This paper presents a design of a monolithic transformer using bond wires. The proposed transformer structure has several advantages such as high power handling and high efficiency. It shows that the measured insertion loss at the 1.9 GHz range is -1.54 dB (70%), which is higher than the spiral transformer of the same size. Also, it shows a phase error of less than 1 degree.

Heart sound is an acoustic wave generated by the mechanical movement of the heart and blood flow, and is a complicated, non-stationary signal composed of many signal sources. It can be divided into normal heart sounds and heart murmurs. Murmurs are abnormal signals that appear over wider ranges of frequency than normal heart sounds. They are generated at random spots in the whole period of heart sounds. The recognition of heart sounds is to differentiate heart murmurs through patterns that appear according to the generation time of murmurs. In this paper, a group of heart sounds was classified into normal heart sounds, pre-systolic murmurs, early systolic murmurs, late systolic murmurs, early diastolic murmurs, and continuous murmurs. The suggested algorithm was standardized by re-sampling and then added as an input to the neural network through wavelet transform. The neural network used Error Back - Propagation algorithm, which is a representative learning method, and controlled the number of hidden layers and the learning rate for optimal construction of networks. As a result of recognition, the suggested algorithm obtained a higher recognition rate than that of existing research methods. The best result was obtained with the average of 88% of the recognition rate when it consisted of 15 hidden layers. The suggested algorithm was considered effective for the recognition of automatic diagnosis of heart sound recognition programs.

In cognitive radio networks, secondary users exchange control information to utilize the available channels efficiently, to maintain connectivity, to negotiate for data communication such as sender-receiver handshakes, for neighbor discovery etc. This task is not trivial in cognitive radio networks due to the dynamic nature of network environment. Generally, this problem is tackled by using two famous approaches. The first one is the use of common control channel (CCC) and the second one is using channel hopping (a.k.a sequence-based protocols). The use of CCC simplifies the processes of MAC protocols. However, it may not be feasible in cognitive radio networks as the available channels, including control channel, are dynamically changing according to primary user activities. Channel hopping approaches can tolerate the failure of network due to primary user activities. But it causes significant amount of channel access delay which is known as time to rendezvous (TTR). In this paper, we propose a hybrid protocol of these two mechanisms. This hybrid protocol can maintain connectivity and it can guarantee the secondary users to be able to exchange necessary control information in dynamic environment. In our hybrid protocol, we use multiple control channels. If some control channels are unavailable in case of primary user appearances, secondary users still can communicate on different control channels, so it can be more tolerable primary user activities than normal CCC approaches. Channel hopping is performed only for control channels, so it provides relatively small amount of channel access delay.

A fully integrated CMOS wideband Low Noise Amplifier (LNA) operating over 2.3-7 GHz is designed and fabricated using a 0.18 µm CMOS process. The proposed structure is a common source-common source (CS-CS) cascode amplifier with a coupling capacitor. It realizes both low voltage drop at load resistor (Rload) and high gain over 2.3-7 GHz with simultaneous noise and input matching and low power consumption. This paper presents the proposed design technique of a wideband LNA, and verifies its performance by simulation and measurement. This wideband LNA achieves an average gain (S21) of 16.5 (dB), an input return loss (S11) less than -8 dB, a noise figure (NF) of 3.4-6.7 dB, and a third order input interception point (IIP3) of -7.5-3 dBm at 2.3-7 GHz with power consumption of 10.8 mW under 1.8 V VDD.

This paper presents the design of a CMOS RF Power Detector (PD) using 0.18 µm standard CMOS technology. The PD is an improved unbalanced source coupled pair incorporating an output differential amplifier and sink current steering. It realizes an input detectable power range of -30 to -20 dBm over 0.1-1 GHz. Also it shows a maximum data rate of 30 Mbps with 2 pF output loading under OOK modulation. The overall current consumption is 1.9 mA under a 1.5 V supply.

This letter analyzes a resource chain trust model for P2P reputation-based systems. Many researchers have given a lot of efforts to reputation-based system area and some of them have made good theoretical models. Problems are to spread malicious contents whereas the remark that such models only concentrate on the relationship between the node and its direct neighbors is still controversial. To solve the problems, we introduced the RCM (Resource Chain Model) and the Enhanced RCM. In this letter, we analyze the models and then show usage of our models can help us to find the best and safest location efficiently and decrease the number of malicious transaction.

