In this paper, we propose a simple algorithm for detecting a vehicle trapped in flooded urban area by using quad-polarimetric SAR data. The four-component scattering power decomposition and phase difference of HH-VV co-pol ratio are effectively used in the proposed algorithm. Here we carry out polarimetric scattering measurement for a scaled vehicle model surrounded by two buildings model in an anechoic chamber, to acquire the quad-polarimetric SAR data. It is confirmed from the results of the image analysis for the measured SAR data that the proposed algorithm for vehicle detection works well even under severe environment where the vehicle is set in the shadow of the building and/or the alignment of the vehicle or the buildings is obliquely oriented to direction of the radar line of sight.

An autoencoder has the potential ability to compress and decompress information. In this work, we consider the process of generating a stego-image from an original image and watermarks as compression, and the process of recovering the original image and watermarks from the stego-image as decompression. We propose embedder and extractor neural networks based on the autoencoder. The embedder network learns mapping from the DCT coefficients of the original image and a watermark to those of the stego-image. The extractor network learns mapping from the DCT coefficients of the stego-image to the watermark. Once the proposed neural network has been trained, the network can embed and extract the watermark into unlearned test images. We investigated the relation between the number of neurons and network performance by computer simulations and found that the trained neural network could provide high-quality stego-images and watermarks with few errors. We also evaluated the robustness against JPEG compression and found that, when suitable parameters were used, the watermarks were extracted with an average BER lower than 0.01 and image quality over 35 dB when the quality factor Q was over 50. We also investigated how to represent the watermarks in the stego-image by our neural network. There are two possibilities: distributed representation and sparse representation. From the results of investigation into the output of the stego layer (3rd layer), we found that the distributed representation emerged at an early learning step and then sparse representation came out at a later step.

Forward converter is most suitable for low voltage and high current applications such as LEDs, battery chargers, EHV etc. The active clamp transformer reset technique offers many advantages over conventional single-ended reset techniques, including lower voltage stress on the main switch, the ability to switch at zero voltage and duty cycle operation above 50 percent. Several papers have compared the functional merits of the active clamp over the more extensively used RCD clamp, third winding and resonant reset techniques. This paper discusses about a center clamp technique with one common core reset circuit making it suitable for wide input voltage applications with extended duty cycle.

Dual-rotor machine (DRM) is a multiple input and output electromechanical device with two electrical and two mechanical ports which make it an optimal transmission system for hybrid electric vehicles. In attempt to boost its performance and efficiency, this work presents a dual-rotor permanent magnet (DR-PM) machine system used for continuously variable transmission (CVT) in HEVs. The proposed DR-PM machine is analyzed, and modeled in consideration of vehicle driving requirements. Considering energy conversion modes and torque transfer modes, operation conditions of the DR-PM machine system used for CVT are illustrated in detail. Integrated control model of the system is carried out, besides, intelligent speed ratio control strategy is designed by analyzing the dynamic coupling modes upon the integrated models to satisfy the performance requirements, reasonable energy-split between machine and engine, and optimal fuel economy. Experimental results confirm the validity of the mathematical model of the DR-PM machine system in the application of CVT, and the effectiveness of the intelligent speed ratio control strategy.

Asymmetrically designed polycrystalline silicon (poly-Si) thin film transistors (TFT) were fabricated and investigated to suppress kink effect and to improve electrical reliability. Asymmetric dual channel length poly-Si TFT (ADCL) shows the best reduction of kink and leakage currents. Technology computer-aided design simulation proves that ADCL can induce properly high voltage at floating node of the TFT at high drain-source voltage (VDS), which can mitigate the impact ionization and the degradation of the transconductance of the TFT showing high reliability under the hot carrier stress.

In the case that images are shared via social networking services (SNS) and cloud photo storage services (CPSS), it is known that the JPEG images uploaded to the services are mostly re-compressed by the providers. Because of such a situation, a new image identification scheme for double-compressed JPEG images is proposed in this paper. The aim is to detect a single-compressed image that has the same original image as the double-compressed ones. In the proposed scheme, a feature extracted from only DC coefficients in DCT coefficients is used for the identification. The use of the feature allows us not only to robustly avoid errors caused by double-compression but also to perform the identification for different size images. The simulation results demonstrate the effectiveness of the proposed one in terms of the querying performance.

