A novel rendering algorithm with a best-matching patch is proposed to address the noise artifacts associated with Monte Carlo renderings. First, in the sampling stage, the representative patch is selected through a modified patch shift procedure, which gathers homogeneous pixels together to stay clear of the edges. Second, each pixel is filtered over a discrete set of filters, where the range kernel is computed using the selected patches. The difference between the selected patch and the filtered value is used as the pixel error, and the single filter that returns the smallest estimated error is chosen. In the reconstruction stage, pixel colors are combined with features of depth, normal and texture to form a cross bilateral filter, which highly preserves scene details while effectively removing noise. Finally, a heuristic metric is calculated to allocate additional samples in difficult regions. Compared with state-of-the art methods, the proposed algorithm performs better both in visual image quality and numerical error.

The High efficiency video coding (HEVC) standard defines two in-loop filters to improve the objective and subjective quality of the reconstructed frames. Through analyzing the effectiveness of the in-loop filters, it is noted that band offset (BO) process achieves much more coding gains for text region which mostly employ intra block copy (IntraBC) prediction mode. The intraBC prediction process in HEVC is performed by using the already reconstructed region for block matching, which is similar to motion compensation. If BO process is applied after one coding tree unit (CTU) encoded, the distortion between original and reconstructed samples copied by the IntraBC prediction will be further reduced, which is simple to operate and can obtain good coding efficiency. Experimental results show that the proposed scheme achieves up to 3.4% BD-rate reduction in All-intra (AI) for screen content sequences with encoding and decoding time no increase.

Quantized congestion notification (QCN), discussed in IEEE 802.1Qau, is one of the most promising Layer 2 congestion control methods for data center networks. Because data center networks have fundamentally symmetric structures and links are designed to have high link utilization, data center flows often pass through multiple bottleneck links. QCN reduces its transmission rate in a probabilistic manner with each congestion notification feedback reception, which might cause excessive regulation of the transmission rate in a multiple-bottleneck case because each bottleneck causes congestion feedbacks. We have already proposed QCN with bottleneck selection (QCN/BS) for multicast communications in data center networks. Although QCN/BS was originally proposed for multicast communications, it can also be applied to unicast communications with multiple bottleneck points. QCN/BS calculates the congestion level for each switch based on feedback from the switch and adjusts its transmission rate to the worst congestion level. In this paper, we preliminarily evaluate QCN/BS in unicast communications with multiple tandem bottleneck points. Our preliminary evaluation reveals that QCN/BS can resolve the excessive rate regulation problem of QCN but has new fairness problems for long-hop flows. To resolve this, we propose a new algorithm that integrates QCN/BS and our already proposed Adaptive BC_LIMIT. In Adaptive BC_LIMIT, the opportunities for rate increase are almost the same for all flows even if their transmission rates differ, enabling an accelerated convergence of fair rate allocation among flows sharing a bottleneck link. The integrated algorithm is the first congestion control mechanism that takes into account unicast flows passing through multiple tandem bottleneck points based on QCN. Furthermore, it does not require any modifications of switches used in QCN. Our simulation results show that our proposed integration of QCN/BS and Adaptive BC_LIMIT significantly mitigates the fairness problem for unicast communications with multiple bottleneck points in data center networks.

Recently, multi-user multiple-input multiple-output (MU-MIMO) systems are being widely studied. For interference cancellation, MU-MIMO commonly uses spatial precoding techniques. These techniques, however, require the transmitters to have perfect knowledge of the downlink channel state information (CSI), which is hard to achieve in high mobility environments. Instead of spatial precoding, a collaborative interference cancellation (CIC) technique can be implemented for these environments. In CIC, mobile stations (MSs) collaborate and share their received signals to increase the demultiplexing capabilities. To obtain efficient signal-exchange between collaborating users, signal selection can be implemented. In this paper, a signal selection scheme suitable for a QRM-MLD algorithm is proposed. The proposed scheme uses the minimum Euclidean distance criterion to obtain an optimum bit error rate (BER) performance. Numerical results obtained through computer simulations show that the proposed scheme is able to provide BER performance near to that of MLD even when the number of candidates in QRM-MLD is relatively small. In addition, the proposed scheme is feasible to implement owing to its low computational complexity.

Personal Wi-Fi Hotspot, the Wi-Fi tethering function, is widely deployed on mobile devices to allow other wireless clients to share Internet access via a broadband connection. Its advantages include no connection fee and support of non-3G/LTE devices. However, utilizing this function can rapidly deplete the battery power of the tethering device because both interface connections (3G/LTE and Wi-Fi) are always on. To address this problem, this paper proposes the Energy Management Mechanism for Wi-Fi Tethering Mode on Mobile Devices (EMWT). The mechanism is designed to effectively manage both interfaces by adjusting certain sleep durations according to the incoming traffic. Short, Long, and Deep sleep durations are introduced for saving energy. EMWT can also guarantee the packet delay bound by limiting the maximum sleep period. Five traffic rates, composed of very low, low, medium, high, and very high, are evaluated. NS-3 simulation results reveal that energy savings of up to 52.52% can be achieved with only a slight impact on system performance, in terms of end-to-end delay, throughput, and packet loss.

