We have proposed and demonstrated a mode selective active-MMI (multimode interferometer) laser diode as a mode selective light source so far. This laser diode features; 1) lasing at a selected space mode, and 2) high modulation bandwidth. Based on these, it is expected to enable high speed interconnection into future personal and mobile devices. In this paper, we explain the mode selection, and the high speed modulation principles. Then, we present our recent results concerning high speed frequency response of the fundamental and first order space modes.

This paper concerned the research on a memristive chaotic system and the generated random sequence; by constructing a piecewise-linear memristor model, a kind of chaotic system is constructed, and corresponding numerical simulation and dynamical analysis are carried out to show the dynamics of the new memristive chaotic system. Finally the proposed memristive chaotic system is used to generate random sequence for the possible application in encryption field.

The IEEE 802.11p Enhanced Distributed Channel Access (EDCA) is a standardization for vehicle-to-vehicle and road-to-vehicle communications. The saturated throughputs of the IEEE 802.11p EDCA obtained from previous analytical expressions differ from those of simulations. The purpose of this paper is to explain the reason why the differences appear in the previous analytical model of the EDCA. It is clarified that there is a special state wherein the Backoff Timer (BT) is decremented in the first time slot of after a frame transmission, which cannot be expressed in the previous Markov model. In addition, this paper proposes modified Markov models, which allow the IEEE 802.11p EDCA to be correctly analyzed. The proposed models describe BT-decrement procedure in the first time slot accurately by adding new states to the previous model. As a result, the proposed models provide accurate transmission probabilities of network nodes. The validity of the proposed models is confirmed by the quantitative agreements between analytical predictions and simulation results.

We report our recent progress in silicon photonics integrated device technology targeting on-chip-level large-capacity optical interconnect applications. To realize high-capacity data transmission, we successfully developed on-package-type silicon photonics integrated transceivers and demonstrated simultaneous 400 Gbps operation. 56 Gbps pulse-amplitude-modulation (PAM) 4 and wavelength-division-multiplexing technologies were also introduced to enhance the transmission capacity.

In this letter, a flexible and compatible with fine resolution radar frequency measurement receiver is designed. The receiver is implemented on the platform of Virtex-5 Field Programmable Grid Array (FPGA) from Xilinx. The Digital Down Conversion (DDC) without mixer based on polyphase filter has been successfully introduced in this receiver to obtain lower speed data flow and better resolution. This receiver can adapt to more modulation types and higher density of pulse flow, up to 200000 pulses per second. The measurement results indicate that the receiver is capable of detecting radar pulse signal of 0.2us to 2.5ms width with a major frequency root mean square error (RMSE) within 0.44MHz. Moreover, the wider pulse width and the higher decimation rate of DDC result in better performance. This frequency measurement receiver has been successfully used in a spaceborne radar system.

Spectral graph theory, based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights, provides a useful approach for analyzing network structure. However, in large scale and complex social networks, since it is difficult to completely know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we propose a method for indirectly determining the Laplacian matrix by estimating its eigenvalues and eigenvectors using the resonance of oscillation dynamics on networks.

Ethernet has become an indispensable technology for communications, and has come into use for many applications. At the IEEE, high-speed standardization has been discussed and has seen the adoption of new technologies such as multi-level modulation formats, high baud rate modulation and dense wave length division multiplexing. The MSA transceiver form factor has also been discussed following IEEE standardization. Optical devices such as TOSA and ROSA have been required to become more compact and higher-speed, because each transceiver form factor has to be miniaturized for high-density construction. We introduce the technologies for realizing 100GbE and those applicable to 400GbE. We also discuss future packages for optical devices. There are many similarities between optical device packages and electrical device packages, and we predict that optical device packages will follow the trends seen in electrical devices. But there are also differences between optical and electrical devices. It is necessary to utilize new technology for specific optical issues to employ advanced electrical packaging and catch up the trends.

All digital phased arrays generate multiple beams concurrently through the digital beam forming technique, which features digital processing with multiple identical receiving/transmitting channels in RF or microwave frequencies. However, the performance of this process strongly depends on accurately matching the amplitude and phase of the channels, as mismatching is likely to degrade radar performance. In this paper, we present a method to calibrate receiving array by using NCO phase increasing algorithm, which simplifies array system by removing the external far-field calibration signals often needed in array systems. Both analysis and simulation results suggest that the proposed method attains better calibration performance than existing approaches, even with a low SNR input signal. Experiments also varify that the proposed calibration method is effective and achieves a desired radiation pattern. We can further boost calibration accuracy and reduce calibration time by programming NCO phase width and NCO phase resolution.

