Cognitive radio technology is still the key technology of future mobile communication systems. Previous studies have focused on improving spectrum utilization and less energy consumption. In this paper, we propose an Overhead Reduced Scheme (ORS) for green cooperative spectrum sensing. Compared to traditional cooperative sensing scheme, ORS scheme divides the sensing time into three time slots and selects the best multi-mode user to report decisions. In consideration of reporting channel deviation, we derive closed-form expressions for detection probability and false alarm probability of ORS scheme based on Rayleigh fading channel. Simulation results show that ORS scheme can improve the perception accuracy while reducing the perceived delay and energy consumption in the process of perception, so as to realize the green communication.

In this paper, we design a segment training based individual channel estimation (STICE) scheme for the classical two-way relay network (TWRN) with multi-pair sources (MPS) and amplify-and-forward (AF). We adopt the linear minimum mean square error (LMMSE) channel estimator to minimize the mean square error (MSE) without channel estimation error, where the optimal power allocation strategy from the relay for different sources is obtained. Then the MSE gains are given with different source pairs among the proposed power allocation scheme and the existing power allocation schemes. Numerical results show that the proposed method outperforms the existing ones.

In this paper we propose a QoS-enhanced intelligent stochastic optimal fair real-time packet scheduler, QUEST, for 4G LTE traffic in routers. The objective of this research is to maximize the system QoS subject to the constraint that the processor utilization is kept nearly at 100 percent. The QUEST has following unique advantages. First, it solves the challenging problem of starvation for low priority process - buffered streaming video and TCP based; second, it solves the major bottleneck of the scheduler Earliest Deadline First's failure at heavy loads. Finally, QUEST offers the benefit of arbitrarily pre-programming the process utilization ratio.Three classes of multimedia 4G LTE QCI traffic, conversational voice, live streaming video, buffered streaming video and TCP based applications have been considered. We analyse two most important QoS metrics, packet loss rate (PLR) and mean waiting time. All claims are supported by discrete event and Monte Carlo simulations. The simulation results show that the QUEST scheduler outperforms current state-of-the-art benchmark schedulers. The proposed scheduler offers 37 percent improvement in PLR and 23 percent improvement in mean waiting time over the best competing current scheduler Accuracy-aware EDF.

Nowadays, SMS spam has been overflowing in many countries. In fact, the standards of filtering SMS spam are different from country to country. However, the current technologies and researches about SMS spam filtering all focus on dividing SMS message into two classes: legitimate and illegitimate. It does not conform to the actual situation and need. Furthermore, they are facing several difficulties, such as: (1) High quality and large-scale SMS spam corpus is very scarce, fine categorized SMS spam corpus is even none at all. This seriously handicaps the researchers' studies. (2) The limited length of SMS messages lead to lack of enough features. These factors seriously degrade the performance of the traditional classifiers (such as SVM, K-NN, and Bayes). In this paper, we present a new fine categorized SMS spam corpus which is unique and the largest one as far as we know. In addition, we propose a classifier, which is based on the probability topic model. The classifier can alleviate feature sparse problem in the task of SMS spam filtering. Moreover, we compare the approach with three typical classifiers on the new SMS spam corpus. The experimental results show that the proposed approach is more effective for the task of SMS spam filtering.

Recently, a number of studies have drawn attention to purchasing online game items. Most of the studies are based on the assumption that consumers behave rationally. Accordingly, TRA- or value-based approaches have been mainly employed to understand the online purchases of game items. However, the purchasing behavior of consumers involves not only making rational decisions, but also making irrational decisions. Hence, their purchase behavior is affected by propensities for conspicuous consumption, impulsive consumption, and habitual consumption. Playing games can be highly addictive, and players often display such addictive behaviors. Our study explored both the rational and irrational factors in purchase behavior to understand how they are associated with purchasing game items. A total of 366 pieces of data were collected from Korean online game users through a survey. Regression analyses of the collected data showed that the behavior of buying game items was influenced not only by the intention to purchase which is a rational factor in consumption, but also by such irrational factors as habit, impulse, and ostentation which should be further emphasized in future studies.

This paper proposes a novel algorithm for lane detection based on inverse perspective transformation and Kalman filter. A simple inverse perspective transformation method is presented to remove perspective effects and generate a top-view image. This method does not need to obtain the internal and external parameters of the camera. The Gaussian kernel function is used to convolute the image to highlight the lane lines, and then an iterative threshold method is used to segment the image. A searching method is applied in the top-view image obtained from the inverse perspective transformation to determine the lane points and their positions. Combining with feature voting mechanism, the detected lane points are fitted as a straight line. Kalman filter is then applied to optimize and track the lane lines and improve the detection robustness. The experimental results show that the proposed method works well in various road conditions and meet the real-time requirements.

