Generation of Intense Narrow-Band Tunable Terahertz Radiation from Highly Bunched Electron Pulse Train

We present the analysis and start-to-end simulation of an intense narrow-band terahertz (THz) source with a broad tuning range of radiation frequency, using a single-pass free electron laser (FEL) driven by a THz-pulse-train photoinjector. The fundamental radiation frequency, corresponding to the spacing between the electron microbunches, can be easily tuned by varying the spacing time between the laser micropulses. Since the prebunched electron beam is highly bunched at the first several harmonics, with the harmonic generation technique, the radiation frequency range can be further enlarged by several times. The start-to-end simulation results show that this FEL is capable of generating a few tens megawatts power, several tens micro-joules pulse energy, and a few percent bandwidth at the frequencies of 0.5–5 THz. In addition, several practical issues are considered.     

Further Characterization of 394-GHz Gyrotron FU CW GII with Additional PID Control System for 600-MHz DNP-SSNMR Spectroscopy

A 394-GHz gyrotron, FU CW GII, has been designed at the University of Fukui, Japan, for dynamic nuclear polarization (DNP)-enhanced solid-state nuclear magnetic resonance (SSNMR) experiments at 600-MHz ¹H resonant frequency. After installation at the Institute for Protein Research (IPR), Osaka University, Japan, a PID feedback control system was equipped to regulate the electron gun heater current for stabilization of the electron beam current, which ultimately achieved stabilization of output power when operating in continuous wave (CW) mode. During exploration to further optimize operating conditions, a continuous tuning bandwidth of approximately 1 GHz was observed by varying the operating voltage at a fixed magnetic field. In the frequency range required for positive DNP enhancement, the output power was improved by increasing the magnetic field and the operating voltage from their initial operational settings. In addition, fine tuning of output frequency by varying the cavity cooling water temperature was demonstrated. These operating conditions and ancillary enhancements are expected to contribute to further enhancement of SSNMR signal.     

Practical Analog Circuit Diagnosis Based on Fault Features with Minimum Ambiguities

As numerous faults exist in practical analog circuits, new challenges arise in the field of diagnosis with large-scale target faults as well as fault features. To address this issue, firstly, an ambiguity model is built to measure the distinguishability between two faults. Then, the optimal fault features are obtained by analyzing the response curves of the circuit under test (CUT) to minimize the ambiguities among the faults. Finally, comparisons are made among three classification methods, including the maximum likelihood classifier (MLC), artificial neural networks (ANNs) and support vector machine (SVM), to demonstrate their own diagnostic abilities for practical use. Two examples are illustrated, and taking advantage of an automated implementation framework, 92 faults in total are examined in the second example. The experimental results show that good diagnostic performances can be obtained with the proposed method. However, when a practical case is encountered, the ANNs method may fail due to its high time and space complexity, while the MLC and SVM methods are still applicable.     

Side-Channel Information Characterisation Based on Cascade-Forward Back-Propagation Neural Network

Traditional cryptanalysis assumes that an adversary only has access to input and output pairs, but has no knowledge about internal states of the device. However, the advent of side-channel analysis showed that a cryptographic device can leak critical information. In this circumstance, Machine learning is known as a powerful and promising method of analysing of side-channel information. In this paper, an experimental investigation on a FPGA implementation of elliptic curve cryptography (ECC) was conducted to explore the efficiency of side-channel information characterisation based on machine learning techniques. In this work, machine learning is used in terms of principal component analysis (PCA) for the preprocessing stage and a Cascade-Forward Back-Propagation Neural Network (CFBP) as a multi-class classifier. The experimental results show that CFBP can be a promising approach in characterisation of side-channel information.     

