This paper reviews our developed wide band human body communication technology for wearable and implantable robot control. The wearable and implantable robots are assumed to be controlled by myoelectric signals and operate according to the operator's will. The signal transmission for wearable robot control was shown to be mainly realized by electrostatic coupling, and the signal transmission for implantable robot control was shown to be mainly determined by the lossy frequency-dependent dielectric properties of human body. Based on these basic observations on signal transmission mechanisms, we developed a 10-50MHz band impulse radio transceiver based on human body communication technology, and applied it for wireless control of a robotic hand using myoelectric signals in the first time. In addition, we also examined its applicability to implantable robot control, and evaluated the communication performance of implant signal transmission using a living swine. These experimental results showed that the proposed technology is well suited for detection and transmission of biological signals for wearable and implantable robot control.

In this paper, the influence of high-temperature sputtering on the nitrogen-doped (N-doped) LaB6 thin film formation utilizing RF sputtering was investigated. The N-doped LaB6/SiO2/p-Si(100) MOS diode and N-doped LaB6/p-Si(100) of Schottky diode were fabricated. A 30 nm thick N-doped LaB6 thin film was deposited from room temperature (RT) to 150°C. It was found that the resistivity was decreased from 1.5 mΩcm to 0.8 mΩcm by increasing deposition temperature from RT to 150°C. The variation of work function was significantly decreased in case that N-doped LaB6 thin film deposited at 150°C. Furthermore, Schottky characteristic was observed by increasing deposition temperature to 150°C. In addition, the crystallinity of N-doped LaB6 thin film was improved by increasing deposition temperature.

This work reports a new technique to suppress the undesirable multiple-triggering effect in the typical diode triggered silicon controlled rectifier (DTSCR), which is frequently used as an ESD protection element in the advanced CMOS technologies. The technique is featured by inserting additional N-Well areas under the N+ region of intrinsic SCR, which helps to improve the substrate resistance. As a consequence, the delay of intrinsic SCR is reduced as the required triggering current is largely decreased and multiple-triggering related higher trigger voltage is removed. The novel DTSCR structures can alter the stacked diodes to achieve the precise trigger voltage to meet different ESD protection requirements. All explored DTSCR structures are fabricated in a 65-nm CMOS process. Transmission-line-pulsing (TLP) and Very-Fast-Transmission-line-pulsing (VF-TLP) test systems are adopted to confirm the validity of this technique and the test results accord well with our analysis.

This paper proposes a low complexity soft input decoding in an iterative linear receiver for overloaded MIMO. The proposed soft input decoding applies two types of lattice reduction-aided linear filters to estimate log-likelihood ratio (LLR) in order to reduce the computational complexity. A lattice reduction-aided linear with whitening filter is introduced for the LLR estimation in the proposed decoding. The equivalent noise caused by the linear filter is mitigated with the decoder output stream and the LLR is re-estimated after the equivalent noise mitigation. Furthermore, LLR clipping is introduced in the proposed decoding to avoid the performance degradation due to the incorrect LLRs. The performance of the proposed decoding is evaluated by computer simulation. The proposed decoding achieves about 2dB better BER performance than soft decoding with the exhaustive search algorithm, so called the MLD, at the BER of 10-4, even though the complexity of the proposed decoding is 1/10 as small as that of soft decoding with the exhaustive search.

A fully integrated 16-phase 8-branch Dickson charge pump is proposed and implemented to decrease the power dissipation due to parasitic capacitance at the bottom plate of the boost capacitor. By using the charge recycling concept, 87% of the power consumption related to parasitic capacitance is saved. In a 4-stage version of this charge pump, a maximum power efficiency of 41% is achieved at 35µA output current and 11V output voltage from a 3.3V supply voltage. The proposed multi-branch charge pump can also reach a very low output voltage ripple of only 0.146% at a load resistance of 1MΩ, which is attributed to the fact that the 8-branch charge pump can transfer charges to the output node eight times consecutively during one clock period. In addition, a high voltage gain of 4.6 is achieved in the 4-stage charge pump at light load conditions. The total chip area is 0.57mm2 in a 0.35µm HV CMOS technology.

The two issues of art image creation and data hiding are integrated into one and solved by a single approach in this study. An automatic method for generating a new type of computer art, called stained glass image, which imitates the stained-glass window picture, is proposed. The method is based on the use of a tree structure for region growing to construct the art image. Also proposed is a data hiding method which utilizes a general feature of the tree structure, namely, number of tree nodes, to encode the data to be embedded. The method can be modified for uses in three information protection applications, namely, covert communication, watermarking, and image authentication. Besides the artistic stego-image content which may distract the hacker's attention to the hidden data, data security is also considered by randomizing both the input data and the seed locations for region growing, yielding a stego-image which is robust against the hacker's attacks. Good experimental results proving the feasibility of the proposed methods are also included.