Compression history detection plays an important role in digital multimedia forensics. Most existing works, however, mainly focus on digital image and video. Additionally, the existed audio compression detection algorithms aim to detect the trace of double compression. In real forgery scenario, multiple compression is more likely to happen. In this paper, we proposed a detection algorithm to reveal the compression history for MP3 audio. The statistics of the scale factor and Huffman table index which are the parameters of MP3 codec have been extracted as the detecting features. The experimental results have shown that the proposed method can effectively identify whether the testing audio has been previously treated with single/double/triple compression.

Saliency detection in neurobiology is a vehement research during the last few years, several cognitive and interactive systems are designed to simulate saliency model (an attentional mechanism, which focuses on the worthiest part in the image). In this paper, a bottom up saliency detection model is proposed by taking into account the color and luminance frequency features of RGB, CIE $L^*a^*b^*$ color space of the image. We employ low-level features of image and apply band pass filter to estimate and highlight salient region. We compute the likelihood probability by applying Bayesian framework at pixels. Experiments on two publically available datasets (MSRA and SED2) show that our saliency model performs better as compared to the ten state of the art algorithms by achieving higher precision, better recall and F-Measure.

In this paper, we propose a compact Convolutional Neural Network (CNN)-based tracker in conjunction with a particle filter architecture, in which the CNN model operates as an accurate candidates estimator, while the particle filter predicts the target motion dynamics, lowering the overall number of calculations and refines the resulting target bounding box. Experiments were conducted on the Online Object Tracking Benchmark (OTB) [34] dataset and comparison analysis in respect to other state-of-art has been performed based on accuracy and precision, indicating that the proposed algorithm outperforms all state-of-the-art trackers included in the OTB dataset, specifically, TLD [16], MIL [1], SCM [36] and ASLA [15]. Also, a comprehensive speed performance analysis showed average frames per second (FPS) among the top-10 trackers from the OTB dataset [34].

This paper presents an extended particle filter to increase the accuracy and decrease the computation load of vehicle tracking. Particle filter has been the subject of extensive interest in video-based tracking which is capable of solving nonlinear and non-Gaussian problems. However, there still exist problems such as preventing unnecessary particle consumption, reducing the computational burden, and increasing the accuracy. We aim to increase the accuracy without an increase in computation load. In proposed method, we calculate the direction angle of the target vehicle. The angular difference between the direction of the target vehicle and each particle of the particle filter is observed. Particles are filtered and weighted, based on their angular difference. Particles with angular difference greater than a threshold is eliminated and the remaining are stored with greater weights in order to increase their probability for state estimation. Threshold value is very critical for performance. Thus, instead of having a constant threshold value, proposed algorithm updates it online. The first advantage of our algorithm is that it prevents the system from failures caused by insufficient amount of particles. Second advantage is to reduce the risk of using unnecessary number of particles in tracking which causes computation load. Proposed algorithm is compared against camshift, direction-based particle filter and condensation algorithms. Results show that the proposed algorithm outperforms the other methods in terms of accuracy, tracking duration and particle consumption.

The hash function RIPEMD-160 is a worldwide ISO/IEC standard and the hash function HAS-160 is the Korean hash standard and is widely used in Korea. On the basis of differential meet-in-the-middle attack and biclique technique, a preimage attack on 34-step RIPEMD-160 with message padding and a pseudo-preimage attack on 71-step HAS-160 without message padding are proposed. The former is the first preimage attack from the first step, the latter increases the best pseudo-preimage attack from the first step by 5 steps. Furthermore, we locate the linear spaces in another message words and exchange the bicliques construction process and the mask vector search process. A preimage attack on 35-step RIPEMD-160 and a preimage attack on 71-step HAS-160 are presented. Both of the attacks are from the intermediate step and satisfy the message padding. They improve the best preimage attacks from the intermediate step on step-reduced RIPEMD-160 and HAS-160 by 4 and 3 steps respectively. As far as we know, they are the best preimage and pseudo-preimage attacks on step-reduced RIPEMD-160 and HAS-160 respectively in terms of number of steps.

