This paper presents wireless systems for use in disaster recovery operations. The Great East Japan Earthquake of March 11, 2011 reinforced the importance of communications in, to, and between disaster areas as lifelines. It also revealed that conventional wireless systems used for disaster recovery need to be renovated to cope with technological changes and to provide their services with easier operations. To address this need we have developed new systems, which include a relay wireless system, subscriber wireless systems, business radio systems, and satellite communication systems. They will be chosen and used depending on the situations in disaster areas as well as on the required services.

A power allocation to active and reactive power in stochastic resonance is discussed for energy harvesting from noise. It is confirmed that active power can be increased at stochastic resonance, in the same way of the relationship between energy and phase at an appropriate setting in resonance.

As process technologies advance, timing-error correction techniques have become important as well. A suspicious timing-error prediction (STEP) technique has been proposed recently, which predicts timing errors by monitoring the middle points, or check points of several speed-paths in a circuit. However, if we insert STEP circuits (STEPCs) in the middle points of all the paths from primary inputs to primary outputs, we need many STEPCs and thus require too much area overhead. How to determine these check points is very important. In this paper, we propose an effective STEPC insertion algorithm minimizing area overhead. Our proposed algorithm moves the STEPC insertion positions to minimize inserted STEPC counts. We apply a max-flow and min-cut approach to determine the optimal positions of inserted STEPCs and reduce the required number of STEPCs to 1/10-1/80 and their area to 1/5-1/8 compared with a naive algorithm. Furthermore, our algorithm realizes 1.12X-1.5X overclocking compared with just inserting STEPCs into several speed-paths.

In 1919 the Department of Electrical Engineering (EE) was established in Tohoku University (at that time, Tohoku Imperial University). In this Department a growing tendency towards research featured in science and technology for electrical communication. Great efforts made in these fields produced pioneering studies such as those of the Yagi-Uda antenna and slotted-anode type magnetrons in the late 1920s. The purpose of this article is to introduce the history of development of microwave electron-tube at Tohoku University, which was started with the Okabe's magnetron.

This paper presents a novel CMOS bias topology serving as not only a bias circuit but also an adaptive linearizer for SiGe HBT power amplifier (PA) IC. The novel bias circuit can well keep the base-to-emitter voltage (Vbe) of RF amplifying HBT constant and adaptively increase the base current (Ib) with the increase of the input power. Therefore, the gain compression and phase distortion performance of the PA is improved. A three-stage 5-GHz band PA IC with the novel bias circuit for WLAN applications is designed and fabricated in IBM 0.35µm SiGe BiCMOS technology. Under 54Mbps OFDM signal at 5.4GHz, the PA IC exhibits a measured small-signal gain of 29dB, an EVM of 0.9% at 17dBm output power and a DC current consumption of 284mA.

Novel multi-band mixers that can receive multiple band signals concurrently are proposed and evaluated. The mixers achieve independent gain control through novel relative power control method of the multiple local oscillator (LO) signals. Linear control is also achieved through multiple LO signal input with total LO power control. Theoretical analysis shows that odd-order nonlinearity components of the multiple LO signals support linear conversion gain control. Dual- and triple-band tests are conducted using typical three MOSFET mixers fabricated by a 0.25 µm SiGe BiCMOS process. Measurements confirm over 40 dB independent control of conversion gain, linear control achieved through LO input power control. The proposed mixers have high input linearity with a 5 dBm output third intercept point. A method is also proposed to reduce interference caused by mixing between multiple LO signals.

Breast cancer is a serious disease across the world, and it is one of the largest causes of cancer death for women. The traditional diagnosis is not only time consuming but also easily affected. Hence, artificial intelligence (AI), especially neural networks, has been widely used to assist to detect cancer. However, in recent years, the computational ability of a neuron has attracted more and more attention. The main computational capacity of a neuron is located in the dendrites. In this paper, a novel neuron model with dendritic nonlinearity (NMDN) is proposed to classify breast cancer in the Wisconsin Breast Cancer Database (WBCD). In NMDN, the dendrites possess nonlinearity when realizing the excitatory synapses, inhibitory synapses, constant-1 synapses and constant-0 synapses instead of being simply weighted. Furthermore, the nonlinear interaction among the synapses on a dendrite is defined as a product of the synaptic inputs. The soma adds all of the products of the branches to produce an output. A back-propagation-based learning algorithm is introduced to train the NMDN. The performance of the NMDN is compared with classic back propagation neural networks (BPNNs). Simulation results indicate that NMDN possesses superior capability in terms of the accuracy, convergence rate, stability and area under the ROC curve (AUC). Moreover, regarding ROC, for continuum values, the existing 0-connections branches after evolving can be eliminated from the dendrite morphology to release computational load, but with no influence on the performance of classification. The results disclose that the computational ability of the neuron has been undervalued, and the proposed NMDN can be an interesting choice for medical researchers in further research.

