This paper proposes a path loss model for crowded outdoor environments that can consider the density of people. Measurement results in an anechoic chamber with three blocking persons showed that multiple human body shadowing can be calculated by using finite width screens. As a result, path loss in crowded environments can be calculated by using the path losses of the multipath and the multiple human body shadowing on those paths. The path losses of the multipath are derived from a ray tracing simulation, and the simulation results are then used to predict the path loss in crowded environments. The predicted path loss of the proposed model was examined through measurements in the crowded outdoor station square in front of Shibuya Station in Tokyo, and results showed that it can accurately predict the path loss in crowded environments at the frequencies of 4.7GHz and 26.4GHz under two different conditions of antenna height and density of people. The RMS error of the proposed model was less than 4dB.

Symbolic execution is capable of automatically generating tests that achieve high coverage. However, its practical use is limited by the scalability problem. To mitigate it, this paper proposes State Concretization based Symbolic Execution (SCSE). SCSE speeds up symbolic execution via state concretization. Specifically, by introducing a concrete store, our approach avoids invoking the constraint solver to check path feasibility at conditional instructions. Intuitively, there is no need to check the feasibility of a path along a concrete execution since it is always feasible. With state concretization, the number of solver queries greatly decreases, thus improving the efficiency of symbolic execution. Through experimental evaluation on real programs, we show that state concretization helps to speed up symbolic execution significantly.

In this work, we investigate a joint transmit beamforming and artificial noise (AN) covariance matrix design in a multiple-input multiple-output (MIMO) cognitive radio (CR) downlink network with simultaneous wireless information and power transfer (SWIPT), where the malicious energy receivers (ERs) may decode the desired information and hence can be treated as potential eavesdroppers (Eves). In order to improve the secure performance of the transmission, AN is embedded to the information-bearing signal, which acts as interference to the Eves and provides energy to all receivers. Specifically, this joint design is studied under a practical non-linear energy harvesting (EH) model, our aim is to maximize the secrecy rate at the SR subject to the transmit power budget, EH constraints and quality of service (QoS) requirement. The original problem is not convex and challenging to be solved. To circumvent its intractability, an equivalent reformulation of this secrecy rate maximization (SRM) problem is introduced, wherein the resulting problem is primal decomposable and thus can be handled by alternately solving two convex subproblems. Finally, numerical results are presented to verify the effectiveness of our proposed scheme.

This paper describes the results of outdoor mobility measurements and high-speed vehicle tests that clarify the 4-by-8 multiple-input multiple-output (MIMO) throughput performance when applying distributed MIMO with narrow antenna-beam tracking in a 28-GHz frequency band in the downlink of a 5G cellular radio access system. To clarify suitable transmission point (TP) deployment for mobile stations (MS) moving at high speed, we examine two arrangements for 3TPs. The first sets all TPs in a line along the same side of the path traversed by the MS, and the other sets one TP on the other side of the path. The experiments in which the MS is installed on a moving wagon reveal that the latter deployment case enables a high peak data rate and high average throughput performance exhibiting the peak throughput of 15Gbps at the vehicle speed of 3km/h. Setting the MS in a vehicle travelling at 30km/h yielded the peak throughput of 13Gbps. The peak throughput of 11Gbps is achieved at the vehicle speed of 100km/h, and beam tracking and intra-baseband unit hand over operation are successfully demonstrated even at this high vehicle speed.

In LTE (Long Term Evolution) / LTE-Advanced (LTE-A) system, the user-plane for a user equipment (UE) is provided by tunneling, which increases header overhead, processing overhead, and management overhead. In addition, the LTE-A system does not support moving cells which are composed of a mobile Relay Node (RN) and UEs attached to the mobile RN. Although there are several proposals for moving cells in the LTE-A system and the 5G system, all of them rely on tunneling for the user-plane, which means that none of them avoid the tunneling overheads. This paper proposes MocLis, a moving cell support protocol based on a Locator/ID split approach. MocLis does not use tunneling. Nested moving cells are supported. Signaling cost for handover of a moving cell is independent of the number of UEs and nested RNs in the moving cell. A MocLis prototype, implemented in Linux, includes user space daemons and modified kernel. Measurements show that the attachment time and handover time are short enough for practical use. MocLis has higher TCP throughput than the tunneling based approaches.