Information Technology (IT) plays an increasingly important role for small and medium-sized enterprises. It has become fundamental for these companies to protect information and IT assets in relation to risks and threats that have grown in recent years. This study aims to understand the importance and structure of an information security policy, using a quantitative study that intends to identify the most important and least relevant elements of an information security policy document. The findings of this study reveal that the top three most important elements in the structure of a security policy are the asset management, security risk management and define the scope of the policy. On the other side, the three least relevant elements include the executive summary, contacts and manual inspection. Additionally, the study reveals that the importance given to each element of the security policy is slightly changed according to the sectors of activity. The elements that show the greatest variability are the review process, executive summary and penalties. On the other side, the purpose of the policy and the asset management present a stable importance for all sectors of activity.

In this paper, we present a new public key encryption scheme with equality test (PKEwET). Compared to other PKEwET schemes, we find that its security can be improved since the proposed scheme is based on non-Abelian factorization problems. To our knowledge, it is the first scheme regarding equality test that can resist quantum algorithm attacks. We show that our scheme is one-way against chosen-ciphertext attacks in the case that the computational Diffie-Hellman problem is hard for a Type-I adversary. It is indistinguishable against chosen-ciphertext attacks in the case that the Decisional Diffie-Hellman problem is hard in the random oracle model for a Type-II adversary. To conclude the paper, we demonstrate that our scheme is more efficient.

Recently, NFV has attracted attention as a next-generation network virtualization technology for hardware -independent and efficient utilization of resources. NFV is a technology that not only virtualize computing, server, storage, network resources based on cloud computing but also connect Multi-Tenant of VNFs, a software network function. Therefore, it is possible to reduce the cost for constructing a physical network and to construct a logical network quickly by using NFV. However, in NFV, when a new VNF is added to a running Tenant, authentication between VNFs is not performed. Because of this problem, it is impossible to identify the presence of Fake-VNF in the tenant. Such a problem can cause an access from malicious attacker to one of VNFs in tenant as well as other VNFs in the tenant, disabling the NFV environment. In this paper, we propose Auto-configurable Security Mechanism in NFV including authentication between tenant-internal VNFs, and enforcement mechanism of security policy for traffic control between VNFs. This proposal not only authenticate identification of VNF when the VNF is registered, but also apply the security policy automatically to prevent malicious behavior in the tenant. Therefore, we can establish an independent communication channel for VNFs and guarantee a secure NFV environment.

In recent years, video surveillance research has been able to recognize various behaviors of pedestrians and analyze the overall situation of objects by combining image analysis technology and deep learning method. Human Activity Recognition (HAR), which is important issue in video surveillance research, is a field to detect abnormal behavior of pedestrians in CCTV environment. In order to recognize human behavior, it is necessary to detect the human in the image and to estimate the pose from the detected human. In this paper, we propose a novel approach for 2D Human Pose Estimation based on object detection using RGB-D information. By adding depth information to the RGB information that has some limitation in detecting object due to lack of topological information, we can improve the detecting accuracy. Subsequently, the rescaled region of the detected object is applied to ConVol.utional Pose Machines (CPM) which is a sequential prediction structure based on ConVol.utional Neural Network. We utilize CPM to generate belief maps to predict the positions of keypoint representing human body parts and to estimate human pose by detecting 14 key body points. From the experimental results, we can prove that the proposed method detects target objects robustly in occlusion. It is also possible to perform 2D human pose estimation by providing an accurately detected region as an input of the CPM. As for the future work, we will estimate the 3D human pose by mapping the 2D coordinate information on the body part onto the 3D space. Consequently, we can provide useful human behavior information in the research of HAR.

Modern software, especially web-based software, is broadly used in various fields. Most web applications employ design patterns, such as a model-view-controller (MVC) pattern and a factory pattern as development technology, so the application can have a good architecture to facilitate maintenance and productivity. A web application, however, may have defects and developers must fix the defects when a user submits bug reports. In this paper, we propose a novel approach to improving fault traceability in web application by using software revision information and software behavior model to reduce costs and effectively handle the software defect. We also provide a case study to show effectiveness of our approach.

Unmanned Aerial Vehicle (UAV) is diversely utilized in our lives such as daily hobbies, specialized video image taking and disaster prevention activities. New ways of UAV application have been explored recently such as UAV-based delivery. However, most UAV systems are being utilized in a passive form such as real-time video image monitoring, filmed image ground analysis and storage. For more proactive UAV utilization, there should be higher-performance UAV and large-capacity memory than those presently utilized. Against this backdrop, this study described the general matters on proactive software platform and high-performance UAV hardware for real-time target tracking; implemented research on its design and implementation, and described its implementation method. Moreover, in its established platform, this study measured and analyzed the core-specific CPU consumption.

