Encoding multiple SIFT descriptors into a single vector is a key technique for efficient object image retrieval. In this paper, we propose an extension of local coordinate system (LCS) for image representation. The previous LCS approaches encode each SIFT descriptor by a single local coordinate, which is not adequate for localizing its position in the descriptor space. Instead, we use multiple local coordinates to represent each descriptor with PCA-based decorrelation. Experiments show that this simple modification can improve retrieval performance significantly.

This paper presents a wideband digital predistortion (DPD) architecture suitable for wideband wireless systems, such as IEEE 802.11ad/WiGig, where low oversampling ratio of the digital-to-analog converter (DAC) is a bottleneck for available linearization bandwidth. In order to overcome the bandwidth limitation in the conventional DPD, the proposed DPD introduces a complex coefficient filter in the DPD signal processing, which enables it to achieve asymmetric linearization. This approach effectively suppresses one side of adjacent channel leakages with twice the bandwidth as compared to the conventional DPD. The concept is verified through system simulation and measurements. Using a scaled model of a 2 GHz RF carrier frequency, the measurement shows a 4.2 dB advantage over the conventional DPD in terms of adjacent channel leakage.

Sensor networks are often used to understand underlying phenomena that are reflected through sensing data. In real world applications, this understanding supports decision makers attempting to access a disaster area or monitor a certain event regularly and thus necessary actions can be triggered in response to the problems. Practitioners designing such systems must overcome difficulties due to the practical limitations of the data and the fidelity of a network condition. This paper explores the design of a network solution for the data acquisition domain with the goal of increasing the efficiency of data gathering efforts. An unmanned aerial vehicle (UAV) is introduced to address various real-world sensor network challenges such as limited resources, lack of real-time representative data, and mobility of a relay station. Towards this goal, we introduce a novel cooperative path selection framework to effectively collect data from multiple sensor sources. The framework consists of six main parts ranging from the system initialization to the UAV data acquisition. The UAV data acquisition is useful to increase situational awareness or used as inputs for data manipulation that support response efforts. We develop a system-based simulation that creates the representative sensor networks and uses the UAV for collecting data packets. Results using our proposed framework are analyzed and compared to existing approaches to show the efficiency of the scheme.

This paper proposes a new and simple microstrip bandpass filter structure for the design of a fully canonical transversal array filter. The filter is constructed by the parallel arrangement of microstrip even- and odd-mode half-wavelength resonators. In this filter, transmission zeros (TZs) are not produced by cross couplings used in conventional filter designs, but by an intrinsic negative coupling of the odd-mode resonators having open ends with respect to the even-mode resonators with shorted ends. Thus, the control of the resonant frequency and the external Q factor of each resonator makes it possible to form both a specified passband and TZs. As an example, a fully canonical bandpass filter with 2-GHz center frequency, 6% bandwidth, and four TZs is synthesized with a coupling-matrix optimization, and its structural parameters are designed. The designed filter achieves a rapid roll-off and low-loss passband response, which can be confirmed numerically and experimentally.

Along with remarkable advancement of radiocommunication services including satellite services, the radio-frequency spectrum and geostationary-satellite orbit are getting congested. WRC-15 was held in November 2015 to study and implement efficient use of those natural resources. There were a number of satellite-related agenda items associated with frequency allocation, new usages of satellite communications and satellite regulatory issues. This paper overviews the outcome from these agenda items of WRC-15 as well as the agenda items for the next WRC (i.e. the WRC-19).

NAND flash memory has been widely used in storage systems. Aiming to design an efficient buffer policy for NAND flash memory, a life-aware buffer management algorithm named LAB-LRU is proposed, which manages the buffer by three LRU lists. A life value is defined for every page and the active pages with higher life value can stay longer in the buffer. The definition of life value considers the effect of access frequency, recency and the cost of flash read and write operations. A series of trace-driven simulations are carried out and the experimental results show that the proposed LAB-LRU algorithm outperforms the previous best-known algorithms significantly in terms of the buffer hit ratio, the numbers of flash write and read operations and overall runtime.

Design of lumped-element immittance inverters which support dual-frequency operation and tuning of both operational frequencies is presented. Unique properties of the dual-composite right/left-handed transmission lines (D-CRLH TL) give an opportunity to design immittance inverters with two non-multiple operational frequencies and a stop band between them. Replacement of capacitors of D-CRLH TL unit cells with variable ones enables inverter tunability. Tunability analysis of such immittance inverters is given. It is shown that a tuning range of the operational frequencies is limited by a tolerable variation of the inverter parameter. The design concept is verified by results of electromagnetic simulation and measured frequency characteristics of fixed (non-tunable) as well as tunable dual-frequency immittance inverters and dual-band filters using the inverters.