A 65nm CMOS Ramp Generator Design and its Application Towards a BIST Implementation of the Reduced-Code Static Linearity Test Technique for Pipeline ADCs

This work presents an efficient on-chip ramp generator targeting to facilitate the deployment of Built-In Self-Test (BIST) techniques for ADC static linearity characterization. The proposed ramp generator is based on a fully-differential switched-capacitor integrator that is conveniently modified to produce a very small integration gain, such that the ramp step size is a small fraction of the LSB of the target ADC. The proposed ramp generator is employed in a servo-loop configuration to implement a BIST version of the reduced-code linearity test technique for pipeline ADCs, which drastically reduces the volume of test data and, thereby, the test time, as compared to the standard test based on a histogram. The demonstration of the pipeline ADC BIST is carried out based on a mixture of transistor-level and behavioral-level simulations that employ actual production test data.     

A Jitter Injection Signal Generation and Extraction System for Embedded Test of High-Speed Data I/O

An instrument for on-chip measurement of transceiver transmission capability is described that is fully realizable in CMOS technology and embeddable within an SoC. The instrument can be used to inject and extract the timing and voltage information associated with signals in high-speed transceiver circuits that are commonly found in data communication applications. At the core of this work is the use of ΣΔ amplitude- and phase-encoding techniques to generate both the voltage and timing (phase) references, or strobes used for high-speed sampling. The same technique is also used for generating the test stimulant for the device-under-test.     

Adaptive VM Management with Two Phase Power Consumption Cost Models in Cloud Datacenter

As cloud computing models have evolved from clusters to large-scale data centers, reducing the energy consumption, which is a large part of the overall operating expense of data centers, has received much attention lately. From a cluster-level viewpoint, the most popular method for an energy efficient cloud is Dynamic Right Sizing (DRS), which turns off idle servers that do not have any virtual resources running. To maximize the energy efficiency with DRS, one of the primary adaptive resource management strategies is a Virtual Machine (VM) migration which consolidates VM instances into as few servers as possible. In this paper, we propose a Two Phase based Adaptive Resource Management (TP-ARM) scheme that migrates VM instances from under-utilized servers that are supposed to be turned off to sustainable ones based on their monitored resource utilizations in real time. In addition, we designed a Self-Adjusting Workload Prediction (SAWP) method to improve the forecasting accuracy of resource utilization even under irregular demand patterns. From the experimental results using real cloud servers, we show that our proposed schemes provide the superior performance of energy consumption, resource utilization and job completion time over existing resource allocation schemes.     

Efficient Resource Management for Cloud-enabled Video Surveillance over Next Generation Network

The next generation video surveillance systems are expected to face challenges in providing computation support for an unprecedented amount of video streams from multiple video cameras in a timely and scalable fashion. Cloud computing offers huge computation resources for large-scale storage and processing on demand, which are deemed suitable for video surveillance tasks. Cloud also provides quality of service guaranteed hardware and software solutions with the virtual machine (VM) technology using a utility-like service costing model. In cloud-based video surveillance context, the resource requests to handle video surveillance tasks are translated in the form of VM resource requests, which in turn are mapped to VM resource allocation referring to physical server resources hosting the VMs. Due to the nature of video surveillance tasks, these requests are highly time-constrained, heterogeneous and dynamic in nature. Hence, it is very challenging to actually manage the cloud resources from the perspective of VM resource allocation given the stringent requirements of video surveillance tasks. This paper proposes a computation model to efficiently manage cloud resources for surveillance tasks allocation. The proposed model works on optimizing the trade-off between average service waiting time and long-term service cost, and shows that long-term service cost is inversely proportional to high and balanced utilization of cloud resources. Experiments show that our approach provides a near-optimal solution for cloud resource management when handling the heterogeneous and unpredictable video surveillance tasks dynamically over next generation network.     

Bioinspired Security Analysis of Wireless Protocols

Fraglets represent an execution model for communication protocols that resembles the chemical reactions in living organisms. The strong connection between their way of transforming and reacting and formal rewriting systems makes a fraglet program amenable to automatic verification. Grounded on past work, this paper investigates feasibility of adopting fraglets as model for specifying security protocols and analysing their properties. In particular, we give concrete sample analyses over a secure RFID protocol, showing evolution of the protocol run as chemical dynamics and simulating an adversary trying to circumvent the intended steps. The results of our analysis confirm the effectiveness of the cryptofraglets framework for the model and analysis of security properties and eventually show its potential to identify and uncover protocol flaws.