Cybersecurity has emerged as a serious problem in Korea and there have been relevant movements to improve domestic cybersecurity policy and system. However, discussions have yet to result in actual progress and the legislation for improvement of cybersecurity policy and system have been stagnant until now. As evidenced by the introduction of primary government legislation bill for national cybersecurity in 2017, the preparations for improvements to the policy and system are still in progress. However, we cannot be positive about the possibility of implementing these improvements during the process. Recognition of the importance of cybersecutiry has gradually risen and is more prevalent than in years past, however, in-depth discussions are not being made. In principle, misunderstandings about cybersecurity itself and insufficient understandings of the relevant legislation seem to cause such problems. Therefore, it is necessary to review key issues related to the improvement of cybersecurity policy and system and reconsider tasks for the future. Such issues include the relationship between cybersecurity and fundamental rights, establishing responsibility and capability of each of entities for cybersecurity, and the role of the military in cybersecurity. This type of in-depth discussion will be helpful for finding ways to improve upon cybersecurity policy and system. Moreover, this study aims to key issues with questionnaire survey and political and normative inquiry.

In this Paper, we propose 8K UHD (Ultra High Definition) encapsulation method for efficient transmission and reception based on MMT (MPEG Media Transport). Broadcasting services for 8K UHD allow users to feel the maximized reality. However, present technology is difficult to provide 8K UHD in broadcasting networks, because the 8K UHD bitrate is too high to be transmitted in the current broadcasting networks. Research for transmitting 8K UHD is underway. In some researches, a receiver is implemented with four 4K UHD display instead of a 8K UHD display. In order to transmit 8K UHD within the limited transmission bitrate of broadcasting network, 8K UHD contents encoded by SHVC (Scalable High Efficiency Video Coding) and then transmitted over heterogeneous network. For using the broadcasting and communication networks, MMT standard is used. MMT is IP based transmission protocol as the next generation transmission protocol. According to the MMT standard, video stream encapsulated and transmitted in MMTP (MMT Protocol) packet. IP-based broadcasting and communication networks can be used to transmit simultaneously, and the receiver can synchronize and play it. We propose an encapsulation method that can efficiently transmit and receive 8K UHD. The proposed method increases a payload rate and decreases an initial delay at the receiver. We show that the efficiency of the proposed method is verified by experimental tests.

This paper suggests an automatic approach to extracting Java method implementation patterns associated with method identifiers using Cascade K-Medoids. Java method implementation patterns indicate recurring implementations for achieving the purpose described in the method identifier with the given parameters and return type. If the implementation is different from the purpose, readers of the code tend to take more time to comprehend the method, which eventually affects to the increment of software maintenance cost. In order to automatically identify implementation patterns and its representative sample code, we first propose three groups of feature vectors for characterizing the Java method signature, method body and their relation. Then, we apply Cascade K-Medoids by enhancing the K-Medoids algorithm with the Calinski and Harrabasez algorithm. As the evaluation of our approach, we identified 16,768 implementation patterns of 7,169 method identifiers from 50 open source projects. The implementation patterns have been validated by the 30 industrial practitioners with from 1 to 6 years industrial experience, resulting in 86% of the precision.

In smart home environment, certificate based signature technology is being studied by communication with Internet of Things(IoT) device. However, block - chain technology has attracted much attention because of the problems such as single - point error and management overhead of the trust server. Among them, Keyless Signature Infrastructure(KSI) provides integrity by configuring user authentication and global timestamp of distributed server into block chain by using hash-based one-time key. In this paper, we provide confidentiality by applying group key and key management based on multi - solution chain. In addition, we propose a smart home environment that can reduce the storage space by using Extended Merkle Tree and secure and efficient KSI-based authentication and communication with enhanced security strength.

The advent of the Internet of Things (IoT) technology, which brings many benefits to our lives, has resulted in numerous IoT devices in many parts of our living environment. However, to adapt to the rapid changes in the IoT market, numerous IoT devices were widely deployed without implementing security by design at the time of development. As a result, malicious attackers have targeted IoT devices, and IoT devices lacking security features have been compromised by attackers, resulting in many security incidents. In particular, an attacker can take control of an IoT device, such as Mirai Botnet, that has insufficient security features. The IoT device can be used to paralyze numerous websites by performing a DDoS attack against a DNS service provider. Therefore, this study proposes a scheme to minimize security vulnerabilities and threats in IoT devices to improve the security of the IoT service environment.

