This paper presents FIR digital filters based on stochastic/binary hybrid computation with reduced hardware complexity and high computational accuracy. Recently, some attempts have been made to apply stochastic computation to realization of digital filters. Such realization methods lead to significant reduction of hardware complexity over the conventional filter realizations based on binary computation. However, the stochastic digital filters suffer from lower computational accuracy than the digital filters based on binary computation because of the random error fluctuations that are generated in stochastic bit streams, stochastic multipliers, and stochastic adders. This becomes a serious problem in the case of FIR filter realizations compared with the IIR counterparts because FIR filters usually require larger number of multiplications and additions than IIR filters. To improve the computational accuracy, this paper presents a stochastic/binary hybrid realization, where multipliers are realized using stochastic computation but adders are realized using binary computation. In addition, a coefficient-scaling technique is proposed to further improve the computational accuracy of stochastic FIR filters. Furthermore, the transposed structure is applied to the FIR filter realization, leading to reduction of hardware complexity. Evaluation results demonstrate that our method achieves at most 40dB improvement in minimum stopband attenuation compared with the conventional pure stochastic design.

This letter presents a successive partial interference cancellation (SPIC) scheme for full-duplex (FD) and multiple-input multiple-output (MIMO) relaying system. The proposed scheme coordinates the cancellation for the self-interference and inter-stream interference. The objective for the coordination focuses on simultaneously minimizing the two interferences. Simulation results under the measured data show that the system with the proposed scheme can achieve a significant performance gain compared to the conventional FD and half-duplex (HD) systems.

We developed a novel, proof-of-concept side-channel attack framework called RouteDetector, which identifies a route for a train trip by simply reading smart device sensors: an accelerometer, magnetometer, and gyroscope. All these sensors are commonly used by many apps without requiring any permissions. The key technical components of RouteDetector can be summarized as follows. First, by applying a machine-learning technique to the data collected from sensors, RouteDetector detects the activity of a user, i.e., “walking,” “in moving vehicle,” or “other.” Next, it extracts departure/arrival times of vehicles from the sequence of the detected human activities. Finally, by correlating the detected departure/arrival times of the vehicle with timetables/route maps collected from all the railway companies in the rider's country, it identifies potential routes that can be used for a trip. We demonstrate that the strategy is feasible through field experiments and extensive simulation experiments using timetables and route maps for 9,090 railway stations of 172 railway companies.

An enormous number of malware samples pose a major threat to our networked society. Antivirus software and intrusion detection systems are widely implemented on the hosts and networks as fundamental countermeasures. However, they may fail to detect evasive malware. Thus, setting a high priority for new varieties of malware is necessary to conduct in-depth analyses and take preventive measures. In this paper, we present a traffic model for malware that can classify network behaviors of malware and identify new varieties of malware. Our model comprises malware-specific features and general traffic features that are extracted from packet traces obtained from a dynamic analysis of the malware. We apply a clustering analysis to generate a classifier and evaluate our proposed model using large-scale live malware samples. The results of our experiment demonstrate the effectiveness of our model in finding new varieties of malware.

This paper expands Bartlett's bisection theorem. The theory of modal S-parameters and their circuit representation is constructed from a group-theoretic perspective. Criteria for the division of a circuit at a fixed node whose state is distinguished by the irreducible representation of its stabilizer subgroup are obtained, after being inductively introduced using simple circuits as examples. Because these criteria use only circuit symmetry and do not require human judgment, the distinction is reliable and implementable in a computer. With this knowledge, the entire circuit can be characterized by a finite combination of smaller circuits. Reducing the complexity of symmetric circuits contributes to improved insights into their characterization, and to savings of time and effort in calculations when applied to large-scale circuits. A three-phase filter and a branch-line coupler are analyzed as application examples of circuit and electromagnetic field analysis, respectively.

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.

