The hybrid precoding (HP) technique has been widely considered as a promising approach for millimeter wave communication systems. In general, the existing HP structure with a complicated high-resolution phase shifter network can achieve near-optimal spectral efficiency, however, it involves high energy consumption. The HP architecture with an energy-efficient switch network can significantly reduce the energy consumption. To achieve maximum energy efficiency, this paper focuses on the HP architecture with switch network and considers a novel adaptive analog network HP structure for such mmWave MIMO systems, which can provide potential array gains. Moreover, a multiuser adaptive coordinate update algorithm is proposed for the HP design problem of this new structure. Simulation results verify that our proposed design can achieve better energy efficiency than other recently proposed HP schemes when the number of users is small.

We previously developed a new terahertz (THz) wave detection method that utilizes the effect of nonlinear optical (NLO) polymers. The new method provided us with a gapless detection, a wide detection bandwidth, and a simpler optical geometry in the THz wave detection. In this paper, polarization dependences in THz wave detection by the Stark effect were investigated. The projection model was employed to analyze the polarization dependences and the consistency with experiments was observed qualitatively, surely supporting the use of the first-order Stark effect in this method. The relations between THz wave detection by the Stark effect and Stark spectroscopy or electroabsorption spectroscopy are also discussed.

We fabricated silicon nanowires (SiNWs) using a metal-catalyzed electroless etching method, which is known to be a low-cost and simple technique. The SiNW arrays with a length of 540 nm were used as a substrate of SiNWs/PEDOT:PSS hybrid solar cell. Furthermore, gold nanoparticles (AuNPs) were used to improve the light absorption of the device due to localized surface plasmon excitation. The results show that the short-circuit current density and the power conversion efficiency increased from 22.1 mA/cm2 to 26.0 mA/cm2 and 6.91% to 8.56%, respectively. The advantage of a higher interface area between the organic and inorganic semiconductors was established by using SiNW arrays and higher absorption light incorporated with AuNPs for improving the performance of the developed solar cell.

In this paper, we explore the physical layer security of an Internet of Things (IoT) network comprised of multiple relay-user pairs in the presence of multiple malicious eavesdroppers and channel estimation error (CEE). In order to guarantee secure transmission with channel estimation error, we propose a channel estimation error oriented joint relay-user pair and friendly jammer selection (CEE-JRUPaFJS) scheme to improve the physical layer security of IoT networks. For the purpose of comparison, the channel estimation error oriented traditional round-robin (CEE-TRR) scheme and the channel estimation error oriented traditional pure relay-user pair selection (CEE-TPRUPS) scheme are considered as benchmark schemes. The exact closed-form expressions of outage probability (OP) and intercept probability (IP) for the CEE-TRR and CEE-TPRUPS schemes as well as the CEE-JRUPaFJS scheme are derived over Rayleigh fading channels, which are employed to characterize network reliability and security, respectively. Moreover, the security-reliability tradeoff (SRT) is analyzed as a metric to evaluate the tradeoff performance of CEE-JRUPaFJS scheme. It is verified that the proposed CEE-JRUPaFJS scheme is superior to both the CEE-TRR and CEE-TPRUPS schemes in terms of SRT, which demonstrates our proposed CEE-JRUPaFJS scheme are capable of improving the security and reliability performance of IoT networks in the face of multiple eavesdroppers. Moreover, as the number of relay-user pairs increases, CEE-TPRUPS and CEE-JRUPaFJS schemes offer significant increases in SRT. Conversely, with an increasing number of eavesdroppers, the SRT of all these three schemes become worse.

The optical properties of new tricyanopyrroline (TCP)-based chromophores with a benzyloxy group bound to aminobenzene donor unit were characterized by hyper-Rayleigh scattering (HRS), absorption spectrum, and 1H-NMR measurements, and the influence of the benzyloxy group on TCP-based chromophores was discussed based on the data. A positive effect of NLO properties was found in TCP-based NLO chromophores with a benzyloxy group compared with benchmark NLO chromophores without the benzyloxy group, suggesting an influence of intra-molecular hydrogen bond. Furthermore, we propose a formation of double intra-molecular hydrogen bonds in the TCP chromophore with monoene as the π-conjugation bridge and aminobenzene with a benzyloxy group as the donor unit.