In this study, we developed textile proximity sensor using conductive textile to develop a Medication adherence management system. The textile proximity sensor utilizes the principle of the capacitor, and the ring type sensor used to reduce the fringe-effect. When take medicines, we made a custom PCB that converts the change of the data measured by the sensor into a digital value so that transmitted the PC. In order to compare the performance of the system, we evaluated the correlation between the data variation according to the quantity of pills in the electronic compact scale and the data in this system. As a result, Pearson's correlation coefficient was 0.956(p<0.01), confirmed a good correlation between the scale and our system. Therefore, we concluded that our system evaluated able to whether or not to take medication.

Everything in the world is becoming connected and interactive due to the Internet. The future of interactive smart environments such as smart cities, smart industries, or smart farms demand high network bandwidth, high network flexibility, and self-organization systems without costly hardware upgrades, and they provide a sustainable, scalable, and replicable smart environment backbone infrastructure. This paper presents a new Hybrid Software-Defined architecture for integrating Internet-of-Things technologies that are essential technologies for smart environments. It combines a software-defined networking infrastructure and a real-time distributed network framework with an advanced optimization to enable self-configuration, self-management, and self-adaption for providing seamless communication and efficiently managing a vast number of smart heterogeneous devices.

Recently interest in Internet of Things(IoT) has attracted significant attention at national level. IoT can create new services as a technology to exchange data through connections among a huge number of objects around the user. Data communication between objects provides not only information collected in the surrounding environment but also various personalized information. IoT services which provide these various types of data are exposed to numerous security vulnerabilities. If data is maliciously collected and used by an attacker in an IoT environment that deals with various data, security threats are greater than those in existing network environments. Therefore, security of all data exchanged in the IoT environment is essential. However, lightweight terminal devices used in the IoT environment are not suitable for applying the existing encryption algorithm. In addition, IoT networks consisting of many sensors require group communication. Therefore, this paper proposes a secure multicast scheme using the proxy re-encryption method based on Vehicular ad-hoc networks(VANET) environment. The proposed method is suitable for a large-scale dynamic IoT network environment using unreliable servers.

This paper presents design for error correcting algorithm of the time of flight (ToF) detection value in the light detection and ranging (LIDAR) system sensor. The walk error of ToF value is generated by change of the received signal power depending on distance between the LIDAR sensor and object. The proposed method efficiently compensates the ToF value error by the independent ToF value calculation from the received signal using both rising point and falling point. A constant error of ~0.05 m is obtained after the walk error correction while an increasing error up to ~1 m is obtained with conventional method.

This paper presents a novel and efficient data transmission scheme based on mobile edge computing for the massive IoT environments which should support various type of services and devices. Based on an accurate and precise synchronization process, it maximizes data transmission throughput, and consistently maintains a flow's latency. To this end, the proposed efficient software defined data transmission scheme (ESD-DTS) configures and utilizes synchronization zones in accordance with the 4 usage cases, which are end node-to-end node (EN-EN), end node-to-cloud network (EN-CN), end node-to-Internet node (EN-IN), and edge node-to-core node (EdN-CN); and it transmit the data by the required service attributes, which are divided into 3 groups (low-end group, medium-end group, and high-end group). In addition, the ESD-DTS provides a specific data transmission method, which is operated by a buffer threshold value, for the low-end group, and it effectively accommodates massive IT devices. By doing this, the proposed scheme not only supports a high, medium, and low quality of service, but also is complied with various 5G usage scenarios. The essential difference between the previous and the proposed scheme is that the existing schemes are used to handle each packet only to provide high quality and bandwidth, whereas the proposed scheme introduces synchronization zones for various type of services to manage the efficiency of each service flow. Performance evaluations show that the proposed scheme outperforms the previous schemes in terms of throughput, control message overhead, and latency. Therefore, the proposed ESD-DTS is very suitable for upcoming 5G networks in a variety of massive IoT environments with supporting mobile edge computing (MEC).

Arrhythmia has recently emerged as one of the major causes of death in Koreans. Premature Ventricular Contraction (PVC) is the most common arrhythmia that can be found in clinical practice, and it may be a precursor to dangerous arrhythmias, such as paroxysmal insomnia, ventricular fibrillation, and coronary artery disease. Therefore, we need for a method that can detect an abnormal heart beat and diagnose arrhythmia early. We extracted the features corresponding to the QRS pattern from the subject's ECG signal and classify the premature ventricular contraction waveform using the features. We modified the weighting and bias values based on the error back-propagation algorithm through learning data. We classify the normal signal and the premature ventricular contraction signal through the modified weights and deflection values. MIT-BIH arrhythmia data sets were used for performance tests. We used RR interval, QS interval, QR amplitude and RS amplitude features. And the hidden layer with two nodes is composed of two layers to form a total three layers (input layer 0, output layer 3).