A vibration minimized input design based on a polynomial input

For information devices such as hard disk drives, actuators must be controlled quickly and precisely as much as possible. To satisfy these requirements, feedforward input design is very important. For this problem, we have proposed a frequency-shaped final-state control method. This method can obtain a feedforward input that has lower spectrum at desired frequency points so as to reduce residual vibration after positioning. Furthermore, the method has been extended to generate the feedforward input by a polynomial. By using the polynomial, the required memory size can be drastically reduced because it only requires to store the coefficients of polynomial. However, in the frequency-shaped final-state control method by polynomial input, it is not guaranteed that the solution always exist theoretically. In order to solve this problem, final-state control methods in which the feedforward input is generated by both of a polynomial and time-series data are proposed. The effectiveness of the proposed methods are verified by simulation using the plant model of the HDD benchmark problem. The simulation results show that the proposed method outperform the conventional methods.

For information devices such as hard disk drives, actuators must be controlled as quickly and precisely as possible. The design of the feedforward input is very important in satisfying these requirements. Therefore, we have proposed design methods based on final-state control so as to reduce its spectrum at the desired frequency points. Furthermore, the final-state control method has been extended to generate the feedforward input using a polynomial. By using a polynomial, the required memory size can be drastically reduced because only the coefficients of the polynomial must be stored. However, when the step number of the input increases, the method may not produce a correct solution because of a numerically ill-posed problem. In this paper, we propose a design method for the final-state control based on a time-symmetric polynomial input. The simulation results show that the proposed method contributes to the improvement of not only the numerically ill-posed problem but also the performance.

As a method to generate a feedforward input that achieves high speed and high precision positioning,a polynomial-input-type final-state control has been proposed.This method can generatea feedforward input that which drives an initial state to a final state in a finite time.However,the existence of the solution is not always ensured for any initial and final states.To solve this problem, a final-state control method using a polynomial and time-series data has been proposed,though,it requires an extra memory to store the time-series data.In this research,we propose a method that does not require the time-series data by introducing a negligibly-sinall final-state error.The effectiveness of the proposed method is verified by performing simulations.

This paper proposes a new design method of track-seeking trajectory for hard disk drives by using final-state control method. The proposed method can obtain a feedforward control input whose frequency components are minimum at the desired frequency points. In our approach, the control input is evaluated in continuous-time domain, and the frequency components higher than the Nyquist frequency can be also optimized. Simulation and experimental results are shown to evaluate the effectiveness of the proposed method.

Computer systems are constantly under attack and illegal access is a constant threat which makes security even more critical. A system can be broken into and secret information, e.g. decryption key, may be exposed. Very recently, a new framework for the protection against such key exposure problem was suggested and was called, key-insulated encryption (KIE). In this paper, we study key insulated cryptosystems without computational assumptions. First, we define the model of KIE in unconditional setting and show lower bounds on required memory sizes. Our bounds are all tight since our concrete construction of KIE achieves the bounds. In other words, this construction is optimal in terms of memory sizes of a user, a trusted device and a sender. We then, extend the concept of it further, and add an extra property so that any pair of users in the system can communicate with each other. We called the version with this added extension, dynamic and mutual key insulated encryption (DMKIE), and concrete implementations of DMKIE are also shown. Finally, we discuss the relationship among KIE, key predistribution schemes (KPS) and broadcast encryption schemes (BES), specifically, showing that DMKIE can be constructed from KPS or BES.

A phase-searching look-up table (PS-LUT) method is proposed to dramatically reduce the memory size of the new look-up table (N-LUT) method as well as to keep its advantage of fast computational speed. Small quantities of samples are chosen as the basic phase points (BPPs) in the principle fringe pattern (PFP), and phases of the object points for BPPs are precalculated and stored in the table. The phases of object point for the whole PFP could be quickly obtained through a phase-searching method. With proper reference beam phases, PFP could be rapidly generated. The experimental results reveal that the computational speed of the proposed method is about 24 times faster than that of the ray-tracing method, and the required memory size is 1731 times less than that of the N-LUT method. To eliminate the coherent noise of the N-LUT and PS-LUT, the random PFP N-LUT (RN-LUT) and PS-LUT (RPS-LUT), in which the PFP is randomly chosen for every object point, are put forward. The reconstructed images with a spatial light modulator indicate that both the RN-LUT and RPS-LUT methods are extremely effective in improving the quality of the reconstructed images.