Multiple-way (N-way) asynchronous arbitration is an important issue in asynchronous system design. In this paper, novel implementation methods of N-way asynchronous arbiters are presented. We first present N-way rectangle mesh arbiters using 2-way mutual exclusion elements. Then, N-way token-ring arbiters based on the non-return-to-zero signaling is also presented. The former can issue grant signals with the same percentage for all the arriving request signals while the latency is proportional to the number of inputs. The latter can achieve low latency and low energy arbitration for a heavy workload environment and a large number of inputs. In this paper, we compare their performances using the 28nm FD-SOI process technologies qualitatively and quantitatively.

In this paper, we propose a tightly coupled asymmetrically tapered bend to suppress differential-to-common mode conversion caused by bend discontinuity in a pair of differential lines. Tightly coupled symmetrically tapered bends have been so far proposed to suppress the mode conversion by decreasing the path difference in the bend. This approach makes the path difference shorter so that the differential lines are coupled more tightly but the path difference of twice the sum of the line width and the line separation still remains. To suppress the remaining path difference, this paper introduces the use of asymmetric tapers. In addition, two-section tapers are applied to reduce differential-mode reflection increased by the tapers and hence improve differential-mode propagation. A full-wave simulation of a right-angled bend demonstrates that the forward differential-to-common mode conversion is decreased by almost 30 dB compared to the symmetrically tapered bend and that the differential-mode reflection coefficient is reduced to the same amount as that of the classic bend. Also, the generality of the proposed bend structure is discussed.

Near-threshold computing has emerged as one of the most promising solutions for enabling highly energy efficient and high performance computation of microprocessors. This paper proposes architecture-level statistical static timing analysis (SSTA) models for the near-threshold voltage computing where the path delay distribution is approximated as a lognormal distribution. First, we prove several important theorems that help consider architectural design strategies for high performance and energy efficient near-threshold computing. After that, we show the numerical experiments with Monte Carlo simulations using a commercial 28nm process technology model and demonstrate that the properties presented in the theorems hold for the practical near-threshold logic circuits.

In emerging wearable sensor systems, it is crucial to save energy because these systems are severely energy-constrained. For making the sensors in these systems energy efficient, transmission power control (TPC) is widely used, and thus far, many TPC algorithms have been proposed in the literature. However, these TPC algorithms do not always work well in all wireless body channel conditions, which are capriciously varied due to diverse sensor environments such as sensor placements, body movements, and body locations. In this paper, we propose a simple TPC algorithm that quickly and stably approaches the optimal transmission power level and works well in all wearable sensor environments. We experimentally evaluated the proposed TPC algorithm and proved that it works well under all wireless body channel conditions.

The mode of intra prediction in H.264/AVC is encoded based on the most probable mode (MPM). To increase coding efficiency, the probability of the case that MPM is equal to coding mode of the current block should increase. In this paper we propose an efficient scheme to make MPM which is matched for the spatial direction property of pixels in the current block. Simulation results show that the proposed scheme gives significant coding gains when compared with the conventional techniques.

For the electric demand prediction problem, a modification mechanism of predicted demand data has been proposed in the previous work. In this paper, we analyze the performance of the modification mechanism in power balancing control. Then, we analytically derive an upper bound of the performance, which is characterized by system parameters and prediction precision.

