This paper presents a system for the automatic generation of Galois-field (GF) arithmetic circuits, named the GF Arithmetic Module Generator (GF-AMG). The proposed system employs a graph-based circuit description called the GF Arithmetic Circuit Graph (GF-ACG). First, we present an extension of the GF-ACG to handle GF(pm) (p≥3) arithmetic circuits, which can be efficiently implemented by multiple-valued logic circuits in addition to the conventional binary circuits. We then show the validity of the generation system through the experimental design of GF(pm) multipliers for different p-values. In addition, we evaluate the performance of three types of GF(2m) multipliers and typical GF(pm) multipliers (p≥3) empirically generated by our system. We confirm from the results that the proposed system can generate a variety of GF parallel multipliers, including practical multipliers over GF(pm) having extension degrees greater than 128.

Wireless sensor network (WSN) has attracted many researchers to investigate it in recent years. It can be widely used in the areas of surveillances, health care and agriculture. The location information is very important for WSN applications such as geographic routing, data fusion and tracking. So the localization technology is one of the key technologies for WSN. Since the computational complexity of the traditional source localization is high, the localization method can not be used in the sensor node. In this paper, we firstly introduce the Neyman-Pearson criterion based detection model. This model considers the effect of false alarm and missing alarm rate, so it is more realistic than the binary and probability model. An affinity propagation algorithm based localization method is proposed. Simulation results show that the proposed method provides high localization accuracy.

Recently, numerous methods have been proposed to tackle the problem of fine-grained image classification. However, rare of them focus on the pre-processing step of image alignment. In this paper, we propose a new pre-processing method with the aim of reducing the variance of objects among the same class. As a result, the variance of objects between different classes will be more significant. The proposed approach consists of four procedures. The “parts” of the objects are firstly located. After that, the rotation angle and the bounding box could be obtained based on the spatial relationship of the “parts”. Finally, all the images are resized to similar sizes. The objects in the images possess the properties of translation, scale and rotation invariance after processed by the proposed method. Experiments on the CUB-200-2011 and CUB-200-2010 datasets have demonstrated that the proposed method could boost the recognition performance by serving as a pre-processing step of several popular classification algorithms.

This paper presents beamforming and beam tracking techniques and downlink performance results from field experiments using a Proof-of-Concept (PoC) system. The PoC implements a 5G mobile radio access system in the millimeter wave band and utilizes beamforming and beam tracking techniques. These techniques are realized with a dielectric lens antenna fed by a switched antenna feeder array. The half-power beamwidth of the antenna is 3° corresponding to massive MIMO using approximately 1000 antenna elements. The system bandwidth is 1GHz and the center frequency is 73.5GHz. Adaptive modulation and coding using four modulation and coding schemes is implemented. The field experiment is conducted in the following small cell environments: a courtyard, a shopping mall and a street canyon. The majority of the test area is Line-Of-Sight (LOS) however the shopping mall course contains 69% Non-LOS (NLOS) conditions. The results show that the maximum throughput of over 2Gbps using rate 7/8 coded 16QAM modulation is achieved in 87%, 34% and 28% of each of the respective environments. The beam tracking achieves high availability of coverage and seamless mobility not only in LOS environments but also under NLOS conditions through the reflected paths.

E-mails, which vary in length, are a special form of text. The difference in the lengths of e-mails increases the difficulty of text analysis. To better analyze e-mail, our models must analyze not only long e-mails but also short e-mails. Unlike normal documents, short texts have some unique characteristics, such as data sparsity and ambiguity problems, making it difficult to obtain useful information from them. However, long text and short text cannot be analyzed in the same manner. Therefore, we have to analyze the characteristics of both. We present the Biterm Author Topic in the Sentences Model (BATS) model; it can discover relevant topics of corpus and accurately capture the relationship between the topics and authors of e-mails. The Author Topic (AT) model learns from a single word in a document, while the BATS is modeled on word co-occurrence in the entire corpus. We assume that all words in a single sentence are generated from the same topic. Accordingly, our method uses only word co-occurrence patterns at the sentence level, rather than the document or corpus level. Experiments on the Enron data set indicate that our proposed method achieves better performance on e-mails than the baseline methods. What's more, our method analyzes long texts effectively and solves the data sparsity problems of short texts.