Address lookup is one of the main functions of the Internet routers and a very important feature in evaluating router performance. As the Internet traffic keeps growing and the number of routing table entries is continuously growing, efficient address-lookup mechanism is essential. In recent years, various fast address-lookup schemes have been proposed, but most of those schemes are not practical in terms of the memory size required for routing table and the complexity required in table update. In this paper, we have proposed a parallel IP address lookup architecture based on multiple hashing. The proposed scheme has advantages in required memory size, the number of memory accesses, and table update. We have evaluated the performance of the proposed scheme through simulation using data from MAE-WEST router. The simulation result shows that the proposed scheme requires a single memory access for the address lookup of each route when 203 kbytes of memory and a few-hundred-entry TCAM are used.

The computational requirements in the simulations of geopotential estimation from satellite gravity gradiometry are discussed. Fast algorithms for spherical harmonic synthesis and least squares accumulation on a vectorizing supercomputers are presented. Using these methods, in a test case estimation of 2595 coefficients of a degree and order 50 gravity field, sustained program execution speeds of 275 Mflops (87 % peak machine speed) on a single processor of a CRAY Y-MP were achieved, with spherical harmonics computation accounting for less than 1 % of total cost. From the results, it appears that brute-force estimation of a degree and order 180 field would require 537 Million Words of memory and 85 hours of CPU time, assuming mission duration of 1 month, and execution speed of 1 Gflops. Both memory size and execution speed requirements are within the capabilities of modern multi-processor supercomputers.

Hadoop and Spark are well-known big data processing platforms. The main technologies of Hadoop are Hadoop Distributed File System and MapReduce processing. Hadoop stores intermediary data on Hadoop Distributed File System, which is a disk-based distributed file system, while Spark stores intermediary data in the memories of distributed computing nodes as Resilient Distributed Dataset. In this paper, we show how memory size affects distributed processing of large volume of data, by comparing the running time of K-means algorithm of HiBench benchmark on Hadoop and Spark clusters, with different size of memories allocated to data nodes. Our results show that Spark cluster is faster than Hadoop cluster as long as the memory size is big enough for the data size. But, with the increase of the data size, Hadoop cluster outperforms Spark cluster. When data size is bigger than memory cache, Spark has to replace disk data with memory cached data, and this situation causes performance degradation.

Distributed local memories, or scratchpads, have been shown to effectively reduce cost and power consumption of application-specific accelerators while maintaining performance. The design of the local memory organization must take several factors into account, including the memory bandwidth and size requirements of the program and the distribution of program data among the memories. In addition, when register structures and function units in the accelerator are clustered, the effects of intercluster communication should be taken into account. This work proposes a technique to synthesize the local memory architecture of a clustered accelerator using a phase-ordered approach. First, the dataflow graph is pre-partitioned to define a performance-centric grouping of the operations. Second, memory synthesis is performed by combining multiple data structures into a set of physical memories that minimizes cost while maintaining a performance threshold. Finally, post-partitioning is performed to determine the final assignment of operations to clusters given the memory organization. Results show that customization reduces memory cost from 2% to 59% over a naive scheme that utilizes one physical memory per program data structure. Further, pre-partitioning is shown to reduce the intercluster communication required to achieve a fixed performance.

This paper presents an approach for symbolic analysis of time-series data from a dynamical system perspective that uses Markov modeling of symbol sequences. A key aspect of this approach is the selection of relevant histories or depth of the symbol sequence to capture the evolution of the observed dynamical system in its phase-space. An approach based on the spectral properties of the one-step transition matrix is proposed. A key advantage of this approach compared to the state-of-the-art is that it does not require several passes through the time-series data to search for the optimal model given some metric. The proposed approach makes use of the decay-rate of conditional influence of the current symbol to the n-step future of the dynamical system. Using a bound on this decay rate, the optimal depth can be computed in exactly one pass though the time-series data. The effectiveness of the proposed methodology is demonstrated on a known low-dimensional chaotic system and the efficacy of the approach for anomaly detection is demonstrated.