Power supply noise generated by integrated circuits is one of the major sources of electromagnetic radiation from printed circuit boards (PCB). The reduction of power supply noise can be realized by means of devices that bypass the current among power supply planes, such as bypass capacitors and ground vias. In the present work, the effect of current bypass devices on the far field radiation from multilayer PCBs is represented in terms of the ratio between the far field after and before their introduction, and it is estimated by means of the power transported by the ‘radiation effective forward wave’ in infinite power supply planes. This approach is computationally very efficient and yelds improved EMC designs for power supply planes in realistic PCBs, for example by selecting the position of stitching ground vias. The results are confirmed by a comparison with commercial tools. Forward wave analysis can be used also to study the vertical distribution of the power supply noise in multilayer PCBs. This allows to understand some important noise propagation mechanisms that are related to power and signal integrity as well, and to take low-cost countermeasures at early stage of PCB design.

Graphical User Interfaces (GUIs) are critical for the security, safety and reliability of software systems. Injection attacks, for instance via SQL, succeed due to insufficient input validation and can be avoided if contract-based approaches, such as Design by Contract, are followed in the software development lifecycle of GUIs. This paper proposes a model-based testing approach for detecting GUI data contract violations, which may result in serious failures such as system crash. A contract-based model of GUI data specifications is used to develop test scenarios and to serve as test oracle. The technique introduced uses multi terminal binary decision diagrams, which are designed as an integral part of decision table-augmented event sequence graphs, to implement a GUI testing process. A case study, which validates the presented approach on a port scanner written in Java programming language, is presented.

In wireless sensor networks, or WSNs, a malicious node is able to cover itself by switching between good and bad behaviors. Even when running under a reputation mechanism, such a node can still behave maliciously now and then so long as its reputation is within the acceptable level. To address this inconsistent behavior issue, a combined approach of statistic reputation and time series is proposed in this study, in which the negative binomial reputation is applied to rate the nodes' reputation and concept of time series is borrowed to analyze the reputation results. Simulations show that the proposed method can effectively counter inconsistent behavior nodes and thus improves the overall system performance.

We present a low-jitter design for a 10-bit second-order frequency shift oscillator time-to-digital converter (FSOTDC). As described herein, we analyze the relation between performance and FSOTDC parameters and provide insight to support the design of the FSOTDC. Results show that an oscillator jitter limits the FSOTDC resolution, particularly during the first stage. To estimate and design an FSOTDC, the frequency shift oscillator requires an inverter of a certain size. In a standard 65-nm CMOS process, an SNDR of 64dB is achievable at an input signal frequency of 10kHz and a sampling clock of 2MHz. Measurements of the test chip confirmed that the measurements match the analyses.

This paper describes an evaluation of a temporally stable spectral envelope estimator proposed in our past research. The past research demonstrated that the proposed algorithm can synthesize speech that is as natural as the input speech. This paper focuses on an objective comparison, in which the proposed algorithm is compared with two modern estimation algorithms in terms of estimation performance and temporal stability. The results show that the proposed algorithm is superior to the others in both aspects.

Magnetic resonant coupling between two coils allows effective wireless transfer of power over distances in the range of tens of centimeters to a few meters. The strong resonant magnetic field also extends to the immediate surroundings of the power transfer system. When a user or bystander is exposed to this magnetic field, electric fields are induced in the body. For the purposes of human and product safety, it is necessary to evaluate whether these fields satisfy the human exposure limits specified in international guidelines and standards. This work investigates the effectiveness of the quasistatic approximation for computational modeling human exposure to the magnetic fields of wireless power transfer systems. It is shown that, when valid, this approximation can greatly reduce the computational requirements of the assessment of human exposure. Using the quasistatic modeling approach, we present an example of the assessment of human exposure to the non-uniform magnetic field of a realistic WPT system for wireless charging of an electric vehicle battery, and propose a coupling factor for practical determination of compliance with the international exposure standards.

Using traditional single-layer dictionary learning methods, it is difficult to reveal the complex structures hidden in the hyperspectral images. Motivated by deep learning technique, a deep dictionary learning approach is proposed for hyperspectral image denoising, which consists of hierarchical dictionary learning, feature denoising and fine-tuning. Hierarchical dictionary learning is helpful for uncovering the hidden factors in the spectral dimension, and fine-tuning is beneficial for preserving the spectral structure. Experiments demonstrate the effectiveness of the proposed approach.