In this paper, a novel method for an effective allocation of non-zero digits in design of CSD (Canonic Signed-Digit) coefficient FIR (Finite Impulse Response) filters is proposed. The design problem can be formulated as a mixed integer programming problem, which is well-known as a NP-hard problem. Recently, a heuristic approach using the PSO (Particle Swarm Optimization) for solving the problem has been proposed, in which the maximum number of non-zero digits was limited in each coefficient. On the other hand, the maximum number of non-zero digits is limited in total in the proposed method and 0-1PSO is applied. It enables an effective allocation of non-zero digits, and provides a good design. Several examples are shown to present the efficiency of the proposed method.

As an important extension of high-efficiency video coding (HEVC), screen content coding (SCC) includes various new coding modes, such as Intra Block Copy (IBC), Palette-based coding (Palette), and Adaptive Color Transform (ACT). These new tools have improved screen content encoding performance. This paper proposed a novel and fast algorithm by classifying Code Units (CUs) as text CUs or non-text CUs. For text CUs, the Intra mode was skipped in the compression process, whereas for non-text CUs, the IBC mode was skipped. The current CU depth range was then predicted according to its adjacent left CU depth level. Compared with the reference software HM16.7+SCM5.4, the proposed algorithm reduced encoding time by 23% on average and achieved an approximate 0.44% increase in Bjøntegaard delta bit rate and a negligible peak signal-to-noise ratio loss.

With the surge of social media platform, users' profile information become treasure to enhance social network services. However, attributes information of most users are not complete, thus it is important to infer latent attributes of users. Contemporary attribute inference methods have a basic assumption that there are enough labeled data to train a model. However, in social media, it is very expensive and difficult to label a large amount of data. In this paper, we study the latent attribute inference problem with very small labeled data and propose the SRW-COND solution. In order to solve the difficulty of small labeled data, SRW-COND firstly extends labeled data with a simple but effective greedy algorithm. Then SRW-COND employs a supervised random walk process to effectively utilize the known attributes information and link structure of users. Experiments on two real datasets illustrate the effectiveness of SRW-COND.

Public auditing is a new technique to protect the integrity of outsourced data in the remote cloud. Users delegate the ability of auditing to a third party auditor (TPA), and assume that each result from the TPA is correct. However, the TPA is not always trustworthy in reality. In this paper, we consider a scenario in which the TPA may lower the reputation of the cloud server by cheating users, and propose a novel public auditing scheme to address this security issue. The analyses and the evaluation prove that our scheme is both secure and efficient.

The most commonly used scattering parameters (S parameters) are normalized to a real reference resistance, typically 50Ω. In some cases, the use of S parameters normalized to some complex reference impedance is essential or convenient. But there are different definitions of complex-referenced S parameters that are incompatible with each other and serve different purposes. To make matters worse, different simulators implement different ones and which ones are implemented is rarely properly documented. What are possible scenarios in which using the right one matters? This tutorial-style paper is meant as an informal and not overly technical exposition of some such confusing aspects of S parameters, for those who have a basic familiarity with the ordinary, real-referenced S parameters.

The indexes of the degradation of C/N, ΔT/T and I/N, which can be converted from one to another, are used to evaluate the impact of interference on the satellite link. However, it is not suitable to intuitively understand how these parameters degrade the quality of services. In this paper, we propose to evaluate the impact of interference on the performance of BSS (Broadcasting Satellite Services) in terms of the increase rate of the outage time caused by the rain attenuation. Some calculation results are given for the 12GHz band BSS in Japan.

We propose a new visual tracking method, where the target appearance is represented by combining color distribution and keypoints. Firstly, the object is localized via a keypoint-based tracking and matching strategy, where a new clustering method is presented to remove outliers. Secondly, the tracking confidence is evaluated by the color template. According to the tracking confidence, the local and global keypoints matching can be performed adaptively. Finally, we propose a target appearance update method in which the new appearance can be learned and added to the target model. The proposed tracker is compared with five state-of-the-art tracking methods on a recent benchmark dataset. Both qualitative and quantitative evaluations show that our method has favorable performance.

