Wireless sensor networks (WSN) are composed of a large number of sensor nodes. Battery-powered sensor nodes have limited coverage; therefore, it is more efficient to transmit data via multi-hop communication. The network lifetime is a crucial issue in WSNs and the multi-hop routing protocol should be designed to prolong the network lifetime. Prolonging the network lifetime can be achieved by minimizing the power consumed by the nodes, as well as by balancing the power consumption among the nodes. A power imbalance can reduce the network lifetime even if several nodes have sufficient (battery) power. In this paper, we propose a routing protocol that prolongs the network lifetime by balancing the power consumption among the nodes. To improve the balance of power consumption and improve the network lifetime, the proposed routing scheme adaptively controls the transmission range using a power control according to the residual power in the nodes. We developed a routing simulator to evaluate the performance of the proposed routing protocol. The simulation results show that the proposed routing scheme increases power balancing and improves the network lifetime.

We present a practical design algorithm for quantizers at nodes in distributed systems in which each local measurement is quantized without communication between nodes and transmitted to a fusion node that conducts estimation of the parameter of interest. The benefits of vector quantization (VQ) motivate us to incorporate the VQ strategy into our design and we propose a low-complexity design technique that seeks to assign vector codewords into sets such that each codeword in the sets should be closest to its associated local codeword. In doing so, we introduce new distance metrics to measure the distance between vector codewords and local ones and construct the sets of vector codewords at each node to minimize the average distance, resulting in an efficient and independent encoding of the vector codewords. Through extensive experiments, we show that the proposed algorithm can maintain comparable performance with a substantially reduced design complexity.

Adversarial attacks on artificial intelligence (AI) systems use adversarial examples to achieve the attack objective. Adversarial examples consist of slightly changed test data, causing AI systems to make false decisions on these examples. When used as a tool for attacking AI systems, this can lead to disastrous results. In this paper, we propose an ensemble of degraded convolutional neural network (CNN) modules, which is more robust to adversarial attacks than conventional CNNs. Each module is trained on degraded images. During testing, images are degraded using various degradation methods, and a final decision is made utilizing a one-hot encoding vector that is obtained by summing up all the output vectors of the modules. Experimental results show that the proposed ensemble network is more resilient to adversarial attacks than conventional networks, while the accuracies for normal images are similar.

In this paper, we present a three-dimensional (3D) optical encryption technique for quick response (QR) code using computational synthesized integral imaging, computational volumetric reconstruction, and double random phase encryption. Two-dimensional (2D) QR code has many advantages, such as enormous storage capacity and high reading speed. However, it does not protect primary information. Therefore, we present 3D optical encryption of QR code using double random phase encryption (DRPE) and an integral imaging technique for security enhancement. We divide 2D QR code into four parts with different depths. Then, 2D elemental images for each part of 2D QR code are generated by computer synthesized integral imaging. Generated 2D elemental images are encrypted using DRPE, and our method increases the level of security. To validate our method, we report simulations of 3D optical encryption of QR code. In addition, we calculated the peak side-lobe ratio (PSR) for performance evaluation.

This paper designed and developed the image processing system of integrating feature extraction and matching by using convolutional neural network (CNN), rather than relying on the simple method of processing feature extraction and matching separately in the image processing of conventional image recognition system. To implement it, the proposed system enables CNN to operate and analyze the performance of conventional image processing system. This system extracts the features of an image using CNN and then learns them by the neural network. The proposed system showed 84% accuracy of recognition. The proposed system is a model of recognizing learned images by deep learning. Therefore, it can run in batch and work easily under any platform (including embedded platform) that can read all kinds of files anytime. Also, it does not require the implementing of feature extraction algorithm and matching algorithm therefore it can save time and it is efficient. As a result, it can be widely used as an image recognition program.

A pseudo-random sequence generated by using a primitive polynomial, trace function, and Legendre symbol has been researched in our previous work. Our previous sequence has some interesting features such as period, autocorrelation, and linear complexity. A pseudo-random sequence widely used in cryptography. However, from the aspect of the practical use in cryptographic systems sequence needs to generate swiftly. Our previous sequence generated by utilizing a primitive polynomial, however, finding a primitive polynomial requires high calculating cost when the degree or the characteristic is large. It’s a shortcoming of our previous work. The main contribution of this work is to find some relation between the generated sequence and irreducible polynomials. The purpose of this relationship is to generate the same sequence without utilizing a primitive polynomial. From the experimental observation, it is found that there are (p - 1)/2 kinds of polynomial, which generates the same sequence. In addition, some of these polynomials are non-primitive polynomial. In this paper, these relationships between the sequence and the polynomials are shown by some examples. Furthermore, these relationships are proven theoretically also.

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

This paper proposes a quick response (QR) barcode watermarking algorithm in order to improve readability of the QR barcode in a JPEG image. The proposed algorithm embeds a watermark in a wavelet based-low frequency band for watermark robustness, and visually extracts a QR barcode after embedding the QR barcode image with confidential information into the original image via imperceptible watermarking technology. The visually extracted QR barcode watermark uses an approach to authenticate the ownership more easily through a common hardware and software-based mobile barcode reader app. Therefore, the QR barcode watermark may improve the ability to easily detect watermarks efficiently as well as imperceptibility and robustness, which is the main watermark requirement by embedding the QR barcode with watermark text information in a digital image and when compare to conventional watermarks.

Player's actions in a game occur in the process of gameplay experiences in a play space designed by the developer according to preset gameplay beats. Focusing on beats that induce a first-person shooter (FPS) game's main gameplay, this paper analyzes the differences in eye gaze information found in different players during the course of gameplay. For this research goal, the study divides the beat areas in which play actions appear in association with gameplay beats at a typical FPS game level, repeatedly conducts tests in accordance with a player's experience level (novice and expert group), and collects and analyzes eye gaze information data in three types of beat areas. The analysis result suggests concrete guidelines for game level design for different beat areas based on an FPS game player's experience level. This empirical experiment method and result can lessen repetitive modification work for game level design and consequently be utilized for optimizing the game level to the developer's intention.

This paper reveals the impact of the insertion of electromagnetic band gap (EBG) structures on the performance of circularly polarized (CP) patch-slot antenna with offset slot. Several optimizations are necessary to precise physical parameters in the aim to fix the resonance frequency at 3.2 GHz. The proposed antenna possesses lightweight, simplicity, low cost, and circular polarization ensured by two feeding sources to permit right-hand and left-hand circular polarization process (RHCP and LHCP). The measured results compared with simulation results of the proposed circularly polarized EBG antenna with offset slot show good band operations with –10 dB impedance bandwidths of 9.1% and 36.2% centered at 3.2 GHz, which cover weather radar, surface ship radar, and some communications satellites bands. Our investigation will confirm the simulation and experimental results of the EBG antenna involving new EBG structures.