To reduce the backbone video traffic generated by video surveillance, we propose an intelligent video surveillance system that offers multi-modal sensor-based event detection and event-driven video rate adaptation. Our proposed system can detect pedestrian existence and movements in the monitoring area by using multi-modal sensors (camera, laser scanner and infrared distance sensor) and control surveillance video quality according to the detected events. We evaluate event detection accuracy and video traffic volume in the experiment scenarios where up to six pedestrians pass through and/or stop at the monitoring area. Evaluation results conclude that our system can significantly reduce video traffic while ensuring high-quality surveillance.

This paper describes the design and experiment results of a 25Gbps vertical-cavity surface emitting laser (VCSEL) driver circuit for a multi channel optical transmitter. To compensate for the non-linearity of the VCSEL and achieve high speed data rate communication, an asymmetric pre-emphasis technique is proposed for the VCSEL driver. An asymmetric pre-emphasis signal can be created by adjusting the duty ratio of the emphasis signal. The VCSEL driver adopts a double cascode connection that can apply a drive current from a high voltage DC bias and feed-forward compensation that can enhance the band-width for common-cathode VCSEL. For the design of the optical module structure, a two-tier low temperature co-fired ceramics (LTCC) package is adopted to minimize the wire bonding between the signal pad on the LTCC and the anode pad on the VCSEL. This structure and circuit reduces the simulated deterministic jitter from 12.7 to 4.1ps. A test chip was fabricated with the 65-nm standard CMOS process and demonstrated to work as an optical transmitter. An experimental evaluation showed that this VCSEL driver with asymmetric pre-emphasis reduced the total deterministic jitter up to 8.6ps and improved the vertical eye opening ratio by 3% compared with symmetric pre-emphasis at 25Gbps with a PRBS=29-1 test signal. The power consumption of the VCSEL driver was 3.0mW/Gbps/ch at 25Gbps. An optical transmitter including the VCSEL driver achieved 25-Gbps, 4-ch fully optical links.

In industrial control processes, control performance influences the quality of products and utilization efficiency of energy; hence, the controller is necessarily designed according to user-desired control performance. Ideal control performance requires fast response for transient state and maintaining user-specified control performance for steady state. Hence, an algorithm to tune controller parameters to match the requirements for transient state and steady state is proposed. Considering the partial learning ability of the cerebellar model articulation controller (CMAC) neural network, it is utilized as a “tuner” of controller parameters in this study, since then the controller parameters can be tuned in both transient and steady states. Moreover, the fictitious reference iterative tuning (FRIT) algorithm is combined with CMAC in order to avoid problems, which may be caused by system modeling error and by using only a set of closed-loop data, the desired controller can be calculated in an off-line manner. In addition, the controller selected is a proportional-integral-derivative (PID) controller. Finally, the effectiveness of the proposed method is numerically verified by using some simulation and experimental examples.

In the case of drive-by download attacks, most malicious web sites identify the software environment of the clients and change their behavior. Then we cannot always obtain sufficient information appropriate to the client organization by automatic dynamic analysis in open services. It is required to prepare for expected incidents caused by re-accessing same malicious web sites from the other client in the organization. To authors' knowledge, there is no study of utilizing analysis results of malicious web sites for digital forensic on the incident and hedging the risk of expected incident in the organization. In this paper, we propose a system for evaluating the impact of accessing malicious web sites by using the results of multi-environment analysis. Furthermore, we report the results of evaluating malicious web sites by the multi-environment analysis system, and show how to utilize analysis results for forensic analysis and risk hedge based on actual cases of analyzing malicious web sites.

Crowd modeling and simulation is an active research field that has drawn increasing attention from industry, academia and government recently. In this paper, we present a generic data-driven approach to generate crowd behaviors that can match the video data. The proposed approach is a bi-layer model to simulate crowd behaviors in pedestrian traffic in terms of exclusion statistics, parallel dynamics and social psychology. The bottom layer models the microscopic collision avoidance behaviors, while the top one focuses on the macroscopic pedestrian behaviors. To validate its effectiveness, the approach is applied to generate collective behaviors and re-create scenarios in the Informatics Forum, the main building of the School of Informatics at the University of Edinburgh. The simulation results demonstrate that the proposed approach is able to generate desirable crowd behaviors and offer promising prediction performance.

