This paper proposes a new small multiband printed antenna for wireless telecommunications modules that can realize Machine-to-Machine applications. We reconfigure our previous antenna to cover the 700MHz, 800MHz, and 900MHz bands, and add two new elements (second strips) to cover the 2GHz band. The new antenna achieves operation in quad-bands: 700MHz, 800MHz, 900MHz, and 2GHz. Frequency characteristics are analyzed using electromagnetic-simulation software based on the method of moments, and the validity of the numerical results is shown based on measured Voltage Standing Wave Ratio (VSWR) characteristics and the radiation patterns of a prototype antenna. The proposed antenna is compact with a VSWR bandwidth (≤2) of 27.5% in bands including 700MHz, 800MHz, and 900MHz, and a VSWR bandwidth (≤2) of 10.6% in the band including 2GHz. We clarify that the operating mechanism in the 2GHz band is equivalent to that of a one wavelength folded offset fed dipole antenna comprising a monopole element and second strips, and that the operating frequency in the 2GHz band can be determined by the path length from the tip of the monopole element to the tip of the second strip via a feeding point.

Underfill materials are used in a packaging of millimeter wave IC. However, there are few reports for dielectric properties of underfill materials in millimeter wave region. A cut-off circular waveguide method is one of a powerful technique to evaluate precisely complex permittivity in millimeter wave region. This method may be useful not only for low-loss materials, but also for mid-loss ones with loss tangent of 10$^{-2}$ order. In this paper, an evaluation technique based on the cut-off circular waveguide method is presented to measure mid-loss underfill materials. As a result, the relative permittivity $arepsilon_{r}$ and the loss tangent tan$delta$ are in the range of 2.8$sim $3.4 and (1.0$sim$1.6)$ imes10^{-2}$, respectively. Also, the measurement precision is 2.3% for $arepsilon_{r} approx 3$ and 40% for tan$delta approx 10^{-2}$.

For two-dimensional IIR digital filters described by the Fornasini-Marchesini second model, the problem of jointly optimizing high-order error feedback and realization to minimize the effects of roundoff noise at the filter output subject to l2-scaling constraints is investigated. The problem at hand is converted into an unconstrained optimization problem by using linear-algebraic techniques. The unconstrained optimization problem is then solved iteratively by applying an efficient quasi-Newton algorithm with closed-form formulas for key gradient evaluation. Finally, a numerical example is presented to illustrate the validity and effectiveness of the proposed technique.

A ferroelectric-based (FE-based) non-volatile logic is proposed for low-power LSI. Standby currents in a logic circuit can be cut off by using FE-based non-volatile flip-flops (NVFFs), and the standby power can be reduced to zero. The FE capacitor is accessed only when the power turns on/off, performance of the NVFF is almost as same as that of the conventional flip-flop (FF) in a logic operation. The use of complementarily stored data in coupled FE capacitors makes it possible to realize wide read voltage margin, which guarantees 10 years retention at 85 degree Celsius under less than 1.5V operation. The low supply voltage and electro-static discharge (ESD) detection technique prevents data destruction caused by illegal access for the FE capacitor during standby state. Applying the proposed circuitry in CPU, the write and read operation for all FE capacitors in 1.6k-bit NVFFs are performed within 7µs and 3µs with access energy of 23.1nJ and 8.1nJ, respectively, using 130nm CMOS with Pb(Zr,Ti)O3(PZT) thin films.

For an integer q≥2, new sets of q-phase aperiodic complementary sequences (ACSs) are constructed by using known sets of q-phase ACSs and certain matrices. Employing the Kronecker product to two known sets of q-phase ACSs, some sets of q-phase aperiodic complementary sequences with a new length are obtained. For an even integer q, some sets of q-phase ACSs with new parameters are generated, and their equivalent matrix representations are also presented.

This paper presents a graph-based approach to designing arithmetic circuits over Galois fields (GFs) using normal basis representations. The proposed method is based on a graph-based circuit description called Galois-field Arithmetic Circuit Graph (GF-ACG). First, we extend GF-ACG representation to describe GFs defined by normal basis in addition to polynomial basis. We then apply the extended design method to Massey-Omura parallel multipliers which are well known as typical multipliers based on normal basis. We present the formal description of the multipliers in a hierarchical manner and show that the verification time can be greatly reduced in comparison with those of the conventional techniques. In addition, we design GF exponentiation circuits consisting of the Massey-Omura parallel multipliers and an inversion circuit over composite field GF(((22)2)2) in order to demonstrate the advantages of normal-basis circuits over polynomial-basis ones.

For break arcs occurring between Ag and Ag/SnO$_2$ 12,wt% electrical contact pairs, the electrical conductivity, viscosity and specific heat at constant pressure are calculated as thermodynamic and transport properties. Mixture rates of contact material vapor are 0%, 1%, 10% and 100%. Influence of the contact material on the properties is investigated. Temperature for the calculation ranges from 2000,K to 20000,K. Following results are shown. When the mixture rate is changed, the electrical conductivity varies at lower temperature (< 10000,K), and the viscosity and specific heat vary widely at all temperature range. The electrical conductivity is independent of the mixture rate when the temperature is exceeding 10000,K. The thermodynamic and transport properties are independent of the kind of the contact materials.