We propose a novel method for estimating acoustic scenes such as user activities, e.g., “cooking,” “vacuuming,” “watching TV,” or situations, e.g., “being on the bus,” “being in a park,” “meeting,” utilizing the information of acoustic events. There are some methods for estimating acoustic scenes that associate a combination of acoustic events with an acoustic scene. However, the existing methods cannot adequately express acoustic scenes, e.g., “cooking,” that have more than one subordinate category, e.g., “frying ingredients” or “plating food,” because they directly associate acoustic events with acoustic scenes. In this paper, we propose an acoustic scene estimation method based on a hierarchical probabilistic generative model of an acoustic event sequence taking into account the relation among acoustic scenes, their subordinate categories, and acoustic event sequences. In the proposed model, each acoustic scene is represented as a probability distribution over their unsupervised subordinate categories, called “acoustic sub-topics,” and each acoustic sub-topic is represented as a probability distribution over acoustic events. Acoustic scene estimation experiments with real-life sounds showed that the proposed method could correctly extract subordinate categories of acoustic scenes.

Embedded memory is extensively being used in SoCs, and is rapidly growing in size and density. It contributes to SoCs to have greater features, but at the expense of taking up the most area. Due to continuous scaling of nanoscale device technology, large area size memory introduces aging-induced faults and soft errors, which affects reliability. In-field test and repair, as well as ECC, can be used to maintain reliability, and recently, these methods are used together to form a combined approach, wherein uncorrectable words are repaired, while correctable words are left to the ECC. In this paper, we propose a novel in-field repair strategy that repairs uncorrectable words, and possibly correctable words, for an ECC-based memory architecture. It executes an adaptive reconfiguration method that ensures 'fresh' memory words are always used until spare words run out. Experimental results demonstrate that our strategy enhances reliability, and the area overhead contribution is small.

This study proposes a two-pass spoken term detection (STD) method. The first pass uses a phoneme-based dynamic time warping (DTW)-based STD, and the second pass recomputes detection scores produced by the first pass using conditional random fields (CRF)-based triphone detectors. In the second-pass, we treat STD as a sequence labeling problem. We use CRF-based triphone detection models based on features generated from multiple types of phoneme-based transcriptions. The models train recognition error patterns such as phoneme-to-phoneme confusions in the CRF framework. Consequently, the models can detect a triphone comprising a query term with a detection probability. In the experimental evaluation of two types of test collections, the CRF-based approach worked well in the re-ranking process for the DTW-based detections. CRF-based re-ranking showed 2.1% and 2.0% absolute improvements in F-measure for each of the two test collections.

Deep Neural Network (DNN) is a powerful machine learning model that has been successfully applied to a wide range of pattern classification tasks. Due to the great ability of the DNNs in learning complex mapping functions, it has been possible to train and deploy DNNs pretty much as a black box without the need to have an in-depth understanding of the inner workings of the model. However, this often leads to solutions and systems that achieve great performance, but offer very little in terms of how and why they work. This paper introduces Sensitivity-characterised Activity Neorogram (SCAN), a novel approach for understanding the inner workings of a DNN by analysing and visualising the sensitivity patterns of the neuron activities. SCAN constructs a low-dimensional visualisation space for the neurons so that the neuron activities can be visualised in a meaningful and interpretable way. The embedding of the neurons within this visualisation space can be used to compare the neurons, both within the same DNN and across different DNNs trained for the same task. This paper will present the observations from using SCAN to analyse DNN acoustic models for automatic speech recognition.

Considering that existing clutter cancellation methods process information either in the time domain or in the spatial domain, this paper proposes a new clutter cancellation method that utilizes joint multi-domain information for passive radar. Assuming that there is a receiving array at the surveillance channel, firstly we propose a multi-domain information clutter cancellation model by constructing a time domain weighted matrix and a spatial weighted vector. Secondly the weighted matrix and vector can be updated adaptively utilizing the constant modulus constraint. Finally the weighted matrix is derived from the principle of optimal filtering and the recursion formula of weighted vector is obtained utilizing the Gauss-Newton method. Making use of the information in both time and spatial domain, the proposed method attenuates the noise and residual clutter whose directions are different from that of the target echo. Simulation results prove that the proposed method has higher clutter attenuation (CA) compared with the traditional methods in the low signal to noise ratio condition, and it also improves the detection performance of weak targets.

A novel method for illumination-invariant face representation is presented based on the orthogonal decomposition of the local image structure. One important advantage of the proposed method is that image gradients and corresponding intensity values are simultaneously used with our decomposition procedure to preserve the original texture while yielding the illumination-invariant feature space. Experimental results demonstrate that the proposed method is effective for face recognition and verification even with diverse lighting conditions.