An incident response organization such as a CSIRT contributes to preventing the spread of malware infection by analyzing compromised websites and sending abuse reports with detected URLs to webmasters. However, these abuse reports with only URLs are not sufficient to clean up the websites. In addition, it is difficult to analyze malicious websites across different client environments because these websites change behavior depending on a client environment. To expedite compromised website clean-up, it is important to provide fine-grained information such as malicious URL relations, the precise position of compromised web content, and the target range of client environments. In this paper, we propose a new method of constructing a redirection graph with context, such as which web content redirects to malicious websites. The proposed method analyzes a website in a multi-client environment to identify which client environment is exposed to threats. We evaluated our system using crawling datasets of approximately 2,000 compromised websites. The result shows that our system successfully identified malicious URL relations and compromised web content, and the number of URLs and the amount of web content to be analyzed were sufficient for incident responders by 15.0% and 0.8%, respectively. Furthermore, it can also identify the target range of client environments in 30.4% of websites and a vulnerability that has been used in malicious websites by leveraging target information. This fine-grained analysis by our system would contribute to improving the daily work of incident responders.

Verification of logic designs has been a long-standing bottleneck in the process of hardware design, where its automation and improvement of efficiency has demanding needs. Mainly simulation-based verification has been used for this purpose, and recently, coverage-driven verification has been widely used, of which target is improvement of some metric called coverage. Our target is the metric called toggle coverage. To find input patterns which cause some toggles on each signal, a SAT solver could be used, but this is computationally costly. In this paper, we study the effect of combination of random simulation and usage of a SAT solver. In particular, we use a SAT solver which can find multiple “diverse” solutions. With this solver, we can avoid generating similar patterns, which are unlikely to improve coverage. The experimental results show that, a small number of calls of a SAT solver can improve entire toggle coverage effectively, compared with simple random simulation.

This report presents experimental measurements of mixed-mode oscillations (MMOs) generated by a weakly driven four-segment piecewise linear Bonhoeffer-van der Pol (BVP) oscillator. Such a roughly approximated simple piecewise linear circuit can generate MMOs and mixed-mode oscillation-incrementing bifurcations (MMOIBs). The laboratory experiments well agree with numerical results. We experimentally and numerically observe time series and Lorenz plots of MMOs generated by successive and nonsuccessive MMOIBs.

This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

In order to realize solid-state drives (SSDs) with high performance, low energy consumption and high reliability, storage class memory (SCM)/multi-level cell (MLC) NAND flash hybrid SSD has been proposed. Algorithm of the hybrid SSD should be designed according to SCM specifications and workload characteristics. In this paper, SCMs are used as non-volatile cache. Cache operation guidelines and optimal SCM specifications for the hybrid SSD are provided for various workload characteristics. Three kinds of non-volatile cache operation for the hybrid SSD are discussed: i) write cache, ii) read-write cache without space control (RW cache) and iii) read-write cache with space control (RW cache w/ SC). SSD workloads are categorized into eight according to read/write ratio, access frequency and access data size. From evaluation result, the write cache algorithm is suitable for write-intensive workloads and read-cold-sequential workloads, while the RW cache algorithm is suitable for read-cold-random workloads to achieve the highest performance of the hybrid SSD. In contrast, as for read-hot-random workloads, write cache is appropriate when the SCM capacity is less than 3% of the NAND flash capacity. On the other hand, RW cache should be used in case that SCM capacity is more than 5% of NAND flash capacity. The effect of Memory-type SCM (M-SCM) and Storage-type SCM (S-SCM) on the hybrid SSD performance is also analyzed. The M-SCM latency is below 1 us (high speed) but the capacity is only 2% of the NAND flash capacity (small capacity). On the other hand, the S-SCM capacity is assumed to be 5% of the NAND flash capacity (large capacity) but S-SCM speed is larger than 1 us (low speed). If the additional SCM cost is limited to 20% of MLC NAND flash cost, up to 7-times and 8-times performance improvement are achieved in write-hot-random workload and read-hot-random workloads, respectively. Moreover, if the additional SCM cost is the same as MLC NAND flash cost, M-SCM/MLC NAND flash hybrid SSD achieves 24-times performance improvement.