This paper presents a mechanism for detecting dynamic loop and procedure nesting during the actual program execution on-the-fly. This mechanism aims primarily at making better strategies for performance tuning or parallelization. Using a pre-compiled application executable machine code as an input, our mechanism statically generates simple but precise markers that indicate loop entries and loop exits, and dynamically monitors loop nesting that appears during the actual execution together with call context tree. To keep precise loop structures all the time, we monitor the indirect jumps that enter the loop regions and the setjmp/longjmp functions that cause irregular function call transfers. We also present a novel representation called Loop-Call Context Graph that can keep track of inter-procedural loop nests. We implement our mechanism and evaluate it using SPEC CPU2006 benchmark suite. The results confirm that our mechanism can successfully reveal the precise inter-procedural loop nest structures from all of SPEC CPU2006 benchmark executions without any particular compiler support. The results also show that it can reduce runtime loop detection overheads compared with the existing loop profiling method.

Local descriptors, Local Binary Pattern (LBP) and Scale Invariant Feature Transform (SIFT) are widely used in various computer applications. They emphasize different aspects of image contents. In this letter, we propose to combine them in sparse coding for categorizing scene images. First, we regularly extract LBP and SIFT features from training images. Then, corresponding to each feature, a visual word codebook is constructed. The obtained LBP and SIFT codebooks are used to create a two-dimensional table, in which each entry corresponds to an LBP visual word and a SIFT visual word. Given an input image, LBP and SIFT features extracted from the same positions of this image are encoded together based on sparse coding. After that, spatial max pooling is adopted to determine the image representation. Obtained image representations are converted into one-dimensional features and classified by utilizing SVM classifiers. Finally, we conduct extensive experiments on datasets of Scene Categories 8 and MIT 67 Indoor Scene to evaluate the proposed method. Obtained results demonstrate that combining features in the proposed manner is effective for scene categorization.

A new real-time sound field transmission system is presented. To construct this system, a large listening area needs to be reproduced at not less than a constant height. Additionally, the driving signals of the loudspeakers should be obtained only from received signals of microphones. Wave field reconstruction (WFR) filtering for linear arrays of microphones and loudspeakers is considered to be suitable for this kind of system. An experimental system was developed to show the feasibility of real-time sound field transmission using the WFR filter. Experiments to measure the reproduced sound field and a subjective listening test of sound localization were conducted to evaluate the proposed system. Although the reproduced sound field included several artifacts such as spatial aliasing and faster amplitude decay, the experimental results indicated that the proposed system was able to provide sound localization accuracy for virtual sound sources comparable to that for real sound sources in a large listening area.

It is known that the IP Multimedia Subsystem (IMS) provides various telecom services e.g., VoIP, instant messaging, and video conferencing. In the IMS, these services are provided with session initiation protocol (SIP) handled by call/session control function servers (CSCFs). Completing the SIP signaling call flow without delay is vital to provide satisfactory services to the users. For service continuity, previous studies redundantly synchronized session states of CSCFs with backup servers. This paper proposes an IMS restoration system that selectively stores the session states. This is achieved by utilizing the retransmission mechanism of SIP. Time-based simulation emulating the process of backup servers shows that the proposed system reduces the number of backup servers to less than 38% compared to the previous studies, without degrading the service quality.

Visual quality evaluation is crucially important for various video and image processing systems. Traditionally, subjective image quality assessment (IQA) given by the judgments of people can be perfectly consistent with human visual system (HVS). However, subjective IQA metrics are cumbersome and easily affected by experimental environment. These problems further limits its applications of evaluating massive pictures. Therefore, objective IQA metrics are desired which can be incorporated into machines and automatically evaluate image quality. Effective objective IQA methods should predict accurate quality in accord with the subjective evaluation. Motivated by observations that HVS is highly adapted to extract irregularity information of textures in a scene, we introduce multifractal formalism into an image quality assessment scheme in this paper. Based on multifractal analysis, statistical complexity features of nature images are extracted robustly. Then a novel framework for image quality assessment is further proposed by quantifying the discrepancies between multifractal spectrums of images. A total of 982 images are used to validate the proposed algorithm, including five type of distortions: JPEG2000 compression, JPEG compression, white noise, Gaussian blur, and Fast Fading. Experimental results demonstrate that the proposed metric is highly effective for evaluating perceived image quality and it outperforms many state-of-the-art methods.