In-phase and quadrature-phase imbalance (IQI) at transceivers is one of the serious hardware impairments degrading system performance. In this paper, we study the overall performance of massive multi-user multi-input multi-output (MU-MIMO) systems with IQI at both the base station (BS) and user equipments (UEs), including the estimation of channel state information, required at the BS for the precoding design. We also adopt a widely-linear precoding based on the real-valued channel model to make better use of the image components of the received signal created by IQI. Of particular importance, we propose estimators of the real-valued channel and derive the closed-form expression of the achievable downlink rate. Both the analytical and simulation results show that IQI at the UEs limits the dowlink rate to finite ceilings even when an infinite number of BS antennas is available, and the results also prove that the widely-linear precoding based on the proposed channel estimation method can improve the overall performance of massive MU-MIMO systems with IQI.

This paper introduces an energy-efficient transmit design for multiple-input single-output (MISO) energy-harvesting cognitive radio (CR) networks in the presence of external eavesdroppers (Eves). Due to the inherent characteristics of CR network with simultaneous wireless information and power transfer (SWIPT), Eves may illegitimately access the primary user (PU) bands, and the confidential message is prone to be intercepted in wireless communications. Assuming the channel state information (CSI) of the Eves is not perfectly known at the transmitter, our approach to guaranteeing secrecy is to maximize the secrecy energy efficiency (SEE) by jointly designing the robust beamforming and the power splitting (PS) ratio, under the constraints of total transmit power, harvested energy at secondary receiver (SR) and quality of service (QoS) requirement. Specifically, a non-linear energy harvesting (EH) model is adopted for the SR, which can accurately characterize the property of practical RF-EH circuits. To solve the formulated non-convex problem, we first employ fractional programming theory and penalty function to recast it as an easy-to-handle parametric problem, and then deal with the non-convexity by applying S-Procedure and constrained concave convex procedure (CCCP), which enables us to exploit the difference of concave functions (DC) programming to seek the maximum worst-case SEE. Finally, numerical results are presented to verify the performance of the proposed scheme.

This paper describes transmission line couplers for non-contact connecters. Their characteristics are formulated in closed forms and design methodologies are presented. As their applications, three different types of transmission line couplers, two-fold transmission line coupler, single-ended to differential conversion transmission line coupler, and rotatable transmission line coupler are reviewed.

Due to progressing process technology, yield of chips is reduced by timing violation caused by delay variation of gates and wires in fabrication. Recently, post-silicon delay tuning, which inserts programmable delay elements (PDEs) into clock trees before the fabrication and adjusts the delays of the PDEs to recover the timing violation after the fabrication, is promising to improve the yield. Although post-silicon delay tuning improves the yield, it increases circuit area and power consumption since the PDEs are inserted. In this paper, a PDE structure is taken into consideration to reduce the circuit area and the power consumption. Moreover, a delay selection algorithm, and a clustering method, in which some PDEs are merged into a PDE and the PDE is inserted for multiple registers, are proposed to reduce the circuit area and the power consumption. In computational experiments, the proposed method reduced the circuit area and the power consumption in comparison with an existing method.

Recently, ZigBee has been attracting attention as a low-power and short-range wireless communication standard. In ZigBee networks, it is necessary to suppress frame transfer load because ZigBee needs to operate within severe capacity constraints and with low power consumption. However, in the typical environments in which ZigBee is used, such as smart home networks, WLAN (Wireless LAN) generally coexists, and radio interference occurs between the two networks. Existing studies focused on only interference avoidance. On the other hand, in this paper, we focus on adaptive cooperation between ZigBee network and WLAN. Specifically, from the viewpoints of WLANs that have wider communication range but have many idle periods in some environments like homes, we propose and study a hop count reduction method of ZigBee frame transfer by partially employing WLAN communication to transfer ZigBee frames. To the best of our knowledge, this is the first paper that considers the adaptive cooperation between ZigBee network and WLAN, where some ZigBee frames are transferred via WLAN to the sink. This is a completely new approach different from existing interference avoidance approaches. Then, we evaluate the hop count reduction by considering the number and the positions of relay points to transfer ZigBee frames to WLAN, and ZigBee tree topology for frame transfer routes. Through the evaluation, two realistic deployment policies of the relay points are derived. Finally, as specific advantages from the hop count reduction, we demonstrate the performance improvement about sink arrival ratio and end-to-end transfer delay of ZigBee frames, and energy consumption.