Efficient use of heterogeneous wireless access networks is necessary to maximize the capacity of the 5G mobile communications system. The wireless local area networks (WLANs) are considered to be one of the key wireless access networks because of the proliferation of WLAN-capable mobile devices. However, throughput starvation can occur due to the well-known exposed/hidden terminal problem in carrier sense multiple access with collision avoidance (CSMA/CA) based channel access mechanism, and this problem is a critical issue with wireless LAN systems. This paper proposes two novel schemes to identify starved access points (APs) and user equipments (UEs) which throughputs are relatively low. One scheme identifies starved APs by observing the transmission delay of beacon signals periodically transmitted by APs. The other identifies starved UEs by using the miscaptured beacon signals ratio at UEs. Numerous computer simulations verify that that the schemes can identify starved APs and UEs having quite low throughput and are superior to the conventional graph-based identification scheme. In addition, AP and UE management with the proposed schemes has the potential to improve system throughput and reduce the number of low throughput UEs.

Massive multiple-input multiple-output (MIMO) technology is one of the key enablers in the fifth generation mobile communications (5G), in order to accommodate growing traffic demands and to utilize higher super high frequency (SHF) and extremely high frequency (EHF) bands. In the paper, we propose a novel transmit precoding named “nonlinear block multi-diagonalization (NL-BMD) precoding” for multiuser MIMO (MU-MIMO) downlink toward 5G. Our NL-BMD precoding strategy is composed of two essential techniques: block multi-diagonalization (BMD) and adjacent inter-user interference pre-cancellation (IUI-PC). First, as an extension of the conventional block diagonalization (BD) method, the linear BMD precoder for the desired user is computed to incorporate a predetermined number of interfering users, in order to ensure extra degrees of freedom at the transmit array even after null steering. Additionally, adjacent IUI-PC, as a nonlinear operation, is introduced to manage the residual interference partially allowed in BMD computation, with effectively-reduced numerical complexity. It is revealed through computer simulations that the proposed NL-BMD precoding yields up to 67% performance improvement in average sum-rate spectral efficiency and enables large-capacity transmission regardless of the user distribution, compared with the conventional BD precoding.

The compressive sensing (CS) theory has been recognized as a promising technique to achieve the target localization in wireless sensor networks. However, most of the existing works require the prior knowledge of transmitting powers of targets, which is not conformed to the case that the information of targets is completely unknown. To address such a problem, in this paper, we propose a novel CS-based approach for multiple target localization and power estimation. It is achieved by formulating the locations and transmitting powers of targets as a sparse vector in the discrete spatial domain and the received signal strengths (RSSs) of targets are taken to recover the sparse vector. The key point of CS-based localization is the sensing matrix, which is constructed by collecting RSSs from RF emitters in our approach, avoiding the disadvantage of using the radio propagation model. Moreover, since the collection of RSSs to construct the sensing matrix is tedious and time-consuming, we propose a CS-based method for reconstructing the sensing matrix from only a small number of RSS measurements. It is achieved by exploiting the CS theory and designing an difference matrix to reveal the sparsity of the sensing matrix. Finally, simulation results demonstrate the effectiveness and robustness of our localization and power estimation approach.

Hybrid analog/digital precoding has attracted growing attention for millimeter wave (mmWave) communications, since it can support multi-stream data transmission with limited hardware cost. A main challenge in implementing hybrid precoding is that the channels will exhibit frequency-selective fading due to the large bandwidth. To this end, we propose a practical hybrid precoding scheme with finite-resolution phase shifters by leveraging the correlation among the subchannels. Furthermore, we utilize the sparse feature of the mmWave channels to design a low-complexity algorithm to realize the proposed hybrid precoding, which can avoid the complication of the high-dimensionality eigenvalue decomposition. Simulation results show that the proposed hybrid precoding can approach the performance of unconstrained fully-digital precoding but with low hardware cost and computational complexity.