Encryption-then-Compression (EtC) systems have been proposed to securely transmit images through an untrusted channel provider. In this study, EtC systems were applied to social media like Twitter that carry out image manipulations. The block scrambling-based encryption schemes used in EtC systems were evaluated in terms of their robustness against image manipulation on social media. The aim was to investigate how five social networking service (SNS) providers, Facebook, Twitter, Google+, Tumblr and Flickr, manipulate images and to determine whether the encrypted images uploaded to SNS providers can avoid being distorted by such manipulations. In an experiment, encrypted and non-encrypted JPEG images were uploaded to various SNS providers. The results show that EtC systems are applicable to the five SNS providers.

Generation of secure signatures suitable for spread-spectrum video watermarking is proposed. The method embeds a message, which is a two-dimensional binary pattern, into a three-dimensional volume, such as video, by addition of a signature. The message can be a mark or a logo indicating the copyright information. The signature is generated by shuffling or permuting random matrices along the third or time axis so that the message is extracted when they are accumulated after demodulation by the correct key. In this way, a message is hidden in the signature having equal probability of decoding any variation of the message, where the key is used to determine which one to extract. Security of the proposed method, stemming from the permutation, is evaluated as resistance to blind estimation of secret information. The matrix-based permutation allows the message to survive the spatial down-sampling without sacrificing the security. The downside of the proposed method is that it needs more data or frames to decode a reliable information compared to the conventional spread-spectrum modulation. However this is minimized by segmenting the matrices and applying permutation to sub-matrices independently. Message detectability is theoretically analyzed. Superiority of our method in terms of robustness to blind message estimation and down-sampling is verified experimentally.

Activity recognition from sensors is a classification problem over time-series data. Some research in the area utilize time and frequency domain handcrafted features that differ between datasets. Another categorically different approach is to use deep learning methods for feature learning. This paper explores a middle ground in which an off-the-shelf feature extractor is used to generate a large number of candidate time-domain features followed by a feature selector that was designed to reduce the bias toward specific classification techniques. Moreover, this paper advocates the use of features that are mostly insensitive to sensor orientation and show their applicability to the activity recognition problem. The proposed approach is evaluated using six different publicly available datasets collected under various conditions using different experimental protocols and shows comparable or higher accuracy than state-of-the-art methods on most datasets but usually using an order of magnitude fewer features.

The CC-Link proposed by the Mitsubishi Electric Company is an industrial network used exclusively in most industries. However, the probabilities of data loss and interference with equipment control increase if the transmission time is greater than the link scan time of 381µs. The link scan time can be reduced by designing the CC-Link module as an external microprocessor (MPU) interface of R-IN32M3; however, it then suffers from expandability issues. Thus, in this paper, we propose a new CC-Link module utilizing R-IN32M3 to improve the expandability. In our designed CC-Link module, we devise a dual-port RAM (DPRAM) function in an external I/O module, which enables parallel communication between the DPRAM and the external MPU. Our experiment with the implemented CC-Link prototype demonstrates that our CC-Link design improves the communication speed owing to the parallel communication between DPRAM and external MPU, and expandability of remote I/O. Our design achieves miniaturization of the CC-Link module, wiring reduction, and an approximately 30% reduction in the link scan time. Furthermore, because we utilize both the Renesas R-IN32M3 and Xilinx XC95144XL chips widely used in diverse application areas, the designed CC-Link module reduces the investment cost. The proposed design is expected to significantly contribute to the utilization of the programmable logic controller memory and I/O expansion for factory automation and improvement of the investment efficiency in the flat panel display industry.

The reward of the Bitcoin system is designed to be proportional to miner's computational power. However, rogue miners can increase their rewards by using the block withholding attacks. For raising awareness on the Bitcoin reward system, a new attack scheme is proposed, where the attackers infiltrate into an open pool and launch the selfish mining as well as the block withholding attack. The simulation results demonstrate that the proposed attack outperforms the conventional block withholding attacks.