Driver behavior assessment is a hard task since it involves distinctive interconnected factors of different types. Especially in case of insurance applications, a trade-off between application cost and data accuracy remains a challenge. Data uncertainty and noises make smart-phone or low-cost sensor platforms unreliable. In order to deal with such problems, this paper proposes the combination between the Belief and Fuzzy theories with a two-level fusion based architecture. It enables the propagation of information errors from the lower to the higher level of fusion using the belief and/or the plausibility functions at the decision step. The new developed risk models of the Driver and Environment are based on the accident statistics analysis regarding each significant driving risk parameter. The developed Vehicle risk models are based on the longitudinal and lateral accelerations (G-G diagram) and the velocity to qualify the driving behavior in case of critical events (e.g. Zig-Zag scenario). In case of over-speed and/or accident scenario, the risk is evaluated using our new developed Fuzzy Inference System model based on the Equivalent Energy Speed (EES). The proposed approach and risk models are illustrated by two examples of driving scenarios using the CarSim vehicle simulator. Results have shown the validity of the developed risk models and the coherence with the a-priori risk assessment.

The overdrive technique is widely used to eliminate motion blur in liquid-crystal displays (LCDs). However, this technique requires a large frame memory to store the previous frame. A reduction in the frame memory requires an image compression algorithm suitable for real-time data processing. In this paper, we present an algorithm based on multimode-color-conversion block truncation coding (MCC-BTC) to obtain a constant output bit rate and high overdrive performance. The MCC-BTC algorithm uses four compression methods, one of which is selected. The four compression modes either use the single-bitmap-generation method or the subsampling method for chrominance. As shown in the simulation results, the proposed algorithm improves the performance of both coding (up to 2.73dB) and overdrive (up to 2.61dB), and the visual quality is improved in comparison to other competing algorithms in literature.

The high performance GaN power amplifier circuit operating at 7.1 GHz was demonstrated for potential use such as in a space ground station. First, the GaN HEMT chips were investigated for the high power amplifier circuit design. And next, the designed amplifier circuits matching with the load and source impedance of the non-linear models were fabricated. From measurement, the AB-class power amplifier circuit with the four-cell chip showed the power added efficiency (PAE) of 42.6% and output power with 41.7dBm at -3dB gain compression. Finally, the good performance of the power amplifier was confirmed in a 20-way radial power combiner with the PAE of 17.4% and output power of 52.6 dBm at -3dB gain compression.

A map-reduce framework is popular for big data analysis. In the typical map-reduce framework, both master node and worker nodes can use hard-disk drives (HDDs) as local disks for the map-reduce computation. However, because of the inherit mechanical problems of HDDs, the I/O performance is a bottleneck for the map-reduce framework when I/O-intensive applications (e.g., sorting) are performed. Replacing HDDs with solid-state drives (SSDs) is not economical, although SSDs have better performance than HDDs. In this paper, we propose a virtualization-based hybrid storage system for the map-reduce framework. The objective of the paper is to combine the advantages of the fast access property of SSDs and the low cost of HDDs by realizing an economical design and improving I/O performance of a map-reduce framework in a virtualization environment. We propose three storage combinations: SSD-based, HDD-based, and a hybrid of SSD-based and HDD-based storage systems which balances speed, capacity, and lifetime. According to experiments, the hybrid of SSD-based and HDD-based storage systems offers superior performance and economy.

A 50-Gb/s optical transmitter, consisting of a 25-Gb/s-class lens-integrated DFB-LD (with -3-dB bandwidth of 20GHz) and a LD-driver chip based on 0.18-µm SiGe BiCMOS technology for inter and intra-rack transmissions, was developed and tested. The DFB-LD and LD driver chip are flip-chip mounted on an alumina ceramic package. To suppress inter-symbol interference due to a shortage of the DFB-LD bandwidth and signal reflection between the DFB-LD and the package, the LD driver includes a two-tap pre-emphasis circuit and a high-speed termination circuit. Operating at a data rate of 50Gb/s, the optical transmitter enhances LD bandwidth and demonstrated an eye opening with jitter margin of 0.23UI. Power efficiency of the optical transmitter at a data rate of 50Gb/s is 16.2mW/Gb/s.

In recent years, the need to build solid state drive (SSD)-based cloud storage systems has been increasing in order to process the big data generated by lots of Internet of Things devices and Internet users. Because these kinds of cloud systems require high performance and reliable storage, the use of flash-based Redundant Array of Independent Disks (RAID) will increase. But in flash-based RAID storage, parity data must be updated with every data write operation, which can more quickly overwhelm SSD's lifespan. To solve this problem, this letter proposes parity data deduplication for OpenStack cloud storage systems using an all flash array. Unlike the traditional data deduplication method, it only removes parity data, which will be stored in the parity disks of the all flash array. Experiments show that the proposed parity data deduplication method can efficiently reduce the number of parity data write operations, compared to the traditional data deduplication method.