Named Entity Translation Equivalents extraction plays a critical role in machine translation (MT) and cross language information retrieval (CLIR). Traditional methods are often based on large-scale parallel or comparable corpora. However, the applicability of these studies is constrained, mainly because of the scarcity of parallel corpora of the required scale, especially for language pairs of Chinese and Japanese. In this paper, we propose a method considering the characteristics of Chinese and Japanese to automatically extract the Chinese-Japanese Named Entity (NE) translation equivalents based on inductive learning (IL) from monolingual corpora. The method adopts the Chinese Hanzi and Japanese Kanji Mapping Table (HKMT) to calculate the similarity of the NE instances between Japanese and Chinese. Then, we use IL to obtain partial translation rules for NEs by extracting the different parts from high similarity NE instances in Chinese and Japanese. In the end, the feedback processing updates the Chinese and Japanese NE entity similarity and rule sets. Experimental results show that our simple, efficient method, which overcomes the insufficiency of the traditional methods, which are severely dependent on bilingual resource. Compared with other methods, our method combines the language features of Chinese and Japanese with IL for automatically extracting NE pairs. Our use of a weak correlation bilingual text sets and minimal additional knowledge to extract NE pairs effectively reduces the cost of building the corpus and the need for additional knowledge. Our method may help to build a large-scale Chinese-Japanese NE translation dictionary using monolingual corpora.

Cost-effective cloud storage services are attracting users with their convenience, but there is a trade-off between service availability and usage cost. We develop two cloud provider selection models for cloud storage services to minimize the total cost of usage. The models select multiple cloud providers to meet the user requirements while considering unavailability. The first model, called a user-copy (UC) model, allows the selection of multiple cloud providers, where the user copies its data to multiple providers. In addition to the user copy function of the UC model, the second model, which is called a user and cloud-provider copy (UCC) model, allows cloud providers to make copies of the data to deliver them to other cloud providers. The cloud service is available if at least one cloud provider is available. We formulate both models as integer linear programming (ILP) problems. Our performance evaluation observes that both models reduce the total cost of usage, compared to the single cloud provider selection approach. As the cost of bandwidth usage between a user and a cloud provider increases, the UCC model becomes more beneficial than the UC model. We implement the prototype for cloud storage services, and demonstrate our models via Science Information Network 5.

Biomimetics is a new research field that creates innovation through the collaboration of different existing research fields. However, the collaboration, i.e., the exchange of deep knowledge between different research fields, is difficult for several reasons such as differences in technical terms used in different fields. In order to overcome this problem, we have developed a new retrieval platform, “Biomimetics image retrieval platform,” using a visualization-based image retrieval technique. A biological database contains a large volume of image data, and by taking advantage of these image data, we are able to overcome limitations of text-only information retrieval. By realizing such a retrieval platform that does not depend on technical terms, individual biological databases of various species can be integrated. This will allow not only the use of data for the study of various species by researchers in different biological fields but also access for a wide range of researchers in fields ranging from materials science, mechanical engineering and manufacturing. Therefore, our platform provides a new path bridging different fields and will contribute to the development of biomimetics since it can overcome the limitation of the traditional retrieval platform.

Non-contiguous orthogonal frequency-division multiplexing (OFDM) is a promising technique for cognitive radio systems. The secondary users transmit on the selected subcarriers to avoid the frequencies being used by the primary users. However, the out-of-band power (OBP) of the OFDM-modulated tones induces interference to the primary users. Another major drawback of OFDM-based system is their high peak-to-average power ratio (PAPR). In this paper, algorithms are proposed to jointly reduce the OBP and the PAPR for non-contiguous OFDM based on the method of alternating projections onto convex sets. Several OFDM subcarriers are selected to accommodate the adjusting weights for OBP and PAPR reduction. The frequency-domain OFDM symbol is projected onto two convex sets that are defined according to the OBP requirements and the PAPR limits. Each projection iteration solves a convex optimization problem. The projection onto the set constrained by the OBP requirement can be calculated using an iterative algorithm which has low computational complexity. Simulation results show good performance of joint reduction of the OBP and the PAPR. The proposed algorithms converge quickly in a few iterations.

This paper experimentally verifies the potential of higher order space division multiplexing in line-of-sight (LOS) channels for multiuser massive MIMO. We previously proposed an inter-user interference (IUI) cancellation scheme and a simplified user scheduling method for Massive Antenna Systems for Wireless Entrance (MAS-WE). In order to verify the effectiveness of the proposed techniques, channel state information (CSI) for a 1×32 SIMO channel is measured in a real propagation environment with simplified test equipment. Evaluations of the measured CSI data confirm the effectiveness of our proposals; they offer good equal gain transmission (EGT) performance, reduced spatial correlation with enlarged angular gap between users, and quite small channel state fluctuation. Link level simulations elucidate that the simple IUI cancellation method is stable in practical conditions. The degradation in symbol error rate with the measured CSI, relative to that yielded by the output of the theoretical LOS channel model, is insignificant.

