Jong-hyuk Lee

Over 40 cd/A Efficient Green Quantum Dot Electroluminescent Device Comprising Uniquely Large-Sized Quantum Dots

Green CdSe@ZnS quantum dots (QDs) of 9.5 nm size with a composition gradient shell are first prepared by a single-step synthetic approach, and then 12.7 nm CdSe@ZnS/ZnS QDs, the largest among ZnS-shelled visible-emitting QDs available to date, are obtained through the overcoating of an additional 1.6 nm thick ZnS shell. Two QDs of CdSe@ZnS and CdSe@ZnS/ZnS are incorporated into the solution-processed hybrid QD-based light-emitting diode (QLED) structure, where the QD emissive layer (EML) is sandwiched by poly(9-vinlycarbazole) and ZnO nanoparticles as hole and electron-transport layers, respectively. We find that the presence of an additional ZnS shell makes a profound impact on device performances such as luminance and efficiencies. Compared to CdSe@ZnS QD-based devices the efficiencies of CdSe@ZnS/ZnS QD-based devices are overwhelmingly higher, specifically showing unprecedented values of peak current efficiency of 46.4 cd/A and external quantum efficiency of 12.6%. Such excellent results are likely attributable to a unique structure in CdSe@ZnS/ZnS QDs with a relatively thick ZnS outer shell as well as a well-positioned intermediate alloyed shell, enabling the effective suppression of nonradiative energy transfer between closely packed EML QDs and Auger recombination at charged QDs.

Highly Efficient, Color-Pure, Color-Stable Blue Quantum Dot Light-Emitting Devices

For colloidal quantum dot light-emitting diodes (QD-LEDs), blue emissive device has always been inferior to green and red counterparts with respect to device efficiency, primarily because blue QDs possess inherently unfavorable energy levels relative to green and red ones, rendering hole injection to blue QDs from neighboring hole transport layer (HTL) inefficient. Herein, unprecedented synthesis of blue CdZnS/ZnS core/shell QDs that exhibit an exceptional photoluminescence (PL) quantum yield of 98%, extraordinarily large size of 11.5 nm with a shell thickness of 2.6 nm, and high stability against a repeated purification process is reported. All-solution-processed, multilayered blue QD-LEDs, consisting of an HTL of poly(9-vinlycarbazole), emissive layer of CdZnS/ZnS QDs, and electron transport layer of ZnO nanoparticles, are fabricated. Our best device displays not only a maximum luminance of 2624 cd/m(2), luminous efficiency of 2.2 cd/A, and external quantum efficiency of 7.1%, but also no red-shift and broadening in electroluminescence (EL) spectra with increasing voltage as well as a spectral match between PL and EL.

Highly Efficient, Color-Reproducible Full-Color Electroluminescent Devices Based on Red/Green/Blue Quantum Dot-Mixed Multilayer

Over the past few years the performance of colloidal quantum dot-light-emitting diode (QLED) has been progressively improved. However, most of QLED work has been fulfilled in the form of monochromatic device, while full-color-enabling white QLED still remains nearly unexplored. Using red, green, and blue quantum dots (QDs), herein, we fabricate bichromatic and trichromatic QLEDs through sequential solution-processed deposition of poly(9-vinlycarbazole) (PVK) hole transport layer, two or three types of QDs-mixed multilayer, and ZnO nanoparticle electron transport layer. The relative electroluminescent (EL) spectral ratios of constituent QDs in the above multicolored devices are found to inevitably vary with applied bias, leading to the common observation of an increasing contribution of a higher-band gap QD EL over low-band gap one at a higher voltage. The white EL from a trichromatic device is resolved into its primary colors through combining with color filters, producing an exceptional color gamut of 126% relative to National Television Systems Committee (NTSC) color space that a state-of-the-art full-color organic LED counterpart cannot attain. Our trichromatic white QLED also displays the record-high EL performance such as the peak values of 23,352 cd/m(2) in luminance, 21.8 cd/A in current efficiency, and 10.9% in external quantum efficiency.

A resource management and fault tolerance services in grid computing

In grid computing, resource management and fault tolerance services are important issues. The availability of the selected resources for job execution is a primary factor that determines the computing performance. In this paper, we propose a resource manager for optimal resource selection. Our resource manager automatically selects the set of optimal resources among candidate resources that achieves optimal performance using a genetic algorithm. Typically, the probability of a failure is higher in the grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational grids. And grid services are often expected to meet some minimum levels of Quality of Service (QoS) for a desirable operation. To address this issue, we also propose a fault tolerance service that satisfies QoS requirements. We extend the definition of failures from the conventional notion of failures in distribute systems in order to provide a fault tolerance service that deals with various types of resource failures, which include process failures, processor failures, and network failures. We also design and implement a fault detector and a fault manager. The implementation and simulation results indicate that our approaches are promising in that (1) the resource manager finds the optimal set of resources that guarantees efficient job execution, (2) the fault detector detects the occurrence of resource failures and (3) the fault manager guarantees that the submitted jobs complete and the performance of job execution is improved due to job migration even if some failures occur.