Drive-by download attacks force users to automatically download and install malware by redirecting them to malicious URLs that exploit vulnerabilities of the user's web browser. In addition, several evasion techniques, such as code obfuscation and environment-dependent redirection, are used in combination with drive-by download attacks to prevent detection. In environment-dependent redirection, attackers profile the information on the user's environment, such as the name and version of the browser and browser plugins, and launch a drive-by download attack on only certain targets by changing the destination URL. When malicious content detection and collection techniques, such as honeyclients, are used that do not match the specific environment of the attack target, they cannot detect the attack because they are not redirected. Therefore, it is necessary to improve analysis coverage while countering these adversarial evasion techniques. We propose a method for exhaustively analyzing JavaScript code relevant to redirections and extracting the destination URLs in the code. Our method facilitates the detection of attacks by extracting a large number of URLs while controlling the analysis overhead by excluding code not relevant to redirections. We implemented our method in a browser emulator called MINESPIDER that automatically extracts potential URLs from websites. We validated it by using communication data with malicious websites captured during a three-year period. The experimental results demonstrated that MINESPIDER extracted 30,000 new URLs from malicious websites in a few seconds that conventional methods missed.

Due to the periodic recovery of virtual machines regardless of whether malicious intrusions exist, proactive recovery-based Intrusion Tolerant Systems (ITSs) are being considered for mission-critical applications. However, the virtual replicas can easily be exposed to attacks during their working period, and additionally, proactive recovery-based ITSs are ineffective in eliminating the vulnerability of exposure time, which is closely related to service availability. To address these problems, we propose a novel hybrid recovery-based ITS in this paper. The proposed method utilizes availability-driven recovery and dynamic cluster resizing. The availability-driven recovery method operates the recovery process by both proactive and reactive ways for the system to gain shorter exposure times and higher success rates. The dynamic cluster resizing method reduces the overhead of the system that occurs from dynamic workload fluctuations. The performance of the proposed ITS with various synthetic and real workloads using CloudSim showed that it guarantees higher availability and reliability of the system, even under malicious intrusions such as DDoS attacks.

Storage-Class Memory (SCM) and NAND flash hybrid Solid-State Drive (SSD) has advantages of high performance and low power consumption compared with NAND flash only SSD. In this paper, first, three SSD configurations are investigated. Three different SCMs are used with 0.1 µs, 1 µs and 10 µs read/write latencies, respectively, and the required SCM/NAND flash capacity ratios are analyzed to maintain the same SSD performance. Next, by using the three SSD configurations, the variation of SSD reliability, performance and cost are analyzed by changing error correction strengths. The SSD reliability of acceptable SCM and NAND flash Bit Error Rates (BERs) is limited by achieving specified SSD performance with error correction, and/or limited by SCM and NAND flash parity size and SSD cost. Lastly, the SSD replacement cost is also analyzed by considering the limitation of NAND flash write/erase cycles. The purpose of this paper is to provide a design guideline for obtaining high performance, highly reliable and cost-effective SCM/NAND hybrid structure SSD with ECC.

Modern web users may encounter a browser security threat called drive-by-download attacks when surfing on the Internet. Drive-by-download attacks make use of exploit codes to take control of user's web browser. Many web users do not take such underlying threats into account while clicking URLs. URL Blacklist is one of the practical approaches to thwarting browser-targeted attacks. However, URL Blacklist cannot cope with previously unseen malicious URLs. Therefore, to make a URL blacklist effective, it is crucial to keep the URLs updated. Given these observations, we propose a framework called automatic blacklist generator (AutoBLG) that automates the collection of new malicious URLs by starting from a given existing URL blacklist. The primary mechanism of AutoBLG is expanding the search space of web pages while reducing the amount of URLs to be analyzed by applying several pre-filters such as similarity search to accelerate the process of generating blacklists. AutoBLG consists of three primary components: URL expansion, URL filtration, and URL verification. Through extensive analysis using a high-performance web client honeypot, we demonstrate that AutoBLG can successfully discover new and previously unknown drive-by-download URLs from the vast web space.