It is found that the electrical resistance-length characteristic in an electroactive supercoiled polymer artificial muscle strongly depends on the temperature. This may come from the thermal expansion of coils in the artificial muscle, which increases the contact area of neighboring coils and results in a lower electrical resistance at a higher temperature. On the other hand, the electrical resistance-length characteristic collected during electrical driving seriously deviates from those collected at constant temperatures. Inhomogeneous heating during electrical driving seems to be a key for the deviation.

Improvement of output and lifetime is a problem for biofuel cells. A structure was adopted in which gelation mixed with agarose and fuel (fructose) was sandwiched by electrodes made of graphene-coated carbon fiber. The cathode surface not contacting the gel was exposed to air. In addition, the anode surface not contacting the gel was in contact with fuel liquid to prevent the gel from being dry. The power density of the fuel cell was improved by increasing oxygen supply from air and the lifetime was improved by maintaining wet gel, that is, the proposed structure was a hybrid type having advantages of both fuel gel and fuel liquid. The output increased almost up to that of just using fuel gel and did not decrease significantly over time. The maximum power density in the proposed system was approximately 74.0 µW/cm2, an enhancement of approximately 1.5 times that in the case of using liquid fuel. The power density after 24 h was approximately 46.1 µW/cm2, which was 62% of the initial value.

Recent IoT devices require extremely low standby power consumption, while a certain performance is needed during the active time, and Coarse-Grained Reconfigurable Arrays (CGRAs) have received attention because of their high energy efficiency. For further reduction of the standby energy consumption of CGRAs, the leakage power for their configuration memory must be reduced. Although the power gating is a common technique, the lost data in flip-flops and memory must be retrieved after the wake-up. Recovering everything requires numerous state transitions and considerable overhead both on its execution time and energy. To address the problem, Non-volatile Cool Mega Array (NVCMA), a CGRA providing non-volatile flip-flops (NVFFs) with spin transfer torque type non-volatile memory (NVM) technology has been developed. However, in general, non-volatile memory technologies have problems with reliability. Some NVFFs are stacked-at-0/1, and cannot store the data in a certain possibility. To improve the chip yield, we propose a mapping algorithm to avoid faulty processing elements of the CGRA caused by the erroneous configuration data. Next, we also propose a method to add an error-correcting code (ECC) mechanism to NVFFs for the configuration and constant memory. The proposed method was applied to NVCMA to evaluate the availability rate and reduction of write time. By using both methods, the average availability ratio of 94.2% was achieved, while the average availability ratio of the nine applications was 0.056% when the probability of failure of the FF was 0.01. The energy for storing data becomes about 2.3 times because of the hardware overhead of ECC but the proposed method can save 8.6% of the writing power on average.

Since most sensor data depend on each other, time-series anomaly detection is one of practical applications of IoT devices. Such tasks are handled by Recurrent Neural Networks (RNNs) with a feedback structure, such as Long Short Term Memory. However, their learning phase based on Stochastic Gradient Descent (SGD) is computationally expensive for such edge devices. This issue is addressed by executing their learning on high-performance server machines, but it introduces a communication overhead and additional power consumption. On the other hand, Recursive Least-Squares Echo State Network (RLS-ESN) is a simple RNN that can be trained at low cost using the least-squares method rather than SGD. In this paper, we propose its area-efficient hardware implementation for edge devices and adapt it to human activity anomaly detection as an example of interdependent time-series sensor data. The model is implemented in Verilog HDL, synthesized with a 45 nm process technology, and evaluated in terms of the anomaly capability, hardware amount, and performance. The evaluation results demonstrate that the RLS-ESN core with a feedback structure is more robust to hyper parameters than an existing Online Sequential Extreme Learning Machine (OS-ELM) core. It consumes only 1.25 times larger hardware amount and 1.11 times longer latency than the existing OS-ELM core.