In recent years, a rise in healthy eating has led to various food management applications which have image recognition function to record everyday meals automatically. However, most of the image recognition functions in the existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network (CNN). CNN has improved accuracies of various kinds of image recognition tasks such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates dish locations and food calories simultaneously by multi-task learning of food dish detection and food calorie estimation with a single CNN. It is expected to achieve high speed and small network size by simultaneous estimation in a single network. Because currently there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, in this work we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos annotated with bounding boxes and single-dish food photos with food calories. Our results showed that our multi-task method achieved higher accuracy, higher speed and smaller network size than a sequential model of food detection and food calorie estimation.

An efficient resistive random access memory (ReRAM) structure is developed for accelerating convolutional neural network (CNN) powered by the in-memory computation. A novel ReRAM cell circuit is designed with two-directional (2-D) accessibility. The entire memory system is organized as a 2-D array, in which specific memory cells can be identically accessed by both of column- and row-locality. For the in-memory computations of CNNs, only relevant cells in an identical sub-array are accessed by 2-D read-out operations, which is hardly implemented by conventional ReRAM cells. In this manner, the redundant access (column or row) of the conventional ReRAM structures is prevented to eliminated the unnecessary data movement when CNNs are processed in-memory. From the simulation results, the energy and bandwidth efficiency of the proposed memory structure are 1.4x and 5x of a state-of-the-art ReRAM architecture, respectively.

This paper discusses the impact of stacking on-chip inductor on power/ground network. Stacking inductor on other circuit components can reduce the circuit area drastically, however, the impact on signal and power integrity is not clear. We investigate the impact by a field-solver, a circuit simulator and real chip measurement. We evaluate three types of power/ground network and various multi-layered inductors. Experimental results show that dense power/ground structures reduce noise although the coupling capacitance becomes larger than that of sparse structures. Measurement in a 65-nm CMOS shows a woven structure makes the noise voltage half compared to a sparse structure.

In December 2018, satellite broadcasting for 4K/8K ultra-high-definition television (UHDTV) will begin in Japan. It will be provided in the 12-GHz (11.7 to 12.75GHz) band with right- and left-hand circular polarizations. BSAT-4a, a satellite used for broadcasting UHDTV, was successfully launched in September 2017. This satellite has not only 12-GHz-band right- and left-hand circular polarization transponders but also a 21-GHz-band experimental transponder. The 21-GHz (21.4 to 22.0GHz) band has been allocated as the downlink for broadcasting satellite service in ITU-R Regions 1 (Europe, Africa) and 3 (Asia Pacific). To receive services provided over these two frequency bands and with dual-polarization, we implement and evaluated a dual-band and dual-circularly polarized parabolic reflector antenna fed by 12- and 21-GHz-band microstrip antenna arrays with a multilayer structure. The antenna is used to receive 12- and 21-GHz-band signals from in-orbit satellites. The measured and experimental results prove that the proposed antenna performs as a dual-polarized antenna in those two frequency bands and has sufficient performance to receive satellite broadcasts.

This paper presents a low-power small-area-overhead physical random number generator utilizing SAR ADC embedded in sensor SoCs. An unpredictable random bit sequence is produced by an existing comparator in typical SAR ADCs, which results in little area overhead. Unlike the other comparator-based physical random number generator, this proposed technique does not require an offset calibration scheme since SAR binary search algorithm automatically converges the two input voltages of the comparator to balance the differential circuit pair. Although the randomness slightly depends on an quantization error due to sharing AD conversion scheme, the input signal distribution enhances the quality of random number bit sequence which can use for various security countermeasures such as masking techniques. Fabricated in 180nm CMOS, 1Mb/s random bit generator achieves high efficiency of 0.72pJ/bit with only 400μm2 area overhead, which occupies less than 0.5% of SAR ADC, while remaining 10-bit AD conversion function.

