Many countries have deregulated their electricity retail markets to offer lower electricity charges to consumers. However, many consumers have not switched their suppliers after the deregulation, and electricity suppliers do not tend to reduce their charges intensely. This paper proposes an electricity market model and evolutionary game to analyze the behavior of consumers in electricity retail markets. Our model focuses on switching costs such as an effort at switching, costs in searching for other alternatives, and so on. The evolutionary game examines whether consumers choose a strategy involving exploration of new alternatives with the searching costs as “cooperators” or not. Simulation results demonstrate that the share of cooperators was not improved by simply giving rewards for cooperators as compensation for searching costs. Furthermore, the results also suggest that the degree of cooperators in a network among consumers has a vital role in increasing the share of cooperators and switching rate.

Detecting Android malwares is imperative. As a promising Android malware detection scheme, we focus on the scheme leveraging the differences of traffic patterns between benign apps and malwares. Those differences can be captured even if the packet is encrypted. However, since such features are just statistic based ones, they cannot identify whether each traffic is malicious. Thus, it is necessary to design the scheme which is applicable to encrypted traffic data and supports identification of malicious traffic. In this paper, we propose an Android malware detection scheme based on level of SSL server certificate. Attackers tend to use an untrusted certificate to encrypt malicious payloads in many cases because passing rigorous examination is required to get a trusted certificate. Thus, we utilize SSL server certificate based features for detection since their certificates tend to be untrusted. Furthermore, in order to obtain the more exact features, we introduce required permission based weight values because malwares inevitably require permissions regarding malicious actions. By computer simulation with real dataset, we show our scheme achieves an accuracy of 92.7%. True positive rate and false positive rate are 5.6% higher and 3.2% lower than the previous scheme, respectively. Our scheme can cope with encrypted malicious payloads and 89 malwares which are not detected by the previous scheme.

Regarding the enlarged array size for the 5G new radio (NR) millimeter-wave phased-array transceivers, an improved phase tuning resolution will be required to support the accurate beam control. This paper introduces a CMOS implementation of an active vector-summing phase shifter. The proposed phase shifter realizes a 6-bit phase shifting with an active area of 0.32mm2. To minimize the gain variation during the phase tuning, a gain error compensation technique is proposed. After the compensation, the measured gain variation within the 5G NR band n257 is less than 0.9dB. The corresponding RMS gain error is less than 0.2dB. The measured RMS phase error from 26.5GHz to 29.5GHz is less than 1.2°. Gain-invariant, high-resolution phase tuning is realized by this work. Considering the error vector magnitude (EVM) performance, the proposed phase shifter supports a maximum data rate of 11.2Gb/s in 256QAM with a power consumption of 25.2mW.

In this letter, the performance of a state-of-the-art deep learning (DL) algorithm in [5] is analyzed and evaluated for orthogonal frequency-division multiplexing (OFDM) receivers, in the presence of harmonic spur interference. Moreover, a novel spur cancellation receiver structure and algorithm are proposed to enhance the traditional OFDM receivers, and serve as a performance benchmark for the DL algorithm. It is found that the DL algorithm outperforms the traditional algorithm and is much more robust to spur carrier frequency offset.

MATLAB/Simulink is the de facto standard tool for the model-based development (MBD) of control software for automotive systems. A Simulink model developed in MBD for real automotive systems involves complex computation as well as tens of thousands of blocks. In this paper, we focus on decision coverage (DC), condition coverage (CC) and modified condition/decision coverage (MC/DC) criteria, and propose a Monte-Carlo test suite generation method for large and complex Simulink models. In the method, a candidate test case is generated by assigning random values to the parameters of signal templates with specific waveforms. We try to find contributable candidates in a plausible and understandable search space, specified by a set of templates. We implemented the method as a tool, and our experimental evaluation showed that the tool was able to generate test suites for industrial implementation models with higher coverages and shorter execution times than Simulink Design Verifier. Additionally, the tool includes a fast coverage measurement engine, which demonstrated better performance than Simulink Coverage in our experiments.

In this work, we address a joint energy efficiency (EE) and throughput optimization problem in interweave cognitive radio networks (CRNs) subject to scheduling, power, and stability constraints, which could be solved through traffic admission control, channel allocation, and power allocation. Specifically, the joint objective is to concurrently optimize the system EE and the throughput of secondary user (SU), while satisfying the minimum throughput requirement of primary user (PU), the throughput constraint of SU, and the scheduling and power control constraints that must be considered. To achieve these goals, our algorithm independently and simultaneously makes control decisions on admission and transmission to maximize a joint utility of EE and throughput under time-varying conditions of channel and traffic without a priori knowledge. Specially, the proposed scheduling algorithm has polynomial time efficiency, and the power control algorithms as well as the admission control algorithm involved are simply threshold-based and thus very computationally efficient. Finally, numerical analyses show that our proposals achieve both system stability and optimal utility.