Loading test vectors and unloading test responses in shift mode during scan testing cause many scan flip-flops to switch simultaneously. The resulting shift switching activity around scan flip-flops can cause excessive local IR-drop that can change the states of some scan flip-flops, leading to test data corruption. A common approach solving this problem is partial-shift, in which multiple scan chains are formed and only one group of the scan chains is shifted at a time. However, previous methods based on this approach use random grouping, which may reduce global shift switching activity, but may not be optimized to reduce local shift switching activity, resulting in remaining high risk of test data corruption even when partial-shift is applied. This paper proposes novel algorithms (one optimal and one heuristic) to group scan chains, focusing on reducing local shift switching activity around scan flip-flops, thus reducing the risk of test data corruption. Experimental results on all large ITC'99 benchmark circuits demonstrate the effectiveness of the proposed optimal and heuristic algorithms as well as the scalability of the heuristic algorithm.

Automatic generation of textual stories from visual data representation, known as visual storytelling, is a recent advancement in the problem of images-to-text. Instead of using a single image as input, visual storytelling processes a sequential array of images into coherent sentences. A story contains non-visual concepts as well as descriptions of literal object(s). While previous approaches have applied external knowledge, our approach was to regard the non-visual concept as the semantic correlation between visual modality and textual modality. This paper, therefore, presents new features representation based on a canonical correlation analysis between two modalities. Attention mechanism are adopted as the underlying architecture of the image-to-text problem, rather than standard encoder-decoder models. Canonical Correlation Attention Mechanism (CAAM), the proposed end-to-end architecture, extracts time series correlation by maximizing the cross-modal correlation. Extensive experiments on VIST dataset ( http://visionandlanguage.net/VIST/dataset.html ) were conducted to demonstrate the effectiveness of the architecture in terms of automatic metrics, with additional experiments show the impact of modality fusion strategy.

Online learning is a method which updates the model gradually and can modify and strengthen the previous model, so that the updated model can adapt to the new data without having to relearn all the data. However, the accuracy of the current online multiclass learning algorithm still has room for improvement, and the ability to produce sparse models is often not strong. In this paper, we propose a new Multiclass Truncated Gradient Confidence-Weighted online learning algorithm (MTGCW), which combine the Truncated Gradient algorithm and the Confidence-weighted algorithm to achieve higher learning performance. The experimental results demonstrate that the accuracy of MTGCW algorithm is always better than the original CW algorithm and other baseline methods. Based on these results, we applied our algorithm for phishing website recognition and image classification, and unexpectedly obtained encouraging experimental results. Thus, we have reasons to believe that our classification algorithm is clever at handling unstructured data which can promote the cognitive ability of computers to a certain extent.

SPHINCS+ is a state-of-the-art post-quantum hash-based signature that is a candidate for the NIST post-quantum cryptography standard. For a target bit security, SPHINCS+ supports many different tradeoffs between the signature size and the signing speed. SPHINCS+ provides 6 parameter sets: 3 parameter sets for size optimization and 3 parameter sets for speed optimization. We propose new parameter sets with better performance. Specifically, SPHINCS+ implementations with our parameter sets are up to 26.5% faster with slightly shorter signature sizes.

Existing cyber deception technologies (e.g., operating system obfuscation) can effectively disturb attackers' network reconnaissance and hide fingerprint information of valuable cyber assets (e.g., containers). However, they exhibit ineffectiveness against skilled attackers. In this study, a proactive fingerprint deception method is proposed, termed as Continuously Anonymizing Containers' Fingerprints (CACF), which modifies the container's fingerprint in the cloud resource pool to satisfy the anonymization standard. As demonstrated by experimental results, the CACF can effectively increase the difficulty for attackers.

Deep learning has shown outstanding performance in various fields, and it is increasingly deployed in privacy-critical domains. If sensitive data in the deep learning model are exposed, it can cause serious privacy threats. To protect individual privacy, we propose a novel activation function and stochastic gradient descent for applying differential privacy to deep learning. Through experiments, we show that the proposed method can effectively protect the privacy and the performance of proposed method is better than the previous approaches.