This paper surveys advances in biomedical processor SoC technology for healthcare application and reviews state-of-the-art architecture and circuits used in SoC integration. Particularly, this paper categorizes and describes techniques for improving power efficiency in communication, computation, and sensing. Additionally, it surveys accuracy enhancement techniques for bio-signal measurement and recognition. Finally, we have discussed the potential new directions for development as well as research.

To address the bandwidth bottleneck that exists between LSI chips, we have proposed a novel, high-sensitivity receiver circuit for differential optical transmission on a silicon optical interposer. Both anodes and cathodes of the differential photodiodes (PDs) were designed to be connected to a transimpedance amplifier (TIA) through coupling capacitors. Reverse bias voltage was applied to each of the differential PDs through load resistance. The proposed receiver circuit achieved double the current signal amplitude of conventional differential receiver circuits. The frequency response of the receiver circuit was analyzed using its equivalent circuit, wherein the temperature dependence of the PD was implemented. The optimal load resistances of the PDs were determined to be 5kΩ by considering the tradeoff between the frequency response and bias voltage drop. A small dark current of the PD was important to reduce the voltage drop, but the bandwidth degradation was negligible if the dark current at room temperature was below 1µA. The proposed circuit achieved 3-dB bandwidths of 18.9 GHz at 25°C and 13.7 GHz at 85°C. Clear eye openings in the TIA output waveforms for 25-Gbps 27-1 pseudorandom binary sequence signals were obtained at both temperatures.

To minimize the damage caused by landslides resulting from torrential rain, residents must quickly evacuate to a place of refuge. To make the decision to evacuate, residents must be able to collect and share disaster information. Firstly, this paper introduces the Grass-roots Information Distribution System and a fixed type monitoring system which our research group has been developing. The fixed type monitoring system is deployed at the location of apparent danger, whereas the Grass-roots Information Distribution System distributes disaster information acquired from the fixed type monitoring system through a mobile ad hoc network (MANET) to residents. The MANET is configured using mobile terminals of residents. Next, in this paper, an information dissemination scheme utilizing a MANET and cellular networks to communicate among mobile terminals is proposed and simulated in the area where our research group has been deploying the distribution system. The MANET topology and information distribution obtained from the simulation results for further field experiments are then discussed.

Multi-attributed graphs, in which each node is characterized by multiple types of attributes, are ubiquitous in the real world. Detection and characterization of communities of nodes could have a significant impact on various applications. Although previous studies have attempted to tackle this task, it is still challenging due to difficulties in the integration of graph structures with multiple attributes and the presence of noises in the graphs. Therefore, in this study, we have focused on clusters of attribute values and strong correlations between communities and attribute-value clusters. The graph clustering methodology adopted in the proposed study involves Community detection, Attribute-value clustering, and deriving Relationships between communities and attribute-value clusters (CAR for short). Based on these concepts, the proposed multi-attributed graph clustering is modeled as CAR-clustering. To achieve CAR-clustering, a novel algorithm named CARNMF is developed based on non-negative matrix factorization (NMF) that can detect CAR in a cooperative manner. Results obtained from experiments using real-world datasets show that the CARNMF can detect communities and attribute-value clusters more accurately than existing comparable methods. Furthermore, clustering results obtained using the CARNMF indicate that CARNMF can successfully detect informative communities with meaningful semantic descriptions through correlations between communities and attribute-value clusters.

This study presents a joint dictionary learning approach for speech emotion recognition named locality preserved joint nonnegative matrix factorization (LP-JNMF). The learned representations are shared between the learned dictionaries and annotation matrix. Moreover, a locality penalty term is incorporated into the objective function. Thus, the system's discriminability is further improved.

The linguistic Multi-Criteria Group Decision-Making (MCGDM) problem involves various types of uncertainties. To deal with this problem, a new linguistic MCGDM method combining cloud model and evidence theory is thus proposed. Cloud model is firstly used to handle the fuzziness and randomness of the linguistic concept, by taking both the average level and fluctuation degree of the linguistic concept into consideration. Hence, a method is presented to transform linguistic variables into clouds, and then an asymmetrical weighted synthetic cloud is proposed to aggregate the clouds of decision makers on each criterion. Moreover, evidence theory is used to handle the imprecision and incompleteness of the group assessment, with the belief degree and the ignorance degree. Hence, the conversion from the cloud to the belief degree is investigated, and then the evidential reasoning algorithm is adopted to aggregate the criteria values. Finally, the average utility is applied to rank the alternatives. A numerical example, which is given to confirm the validity and feasibility, also shows that the proposed method can take advantage of cloud model and evidence theory to efficiently deal with the uncertainties caused by both the linguistic concept and group assessment.