Mobile edge computing (MEC) is a new computing paradigm, which provides computing support for resource-constrained user equipments (UEs). In this letter, we design an effective incentive framework to encourage MEC operators to provide computing service for UEs. The problem of jointly allocating communication and computing resources to maximize the revenue of MEC operators is studied. Based on auction theory, we design a multi-round iterative auction (MRIA) algorithm to solve the problem. Extensive simulations have been conducted to evaluate the performance of the proposed algorithm and it is shown that the proposed algorithm can significantly improve the overall revenue of MEC operators.

Systems for Internet of Things (IoT) have generated new requirements in all aspects of their development and deployment, including expanded Quality of Service (QoS) needs, enhanced resiliency of computing and connectivity, and the scalability to support massive numbers of end devices in a variety of applications. The research reported here concerns the development of a reliable and secure IoT/cyber physical system (CPS), providing network support for smart and connected communities, to be realized by means of distributed, secure, resilient Edge Cloud (EC) computing. This distributed EC system will be a network of geographically distributed EC nodes, brokering between end-devices and Backend Cloud (BC) servers. This paper focuses on three main aspects of the CPS: a) resource management in mobile cloud computing; b) information management in dynamic distributed databases; and c) biological-inspired intrusion detection system.

In resource-constrained network function virtualization (NFV) environments, it is expected that data throughput for service chains is maintained by using virtual network functions (VNFs) effectively. In this paper, we formulate an optimization problem for maximizing the total data throughput in resource-constrained NFV environments. Moreover, based on our formulated optimization problem, we propose a heuristic service chain construction algorithm for maximizing the total data throughput. This algorithm also determines the placement of VNFs, the amount of resources for each VNF, and the transmission route for each service chain. It is expected that the heuristic algorithm can construct service chains more quickly than the meta-heuristic algorithm. We evaluate the performance of the proposed methods with simulations, and we investigate the effectiveness of our proposed heuristic algorithm through a performance comparison. Numerical examples show that our proposed methods can construct service chains so as to maximize the total data throughput regardless of the number of service chains, the amount of traffic, and network topologies.

Picross 3D is a popular single-player puzzle video game for the Nintendo DS. It presents a rectangular parallelepiped (i.e., rectangular box) made of unit cubes, some of which must be removed to construct an object in three dimensions. Each row or column has at most one integer on it, and the integer indicates how many cubes in the corresponding 1D slice remain when the object is complete. Kusano et al. showed that Picross 3D is NP-complete and Kimura et al. showed that the counting version, the another solution problem, and the fewest clues problem of Picross 3D are #P-complete, NP-complete, and Σ2P-complete, respectively, where those results are shown for the restricted input that the rectangular parallelepiped is of height four. On the other hand, Igarashi showed that Picross 3D is NP-complete even if the height of the input rectangular parallelepiped is one. Extending the result by Igarashi, we in this paper show that the counting version, the another solution problem, and the fewest clues problem of Picross 3D are #P-complete, NP-complete, and Σ2P-complete, respectively, even if the height of the input rectangular parallelepiped is one. Since the height of the rectangular parallelepiped of any instance of Picross 3D is at least one, our hardness results are best in terms of height.

Embedded system approaches to edge computing in IoT implementations are proposed and discussed. Rationales of edge computing and essential core capabilities for IoT data supply innovation are identified. Then, innovative roles and development of MCU and embedded flash memory are illustrated by technology and applications, expanding from CPS to big-data and nomadic/autonomous elements of IoT requirements. Conclusively, a technology roadmap construction specific to IoT is proposed.

For the RF power amplifier, its exposed input and output are susceptible to damage from Electrostatic (ESD) damage. The bi-direction protection is required at the input in push-pull operating mode. In this paper, considering the process compatibility to the power amplifier, cascaded Grounded-gate NMOS (ggNMOS) and Polysilicon diodes (PDIO) are stacked together to form an ESD clamp with forward and reverse protection. Through Transmission line pulse (TLP) and CV measurements, the clamp is demonstrated as latch-up immune and low parasitic capacitance bi-direction ESD protection, with 18.67/17.34V holding voltage (Vhold), 4.6/3.2kV ESD protection voltage (VESD), 0.401/0.415pF parasitic capacitance (CESD) on forward and reverse direction, respectively.

Kurotto and Juosan are Nikoli's pencil puzzles. We study the computational complexity of Kurotto and Juosan puzzles. It is shown that deciding whether a given instance of each puzzle has a solution is NP-complete.

