In this paper a hardware-efficient local extrema detection (LED) method used for scale-space extrema detection in the SIFT algorithm is proposed. By reformulating the reuse of the intermediate results in taking the local maximum and minimum, the necessary operations in LED are reduced without degrading the detection accuracy. The proposed method requires 25% to 35% less logic resources than the conventional method when implemented in an FPGA with a slight increase in latency.

Return-oriented programming (ROP) attacks, which have been increasing in number recently, are an exploitation technique that can bypass non-executable page protection methods by using codes that exist within benign programs or modules. There have been many studies on defense against ROP attacks, but most of them have high overhead or high time complexity in terms of the detection of gadgets. In this letter, we suggest an ROP defense technique which is fast, space-efficient, and of lower detection time complexity; it uses a compiler-based approach. The most recent ROP defense technique is a compiler-based zero-sum defender suggested by Kim et al., achieving very low overhead. However, it still did not solve the issue of time complexity regarding detection. Our technique performs a specific computation to identify gadgets at the resetting position immediately before and after a return instruction. This method can efficiently identify a series of gadgets performed without calls and defend against them. In our experiment, the performance overhead was 1.62% and the file size overhead was 4.60%; our proposed technique achieved O(1) in terms of time complexity while having almost the same overhead as the zero-sum defender.

In this paper, we propose a novel placement algorithm for mixed-grained reconfigurable architectures (MGRAs). MGRA consists of coarse-grained and fine-grained clusters, in order to implement a combined digital systems of high-speed data paths with multi-bit operands and random logic circuits for state machines and bit-wise operations. For accelerating simulated annealing based FPGA placement algorithm, range limiter has been proposed to control the distance of two blocks to be interchanged. However, it is not applicable to MGRAs due to the heterogeneous structure of MGRAs. Proposed range limiter using connection bounding box effectively keeps the size of range limiter to encourage moves across fine-grain blocks in non-adjacent clusters. From experimental results, the proposed method achieved 47.8% reduction of cost in the best case compared with conventional methods.

Non-volatile memories are paid attention to as a promising alternative to memory design. Data stored in them still may be destructed due to crosstalk and radiation. We can restore the data by using error-correcting codes which require extra bits to correct bit errors. Further, non-volatile memories consume ten to hundred times more energy than normal memories in bit-writing. When we configure them using error-correcting codes, it is quite necessary to reduce writing bits. In this paper, we propose a method to generate a bit-write-reducing code with error-correcting ability. We first pick up an error-correcting code which can correct t-bit errors. We cluster its codeswords and generate a cluster graph satisfying the S-bit flip conditions. We assign a data to be written to each cluster. In other words, we generate one-to-many mapping from each data to the codewords in the cluster. We prove that, if the cluster graph is a complete graph, every data in a memory cell can be re-written into another data by flipping at most S bits keeping error-correcting ability to t bits. We further propose an efficient method to cluster error-correcting codewords. Experimental results show that the bit-write-reducing and error-correcting codes generated by our proposed method efficiently reduce energy consumption. This paper proposes the world-first theoretically near-optimal bit-write-reducing code with error-correcting ability based on the efficient coding theories.

Protanopes and deuteranopes are difficult to distinguish some color pairs. In this letter, a new lightness modification method which considers the Craik-O'Brien effect is proposed. The lightness modification is performed at parts which are difficult to distinguish in the protanopia or deuteranopia. Experiments show the validity of the proposed method.

In this paper, a novel scheme of the adaptive sampling of block compressive sensing is proposed for natural images. In view of the contents of images, the edge proportion in a block can be used to represent its sparsity. Furthermore, according to the edge proportion, the adaptive sampling rate can be adaptively allocated for better compressive sensing recovery. Given that there are too many blocks in an image, it may lead to a overhead cost for recording the ratio of measurement of each block. Therefore, K-means method is applied to classify the blocks into clusters and for each cluster a kind of ratio of measurement can be allocated. In addition, we design an iterative termination condition to reduce time-consuming in the iteration of compressive sensing recovery. The experimental results show that compared with the corresponding methods, the proposed scheme can acquire a better reconstructed image at the same sampling rate.

For the nonlinear acoustic echo cancellation, we present an algorithm to estimate the threshold of the clipping effect and the room impulse response vector by suppressing their time-varying cost function. A common way to suppress the time-varying cost function of a pair of parameters is to alternatingly minimize the function with respect to each parameter while keeping the other fixed, which we refer to as adaptive alternating minimization. However, since the cost function for the threshold is nonconvex, the conventional methods approximate the exact minimizations by gradient descent updates, which causes serious degradation of the estimation accuracy in some occasions. In this paper, by exploring the fact that the cost function for the threshold becomes piecewise quadratic, we propose to exactly minimize the cost function for the threshold in a closed form while suppressing the cost function for the impulse response vector in an online manner, which we call exact-online adaptive alternating minimization. The proposed method is expected to approximate more efficiently the adaptive alternating minimization strategy than the conventional methods. Numerical experiments demonstrate the efficacy of the proposed method.

