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Recently, with the development of renewable energy such as solar power and wind power, the dc grid has garnered attention and the dc circuit breakers essential to the dc grid have been actively studied. The dc solid-state circuit breaker (dc SSCB) can quickly break the fault current within 1 ms compared with a mechanical circuit breaker, which does the same in several tens of milliseconds. The previous thyristor dc SSCB cannot naturally charge the commutation capacitor for fault current breaking, and thus its control of operation is complicated. Thus, this article proposes a new thyristor dc SSCB, which can naturally charge the commutation capacitor without complicated switching operation. It can not only perform the fast breaking of fault current, but also the operating duties of reclosing and rebreaking. The operating characteristics of the proposed dc SSCB are verified by the experimental results. [ABSTRACT FROM AUTHOR]

DC microgrids have received remarkable interest in both the academia and the industry for over a decade, yet the protection of dc microgrids remains as a major challenge. This challenge is rooted in the nonexistence of natural zero-crossing point in dc microgrids which rapidly increase the fault current. In this article, a novel bidirectional dc solid-state circuit breaker (dc SSCB) to reinforce the bidirectional flow of energy which significantly boosts the overall efficiency of the power supply of dc microgrids is presented. The proposed topology can perform the operating duty of reclosing and rebreaking, and the commutation capacitor is charged and recharged naturally without any complex control of the main or auxiliary thyristors, which is considered atypical of existing topologies. A clear and detailed analysis of the operation of the proposed topology is presented. Finally, the experimental results are presented to confirm the effectiveness of the proposed novel bidirectional dc SSCB. [ABSTRACT FROM AUTHOR]

In the conventional approaches using reinforcement learning (RL) for resource allocation in wireless networks, the structure of the policy depends on network circumstances such as the number of users and quality-of-service requirements. Due to this dependence, the policy is hard to be used in a practical system where the network circumstance is dynamically changing. To resolve this issue, we propose a circumstance-independent policy that can effectively address the different network circumstances even with a single policy. Thus, contrary to the conventional RL approaches, the proposed policy can be easily applied in the practical system. We then develop a deep RL algorithm to learn it. Through simulation results, we show that a single proposed policy can be used over different circumstances, and it achieves a close performance to the circumstance-dependent policy for each circumstance, which learns the optimal policy for the corresponding circumstance. [ABSTRACT FROM AUTHOR]

In this letter, to improve data rate over wireless communication channels, we propose a deep Q network (DQN)-based adaptive modulation scheme by using Markov decision process (MDP) model. The proposed algorithm makes the reinforcement learning agent to select rate region boundaries as the states, which divide signal-to-noise ratio (SNR) range into rate regions. The simulation results show that spectral efficiency can be improved on the average by 0.5395 bps/Hz in wide SNR range. [ABSTRACT FROM AUTHOR]

Acoustic-resolution photoacoustic micro-scopy (AR-PAM) is an emerging biomedical imaging modality that combines superior optical sensitivity and fine ultrasonic resolution in an optical quasi-diffusive regime (~1–3 mm in tissues). AR-PAM has been explored for anatomical, functional, and molecular information in biological tissues. Heretofore, AR-PAM systems have suffered from a limited field-of-view (FOV) and/or slow imaging speed, which have precluded them from routine preclinical and clinical applications. Here, we demonstrate an advanced AR-PAM system that overcomes both limitations of previous AR-PAM systems. The new AR-PAM system demonstrates a super wide-field scanning that utilized a 1-axis water-proofing microelectromechanical systems (MEMS) scanner integrated with two linear stepper motor stages. We achieved an extended FOV of 36 × 80 mm2 by mosaicking multiple volumetric images of 36 × 2.5 mm2 with a total acquisition time of 224 seconds. For one volumetric data (i.e., 36 × 2.5 mm2), the B-scan imaging speed over the short axis (i.e., 2.5 mm) was 83 Hz in humans. The 3D volumetric image was also provided by using MEMS mirror scanning along the X-axis and stepper-motor scanning along the Y-axis. The super-wide FOV mosaic image was realized by registering and merging all individual volumetric images. Finally, we obtained multi-plane whole-body in-vivo PA images of small animals, illustrating distinct multi-layered structures including microvascular networks and internal organs. Importantly, we also visualized microvascular networks in human fingers, palm, and forearm successfully. This advanced MEMS-AR-PAM system could potentially enable hitherto not possible wide preclinical and clinical applications. [ABSTRACT FROM AUTHOR]

