understanding of the human brain organization by shedding light on the structure-function relationship in the brain.
Minimally invasive diagnosis, therapy and catheterization are increasingly used by physicians and surgeons. Catheters are extensively employed in wide range of medical procedures procedures such as angiography, intravascular ultrasound, coiling of cerebral aneurysms, stent deployment and treatment of thromboembolic diseases. Navigation of the catheter is performed using guide wires that are manipulated from outside of the body using a combination of pushing, pulling and torque. Catheters then slide over the wires to reach the area of interest to the surgeon. Some disadvantages of this traditional technique including the limitations on its use in narrow and complex passages (such as in the brain), motivate us to propose a polypyrrole controllable micro active catheter. In this project I propose to design and fabricate a controllable miniaturized microcatheter, which can ultimately be inserted into the narrow arteries in the brain with high precision in order to assist in the identification and treatment of acute ischemic stroke and aneurysms. This microactuator is to be employed as a surgical device to mechanically remove a thrombus or inject a clot melting substance at a specific location in the arteries and provide the channel for inserting the very thin platinum wire to fill the aneurysm. I will utilize polypyrrole as the key element to provide active deformation of the catheter and to control its maneuverability and actuation. To form the body of the catheter tip and an electrolyte medium to enable transfer of ions during the actuation of polypyrrole the use of tough hydrogels is suggested. This compliant medium will also enable the tight radius of curvature required for the application.
In this project, our objective is to overlay data derived from pre-operative CT images onto the surgeon's stereo endoscopic view. The overlaid data will include both pre-operative CT images, in the form of 2D slices or 3D volumes, as well as pre-segmented anatomy and pathology (e.g. vasculature and tumor). Moreover, the pre-operative data will be first positioned and then deformed (e.g. stretched, bent, or cut) continuously to match the current state of the patient's anatomy. Lastly, left and right (stereo) projective views of the overlaid data will be streamed onto the left and right camera views provided to the surgeon console, thus augmenting the 3D endoscopic view of the operation field. This MIS enhancement will radically improve the surgeon's experience and efficiency, increase the precision of the surgery, decrease the surgery time, and reduce collateral damage, which in turn will lead to improved surgery outcomes and patient outlook and recovery.
Ashwin Usgaocar A photovoltaic cell and battery using photosynthetic reaction centres
Photosynthetic reaction centres are protein structures that perform the light harvesting step in photosynthesis. These molecules are extremely efficient at harvesting incoming photons, with quantum efficiencies reaching close to 100%. The absorbed photons are stored in a charge separated state with a very long lifetime (~1s), making these molecules attractive candidates for use in a combined solar cell and battery device. This poster describes architectures for such devices and some of the challenges involved in their construction.
Increasing number of devices capable of sensing, transmitting, and recording location information makes the collection of location and mobility trajectory data possible. Location data represents a valuable source of information in both online (e.g. searching for a place) and offline (e.g., urban traffic management) applications. This data has to be shared among third parties and data miners for further analysis. However, publishing individuals' location information may cause serious privacy concerns. Preserving the spatial privacy of individuals is a well known problem and some techniques have been proposed to stop malicious threats. This work presents a new threat model that helps attackers revealing sensitive information of individuals sharing their location in form of trajectory databases. Proposed model is evaluated by considering a real world trajectory database of vehicles.
Mohammad Bajammal Real-time Extraction of Local Phase Features from Volumetric Medical Image Data
We present a novel real-time implementation of local phase feature extraction from volumetric ultrasound based on 3D directional (log-Gabor) filters. We achieve drastic performance gains without compromising the signal-to-noise ratio by pre-computing the filters and adaptive noise estimation parameters, and streamlining the remainder of the computations to efficiently run on a multi-processor graphic processing unit (GPU). We validate our method on clinical ultrasound data and demonstrate a 15-fold speedup in computation time over state-of-the art methods, which could potentially facilitate a wide range of practical applications for real-time image-guided surgery.
Assessing the value of individual users' contributions in peer-production systems is paramount to the design of mechanisms that support collaboration and improve users' experience. For instance, to incentivize contributions, file sharing systems based on the BitTorrent protocol equate value with volume of contributed content and use a prioritization mechanism to reward users who contribute more. This approach and similar techniques used in resource sharing systems rely on the fact that the physical resources shared among users are easily quantifiable. In contrast, information-sharing systems, like social tagging systems, lack the notion of a physical resource unit (e.g., content size, bandwidth) that facilitates the task of evaluating user contributions. For this reason, the issue of estimating the value of user contributions in information sharing systems remains largely unexplored. This presentation outlines a research project to tackle the problem of assessing the value of contributions in social tagging systems.