A method of color scheme is proposed considering contrast of luminance between adjacent regions and design property. This method aims at setting the contrast of luminance high, in order to make the image understandable to visually handicapped people. This method also realizes preferable color design for visually normal people by assigning color components from color combination samples. Interactive evolutionary computing is adopted to design the luminance and the color, so that the luminance and color components are assigned to each region appropriately on the basis of human subjective criteria. Here, the luminance is designed first, and then color components are assigned, keeping the luminance unchanged. Since samples of fine color combinations are applied, the obtained color design is also fine and harmonic. Computer simulations verify the high performance of this system.

This paper develops the generating function method for the discrete-time nonlinear optimal control problem. This method can analytically give the optimal input as state feedforward control in terms of the generating functions. Since the generating functions are nonlinear, we also develop numerical implementations to find their Taylor series expressions. This finally gives optimal solutions expressed only in terms of the pre-computed generating function coefficients and state boundary conditions, such that it is useful for the on-demand optimal solution generation for different boundary conditions. Examples demonstrate the effectiveness of the developed method.

This letter proposes an Index based on Irregular Partition of data identifiers (IIP), to enable clients to quickly access multiple data items on a wireless broadcast channel. IIP improves the access time by reducing the index waiting time when clients access multiple data items, through the use of irregular partitioning of the identifier space of data items. Our performance evaluation shows that with respect to access time, the proposed IIP outperforms the existing index schemes supporting multiple data access.

In this letter, we propose a novel discriminative representation for patterned fabric defect inspection when only limited negative samples are available. Fisher criterion is introduced into the loss function of deep learning, which can guide the learning direction of deep networks and make the extracted features more discriminating. A deep neural network constructed from the encoder part of trained autoencoders is utilized to classify each pixel in the images into defective or defectless categories, using as context a patch centered on the pixel. Sequentially the confidence map is processed by median filtering and binary thresholding, and then the defect areas are located. Experimental results demonstrate that our method achieves state-of-the-art performance on the benchmark fabric images.

This paper proposes a technique for efficient standing multipath mitigation of geostationary earth orbit (GEO) satellites. The performance of traditional filtering methods for GEO multipath errors can be effectively improved by using carrier frequency variation approach. Conventional multipath mitigation methods do not pay much attention to the GEO satellite multipath effects, and they are less effective due to the non-zero average characteristics of GEO multipath errors for short epochs. By varying carrier frequency, the multipath error average becomes approaching zero in short epochs due to the faster multipath carrier phase variation. Therefore, it could enhance the traditional filtering method performance on the multipath signals. By varying the carrier frequency or the carrier phase offset, the average multipath error will approach zero as a result of the frequent multipath carrier phase variations. This method aims to explore the potential for signal pattern design while improving the performance of current satellite navigation systems. The results show that the root mean square error (RMSE) for pseudo-range multipath errors of the proposed approach improves about 0.3m with a frequency variation range of 15MHz.

We investigate the influence of launching conditions on misalignment tolerance of pluggable ballpoint-pen interconnects, where graded-index plastic optical fibers (GI POFs) are coupled with ball lenses mounted on their end faces. The lateral-misalignment tolerance of the ballpoint-pen connector decreased with an increase in the driving current of a vertical cavity surface emitting laser (VCSEL) under the center launching condition. This was attributed to the VCSEL multimode oscillation, which increased the connector coupling loss through the higher-order guided mode launching in the GI POF and the resulting output beam expansion in the ballpoint-pen connector. The driving-current dependence of the connector coupling loss could be decreased using offset launchings. For a radial launching offset of 20µm, we could obtain coupling losses below 1dB for lateral coupling offsets of ±50µm with little dependence on the driving current. This suggests that data transmission quality for misaligned connection of the GI POFs can be improved further by optimizing launching systems for the ballpoint-pen interconnects.

Network intelligence is a discipline that builds on the capabilities of network systems to act intelligently by the usage of network resources for delivering high-quality services in a changing environment. Wide area network intelligence is a class of network intelligence in wide area network which covers the core and the edge of Internet. In this paper, we propose a system based on machine learning for wide area network intelligence. The whole system consists of a core machine for pre-training and many terminal machines to accomplish faster responses. Each machine is one of dual-hemisphere models which are made of left and right hemispheres. The left hemisphere is used to improve latency by terminal response and the right hemisphere is used to improve communication by data generation. In an application on multimedia service, the proposed model is superior to the latest deep feed forward neural network in the data center with respect to the accuracy, latency and communication. Evaluation shows scalable improvement with regard to the number of terminal machines. Evaluation also shows the cost of improvement is longer learning time.