With the huge influx of various data nowadays, extracting knowledge from them has become an interesting but tedious task among data scientists, particularly when the data come in heterogeneous form and have missing information. Many data completion techniques had been introduced, especially in the advent of kernel methods — a way in which one can represent heterogeneous data sets into a single form: as kernel matrices. However, among the many data completion techniques available in the literature, studies about mutually completing several incomplete kernel matrices have not been given much attention yet. In this paper, we present a new method, called Mutual Kernel Matrix Completion (MKMC) algorithm, that tackles this problem of mutually inferring the missing entries of multiple kernel matrices by combining the notions of data fusion and kernel matrix completion, applied on biological data sets to be used for classification task. We first introduced an objective function that will be minimized by exploiting the EM algorithm, which in turn results to an estimate of the missing entries of the kernel matrices involved. The completed kernel matrices are then combined to produce a model matrix that can be used to further improve the obtained estimates. An interesting result of our study is that the E-step and the M-step are given in closed form, which makes our algorithm efficient in terms of time and memory. After completion, the (completed) kernel matrices are then used to train an SVM classifier to test how well the relationships among the entries are preserved. Our empirical results show that the proposed algorithm bested the traditional completion techniques in preserving the relationships among the data points, and in accurately recovering the missing kernel matrix entries. By far, MKMC offers a promising solution to the problem of mutual estimation of a number of relevant incomplete kernel matrices.

We focus on the construction of the digital signature scheme for local broadcast, which allows the devices with limited resources to securely transmit broadcast message. A multi-group authentication scheme that enables a node to authenticate its membership in multi verifiers by the sum of the secret keys has been proposed for limited resources. This paper presents a transformation which converts a multi-group authentication into a multi-group signature scheme. We show that the multi-group signature scheme converted by our transformation is existentially unforgeable against chosen message attacks (EUF-CMA secure) in the random oracle model if the multi-group authentication scheme is secure against impersonation under passive attacks (IMP-PA secure). In the multi-group signature scheme, a sender can sign a message by the secret keys which multiple certification authorities issue and the signature can validate the authenticity and integrity of the message to multiple verifiers. As a specific configuration example, we show the example in which the multi-group signature scheme by converting an error correcting code-based multi-group authentication scheme.

In the past, the security of industrial control systems was guaranteed by their obscurity. However, as devices of industrial control systems became more varied and interaction between these devices became necessary, effective management systems for such networks emerged. This triggered the need for cyber-physical systems that connect industrial control system networks and external system networks. The standards for the protocols in industrial control systems explain security functions in detail, but many devices still use nonsecure communication because it is difficult to update existing equipment. Given this situation, a number of studies are being conducted to detect attacks against industrial control system protocols, but these studies consider only data payloads without considering the case that industrial control systems' availability is infringed owing to packet reassembly failures. Therefore, with regard to the DNP3 protocol, which is used widely in industrial control systems, this paper describes attacks that can result in packet reassembly failures, proposes a countermeasure, and tests the proposed countermeasure by conducting actual attacks and recoveries. The detection of a data payload should be conducted after ensuring the availability of an industrial control system by using this type of countermeasure.

This paper studies the multi-link multi-antenna amplify-and-forward (AF) relay system, in which multiple source-destination pairs communicate with the aid of an energy harvesting (EH)-enabled relay and the relay utilizes the power splitting (PS) protocol to accomplish simultaneous EH and information forwarding (IF). Specifically, independent PS, i.e., allow each antenna to have an individual PS factor, and cooperative power allocation (PA) i.e., adaptively allocate the harvested energy to each channel, are proposed to increase the signal processing degrees of freedom and energy utilization. Our objective is to maximize the minimum rate of all source-destination pairs, i.e., the max-min rate, by jointly optimizing the PS and PA strategies. The optimization problem is first established for the ideal channel state information (CSI) model. To solve the formulated non-convex problem, the optimal forwarding matrix is derived and an auxiliary variable is introduced to remove the coupling of transmission rates in two slots, following which a bi-level iteration algorithm is proposed to determine the optimal PS and PA strategy by jointly utilizing the bisection and golden section methods. The proposal is then extended into the partial CSI model, and the final transmission rate for each source-destination pair is modified by treating the CSI error as random noise. With a similar analysis, it is proved that the proposed bi-level algorithm can also solve the joint PS and PA optimization problem in the partial CSI model. Simulation results show that the proposed algorithm works well in both ideal CSI and partial CSI models, and by means of independent PS and cooperative PA, the achieved max-min rate is greatly improved over existing non-EH-enabled and EH-enabled relay schemes, especially when the signal processing noise at the relay is large and the sources use quite different transmit powers.