Negar Harandi A Minimally Interactive Model-Based Approach for 3D Segmentation of the Tongue in MRI
Subject-specific computer-aided modelling and simulation of the oropharyngeal structures assist with efficient diagnosis and optimization of treatment and surgery. Static MRI partially resolves soft tissue details of the oropharynx. However, delineation of tongue tissue remains a challenge due to the lack of definitive boundary features. We propose a minimally interactive inter-subject mesh-to-image registration scheme to tackle 3D segmentation of the human tongue from MR volumes. A tongue surface-mesh is first initialized using an exemplar expert-delineated template, which is then refined based on local intensity similarities between the source and target volumes. We enable effective minimal user interaction by incorporating additional boundary labels in areas where automatic segmentation is deemed inadequate. We validate our method on 12 normal-subjects using expert manual delineation as the ground truth. Results indicate an average dice accuracy of 0.904 $\pm$ 0.004, achieved within a real-time expert interaction time of 2 $\pm$ 1 minutes per volume.
Roee Diamant Spatial Dependencies Between Velocities of Underwater Drifting Nodes
The ability to navigate underwater is a key requirement for most underwater applications. Mobile devices, drifters, and also human divers, all require precise navigation capability to perform long term missions while being submerged. In the absence of GPS reception, performing underwater navigation (UN) requires an accurate state-space-model (SSM) to allow the tracked node (TN) to self-estimate its location. However, irregularities in nodes motion (mostly due to unpredictable changes of ocean current) makes it hard to reliably determine the SSM. In this paper, we rely on spatial correlation of ocean current, and propose to estimate the drift velocity of the TN as a combination of the drift velocities of anchors, and to directly use it as part of the SSM. This not only augments the amount of information available for the TN to track its own position, but also offers an unbiased velocity estimate which increases the reliability of the SSM. Since ocean current may not be always correlated (for example, in the case of turbulence), we offer two unbiased confidence indexes: one which is based on the range to the anchor, and a second which is based on the homogeneity of the current velocity field. To evaluate the potential of utilizing node spatial dependencies for UN, we collect trajectories of drifting nodes from both model-based simulations and a sea trial performed in Israel. Our results suggest that nodes drifting motion shows strong spatial correlation which could greatly enhance the performance of tracking algorithms.
Transformers play an integral role in a variety of power electronic converter topologies. They are considered bulky and heavy components in switching mode power supplies and considerable design time is required to make sure that cost is minimized while maintaining good performance. Small planar transformers have the benefit of being light and compact, and they can be placed directly on multi-layer printed circuit boards in high frequency power conversion applications. Combined with exceptional thermal characteristics, low leakage inductance, high reproducibility, and easy manufacturability, they are desirable for many applications in consumer electronics and renewable power. This work highlights the circuit modeling of planar transformers and presents several examples of their integration into high-frequency power converters.
The first stage of an ocean energy extraction system is converting the kinetic energy present in tidal currents to mechanical power available in the rotor shaft. This mechanical energy is then converted to electrical by a Permanent Magnet Synchronous Generator. The electrical energy is then taken into a high-efficiency power converter in order to obtain high quality conditioned power. Simulation and emulation of this full renewable energy harvesting system has been developed in order to provide information to the power electronics designer in a fast, predictable and costless manner.
Jason Forbes Increased Frequency Resolution of Active Rectifiers Using Multi-Carrier Digital PWM
A multi-carrier digital PWM (DPWM) scheme is investigated for active rectifiers with the goal of increasing the fundamental frequency resolution attainable with traditional DPWM. The multi-carrier PWM technique overcomes frequency resolution drawbacks inherent in DPWM. In practice, the fractional DPWM technique will improve the frequency resolution of active rectifiers by a factor of 20 to 50. The augmented resolution allows for a much tighter tracking of the source frequency without increasing the cost of the microcontroller, reducing limit-cycling and subharmonic distortion.