Music listening is a very personal and situational behavior for modern people who always carry smartphones in everyday life. Therefore, contextual information, such as user’s current activity and mood state could be used to greatly improve music recommendations. In this paper, we develop a smartphone-based mobile system that includes two core modules for recognizing human activities and then accordingly recommending music. In the proposed method, a deep residual bidirectional gated recurrent neural network is applied to obtain high activity recognition accuracy from accelerometer signals on the smartphone. In order to improve the performance of tempo-oriented music classification, an ensemble of dynamic classification using a long-term modulation spectrum and sequence classification using a short-term spectrogram is used. Music recommendation is performed using the relationship between the recognized human activities and the music files indexed by tempo-oriented music classification that reflects user preference models in order to achieve high user satisfaction. The results of comprehensive experiments on real data confirm the accuracy of the proposed activity-aware music recommendation framework. [ABSTRACT FROM AUTHOR]

To improve energy efficiency (EE) in power line communication (PLC) systems, we proposed a dynamic load-based PLC system model as a new model for EE maximization and an energy-efficient resource allocation strategy optimizing load impedance, transmission power and subchannel allocation as the optimization arguments. Since the load impedance at receivers is influenced by characteristics of a power line channel, optimizing the load impedance is required to maximally induce a received power while considering the channel characteristics. We sought to maximize network EE while satisfying constraints that transmission power of a transmitter cannot be exceeded by its maximum limit and minimum quality of service should be guaranteed. Therefore, we studied a scenario optimizing the three arguments based on orthogonal frequency division multiplexing downlink networks with the non-white Gaussian noise channel in multi-receiver PLC systems. Using nonlinear fractional programming and Lagrange dual method, we provided a tractable solution as an iterative algorithm obtaining the optimal value of the arguments. Simulation results showed that the proposed system is more energy-efficient compared to baseline schemes, and EE is greatly improved by the synergistic effects of the impedance optimization and the subchannel allocation strategy. [ABSTRACT FROM PUBLISHER]

In this letter, we study an interference avoidance scenario in the presence of a smart interferer, which can rapidly observe the transmit power of a backscatter wireless sensor network (WSN) and effectively interrupt backscatter signals. We consider a power control with a sub-channel allocation to avoid interference attacks and a time-switching ratio for backscattering and RF energy harvesting in backscatter WSNs. We formulate the problem based on a Stackelberg game theory and compute the optimal parameters to maximize a utility function against the smart interference. We propose two algorithms for the utility maximization using Lagrangian dual decomposition for the backscatter WSN and the smart interference to prove the existence of the Stackelberg equilibrium. Numerical results show that the proposed algorithms effectively maximize the utility, compared to that of the algorithm based on the Nash game, so as to overcome smart interference in backscatter communications. [ABSTRACT FROM PUBLISHER]

Traffic patterns associated with different primary user (PU) channels may provide different spectral access and energy harvesting opportunities in wireless-powered cognitive radio networks (WP-CRNs). Considering this, we propose traffic-specific optimal spectrum sensing policy such that the expected transmission rate of secondary user (SU) is maximized under the energy causality and PU collision constraints in orthogonal frequency-division multiple access (OFDMA)-based WP-CRN. Toward this, we cluster $N$ subcarriers (subchannels) into $K$ clusters (where $K \leq N$) and derive an optimal energy detection threshold for the SU under each traffic pattern. Using traffic features, we propose an unsupervised and nonparametric classification technique to determine the number of unique traffic patterns $K$ over all subchannels. Then, the traffic patterns are used to predict the idle/busy period statistics for subchannels, based upon which SU identifies harvest and transmit PU subchannels for energy harvesting and data transmission, respectively. We derive an optimal detection threshold based on the harvested energy such that it maximizes the expected transmission rate of SU while protecting PU from collision. We demonstrate the effectiveness of the proposed scheme in terms of rate gains under design constraints and show the optimal detection threshold under various energy arrival rates. [ABSTRACT FROM PUBLISHER]

A Markovian battery model for energy harvesting (EH) secondary users (SUs) is proposed to derive the probability of packet loss due to sensing inaccuracy and energy outage in the EH cognitive radio network (CRN). With the proposed analysis, the packet loss probability can easily be predicted and utilized to optimize the transmission policy (i.e., opportunities for successful transmission and EH) of EH SUs to improve their throughput. Especially, the proposed method can be applied to upper layer (scheduling and routing) optimization. To this end, we validate the proposed analysis through Monte-Carlo simulation and show an agreement between the analysis and simulation results. [ABSTRACT FROM PUBLISHER]