Wireless sensor networks with a star network topology are commonly applied for health monitoring systems. To determine the condition of a patient, sensor nodes are attached to the body to transmit the data to a coordinator. However, this process is inefficient because the coordinator is always communicating with each sensor node resulting in a data processing workload for the coordinator that becomes much greater than that of the sensor nodes. In this paper, a method is proposed to reduce the number of data transmissions from the sensor nodes to the coordinator by establishing a threshold for data from the biological signals to ensure that only relevant information is transmitted. This results in a dramatic reduction in power consumption throughout the entire network.

This paper presents a circuit that improves supply noise rejection using an active inductor circuit. Compared to the conventional designs, the proposed supply noise suppression circuit has better characteristics such as low current consumption and small die size with noise rejection. The circuit was fabricated using 0.13 µm UMC CMOS technology. The experimental results showed that the supply noise was suppressed by 61% with only an increase in size of 20.0 µm 2.5 µm, and the current consumption was under 2 mA.

This letter proposes a new fast network configuration scheme that realizes an IP interface that allows users to view Internet Protocol TV (IPTV) in IPv6 networks more quickly than is possible with the current configuration procedure. The new scheme, a hybrid combination of IPv6, address information, and non-IP information, especially the Domain Name Service, is newly designed based on a technical analysis. The evaluation results show that the proposed scheme is acceptable for real-time television watching in IPv6 networks, even when in motion.

D2D (Device-to-Device) communication has received considerable attention in recent years as one of the key technologies for future communication systems. Among the typical D2D communication systems, FlashLinQ (FLQ) adopted single-tone OFDM (Orthogonal Frequency Division Multiplexing) transmission which enables wide-sense discovery and distributed channel-aware link scheduling. Although synchronization based on a CES (Common External Source) is basically assumed in FLQ, a means to support devices when they are unable to use a CES is still necessary. In most OFDM systems, CFO (Carrier Frequency Offset) induces ICI (Inter Channel Interference) which degrades overall system performance drastically. Especially in D2D systems, ICI can be amplified due to different path losses between link and a precise estimation and correction of CFO is very important. Many CFO estimation algorithms based on DA (Data Aided) and NDA (None Data Aided) were proposed for OFDM systems, but there are several constraint conditions on frequency synchronization in D2D systems. Therefore, in this paper, we propose a new NDA-CFO estimation method for OFDM based D2D systems. The proposed method is based on the characteristics of single-tone OFDM signal, and is composed of two estimation stages: initial estimation and feed-back estimation. In initial estimation, the estimation of CFO is obtained by using two correlation results in a symbol. Also, estimation range can be adaptively defined as the distance between the two windows. In feed-back estimation, the distance between the two correlation results is gradually increased by re-using the estimated CFO and the correlation results. Therefore, more precise CFO estimation can be obtained. A numerical analysis and performance evaluation verify that the proposed method has a large estimation range and achieves precise estimation performance compared to the conventional methods.

Tag collision is a major problem in the field of multi-tag identification in RFID systems. To solve this problem, many RFID systems adopt their own collision arbitration algorithms based on framed-structure slotted Aloha (FSSA) due to the simplicity of implementation. The frame size, meaning the number of slots in a frame, is a very important factor to inventory tags' responses in the FSSA. How to assign the frame size is therefore crucial to the collision arbitration performance. Since the existing collision arbitration methods do not consider the slot times of each slot when assigning frame size, they may increase overall identification time. By involving the slot times, we improve the collision arbitration performance of the conventional methods. Simulation results show that collision arbitration based on the proposed method is superior to that based on the conventional methods, irrespective of the number of tags.

The frequent and time-independent packet loss due to signal noise in the wireless network has been a major obstacle in providing a persistent end-to-end bandwidth in the integrated environment of wired and wireless networks. One approach to cope with this is to develop an efficient transport mechanism which appropriately adapts to the dynamics of the wireless part of the network so that the end-to-end throughput is maximised. The success of new transport mechanisms depends on the quality of information obtained, in particular, from the wireless network. This paper presents a novel mechanism to assess accurately the transmission quality of the wireless part of the integrated CDMA2000 1X networks using the NAK rate obtained from the underlying RLP protocol stack. The experiment results show that the proposed mechanism correctly measures the wireless transmission quality of the CDMA2000 1X network.