This paper proposes a temporal domain difference based secondary background modeling algorithm for surveillance video coding. The proposed algorithm has three key technical contributions as following. Firstly, the LDBCBR (Long Distance Block Composed Background Reference) algorithm is proposed, which exploits IBBS (interval of background blocks searching) to weaken the temporal correlation of the foreground. Secondly, both BCBR (Block Composed Background Reference) and LDBCBR are exploited at the same time to generate the temporary background reference frame. The secondary modeling algorithm utilizes the temporary background blocks generated by BCBR and LDBCBR to get the final background frame. Thirdly, monitor the background reference frame after it is generated is also important. We would update the background blocks immediately when it has a big change, shorten the modeling period of the areas where foreground moves frequently and check the stable background regularly. The proposed algorithm is implemented in the platform of IEEE1857 and the experimental results demonstrate that it has significant improvement in coding efficiency. In surveillance test sequences recommended by the China AVS (Advanced Audio Video Standard) working group, our method achieve BD-Rate gain by 6.81% and 27.30% comparing with BCBR and the baseline profile.

In this paper, we propose a distributed subgradient-based method over quantized and event-triggered communication networks for constrained convex optimization. In the proposed method, each agent sends the quantized state to the neighbor agents only at its trigger times through the dynamic encoding and decoding scheme. After the quantized and event-triggered information exchanges, each agent locally updates its state by a consensus-based subgradient algorithm. We show a sufficient condition for convergence under summability conditions of a diminishing step-size.

In this letter, an active complex filter with finite transmission zeros is proposed. In order to obtain a complex prototype ladder filter including no inductors, a new circuit transformation is proposed. This circuit is classified into the RiCR filter. It is shown that it includes no negative capacitors when it is obtained through a frequency transformation. The validity of the proposed method is confirmed through computer simulation.

Near real-time periodic tasks, which are popular in multimedia streaming applications, have deadline periods that are longer than the input intervals thanks to buffering. For such applications, the conventional frame-based schedulings cannot realize the optimal scheduling due to their shortsighted deadline assumptions. To realize globally energy-efficient executions of these applications, we propose a novel task scheduling algorithm, which takes advantage of the long deadline period. We confirm our approach can take advantage of the longer deadline period and reduce the average power consumption by up to 18%.

Tea sprouts segmentation via machine vision is the core technology of tea automatic picking. A novel method for Tea Sprouts Segmentation based on improved deep convolutional encoder-decoder Network (TS-SegNet) is proposed in this paper. In order to increase the segmentation accuracy and stability, the improvement is carried out by a contrastive-center loss function and skip connections. Therefore, the intra-class compactness and inter-class separability are comprehensively utilized, and the TS-SegNet can obtain more discriminative tea sprouts features. The experimental results indicate that the proposed method leads to good segmentation results, and the segmented tea sprouts are almost coincident with the ground truth.

In this letter, we propose a novel deep domain-adaptive convolutional neural network (DDACNN) model to handle the challenging cross-corpus speech emotion recognition (SER) problem. The framework of the DDACNN model consists of two components: a feature extraction model based on a deep convolutional neural network (DCNN) and a domain-adaptive (DA) layer added in the DCNN utilizing the maximum mean discrepancy (MMD) criterion. We use labeled spectrograms from source speech corpus combined with unlabeled spectrograms from target speech corpus as the input of two classic DCNNs to extract the emotional features of speech, and train the model with a special mixed loss combined with a cross-entrophy loss and an MMD loss. Compared to other classic cross-corpus SER methods, the major advantage of the DDACNN model is that it can extract robust speech features which are time-frequency related by spectrograms and narrow the discrepancies between feature distribution of source corpus and target corpus to get better cross-corpus performance. Through several cross-corpus SER experiments, our DDACNN achieved the state-of-the-art performance on three public emotion speech corpora and is proved to handle the cross-corpus SER problem efficiently.

Based on the License Assisted Access (LAA) small cell architecture, the LAA coexisting with Wi-Fi heterogeneous networks provide LTE mobile users with high bandwidth efficiency as the unlicensed channels are shared among LAA and Wi-Fi. However, the LAA and Wi-Fi will affect each other when both systems are using the same unlicensed channel in the heterogeneous networks. In such a network, unlicensed band allocation for LAA and Wi-Fi is an important issue that may affect the quality of service (QoS) of both systems significantly. In this paper, we propose an analytical model and conduct simulation experiments to study two allocations for the unlicensed band: unlicensed full allocation (UFA), unlicensed time-division allocation (UTA), and the corresponding buffering mechanism for the LAA data packets. We evaluate the performance for these unlicensed band allocations schemes in terms of the acceptance rate of both LAA and Wi-Fi packet data in LAA buffer queue. Our study provides guidelines for designing channel occupation phase and the buffer size of LAA small cell.