Register context-free grammars (RCFG) is an extension of context-free grammars to handle data values in a restricted way. In RCFG, a certain number of data values in registers are associated with each nonterminal symbol and a production rule has the guard condition, which checks the equality between the content of a register and an input data value. This paper starts with RCFG and introduces register type, which is a finite representation of a relation among the contents of registers. By using register type, the paper provides a translation of RCFG to a normal form and ϵ-removal from a given RCFG. We then define a generalized RCFG (GRCFG) where an arbitrary binary relation can be specified in the guard condition. Since the membership and emptiness problems are shown to be undecidable in general, we extend register type for GRCFG and introduce two properties of GRCFG, simulation and progress, which guarantee the decidability of these problems. As a corollary, these problems are shown to be EXPTIME-complete for GRCFG with a total order over a dense set.

We consider the leader election problem in the population protocol model, which Angluin et al. proposed in 2004. A self-stabilizing leader election is impossible for complete graphs, arbitrary graphs, trees, lines, degree-bounded graphs, and so on unless the protocol knows the exact number of nodes. In 2009, to circumvent the impossibility, we introduced the concept of loose stabilization, which relaxes the closure requirement of self-stabilization. A loosely stabilizing protocol guarantees that starting from any initial configuration, a system reaches a safe configuration, and after that, the system keeps its specification (e.g., the unique leader) not forever but for a sufficiently long time (e.g., an exponentially long time with respect to the number of nodes). Our previous works presented two loosely stabilizing leader election protocols for arbitrary graphs; one uses agent identifiers, and the other uses random numbers to elect a unique leader. In this paper, we present a loosely stabilizing protocol that solves leader election on arbitrary graphs without agent identifiers or random numbers. Given upper bounds N and Δ of the number of nodes n and the maximum degree of nodes δ, respectively, the proposed protocol reaches a safe configuration within O(mn2d log n+mNΔ2 log N) expected steps and keeps the unique leader for Ω(NeN) expected steps, where m is the number of edges and d is the diameter of the graph.

A regular pattern is a string consisting of constant symbols and distinct variable symbols. The language of a regular pattern is the set of all constant strings obtained by replacing all variable symbols in the regular pattern with non-empty strings. The present paper deals with the learning problem of languages of regular patterns within Angluin's query learning model, which is an established mathematical model of learning via queries in computational learning theory. The class of languages of regular patterns was known to be identifiable from one positive example using a polynomial number of membership queries, in the query learning model. In present paper, we show that the class of languages of regular patterns is identifiable from one positive example using a linear number of membership queries, with respect to the length of the positive example.

Real-time pulse rate (PR) monitoring based on photoplethysmography (PPG) has been drawn much attention in recent years. However, PPG signal detected under movement is easily affected by random noises, especially motion artifacts (MA), affecting the accuracy of PR estimation. In this paper, a parallel method structure is proposed, which effectively combines wavelet threshold denoising with recursive least squares (RLS) adaptive filtering to remove interference signals, and uses spectral peak tracking algorithm to estimate real-time PR. Furthermore, we propose a parallel structure RLS adaptive filtering to increase the amplitude of spectral peak associated with PR for PR estimation. This method is evaluated by using the PPG datasets of the 2015 IEEE Signal Processing Cup. Experimental results on the 12 training datasets during subjects' walking or running show that the average absolute error (AAE) is 1.08 beats per minute (BPM) and standard deviation (SD) is 1.45 BPM. In addition, the AAE of PR on the 10 testing datasets during subjects' fast running accompanied with wrist movements can reach 2.90 BPM. Furthermore, the results indicate that the proposed approach keeps high estimation accuracy of PPG signal even with strong MA.

Addition is a key fundamental function for many error-tolerant applications. Approximate addition is considered to be an efficient technique for trading off energy against performance and accuracy. This paper proposes a carry-maskable adder whose accuracy can be configured at runtime. The proposed scheme can dynamically select the length of the carry propagation to satisfy the quality requirements flexibly. Compared with a conventional ripple carry adder and a conventional carry look-ahead adder, the proposed 16-bit adder reduced the power consumption by 54.1% and 57.5%, respectively, and the critical path delay by 72.5% and 54.2%, respectively. In addition, results from an image processing application indicate that the quality of processed images can be controlled by the proposed adder. Good scalability of the proposed adder is demonstrated from the evaluation results using a 32-bit length.

In this letter, the performance of a state-of-the-art deep learning (DL) algorithm in [5] is analyzed and evaluated for orthogonal frequency-division multiplexing (OFDM) receivers, in the presence of harmonic spur interference. Moreover, a novel spur cancellation receiver structure and algorithm are proposed to enhance the traditional OFDM receivers, and serve as a performance benchmark for the DL algorithm. It is found that the DL algorithm outperforms the traditional algorithm and is much more robust to spur carrier frequency offset.

In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.