In recent years, the demand for NAND flash-based storage devices has rapidly increased because of the popularization of various portable devices. NAND flash memory (NFM) offers many advantages, such as nonvolatility, high performance, the small form factor, and low-power consumption, while achieving high chip integration with a specialized architecture for bulk data access. A unit of NFM’s read and program operations, the page, has continuously grown. Although increasing page size reduces costs, it adversely affects performance because of the resultant side effects, such as fragmentation and wasted space, caused by the incongruity of data and page sizes. To address this issue, we propose a multiple-page-size NFM architecture and its management. Our method dramatically improves write performance through adopting multiple page sizes without requiring additional area overhead or manufacturing processes. Based on the experimental results, the proposed NFM improves write latency and NFM lifetime by up to 65% and 62%, respectively, compared with the single-page-size NFM. [ABSTRACT FROM AUTHOR]

The disproportionate increase of the resistance of silicon nanowires with reduced diameter is theoretically analyzed, taking into account both the effective conducting cross section and the hole concentration. Previously reported decrease in the conducting cross section and reduction of effective hole concentration that are observed when the nanowire radius is decreased were correlated with an increased influence of the interface state density on the wire's electrical characteristics. By measuring the resistances of doped Si nanowires as a function of nanowire radii and comparing those to an analytical model developed in this study, we extracted both the doping concentration and the interface state density. The measured doping concentration of Si nanowires epitaxially bridged between a pair of doped Si electrodes was found to be 2 × 10^18 cm ^-3 and the corresponding interface trap density is ∼ 2 × 10^12 cm^-2·eV^-1, which contributes to a reduced ionization of impurities resulting in a lower effective charge hole density than the doping concentration. By measuring the nanowire radius, length, and resistance, this analytic model can be used to estimate, control, and optimize the doping concentration and surface treatment method for Si nanowires for the fabrication of nanowire based devices such as field-effect transistors. [ABSTRACT FROM PUBLISHER]

As part of the effort to develop a superconductive microprocessor, we constructed a rapid single flux quantum (RSFQ) 4-bit arithmetic and logic unit (ALU) in a pipelined structure. To speed up the circuit, we used a forward clocking scheme. The 4-bit ALU consisted of ten RSFQ Half Adders, four 2 × 2 switches, and several D flip-flops. By commutating output ports of the half adder, we were able to produce AND, OR, XOR, and ADD functions. The 4-bit ALU was fabricated using., the Korea Photonics Technology Institute's ten-level 1.0 kA/cm² Nb process. The size of the ALU was 3.0 mm × 1.5 mm, fitting in a 5 mm × 5 mm chip. The fabricated 1-bit ALU block was successfully tested at up to a 40 GHz clock frequency. The complete 4-bit ALU operated correctly at up to a 5 GHz clock frequency. [ABSTRACT FROM AUTHOR]

Presents information on a study which examined a local multipoint distribution service system with parallel concatenated convolutional coding. System model; Performance analysis; Simulation results; Conclusions.

Presents information on a study that analyzed the performance of orthogonal frequency division multiplexing (OFDM)/code division multiple access (CDMA) system with turbo coding and simulated in a multipath fading channel. Description of the system model; Performance analysis; Simulation results; Conclusions.

Focuses on the development of a novel low temperature deposition process, using reactive pulsed dc magnetron sputtering, to deposit thin dielectric films. Description of these dielectric films; Use of quality high dielectric thin films in multi-chip module (MCM) and dynamic random access memory (DRAM) applications; Experiments conducted in this research.

AC inorganic electroluminescence (EL) devices were fabricated using the emitting layer composed of phosphor–single-walled carbon nanotubes (SWCNTs) composite (PSC) to utilize the local field enhancement of SWCNTs. In order to enhance the electric field in an emitting layer with minimized current paths, bare long SWCNTs were shortened by cryogenic crushing method. After electrical aging treatment of the PSC device, partly formed short SWCNT networks in the emitting layer were effectively removed during the electrical aging treatment. High performance of 60% and 43% increase in efficiency and luminance, respectively, was achieved. This PSC device with short SWCNTs could lead to high brightness due to increased electron tunneling into phosphor. The PSC device reveals the importance of high electric field by SWCNTs leading to high EL performance. [ABSTRACT FROM PUBLISHER]