This paper proposes a simple source data exchange method for channel switching in space-time block code. If one transmits source data on another antenna, then the receiver should change combining method in order to adapt it. No one except knowing the channel switching sequence can decode the received data correctly. In case of exchanging data for channel switching, four orthogonal frequency division multiplexing symbols are exchanged according to a format of space-time block code. In this paper, I proposes two simple sign exchanges without exchanging four orthogonal-frequency division multiplexing symbols which occurs a different combining and channel switching method in the receiver.

Time-sequence data is high dimensional and contains a lot of information, which can be utilized in various fields, such as insurance, finance, and advertising. Personal data including time-sequence data is converted to anonymized datasets, which need to strike a balance between both privacy and utility. In this paper, we consider low-rank matrix factorization as one of anonymization methods and evaluate its efficiency. We convert time-sequence datasets to matrices and evaluate both privacy and utility. The record IDs in time-sequence data are changed at regular intervals to reduce re-identification risk. However, since individuals tend to behave in a similar fashion over periods of time, there remains a risk of record linkage even if record IDs are different. Hence, we evaluate the re-identification and linkage risks as privacy risks of time-sequence data. Our experimental results show that matrix factorization is a viable anonymization method and it can achieve better utility than existing anonymization methods.

A key driver of software business growth in developing countries is the survival of software small and medium-sized enterprises (SMEs). Quality of products is a critical factor that can indicate the future of the business by building customer confidence. Software development agencies need to be aware of meeting international standards in software development process. In practice, consultants and assessors are usually employed as the primary solution, which can impact the budget in case of small businesses. Self-assessment tools for software development process can potentially reduce time and cost of formal assessment for software SMEs. However, the existing support methods and tools are largely insufficient in terms of process coverage and semi-automated evaluation. This paper proposes to apply a knowledge-based approach in development of a self-assessment and gap analysis support system for the ISO/IEC 29110 standard. The approach has an advantage that insights from domain experts and the standard are captured in the knowledge base in form of decision tables that can be flexibly managed. Our knowledge base is unique in that task lists and work products defined in the standard are broken down into task and work product characteristics, respectively. Their relation provides the links between Task List and Work Product which make users more understand and influence self-assessment. A prototype support system was developed to assess the level of software development capability of the agencies based on the ISO/IEC 29110 standard. A preliminary evaluation study showed that the system can improve performance of users who are inexperienced in applying ISO/IEC 29110 standard in terms of task coverage and user's time and effort compared to the traditional self-assessment method.

Cross-component linear model (CCLM) has been recently adopted as a chroma intra-prediction tool in Versatile Video Coding (VVC), which is being developed as a new video coding standard. CCLM predicts chroma components from luma components through a linear model based on assumption of linear correlation between both components. A linear model is derived from the reconstructed neighboring luma and chroma samples of the current coding block by linear regression. A simplified linear modeling method recently adopted in the test model of VVC (VTM) 3.0 significantly reduces computational complexity of deriving model parameters with considerable coding loss. This letter proposes a method of linear modeling to compensate the coding loss of the simplified linear model. In the proposed method, the model parameters which are quite roughly derived in the existing simplified linear model are refined more accurately using individual method to derive each parameter. Experimental results show that, compared to VTM 3.0, the proposed method gives 0.08%, 0.52% and 0.55% Bjotegaard-Delta (BD)-rate savings, for Y, Cb and Cr components, respectively, in the All-Intra (AI) configuration with negligible computational complexity increase.

The surveillance problem is to find optimal trajectories of agents that patrol a given area as evenly as possible. In this paper, we consider multiple agents with fuel constraints. The surveillance area is given by a weighted directed graph, where the weight assigned to each arc corresponds to the fuel consumption/supply. For each node, the penalty to evaluate the unattended time is introduced. Penalties, agents, and fuels are modeled by a mixed logical dynamical system model. Then, the surveillance problem is reduced to a mixed integer linear programming (MILP) problem. Based on the policy of model predictive control, the MILP problem is solved at each discrete time. In this paper, the feasibility condition for the MILP problem is derived. Finally, the proposed method is demonstrated by a numerical example.

This work develops a system called CLAP that detects and classifies “potentially unwanted applications” (PUAs) such as adware or remote monitoring tools. Our approach leverages DNS queries made by apps. Using a large sample of Android apps from third-party marketplaces, we first reveal that DNS queries can provide useful information for detection and classification of PUAs. We then show that existing DNS blacklists are limited when performing these tasks. Finally, we demonstrate that the CLAP system performs with high accuracy.