CPU transparent protection of OS kernel and hypervisor integrity with programmable DRAM

Increasingly, cyber attacks (e.g., kernel rootkits) target the inner rings of a computer system, and they have seriously undermined the integrity of the entire computer systems. To eliminate these threats, it is imperative to develop innovative solutions running below the attack surface. This paper presents MGuard, a new most inner ring solution for inspecting the system integrity that is directly integrated with the DRAM DIMM devices. More specifically, we design a programmable guard that is integrated with the advanced memory buffer of FB-DIMM to continuously monitor all the memory traffic and detect the system integrity violations. Unlike the existing approaches that are either snapshot-based or lack compatibility and flexibility, MGuard continuously monitors the integrity of all the outer rings including both OS kernel and hypervisor of interest, with a greater extendibility enabled by a programmable interface. It offers a hardware drop-in solution transparent to the host CPU and memory controller. Moreover, MGuard is isolated from the host software and hardware, leading to strong security for remote attackers. Our simulation-based experimental results show that MGuard introduces no speed overhead, and is able to detect nearly all the OS-kernel and hypervisor control data related rootkits we tested.

Pipelined CPU Design With FPGA in Teaching Computer Architecture

This paper presents a pipelined CPU design project with a field programmable gate array (FPGA) system in a computer architecture course. The class project is a five-stage pipelined 32-bit MIPS design with experiments on the Altera DE2 board. For proper scheduling, milestones were set every one or two weeks to help students complete the project on time. The goal of the project is to educate students effectively via hands-on learning, rather than having them achieve a complete and flawless CPU design. This study reveals that 21 MIPS instructions are enough to achieve the purpose. With the addition in 2010 of the properly enforced scheduling and the FPGA system, many more students successfully completed the class project than was the case in 2009. A student survey and the independent samples t-test reveal the effectiveness of the methodology with the FPGA system. This work differs from previous work in that the devised project requires the implementation of a real CPU instead of utilizing simulators or just experimenting with ready-made complete CPU models.

A resource manager for optimal resource selection and fault tolerance service in Grids

In this paper, we address the issues of resource management and fault tolerance in Grids. In Grids, the state of the selected resources for job execution is a primary factor that determines the computing performance. Specifically, we propose a resource manager for optimal resource selection. The resource manager automatically selects the optimal resources among candidate resources using a genetic algorithm. Typically, the probability of failure is higher in Grid computing than in a traditional parallel computing and the failure of resources affects job execution fatally. Therefore, a fault tolerance service is essential in computational Grids and Grid services are often expected to meet some minimum levels of quality of service (QoS) for desirable operation. To address this issue, we also propose fault tolerance service to satisfy QoS requirements. We extend the definition of failures, such as process failure, processor failure, and network failure, and design the fault detector and fault manager. The simulation results indicate that our approaches are promising in that (1) our resource manager finds the optimal set of resources that guarantees the optimal performance; (2) the fault detector detects the occurrence of resource failures; and (3) the fault manager guarantees that the submitted jobs complete and improves the performance of job execution due to job migration even if some failures happen.

A Power Division Reuse Partitioning Scheme with Half Frequency Reuse Factor for OFDMA Downlink Systems

In multicell OFDMA systems, throughput is severely degraded by other cell interference (OCI), especially at cell edge. To satisfy the performance requirement, OCI must be mitigated effectively. Several schemes have been proposed to handle this problem by coordinating the way to allocate spectrum resource to each cell. Since the allocation of spectrum has effects on OCI mitigation and the spectral efficiency, an wise strategy to consider those two factors is required. In this paper, we propose a new scheme named as power division reuse partitioning scheme (PDRPS). PDRPS balances OCI mitigation and spectral efficiency by adopting interference cancellation (IC) scheme. The simulation results show that PDRPS has more throughput and lower outage probability than the conventional schemes.

Adaptive workflow scheduling strategy in service-based grids

During the past several years, the grid application executed same jobs on one or more hosts in parallel, but the recent grid application is requested to execute different jobs linearly. That is, the grid application takes the form of workflow application. In general, efficient scheduling of workflow applications is based on heuristic scheduling method. The heuristic considering relation between hosts would improve execution time in workflow applications. But because of the heterogeneity and dynamic nature of grid resources, it is hard to predict the performance of grid application. In addition, it is necessary to deal with users QoS as like performance guarantee. In this paper, we propose a service model for predicting performance and an adaptive workflow scheduling strategy, which uses maximum flow algorithms for the relation of services and users QoS. Experimental results show that the performance of our proposed scheduling strategy is better than common-used greedy strategies.

Towards Quality Aware Collaborative Video Analytic Cloud

As cloud diversifies into different application fields, understanding and characterizing the specific work load sand application requirements play important roles in the design of efficient cloud infrastructure and system software support. Video analytic is a rapidly advancing field and it is widely used in many application domains (i.e., health, medical care, surveillance, and defense). To support video analytic applications efficiently in cloud, one has to overcome many challenges such as lack of understanding of the relationship and trade off between analytic performance metrics and resource requirements. Furthermore, cloud computing has grown from the early model of resource sharing to data sharing and workflow sharing. To address the challenges and to lever age emerging trends, we propose and experiment with a domain specific cloud environment for video analytic applications. We design a cloud infrastructure framework for sharing video data, analytic software, and workflow. In addition, we create a video analytic quality aware resource plan model to guarantee users QoS and optimize usage of resources based on predictive knowledge of video analytic softwares performance metrics and a resource planning model that optimizes the overall analytic service quality under users constraints (i.e., time and cost).The predictive knowledge is represented as input and analytic software specific predictors. The experimental results show that the video analytic quality aware resource planning model can balance the tradeoff between analytic quality and resource requirements, and achieve optimal or near-optimal planning for video analytic workloads with constraints in a resource shared environment. Simulation studies show that resource planning results using ground truth and video analytic performance predictions are very similar, which indicates that our analytic quality/resource predictors are very accurate.