Urban society relies heavily on critical infrastructure (CI) such as power and water systems. The anticipated prosperity and the national security of society depend on the ability to understand, measure and analyze the vulnerabilities and interdependencies of this system of infrastructures. Only then can emergency responders (ER) react quickly and effectively to any major disruption that the system might face. In this paper, we propose a model to train a reinforcement learning (RL) agent that is able to optimize resource usage following an infrastructure disruption. The novelty of our approach is the use of dynamic programming techniques to build an agent that is able to learn from experience, where the experience is generated by a simulator. The goal of the agent is to maximize an output, which in our case is the number of discharged patients (DP) from hospitals or on-site emergency units. We show that by exposing such an intelligent agent to a large sequence of simulated disaster scenarios, we can capture enough experience to enable the agent to make informed decisions.
Hamed Ahmadi A Robust Framework for Power Distribution System Optimization
The growing concern of energy crisis in the world has made the industry to look for any possibility to both reduce the power consumption and find alternative sources for clean energy. As one of the main parts of power grids, distribution systems play a crucial role in delivering the power to the end-users. Since distribution systems have been build long time ago and have not changed for a long time, their structure and performance does not match the today's requirements and standards. Therefore, there is an urgent need to modify the structure and operational algorithms for these systems. In this study, fast and robust algorithms are developed which use the data available from advanced metering infrastructure to improve the performance of the distribution network. As an example, the implementation of the proposed framework on medium-scale systems has led to about 15%-30% reduction in power losses, up to 4% reduction in total power consumption, and an average factor of 3% increase in power quality at customer level.
William Hoiles Mathematical Models for Sensing Devices Constructed out of Artificial Cell Membranes: Macroscopic to Microscopic
Biosensors have applications in the fields of engineering, medicine, and biology. The recent emergence of biomimetically engineered nanomachine devices capable of measuring femtomolar concentrations of chemical species and the detection of channelopathies (ion channel disorders) make them an attractive tool due to their high sensitivity and rapid detection rates. At the macroscopic scale we have successfully modeled the behavior of these biosensor systems using lumped-circuit components and continuum models utilizing the laws of dilute electrochemical dynamics. The design of novel biosensor systems is limited by our understanding of the microscopic molecular mechanisms in and on the surface of membranes. Classical techniques such as lumped-circuit models and continuum models can not accurately model the physical phenomena at the microscopic (i.e. nanometer) length scale. Given the necessity of mathematical models for the rational design of these emerging biosensor systems, we are currently developing models utilizing the tools of coarse-grained molecular dynamics in combination with stochastic sampling techniques. At the frontiers of medicine and biology is the development of cost-effective test beds for the study of antimicrobial peptides interaction with membrane surfaces. Here we provide an example application of the coarse-grained methodology to study the insertion behavior of the antimicrobial peptide (PGLa) into a lipid membrane. We envisage these continuum and molecular models to be invaluable for the future development of biosensor systems.
Roya Arab Loodaricheh Distributed Resource Allocation for OFDM Based Amplify-and-Forward Cooperative Communication Systems
We propose two distributed resource allocation methods for OFDM based cooperative relay networks. We want to maximize the throughput of the system using minimal channel state information feedback. Numerical results demonstrate that these methods have lower complexity and significantly reduce the signaling overhead while the performance of the system is very close to that of the optimal method.
Tanaya Guha Sparse SNR: Image similarity from sparse reconstruction errors
We present a new approach to measuring the similarity between two images using sparse reconstruction. Our approach alleviates the difficulty of selecting and extracting suitable features from images which usually requires domain specific knowledge. The proposed measure, the Sparse SNR (SSNR), does not use any prior knowledge about the data type or the application. SSNR is generic in the sense that it is applicable, without modification, to a variety of problems involving different types of images. Given a pair of images, a set of basis vectors (dictionary) is learnt for each image such that each image can be represented as a linear combination of a small number of its dictionary elements. Each image is reconstructed by two dictionaries - the one trained on the image itself and the second - trained on the other image. We develop a novel similarity measure based on the resulting reconstruction errors. Excellent classification, clustering and retrieval results are achieved on benchmark datasets involving facial images and textures.