Since cyber attacks such as cyberterrorism against Industrial Control Systems (ICSs) and cyber espionage against companies managing them have increased, the techniques to detect anomalies in early stages are required. To achieve the purpose, several studies have developed anomaly detection methods for ICSs. In particular, some techniques using packet flow regularity in industrial control networks have achieved high-accuracy detection of attacks disrupting the regularity, i.e. normal behaviour, of ICSs. However, these methods cannot identify scanning attacks employed in cyber espionage because the probing packets assimilate into a number of normal ones. For example, the malware called Havex is customised to clandestinely acquire information from targeting ICSs using general request packets. The techniques to detect such scanning attacks using widespread packets await further investigation. Therefore, the goal of this study was to examine high performance methods to identify anomalies even if elaborate packets to avoid alert systems were employed for attacks against industrial control networks. In this paper, a novel detection model for anomalous packets concealing behind normal traffic in industrial control networks was proposed. For the proposal of the sophisticated detection method, we took particular note of packet flow regularity and employed the Markov-chain model to detect anomalies. Moreover, we regarded not only original packets but similar ones to them as normal packets to reduce false alerts because it was indicated that an anomaly detection model using the Markov-chain suffers from the ample false positives affected by a number of normal, irregular packets, namely noise. To calculate the similarity between packets based on the packet flow regularity, a vector representation tool called word2vec was employed. Whilst word2vec is utilised for the culculation of word similarity in natural language processing tasks, we applied the technique to packets in ICSs to calculate packet similarity. As a result, the Markov-chain with word2vec model identified scanning packets assimulating into normal packets in higher performance than the conventional Markov-chain model. In conclusion, employing both packet flow regularity and packet similarity in industrial control networks contributes to improving the performance of anomaly detection in ICSs.

In this paper, we propose a novel algorithm called multi-projection ensemble discriminant clustering (MPEDC) for JPEG steganalysis. The scheme makes use of the optimal projection of linear discriminant analysis (LDA) algorithm to get more projection vectors by using the micro-rotation method. These vectors are similar to the optimal vector. MPEDC combines unsupervised K-means algorithm to make a comprehensive decision classification adaptively. The power of the proposed method is demonstrated on three steganographic methods with three feature extraction methods. Experimental results show that the accuracy can be improved using iterative discriminant classification.

We have proposed a new method for the scattering of electromagnetic waves by inhomogeneous dielectric gratings loaded with parallel perfectly conducting strips using the combination of improved Fourier series expansion method and point matching method. Numerical results aregiven for the transmission and scattering characteristics for TE and TM cases.

Side scan sonar using low frequency can quickly search a wide range, but the images acquired are of low quality. The image super resolution (SR) method can mitigate this problem. The SR method typically uses sparse coding, but accurately estimating sparse coefficients incurs substantial computational costs. To reduce processing time, we propose a region-selective sparse coding based SR system that emphasizes object regions. In particular, the region that contains interesting objects is detected for side scan sonar based underwater images so that the subsequent sparse coding based SR process can be selectively applied. Effectiveness of the proposed method is verified by the reduced processing time required for image reconstruction yet preserving the same level of visual quality as conventional methods.

In this study, transverse magnetic electromagnetic plane wave scatterings by rectangular cylinders have been analyzed by a high frequency asymptotic method. Scattering field can be generated by the equivalent electric and magnetic currents which are obtained approximately from the geometrical optics (GO) fields. Our formulation is found to be exactly the same with the physical optics (PO) for the conducting cylinders, and it can also be applicable for dielectric cylinders. Numerical calculations are made to compare the results with those by other methods, such as the geometrical theory of diffraction (GTD) and HFSS simulation. A good agreement has been observed to confirm the validity of our method.

Location sharing services have recently gained momentum over mobile online social networks (mOSNs), seeing the increasing popularity of GPS-capable mobile devices such as smart phones. Despite the convenience brought by location sharing, there comes severe privacy risks. Though many efforts have been made to protect user privacy during location sharing, many of them rely on the extensive deployment of trusted Cellular Towers (CTs) and some incur excessive time overhead. More importantly, little research so far can support complete privacy including location privacy, identity privacy and social relation privacy. We propose SAM, a new System Architecture for mOSNs, and P3S, a Privacy-Preserving Protocol based on SAM, to address the above issues for privacy-preserving location sharing over mOSNs. SAM and P3S differ from previous work in providing complete privacy for location sharing services over mOSNs. Theoretical analysis and extensive experimental results demonstrate the feasibility and efficiency of the proposed system and protocol.

For capacity expansion of the Quasi-Zenith Satellite System (QZSS) safety confirmation system, frame slotted ALOHA with flag method has previously been proposed as an access control scheme. While it is always able to communicate in an optimum state, its maximum channel efficiency is only 36.8%. In this paper, we propose adding a reservation channel (R-Ch) to the frame slotted ALOHA with flag method to increase the upper limit of the channel efficiency. With an R-Ch, collision due to random channel selection is decreased by selecting channels in multiple steps, and the channel efficiency is improved up to 84.0%. The time required for accommodating 3 million mobile terminals, each sending one message, when using the flag method only and the flag method with an R-Ch are compared. It is shown that the accommodating time can be reduced to less than half by adding an R-Ch to the flag method.