In super-Nyquist wavelength division multiplexed systems, performance of forward error correction (FEC) can be improved by an iterative decoder between a maximum likelihood decoder for polybinary shaping and an FEC decoder. The typical iterative decoder includes not only the iteration between the first and second decoders but also the internal iteration within the FEC decoder. Such two-fold loop configuration would increase the computational complexity for decoding. In this paper, we propose the simplified iterative decoder, where the internal iteration in the FEC decoder is not performed, reducing the computational complexity. We numerically evaluate the bit-error rate performance of polybinary-shaped QPSK signals in the simplified iterative decoder. The numerical results show that the FEC performance can be improved in the simplified scheme, compared with the typical iterative decoder. In addition, the performance of the simplified iterative decoder has been investigated by the extrinsic information transfer (EXIT) chart.

The recent widespread use of high-performance terminals has resulted in a rapid increase in mobile data traffic. Therefore, public wireless local area networks (WLANs) are being used often to supplement the cellular networks. Capacity improvement through the dense deployment of access points (APs) is being considered. However, the effective throughput degrades significantly when many users connect to a single AP. In this paper, users are classified into guaranteed bit rate (GBR) users and best effort (BE) users, and we propose a network model to provide those services. In the proposed model, physical APs and the bandwidths are assigned to each service class dynamically using a virtual AP configuration and a virtualized backhaul network, for reducing the call-blocking probability of GBR users and improving the satisfaction degree of BE users. Finally, we evaluate the performance of the proposed model through simulation experiments and discuss its feasibility.

In this paper, we propose a periodic reactance time function for 2-element electronically steerable passive array radiator (ESPAR) antennas applicable to the receivers of both single-input multiple-output (SIMO) and multiple-input multiple-output (MIMO) systems with 2 outputs. Based on the proposed function, we evaluate the power patterns of the antenna for various distances between two antenna elements. Moreover, for the distances, we discuss the correlation properties and the strength of the two outputs to find the appropriate distance for the receiver. From the discussions, we can conclude that distances from 0.1 to 0.35 times the wavelength are effective in terms of receive diversity.

Chipless RFID tags that use the higher-mode resonances of a transmission line resonator are presented in this paper. We have proposed multimode stepped impedance resonators (SIRs) for this application and reported the fundamental characteristics of an experimental system composed of multimode SIRs with open-circuited ends and a near-field electromagnetic detector using capacitive coupling (electric field) probes for the detector. To improve the frequency response and widen the detection range, we introduced multimode SIRs with short-circuited ends and inductive coupling (magnetic field) probes and measured their properties. To reduce the size of the tag and reader, we examined the frequency responses and found that the optimal configuration consisted of C-shaped tags and detector probes with a spatially orthogonal arrangement. The experimental tag system showed good frequency responses, detection range, and frequency detection accuracy. In particular, the spacing between the tag resonator and the transmission line of the probe, which corresponds to the detection distance, was 5mm or more, and was at least 10 times greater than that of previously reported RFID tag systems using near-field electromagnetic coupling.

This paper focuses on on-demand wireless sensor networks (WSNs) where a wake-up receiver is installed into each node. In on-demand WSNs, each node sends a wake-up signal including a wake-up ID assigned to a specific destination node in order to remotely activate its main radio interface. This wake-up control helps each node to reduce energy consumed during idle periods, however, the wake-up signal transmitted before every data transmission results in overhead, which degrades communication quality and increases energy consumption at each sender node. In order to reduce the overhead for wake-up control, in this paper, we propose three schemes. First, we propose a scheme called Double Modulation (DM), where each node embeds the sensing data to be transmitted into the payload field of a wake-up signal. The destination interprets the wake-up message differently depending on its wake-up state: if it is in a sleep state, it treats the message as a wake-up signal, otherwise it extracts the sensing data from the detected message. Second, we propose a scheme called Overhearing (OH), where each node observes the frames transmitted by a destination node and suppresses the transmission of wake-up signal when detecting the active state of their destination. Finally, we propose a hybrid scheme that combines OH and DM schemes. Our simulation results show that the proposed schemes can effectively reduce the negative impact of wake-up overhead, and significantly improve data collection rate and energy-efficiency in comparison to on-demand WSN without the proposed schemes.

To recognize transportation modes without any additional sensor devices, we demonstrate that the transportation modes can be recognized from communication quality factors. In the demonstration, instead of using global positioning system (GPS) and accelerometer sensors, we collect mobile TCP throughputs, received-signal strength indicators (RSSIs), and cellular base-station IDs (Cell IDs) through in-line network measurement when the user enjoys mobile services, such as video streaming. In accuracy evaluations, we conduct two different field experiments to collect the data in six typical transportation modes (static, walking, riding a bicycle, riding a bus, riding a train and riding a subway), and then construct the classifiers by applying a support-vector machine (SVM), k-nearest neighbor (k-NN), random forest (RF), and convolutional neural network (CNN). Our results show that these transportation modes can be recognized with high accuracy by using communication quality factors as well as the use of accelerometer sensors.