Parastoo Kheirkhah Dehkordi Estimation of heart rate variability from photoplethysmography (PPG) signal in children with and without sleep disordered breathing
Heart Rate Variability (HRV), the variation in the time interval between heartbeats is one of the most promising and widely used quantitative markers of autonomic activity. HRV can be used as a discriminable index for identifying individuals with sleep disordered breathing (SDB).Traditionally, HRV is measured as the series of instantaneous cycle intervals of ECG signal. In this study, we investigated the estimation of variation in heart rate from a photoplethysmography (PPG) signal. The pulsatile feature of the PPG waveform is synchronized with each heartbeat and its fundamental frequency depends on heart rate. We obtained pulse to pulse variability, called pulse rate variability (PRV) from peak to peak time intervals of PPG signal and assessed its accuracy as an estimate of HRV in children with and without sleep disordered breathing (SDB). The PPG was recorded from 63 children undergoing sleep study by an oximeter sensor connected to a mobile phone simultaneously with ECG and other signals in a full channel PSG. We used correlation and Bland Altman analysis for comparing the parameters of HRV and PRV in both groups of children. Significant (p <0.05) correlations (0.90 < r < 1) and good agreement were found between HRV and PRV for mean of intervals, SDNN and RMSSD parameters. However Bland Altman analysis showed the large divergence for LF/HF ratio parameter. In conclusion, PRV can be used instead of HRV but the LF/HF ration should be used by more consideration.
Sara Khosravi Simulated Robust Model Predictive Control of Hypnosis in Adults
The main difficulty in the design of controllers for closed-loop control of anesthesia is the significant intra- and inter-patient variability in response to a standard dose of drug. To attain acceptance for a closed-loop drug delivery from clinicians and regulatory authorities, the control system will require a certification procedure that includes stability and robust performance criteria. The goal of this work was to design a model predictive controller (MPC) that provides an adequate propofol infusion rate for a given population while being robust to the inter-patient variability and the resulting mismatch between the prediction model and the patient response. An attraction of MPC is that it provides the ability to control both hypnosis and analgesia in an uncomplicated manner, with constraints on drug infusion rates and system states. The novel component of this design is the systematic controller tuning and the inclusion of robustness and performance analysis in the controller design. Comparing the results of the MPC controller and a robustly tuned PID controller, reveals that the MPC controller has less overall oscillations for the same population. The MPC controller attains this, while outperforming the PID controller in terms of rise time 5.4 min and 7.87min and overshoot 1.8 % and 3.3%, respectively. The proposed MPC controller can provide adequate and stable depth of hypnosis during simulated induction and maintenance of anesthesia and achieves robustness against patient uncertainty. Future work includes extending the proposed method to a constrained robust MPC controller as well as multivariable control of hypnosis and analgesia.
Majid Dadashi SCRIBE: A hardware infrastructure enabling fine-grained software layer diagnosis
Recent studies have shown that intermittent faults have gained increased prominence on being responsible for computer system failures. This category of faults is harder to diagnose in comparison with permanent faults. Full hardware diagnosis techniques incur significant power and area overheads. Software layer diagnosis techniques have zero area overhead but limited visibility into many micro-architectural structures and hence cannot diagnose faults in them. We propose SCRIBE, a simple hardware infrastructure to enable fine-grained software layer diagnosis. SCRIBE records the detailed micro-architectural resource usage of each instruction in the processor and exposes it to the software diagnosis layer. Our evaluation indicates that SCRIBE has an overhead of 12 to 23% depending on the processor type.
Ehsan Vahedi Analytical Modelling of RFID Generation-2 Protocol Using Absorbing Markov Chain Theorem
Radio frequency identification (RFID) is a ubiquitous wireless technology which allows objects to be identified automatically. An RFID tag is a small electronic device with an antenna and has a unique identification number. RFID tags can be categorized into passive and active tags. For passive tags, there exists a standard communication protocol called EPCglobal Generation-2, or briefly EPC Gen-2 [1]. In this poster, we investigate the EPC Gen-2 protocol and model it using an absorbing Markov chain. We formulate the proposed model and calculate the expected number of queries required to identify all tags in the system. Extensive simulations validate and confirm the accuracy of our proposed analytical model. Without this model, one has to run simulations and average the results to obtain the expected number of required queries for any given number of tags in the system. Using the mathematical formulations provided, there is no need to rely on simulations for studying the behavior of the EPC Gen-2 protocol and we are able to calculate the number of required queries directly. Our proposed analytical model is also useful in studying and comparing other RFID protocols, and in deploying better protocols for RFID systems.