Modeling of temporal patterns to infer generative models from measurement data is critical for dynamic data-driven application systems (DDDAS). Markov models are often used to capture temporal patterns in sequential data for statistical learning applications. This chapter presents a methodology for reduced-order Markov modeling of time-series data based has been used on spectral properties of stochastic matrix and clustering of directed graphs. Instead of the common Hidden Markov model (HMM)-inspired techniques, a symbolic dynamics-based approach to infer an approximate generative Markov model for the data. The time-series data is first symbolized by partitioning of the discrete-valued signal in continuous domain. The size of temporal memory of the discretized symbol sequence is then estimated using spectral properties of the stochastic matrix created from the symbol sequence for a first-order Markov model of the symbol sequence. Then, a graphical method is used to cluster the states of the corresponding high-order Markov model to infer a reduced-size Markov model with a non-deterministic algebraic structure. A Bayesian inference rule captures the parameters of the reduced-size Markov model from the original model. The proposed idea is illustrated by creating Markov models for pressure time-series data from a swirl stabilized combustor where some controlled protocols are used to induce instability. Results demonstrate complexity modeling of the underlying Markov model as the system operating condition changes from stable to unstable which is useful in combustion applications such as detection and control of thermo-acoustic instabilities.

High-performance light detection and ranging (LiDAR) modules are highly demanded for advanced driving assistance system and autonomous driving. With CMOS-fabricated single-photon avalanche diodes (SPADs) and pulsed-operated semiconductor lasers, photon-counting based vehicle LiDARs are one of the most promising candidates for low-cost solution. The high data rate and the long integration time of SPAD LiDARs are two of main obstacles for realizing commercially affordable 3-D imagers. In this work, we develop an evaluation method for the minimum ranging time and investigate the time-bin width effect on the ranging precision. A simple formula for predicting the shortest integration time is proposed and compared with the experimental data. We also demonstrate that a proper time-bin width can reduce the data rate and required memory size without degrading the distance resolution. Our work paves the way for making low-cost vehicle LiDARs through system optimizing and engineering.

Convolution neural networks have become widely used in embedded systems such as automatic driving systems. In these cases, inference functions in convolution neural networks are implemented due to resource limitation in embedded systems. Field-programmable gate array implementation is preferable because of low power consumption and real-time response time in embedded systems. Parameters in a convolution neural network are floating-point numbers, and enormous floating-point calculation is required. This is a challenge because the field-programmable gate array has a limited floating processing unit and in-memory processing. One way to solve the problem is to approximate an enormous number of parameters and to perform efficient computation. In the approximation, floating-point numbers of parameters are implemented using smaller-size integers or numbers having fewer bits. However, the inference accuracy decreases in the approximation, leading to a tradeoff situation. In addition, which layer should be approximated in order to be effective is not clear. In order to solve these problems, we developed an approximation support system. The developed system approximates the parameters and calculates the accuracy of the parameters and the required memory size. Furthermore, using this system, we carry out experiments to evaluate the effectiveness of several approximation methods for a large-scale network and dataset.

Anonymous channels or similar techniques that achieve sender's anonymity play important roles in many applications, e.g. electronic voting. However, they will be meaningless if cryptographic primitives containing sender's identity are carelessly used during the transmission. In computationally secure settings, this problem may be easily overcome by using public key encryption and group signatures. However, in an unconditionally secure setting, in which no computational difficulty is assumed, this is not an easy case as such. As the increasing computational power approaches the point where security policy can no longer assume the difficulty of solving factoring or discrete logarithm problems, it must shift its focus to assuring the solvency of unconditionally secure schemes that provide long-term security. The main contribution of this paper is to study the security primitives for the above problem. In this paper, we first define the unconditionally secure asymmetric encryption scheme, which is an encryption scheme with unconditional security and where it is impossible for a receiver to deduce the identity of a sender from the encrypted message. We also investigate tight lower bounds on required memory sizes from an information theoretic viewpoint and show an optimal construction based on polynomials. It is remarkable to see that these bounds are considerably different from those in Shannon's model of the conventional unconditionally secure symmetric encryption. Other than the polynomial-based scheme, we also show a construction based on combinatorial theory, a non-malleable scheme and a multi-receiver scheme. Then, we define and formalize the group authentication code (GA-code), which is an unconditionally secure authentication code with anonymity like group signatures. In this scheme, any authenticated user will be able to generate and send an authenticated message while the receiver can verify the legitimacy of the message---that it has been sent from a legitimate user but at the same time retains his anonymity. However, by cooperating with the group authority, such as in the case of disputes, the receiver is able to obtain information of the user's identity. For GA-code, we show two concrete constructions.