In this paper, the recent advances in cooperative distributed antenna transmission (CDAT) are introduced for spatial diversity and multi-user spatial multiplexing in 5G mobile communications network. CDAT is an advanced version of the coordinated multi-point (CoMP) transmission. Space-time block coded transmit diversity (STBC-TD) for spatial diversity and minimum mean square error filtering combined with singular value decomposition (MMSE-SVD) for multi-user spatial multiplexing are described under the presence of co-channel interference from adjacent macro-cells. Blind selected mapping (blind SLM) which requires no side information transmission is introduced in order to suppress the increased peak-to-average signal power ratio (PAPR) of the transmit signals when CDAT is applied. Some computer simulation results are presented to confirm the effectiveness of CDAT techniques.

An energy-efficient nonvolatile FPGA with assuring highly-reliable backup operation using a self-terminated power-gating scheme is proposed. Since the write current is automatically cut off just after the temporal data in the flip-flop is successfully backed up in the nonvolatile device, the amount of write energy can be minimized with no write failure. Moreover, when the backup operation in a particular cluster is completed, power supply of the cluster is immediately turned off, which minimizes standby energy due to leakage current. In fact, the total amount of energy consumption during the backup operation is reduced by 66% in comparison with that of a conventional worst-case-based approach where the long time write current pulse is used for the reliable write.

WalkSAT (WSAT) is one of the best performing stochastic local search algorithms for the Boolean Satisfiability (SAT) and the Maximum Boolean Satisfiability (MaxSAT). WSAT is very suitable for hardware acceleration because of its high inherent parallelism. Formal verification of digital circuits is one of the most important applications of SAT and MaxSAT. Structural knowledge such as logic gates and their dependencies can be derived from SAT/MaxSAT instances generated from formal verification of digital circuits. Such that knowledge is useful to solve these instances efficiently. In this paper, we first discuss a heuristic to utilize the structural knowledge for solving these problems by using WSAT. Then, we show its implementation on FPGA. The problem size of the formal verification is typically very large, and most data have to be placed in off-chip DRAMs. In this situation, the acceleration by FPGA is limited by the throughput and access latency of the DRAMs. In our implementation, data are carefully mapped on the on-chip memory banks and off-chip DRAMs so that most data in the off-chip DRAMs can be continuously accessed using burst-read. Furthermore, a variable-way cache memory comprised of the on-chip memory banks is used in order to hide the DRAM access latency by caching the head portion of the continuous read from the DRAMs and giving them to the circuit till the rest portion is started to be given by the burst-read. We evaluate the performance of our proposed method by changing configuration of the variable-way cache and the processing parallelism, and discuss how much acceleration can be achieved.

To drastically increase the splitting ratio of extended-reach (40km span) time- and wavelength-division multiplexed passive optical networks (WDM/TDM-PONs), we modify the gain control scheme of our automatic gain controlled semiconductor optical amplifiers (AGC-SOAs) that were developed to support upstream transmission in long-reach systems. While the original AGC-SOAs are located outside the central office (CO) as repeaters, the new AGC-SOAs are located inside the CO and connected to each branch of an optical splitter in the CO. This arrangement has the potential to greatly reduce the costs of CO-sited equipment as they are shared by many more users if the new gain control scheme works properly even when the input optical powers are low. We develop a prototype and experimentally confirm its effectiveness in increasing the splitting ratio of extended-reach systems to 512.

In the topologies for interconnected nodes, it is desirable to have a low degree and a small diameter. For the same number of nodes, a dual-cube topology has almost half the degree compared to a hypercube while increasing the diameter by just one. Hence, it is a promising topology for interconnection networks of massively parallel systems. We propose here a stochastic fault-tolerant routing algorithm to find a non-faulty path from a source node to a destination node in a dual-cube.