Deep neural networks (DNNs) provide excellent services in machine learning tasks such as image recognition, speech recognition, pattern recognition, and intrusion detection. However, an adversarial example created by adding a little noise to the original data can result in misclassification by the DNN and the human eye cannot tell the difference from the original data. For example, if an attacker creates a modified right-turn traffic sign that is incorrectly categorized by a DNN, an autonomous vehicle with the DNN will incorrectly classify the modified right-turn traffic sign as a U-Turn sign, while a human will correctly classify that changed sign as right turn sign. Such an adversarial example is a serious threat to a DNN. Recently, an adversarial example with multiple targets was introduced that causes misclassification by multiple models within each target class using a single modified image. However, it has the weakness that as the number of target models increases, the overall attack success rate decreases. Therefore, if there are multiple models that the attacker wishes to attack, the attacker must control the attack success rate for each model by considering the attack priority for each model. In this paper, we propose a priority adversarial example that considers the attack priority for each model in cases targeting multiple models. The proposed method controls the attack success rate for each model by adjusting the weight of the attack function in the generation process while maintaining minimal distortion. We used MNIST and CIFAR10 as data sets and Tensorflow as machine learning library. Experimental results show that the proposed method can control the attack success rate for each model by considering each model's attack priority while maintaining minimal distortion (average 3.95 and 2.45 with MNIST for targeted and untargeted attacks, respectively, and average 51.95 and 44.45 with CIFAR10 for targeted and untargeted attacks, respectively).

Although the Digital Rights Management (DRM) systems have been rapidly developed to protect copyrights, they have not considered user privacy because they regard this as an unnecessary element in achieving their goals. However, the protection of user privacy becomes one of the most important issues in DRM systems as the number of people who suffer from accidents caused by the infringement of individual information dramatically increases. This paper suggests a license management protocol which is a more powerful protocol to protect individual information in DRM. To protect the exposure of information of user identification, the proposed protocol uses alias like a TID and a token instead of the identity of content users. Due to using alias, this protocol can guarantee the anonymity of content users. Also, it can prevent the leakage of individual information through encryption of usage information. In this way, it can protect the privacy of content users.

An accurate but energy-efficient estimation of a position is important as the number of mobile computing systems grow rapidly. A challenge is to develop a highly accurate but energy efficient estimation method. A particle filter is a key algorithm to estimate and track the position of an object which exhibits non-linear movement behavior. However, it requires high usage of computation resources and energy. In this paper, we propose a scheme which can dynamically adjust the number of particles according to the accuracy of the reference signal for positioning and reduce the energy consumption by 37% on Cortex A7.

A power analysis attack is a well-known side-channel attack but the efficiency of the attack is frequently degraded by the existence of power components, irrelative to the encryption included in signals used for the attack. To enhance the performance of the power analysis attack, we propose a preprocessing method based on extracting encryption-related parts from the measured power signals. Experimental results show that the attacks with the preprocessed signals detect correct keys with much fewer signals, compared to the conventional power analysis attacks.

We present a latency-aware bus arbitration scheme for real-time embedded systems. Only a few works have addressed the quality of service (QoS) issue for traditional busses or interconnection network. They mostly aimed at minimizing the latencies of several master blocks, resulting in decreasing overall bandwidth and/or increasing the latencies of other master blocks. In our method, the optimization goal is different in that the latency of a master should be as close as a given latency constraint. This is achieved by introducing the concept of "slack". In this method, masters effectively share the given communication architecture so that they all observe expected latencies and the degradation of overall bandwidth is marginal. The experimental results show that our method greatly reduces the number of constraint violations compared to other conventional arbitration schemes while minimizing the bandwidth degradation.

This letter proposes a QoS control method for video streaming service over wireless networks. Based on statistical analysis, the time-varying MAC parameters highly related to channel condition are selected to predict available bitrate. Adaptive bitrate control of scalably-encoded video guarantees continuity in streaming service even if the channel condition changes abruptly.

To reduce RFID tag identification delay, we propose a novel Dynamic Splitting protocol (DS) which is an improvement of the Query tree protocol (QT). DS controls the number of branches of a tree dynamically. An improved performance of DS relative to QT is verified by analytical results and simulation studies.

Query response times are critical for cluster computing applications in data centers. In this letter, we argue that to optimize the network performance, we should consider the latency of the flows suffered loss, which are called tardy flows. We propose two tardy flow scheduling algorithms and show that our work offers significant performance gains through performance analysis and simulations.