This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose various well-known data mining tools as well as ones developed by authors were used. To be more specific, the following techniques were developed and applied for the mentioned problems: artificial neural networks and fuzzy rule-based classifiers. The neural networks were generated by two modifications of the Differential Evolution algorithm based on the NSGA and MOEA/D schemes, proposed for solving multi-objective optimization problems. Fuzzy logic systems were generated by the population-based algorithm called Co-Operation of Biology Related Algorithms or COBRA. However, firstly each person's state was monitored. Thus, databases for problems described in this study were obtained by using non-contact Doppler sensors. Experimental results demonstrated that automatically generated neural networks and fuzzy rule-based classifiers can properly determine the human condition and reaction. Besides, proposed approaches outperformed alternative data mining tools. However, it was established that fuzzy rule-based classifiers are more accurate and interpretable than neural networks. Thus, they can be used for solving more complex problems related to the automated detection of an operator's condition.

We address analysis and design problems of aggregate demand response systems composed of various consumers based on controllability to facilitate to design automated demand response machines that are installed into consumers to automatically respond to electricity price changes. To this end, we introduce a controllability index that expresses the worst-case error between the expected total electricity consumption and the electricity supply when the best electricity price is chosen. The analysis problem using the index considers how to maximize the controllability of the whole consumer group when the consumption characteristic of each consumer is not fixed. In contrast, the design problem considers the whole consumer group when the consumption characteristics of a part of the group are fixed. By solving the analysis problem, we first clarify how the controllability, average consumption characteristics of all consumers, and the number of selectable electricity prices are related. In particular, the minimum value of the controllability index is determined by the number of selectable electricity prices. Next, we prove that the design problem can be solved by a simple linear optimization. Numerical experiments demonstrate that our results are able to increase the controllability of the overall consumer group.

As NAND flash-based storage has been settled, a flash translation layer (FTL) has been in charge of mapping data addresses on NAND flash memory. Many FTLs implemented various mapping schemes, but the amount of mapping data depends on the mapping level. However, the FTL should contemplate mapping consistency irrespective of how much mapping data dwell in the storage. Furthermore, the recovery cost by the inconsistency needs to be considered for a faster storage reboot time. This letter proposes a novel method that enhances the consistency for a page-mapping level FTL running a legacy logging policy. Moreover, the recovery cost of page mappings also decreases. The novel method is to adopt a virtually-shrunk segment and deactivate page-mapping logs by assembling and storing the segments. This segment scheme already gave embedded NAND flash-based storage enhance its response time in our previous study. In addition to that improved result, this novel plan maximizes the page-mapping consistency, therefore improves the recovery cost compared with the legacy page-mapping FTL.

This paper proposes a novel radio access scheme that uses duplicated transmission via multiple frequency channels to achieve mission critical Internet of Things (IoT) services requiring highly reliable wireless communications; the interference constraints that yield the required reliability are revealed. To achieve mission critical IoT services by wireless communication, it is necessary to improve reliability in addition to satisfying the required transmission delay time. Reliability is defined as the packet arrival rate without exceeding the desired transmission delay time. Traffic of the own system and interference from the other systems using the same frequency channel such as unlicensed bands degrades the reliability. One solution is the frequency/time diversity technique. However, these techniques may not achieve the required reliability because of the time taken to achieve the correct reception. This paper proposes a novel scheme that transmits duplicate packets utilizing multiple wireless interfaces over multiple frequency channels. It also proposes a suppressed duplicate transmission (SDT) scheme, which prevents the wastage of radio resources. The proposed scheme achieves the same reliable performance as the conventional scheme but has higher tolerance against interference than retransmission. We evaluate the relationship between the reliability and the occupation time ratio where the interference occupation time ratio is defined as the usage ratio of the frequency resources occupied by the other systems. We reveal the upper bound of the interference occupation time ratio for each frequency channel, which is needed if channel selection control is to achieve the required reliability.

Cascading Style Sheets (CSS) is a popular language for describing the styles of XML documents as well as HTML documents. To resolve conflicts among CSS rules, CSS has a mechanism called specificity. For a DTD D and a CSS code R, due to specificity R may contain “unsatisfiable” rules under D, e.g., rules that are not applied to any element of any document valid for D. In this paper, we consider the problem of detecting unsatisfiable CSS rules under DTDs. We focus on CSS fragments in which descendant, child, adjacent sibling, and general sibling combinators are allowed. We show that the problem is coNP-hard in most cases, even if only one of the four combinators is allowed and under very restricted DTDs. We also show that the problem is in coNP or PSPACE depending on restrictions on DTDs and CSS. Finally, we present four conditions under which the problem can be solved in polynomial time.