Lack of drug penetration and diffusion through the outermost layer of skin, stratum corneum, is a tremendous limitation to transdermal drug delivery. Though many means have been tested and developed to enhance drug penetration through the stratum corneum, none have shown the potential presented by microneedles. Being sub-millimetre needle-like structures, microneedles have the ability to overcome this skin barrier by creating mechanical pathways to bypass the stratum corneum, providing direct routes for subcutaneous drug delivery. Microneedles are thus increasingly used in localized transdermal drug and vaccine delivery applications to great effect, as well as in interstitial fluid extraction. Microneedle-based research conducted in the Stoeber Laboratory at the Department of Electrical and Computer Engineering at UBC involves the development of novel microfabrication techniques for hollow microneedle arrays; characterization of microneedle injections in human, animal and artificial skins. The delivery of drugs and vaccines transdermally and continuously using microneedles, as presented herein, is investigated in collaboration with Hafeli lab in Pharmaceutical Sciences and the Dutz lab at the Skin Care Centre.
John Berring An all-polymer flexural plate wave device for volatile organic compound detection, thin film characterisation and fluid manipulation
We have developed a novel flexural plate wave (FPW) sensor and microfluidic manipulator composed of a piezoelectric polymer substrate, polyvinylidene fluoride (PVDF), conducting polyethylene dixythiophene polystyrene sulfonate (PEDOT:PSS) interdigitated transducers (IDTs), and a polymer sensing layer. When a signal is applied to the input transducers, an anti-symmetric flexural plate wave is generated which propagates across the substrate to the receiving IDTs. The wave velocity, frequency and phase of this wave is determined by the physical properties of the substrate and sensing layer. Output signal characteristics are measured at the receiving IDTs and used to determine the presence of an analyte or property of an applied film. Due to the low stiffness of the substrate, these devices may be used to detect stiffness and stress changes, along with mass variation in an adjacent polymer film. This is a unique property for Lamb or Rayleigh wave devices, most of which are purely gravimetric sensors. In this work, we investigate the capabilities of these devices as volatile organic compound sensors, thin film characterisation tools, and microfluidic pumps.
There are several implementation challenges to sustaining acceleration speedups on FPGAs as the size of the data set to be processed scales. We use the implementation of an FPGA platform for the processing of gigabyte scale biological sequences, to illustrate the significant design changes that must be made to achieve a successful implementation. In doing so, we demonstrate that conventional accelerator architecture design choices that focus on throughput speedup, in isolation of system level IO bandwidth feasibility, cannot sustain their throughput levels as the input data set scales. This is shown to be primarily due to currently unavailable high-bandwidth large-scale data storage and retrieval for FPGAs. As a solution to this problem, we propose a general FPGA based IO infrastructure to utilize high bandwidth hard-drive storage options, as means to achieving sustained throughput in the face of large data.
Safety-critical applications form the main motivation for intelligent transportation systems (ITSs). Studying the major concerns in such applications, i.e., delay and reliability, through mathematical analysis is extremely beneficial because it enables us to design optimized schemes. Such analysis is, however, challenging due to the dynamics of such a network. In this poster, we present a mathematical model to study delay and reliability of emergency message dissemination in vehicular networks. We make some interesting observations from the presented model. First, the end-to-end reliability has a fairly fast transition over time. The second observation from the analytical model confirms the fact that using the vehicle density on the road is a good metric for setting the right forwarding probability in vehicles. We exploit this conclusion and propose a completely distributed forwarding strategy. Simulation studies indicate that our model does capture the delay characteristics of vehicular networks. It also affirms the effectiveness of our warning dissemination scheme in terms of delay and single-hop reliability in comparison with other well-known routing methods. We believe that this is a promising step toward accurate characterization of communication delay and reliability in vehicular networks.
A renaissance in low-dimensional thermoelectrics has prompted research into new opportunities for nanostructures to constitute high figure of merit materials capable of converting temperature differentials to electrical power at comparatively high efficiency. Our research uses molecular dynamics tools to examine the surface effects of nanowire structural features to elucidate nanoscale designs that can reduce phonon propagation and corresponding thermal conductivity. Experimental work is beginning with a focus on nanowire superlattice fabrication through multipotential electrodeposition of suitable alloys on nanowire templates. Quantum dot electronic and phononic properties are first optimized using molecular dynamics, density functional theory, and transport simulations with materials compatible with superlattice electroplating, followed by an investigation into surface modification to further improve performance.