This paper presents a capacitor-loaded 4x4 planar loop array for three-dimensional near-field beamforming of magnetic resonance wireless power transfer (WPT). This planar loop array provides three important functions: beamforming, selective power transfer, and the ability to work alignment free with the receiver. These functions are realized by adjusting the capacitance of each loop. The optimal capacitance of each loop that corresponds to the three functions can be found using a genetic algorithm (GA); the three functions were verified by comparing simulations and measurements at a frequency of 6.78MHz. Finally, the beamforming mechanism of a near-field loop array was investigated using the relationship between the current magnitude and the resonance frequency of each loop, resulting in the findings that the magnitude and the resonance frequency are correlated. This focused current of the specified loop creates a strong magnetic field in front of that loop, resulting in near-field beamforming.

We consider fixed-to-variable length coding with a regular cost function by allowing the error probability up to any constantε. We first derive finite-length upper and lower bounds on the average codeword cost, which are used to derive general formulas of two kinds of minimum achievable rates. For a fixed-to-variable length code, we call the set of source sequences that can be decoded without error the dominant set of source sequences. For any two regular cost functions, it is revealed that the dominant set of source sequences for a code attaining the minimum achievable rate under a cost function is also the dominant set for a code attaining the minimum achievable rate under the other cost function. We also give general formulas of the second-order minimum achievable rates.

In the future, 5G radio access and support for the internet of things (IoT) is becoming more important, which is called machine type communications. Different from current mobile communication systems, machine type communications generates relatively small packets. In order to support such small packets with high reliability, channel coding techniques are inevitable. One of the most effective channel codes in such conditions is the tail-biting convolutional code, since it is used in LTE systems due to its good performance for small packet sizes. By employing a list Viterbi algorithm for the tail-biting convolutional code, the block error rate (BLER) performances is further improved. Therefore, this paper evaluates the BLER performances of several list Viterbi algorithms, i.e., circular parallel list Viterbi algorithm (CPLVA), per stage CPLVA (PSCPLVA), and successive state and sequence estimation (SSSE). In the evaluation, computational complexity is also taken into account. It is shown that the performance of the CPLVA is better in the wide range of computational complexity defined in this paper.

Index generation functions model content-addressable memory, and are useful in virus detectors and routers. Linear decompositions yield simpler circuits that realize index generation functions. This paper proposes a balanced decision tree based heuristic to efficiently design linear decompositions for index generation functions. The proposed heuristic finds a good linear decomposition of an index generation function by using appropriate cost functions and a constraint to construct a balanced tree. Since the proposed heuristic is fast and requires a small amount of memory, it is applicable even to large index generation functions that cannot be solved in a reasonable time by existing heuristics. This paper shows time and space complexities of the proposed heuristic, and experimental results using some large examples to show its efficiency.

This paper analyzes the relationship between the changes of Body Mass Index (BMI) and those of the other health checkup data in one year. We divide all data of the subjects into 13 groups by their BMI changes. We calculate these variations in each group and classify the variations into gender, age, and BMI. As the result by gender, men were more influenced by the changes of BMI than women at Hb-A1c, AC, GPT, GTP, and TG. As the result of classification by age, they were influenced by the changes of BMI at Hb-A1c, GPT, and DTP by age. As the result of classification by BMI, inspection values such as GOT, GPT, and GTP decreased according to the decrement of BMI. Next we show the result on gender-age, gender-BMI, and age-BMI clusters. Our results showed that subjects should reduce BMI values in order to improve lifestyle-related diseases. Several inspection values would be improved according to decrement of BMI. Conversely, it may be difficult for subjects with under 18 of BMI to manage them by BMI. We show a possibility that we could prevent the lifestyle disease by controlling BMI.

The growing demand for high-speed data communication has continued to meet the need for ever-increasing I/O bandwidth in recent VLSI systems. However, signal integrity issues, such as intersymbol interference (ISI) and reflections, make the channel band-limited at high-speed data rates. We propose high-speed data transmission techniques for VLSI systems using Tomlinson-Harashima precoding (THP). Because THP can eliminate ISI by inverting the characteristics of channels with limited peak and average power at the transmitter, it is suitable for implementing advanced low-voltage and high-speed VLSI systems. This paper presents a novel double-rate THP equalization technique especially intended for multi-valued data transmission to further improve THP performance. Simulation and measurement results show that the proposed THP equalization with a double sampling rate can enhance the data transition time and, therefore, improve the eye opening.