In OFDM systems, link performance depends heavily on the estimation of symbol-timing and frequency offsets. Performance sensitivity to these estimates is a major drawback of OFDM systems. Timing errors destroy the orthogonality of OFDM signals and lead to inter-symbol interference (ISI) and inter-carrier interference (ICI). The interference due to timing errors can be exploited as a metric for symbol-timing synchronization. In this paper, we propose a novel method to extract interference components using a DFT of the upsampled OFDM signals. Mathematical analysis and formulation are given for the dependence of interference on timing errors. From a numerical analysis, the proposed interference estimation shows robustness against channel dispersion.

An transfer learning method, called Kernel Canonical Correlation Analysis plus (KCCA+), is proposed for heterogeneous Cross-company defect prediction. Combining the kernel method and transfer learning techniques, this method improves the performance of the predictor with more adaptive ability in nonlinearly separable scenarios. Experiments validate its effectiveness.

Nonnegative matrix factorization (NMF) is one of the most popular machine learning tools for speech enhancement. The supervised NMF-based speech enhancement is accomplished by updating iteratively with the prior knowledge of the clean speech and noise spectra bases. However, in many real-world scenarios, it is not always possible for conducting any prior training. The traditional semi-supervised NMF (SNMF) version overcomes this shortcoming while the performance degrades. In this letter, without any prior knowledge of the speech and noise, we present an improved semi-supervised NMF-based speech enhancement algorithm combining techniques of NMF and robust principal component analysis (RPCA). In this approach, fixed speech bases are obtained from the training samples chosen from public dateset offline. The noise samples used for noise bases training, instead of characterizing a priori as usual, can be obtained via RPCA algorithm on the fly. This letter also conducts a study on the assumption whether the time length of the estimated noise samples may have an effect on the performance of the algorithm. Three metrics, including PESQ, SDR and SNR are applied to evaluate the performance of the algorithms by making experiments on TIMIT with 20 noise types at various signal-to-noise ratio levels. Extensive experimental results demonstrate the superiority of the proposed algorithm over the competing speech enhancement algorithm.

A group signature allows any group member to anonymously sign a message. One of the important issues is an efficient membership revocation. The scheme proposed by Libert et al. has achieved O(1) signature and membership certificate size, O(1) signing and verification times, and O(log N) public key size, where N is the total number of members. However the Revocation List (RL) data is large, due to O(R) signatures in RL, where R is the number of revoked members. The scheme proposed by Nakanishi et al. achieved a compact RL of O(R/T) signatures for any integer T. However, this scheme increases membership certificate size by O(T). In this paper, we extend the scheme proposed by Libert et al., by reducing the RL size to O(R/T) using a vector commitment to compress the revocation entries, while O(1) membership certificate size remains.

This paper presents indoor and outdoor experiments that confirm 4-Gbps throughput based on 400-MHz bandwidth transmission when applying carrier aggregation (CA) with 4 component carriers (CCs) and 4-by-4 single-user multiple-in multiple-out multiplexing (MIMO) in the 15-GHz frequency band in the downlink of 5G cellular radio access. A new radio interface with time division duplexing (TDD) and radio access based on orthogonal frequency-division multiple access (OFDMA) is implemented in a 5G testbed to confirm ultra-high speed transmission with low latency. The indoor experiment in an entrance hall shows that the peak throughput is 4.3Gbps in front of the base station (BS) antenna where the reference signal received power (RSRP) is -40dBm although the channel correlation at user equipment (UE) antenna is 0.8. The outdoor experiment in an open-space parking area shows that the peak throughput is 2.8Gbps in front of a BS antenna with a high RSRP although rank 2 is selected due to the high channel correlation. The results also show that the average throughput of 2Gbps is achieved 120m from the BS antenna. In a courtyard enclosed by building walls, 3.6Gbps is achieved in an outdoor-to-outdoor environment with a high RSRP and in an outdoor-to-indoor environment where the RSRP is lower due to the penetration loss of glass windows, but the multipath rich environment contributes to realizing the low channel correlation.