Wireless body area networks (BANs) are attracting much attention due to their suitable for healthcare and medical applications. Unfortunately, electrostatic discharge (ESD) is a major electromagnetic (EM) noise source that can degrade wireless communication performance. In this study, we measure EM noise power in the 2.4GHz and 30MHz bands for indirect ESD testing specified in IEC 61000-4-2 standard, and derived a statistical ESD noise model from the measurement results. The ESD noise power was found to follow a lognormal distribution in both 2.4GHz and 30MHz bands. We use this ESD noise model to conduct bit error rate (BER) simulations in a communication channel with additive white Gaussian noise (AWGN) plus ESD noise at 2.4GHz and 30MHz bands. The result is that the BER performance is virtually the same in both bands, and decreases with the signal to noise power ratio (SNR). It is also shown that an error floor exists in the BER performances at both frequencies, which, if the ESD noise power is larger than the Gaussian noise, cannot be improved by increasing the SNR. Although the ESD noise power at 2.4GHz band is nearly 30dB smaller than that at 30MHz band, the signal attenuation along the human body at 2.4GHz band is much larger compared to 30MHz band. This may yield a similar SNR level at 30MHz and 2.4GHz bands in an ESD-dominated environment, so that the 2.4GHz band does not have an obvious merit for BAN applications. Since there are so many in-band interference sources at 2.4GHz band, the 30MHz band seems more promising for vital data transmission in a BAN scenario even in an ESD-dominated environment.

Extending the very popular tori interconnection networks[1]-[3], Torus-Connected Cycles (TCC) have been proposed as a novel network topology for massively parallel systems [5]. Here, the set-to-set disjoint paths routing problem in a TCC is solved. In a TCC(k,n), it is proved that paths of lengths at most kn2+2n can be selected in O(kn2) time.

In this paper, a built-in test circuit for an electrical interconnect test method is proposed to detect an open defect occurring at an interconnect between an IC and a printed circuit board. The test method is based on measuring the supply current of an inverter gate in the test circuit. A time-varying signal is provided to an interconnect as a test signal by the built-in test circuit. In this paper, the test circuit is evaluated by SPICE simulation and by experiments with a prototyping IC. The experimental results reveal that a hard open defect is detectable by the test method in addition to a resistive open defect and a capacitive open one at a test speed of 400 kHz.

Hough-based voting approaches have been widely used to solve many detection problems such as object and action detection. These approaches for action detection cast votes for action classes and positions based on the local spatio-temporal features of given videos. The voting process of each local feature is performed independently of the other local features. This independence enables the method to be robust to occlusions because votes based on visible local features are not influenced by occluded local features. However, such independence makes discrimination of similar motions between different classes difficult and causes the method to cast many false votes. We propose a novel Hough-based action detection method to overcome the problem of false votes. The false votes do not occur randomly such that they depend on relevant action classes. We introduce vote distributions, which represent the number of votes for each action class. We assume that the distribution of false votes include important information necessary to improving action detection. These distributions are used to build a model that represents the characteristics of Hough voting that include false votes. The method estimates the likelihood using the model and reduces the influence of false votes. In experiments, we confirmed that the proposed method reduces false positive detection and improves action detection accuracy when using the IXMAS dataset and the UT-Interaction dataset.

We propose subquadratic space complexity multipliers for any finite field $mathbb{F}_{q^n}$ over the base field $mathbb{F}_q$ using the Dickson basis, where q is a prime power. It is shown that a field multiplication in $mathbb{F}_{q^n}$ based on the Dickson basis results in computations of Toeplitz matrix vector products (TMVPs). Therefore, an efficient computation of a TMVP yields an efficient multiplier. In order to derive efficient $mathbb{F}_{q^n}$ multipliers, we develop computational schemes for a TMVP over $mathbb{F}_{q}$. As a result, the $mathbb{F}_{2^n}$ multipliers, as special cases of the proposed $mathbb{F}_{q^n}$ multipliers, have lower time complexities as well as space complexities compared with existing results. For example, in the case that n is a power of 3, the proposed $mathbb{F}_{2^n}$ multiplier for an irreducible Dickson trinomial has about 14% reduced space complexity and lower time complexity compared with the best known results.

Recently, cloud systems have started to be utilized for services which analyze user's data in the field of computer vision. In these services, keypoints are extracted from images or videos, and the data is identified by machine learning with a large database in the cloud. To reduce the number of keypoints which are sent to the cloud, Keypoints of Interest (KOI) extraction has been proposed. However, since its computational complexity is large, hardware implementation is required for real-time processing. Moreover, the hardware resource must be low because it is embedded in devices of users. This paper proposes a hardware-friendly KOI algorithm with low amount of computations and its real-time hardware implementation based on dual threshold keypoint detection by gradient histogram and parallelization of connectivity of adjacent keypoint-utilizing register counters. The algorithm utilizes dual-histogram based detection and keypoint-matching based calculation of motion information and dense-clustering based keypoint smoothing. The hardware architecture is composed of a detection module utilizing descriptor, and grid-region-parallelization based density clustering. Finally, the evaluation results of hardware implementation show that the implemented hardware achieves Full-HD (1920x1080)-60 fps spatio-temporal keypoint extraction. Further, it is 47 times faster than low complexity keypoint extraction on software and 12 times faster than spatio-temporal keypoint extraction on software, and the hardware resources are almost the same as SIFT hardware implementation, maintaining accuracy.