This paper presents a novel filter to keep from over-smoothing the edges and corners and rectify the outliers in the flow field after each incremental computation step, which plays a key role during the process of estimating flow field. This filter works according to the spatial-temporal derivatives distance of the input image and velocity field distance, whose principle is more reasonable in filtering mechanism for optical flow than other existing nonlinear filters. Moreover, we regard the spatial-temporal derivatives as new powerful descriptions of different motion layers or regions and give a detailed explanation. Experimental results show that our proposed method achieves better performance.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

Millimeter-wave synthetic aperture imaging radiometer (SAIR) is a powerful sensor for near-field high-resolution observations. However, the large receiver number and system complexity affect the application of SAIR. To overcome this shortage (receiver number), an accurate imaging algorithm based on compressed sensing (CS) theory is proposed in this paper. For reconstructing the brightness temperature images accurately from the sparse SAIR with fewer receivers, the proposed CS-based imaging algorithm is used to accomplish the sparse reconstruction with fewer visibility samples. The reconstruction is performed by minimizing the $l_{1}$ norm of the transformed image. Compared to the FFT-based methods based on Fourier transform, the required receiver number can be further reduced by this method. The simulation results demonstrate that the proposed CS-based method has higher reconstruction accuracy for the sparse SAIR.

Energy harvesting technology was introduced into wireless sensor networks (WSNs) to solve the problem of the short lifetimes of sensor nodes. The technology gives sensor nodes the ability to convert environmental energy into electricity. Sufficient electrical energy can lengthen the lifetime and improve the quality of service of a WSN. This paper proposes a novel use of mutual information to evaluate data transmission behavior in the energy harvesting WSNs. Data at a sink for a node deteriorates over time until the next periodic transmission from the node is received. In this paper, we suggest an optimized intermittent transmission method for WSNs that harvest energy. Our method overcomes the problem of information deterioration without increasing energy cost. We show that by using spatial correlation between different sensor nodes, our proposed method can mitigate information deterioration significantly at the sink.

A global tree local X-net network (GTLX) is introduced to realize high-performance data transfer in a multiple-valued fine-grain reconfigurable VLSI (MVFG-RVLSI). A global pipelined tree network is utilized to realize high-performance long-distance bit-parallel data transfer. Moreover, a logic-in-memory architecture is employed for solving data transfer bottleneck between a block data memory and a cell. A local X-net network is utilized to realize simple interconnections and compact switch blocks for eight-near neighborhood data transfer. Moreover, multiple-valued signaling is utilized to improve the utilization of the X-net network, where two binary data can be transferred from two adjacent cells to one common adjacent cell simultaneously at each “X” intersection. To evaluate the MVFG-RVLSI, a fast Fourier transform (FFT) operation is mapped onto a previous MVFG-RVLSI using only the X-net network and the MVFG-RVLSI using the GTLX. As a result, the computation time, the power consumption and the transistor count of the MVFG-RVLSI using the GTLX are reduced by 25%, 36% and 56%, respectively, in comparison with those of the MVFG-RVLSI using only the X-net network.

Recently, video surveillance systems have been widely introduced in various places, and protecting the privacy of objects in the scene has been as important as ensuring security. Masking each moving object with a background subtraction method is an effective technique to protect its privacy. However, the background subtraction method is heavily affected by sunshine change, and a redundant masking by over-extraction is inevitable. Such superfluous masking disturbs the quality of video surveillance. In this paper, we propose a moving object masking method combining background subtraction and machine learning based on Real AdaBoost. This method can reduce the superfluous masking while maintaining the reliability of privacy protection. In the experiments, we demonstrate that the proposed method achieves about 78-94% accuracy for classifying superfluous masking regions and moving objects.

We discuss the division of radio resources in the time and frequency domains for wireless local area network (WLAN) devices powered with microwave energy. In general, there are two ways to avoid microwave power transmission (MPT) from influencing data communications: adjacent channel operation of continuous MPT and WLAN data transmission and co-channel operation of intermittent MPT and WLAN data transmission. Experimental results reveal that, even when we implement these methods, several problems arise because WLAN devices have been developed without supposing the existence of MPT. One problem clarified in our experiment is that adjacent channel operation at 2.4GHz does not necessarily perform well owing to the interference from MPT. This interference occurs regardless of the frequency separation at 2.4GHz. The other problem is that intermittent MPT could result in throughput degradation owing to the data rate control algorithm and the association scheme of the WLAN. In addition, the experimental results imply that a microwave energy source and a WLAN device should share information on the timings of intermittent MPT and data transmission to avoid buffer overflow.

In this paper, compact reconfigurable wideband Inverted-F Antenna (IFA) elements are proposed for a Long Term Evolution (LTE) mobile terminal. The proposed inverted-F antenna has a very simple planar arrangement. A capacitive coupling feed strip is employed to modify the input impedance matching. A chip inductor is connected in series to the feeding point to realize the wideband property. The whole Multiple-Input Multiple-Output (MIMO) antenna system consists of two similar inverted-F antenna elements. Antenna element #1 has a wideband property with a 6-dB bandwidth from 0.74 to 0.9GHz. To further expand the bandwidth, antenna element #2 has a reconfigurable property through the use of a Positive Intrinsic-Negative (PIN) diode. Antenna element #2 has a 6-dB bandwidth from 0.74 to 0.84GHz when the PIN diode is on and from 0.79 to 0.9GHz when the PIN diode is off. Moreover, the proposed MIMO antenna has a low radiation-pattern-based envelope correlation coefficient (ECC), which is less than 0.15 over LTE bands 4, 5, 13, and 14.