In this paper, a high-frequency resolution Digital Frequency Discriminator (DFD) IC using a Time to Digital Converter (TDC) and an edge counter for Instantaneous Frequency Measurement (IFM) is proposed. In the proposed DFD, the TDC measures the time of the maximum periods of divided RF short pulse signals, and the edge counter counts the maximum number of periods of the signal. By measuring the multiple periods with the TDC and the edge counter, the proposed DFD improves the frequency resolution compared with that of the measuring one period because it is proportional to reciprocal of the measurement time of TDC. The DFD was fabricated using 0.18-um SiGe-BiCMOS. Frequency accuracy below 0.39MHz and frequency precision below 1.58 MHz-RMS were achieved during 50 ns detection time in 0.3 GHz to 5.5 GHz band with the temperature range from -40 to 85 degrees.

With advances in information technology and the development of big data, manual operation is unlikely to be a smart choice for stock market investing. Instead, the computer-based investment model is expected to bring investors more accurate strategic analysis and more effective investment decisions than human beings. This paper aims to improve investor profits by mining for critical information in the stock data, therefore helping big data analysis. We used the R language to find the technical indicators in the stock market, and then applied the technical indicators to the prediction. The proposed R package includes several analysis toolkits, such as trend line indicators, W type reversal patterns, V type reversal patterns, and the bull or bear market. The simulation results suggest that the developed R package can accurately present the tendency of the price and enhance the return on investment.

To minimize the annotation costs associated with training semantic segmentation models and object detection models, weakly supervised detection and weakly supervised segmentation approaches have been extensively studied. However most of these approaches assume that the domain between training and testing is the same, which at times results in considerable performance drops. For example, if we train an object detection network using only web images showing a large object at the center, it can be difficult for the network to detect multiple small objects. In this paper, we focus on training a CNN with only web images and achieve object detection in the wild. A proposal-based approach can address the problem associated with differences in domains because web images are similar to images of the proposal. In both domains, the target object is located at the center of the image and the ratio of the size of the target object to the size of the image is large. Several proposal methods have been proposed to detect regions with high “object-ness.” However, many of these proposals generate a large number of candidates to increase the recall rate. Considering the recent advent of deep CNNs, methods that generate a large number of proposals exhibit problems in terms of processing time for practical use. Therefore, we propose a CNN-based “food-ness” proposal method in this paper that requires neither pixel-wise annotation nor bounding box annotation. Our method generates proposals through backpropagation and most of these proposals focus only on food objects. In addition, we can easily control the number of proposals. Through experiments, we trained a network model using only web images and tested the model on the UEC FOOD 100 dataset. We demonstrate that the proposed method achieves high performance compared to traditional proposal methods in terms of the trade-off between accuracy and computational cost. Therefore, in this paper, we propose an intermediate approach between the traditional proposal approach and the fully convolutional approach. In particular, we propose a novel proposal method that generates high“food-ness” regions using fully convolutional networks based on the backward approach by training food images gathered from the web.

A novel configuration data compression technique for coarse-grained reconfigurable architectures (CGRAs) is proposed. Reducing the size of configuration data of CGRAs shortens the reconfiguration time especially when the communication bandwidth between a CGRA and a host CPU is limited. In addition, it saves energy consumption of configuration cache and controller. The proposed technique is based on a multicast configuration technique called RoMultiC, which reduces the configuration time by multicasting the same data to multiple PEs (Processing Elements) with two bit-maps. Scheduling algorithms for an optimizing the order of multicasting have been proposed. However, the multicasting is possible only if each PE has completely the same configuration. In general, configuration data for CGRAs can be divided into some fields like machine code formats of general perpose CPUs. The proposed scheme confines a part of fields for multicasting so that the possibility of multicasting more PEs can be increased. This paper analyzes algorithms to find a configuration pattern which maximizes the number of multicasted PEs. We implemented the proposed scheme to CMA (Cool Mega Array), a straight forward CGRA as a case study. Experimental results show that the proposed method achieves 40.0% smaller configuration than a previous method for an image processing application at maximum. The exploration of the multicasted grain size reveals the effective grain size for each algorithm. Furthermore, since both a dynamic power consumption of the configuration controller and a configuration time are improved, it achieves 50.1% reduction of the energy consumption for the configuration with a negligible area overhead.