This paper presents a novel defense scheme for DDoS attacks that uses an image processing method. This scheme especially focused on the prevalence of adjacent neighbor spoofing, called subnet spoofing. It is rarely studied and there is few or no feasible approaches than other spoofing attacks. The key idea is that a “DDoS attack with IP spoofing” is represented as a specific pattern such as a “line” on the spatial image planes, which can be recognized through an image processing technique. Applying the clustering technique to the lines makes it possible to identify multiple attack source networks simultaneously. For the identified networks in which the zombie hosts reside, we then employ a signature-based pattern extraction algorithm, called a pivoted movement, and the DDoS attacks are filtered by correlating the IP and media access control pairing signature. As a result, this proposed scheme filters attacks without disturbing legitimate traffic. Unlike previous IP traceback schemes such as packet marking and path fingerprinting, which try to diagnose the entire attack path, our proposed scheme focuses on identifying only the attack source. Our approach can achieve an adaptive response to DDoS attacks, thereby mitigating them at the source, while minimizing the disruption of legitimate traffic. The proposed scheme is analyzed and evaluated on the IPv4 and IPv6 network topology from CAIDA, the results of which show its effectiveness.

As semiconductor technologies have advanced, the reliability problem caused by soft-errors is becoming one of the serious issues in LSIs. Moreover, multiple component errors due to single soft-errors also have become a serious problem. In this paper, we propose a method to synthesize multiple component soft-error tolerant application-specific datapaths via high-level synthesis. The novel feature of our method is speculative resource sharing between the retry parts and the secondary parts for time overhead mitigation. A scheduling algorithm using a special priority function to maximize speculative resource sharing is also an important feature of this study. Our approach can reduce the latency (schedule length) in many applications without deterioration of reliability and chip area compared with conventional datapaths without speculative resource sharing. We also found that our method is more effective when a computation algorithm possesses higher parallelism and a smaller number of resources is available.

In interactive audio services, users can render audio objects rather freely to match their desires and the spatial audio object coding (SAOC) scheme is fairly good both in the sense of bitrate and audio quality. But rather perceptible audio quality degradation can occur when an object is suppressed or played alone. To complement this, the SAOC scheme with Two-Step Coding (SAOC-TSC) was proposed. But the bitrate of the side information increases two times compared to that of the original SAOC due to the bitrate needed for the residual coding used to enhance the audio quality. In this paper, an efficient residual coding method of the SAOC-TSC is proposed to reduce the side information bitrate without audio quality degradation or complexity increase.

In this paper, a new approach to grammatical evolution is presented. The aim is to generate complete programs using probabilistic modeling and sampling of (probability) distribution of given grammars. To be exact, probabilistic context free grammars are employed and a modified mapping process is developed to create new individuals from the distribution of grammars. To consider problem structures in the individual generation, conditional dependencies between production rules are incorporated into the mapping process. Experiments confirm that the proposed algorithm is more effective than existing methods.

This paper presents a new approach for circuit matching using signatures. We have defined a signature based on topology of the fanin cones of the circuit elements. Given two circuits, first we find all the circuit elements with unique signature between the two input circuits. After that, we try to expand the matching area by our expansion rules as much as possible. We iteratively find the unique matches and expand the matching area until no further matching is possible. Our experiments on IWLS2005 benchmark suite show that our method is able to find the perfect matching between two 160,000-gate IP in 5 minutes. In addition, our method is more than one order of magnitude faster than our previous signature-based matching method, while the size of the matched area is comparable or larger.

An efficient Monte Carlo (MC) method for the calculation of failure probability degradation of an SRAM cell due to negative bias temperature instability (NBTI) is proposed. In the proposed method, a particle filter is utilized to incrementally track temporal performance changes in an SRAM cell. The number of simulations required to obtain stable particle distribution is greatly reduced, by reusing the final distribution of the particles in the last time step as the initial distribution. Combining with the use of a binary classifier, with which an MC sample is quickly judged whether it causes a malfunction of the cell or not, the total number of simulations to capture the temporal change of failure probability is significantly reduced. The proposed method achieves 13.4× speed-up over the state-of-the-art method.

In this letter, an underground facility management system for effective underground facility management is suggested. The present underground facility management system uses a wired and wireless duplex communication method to enable seamless communication, and rapid responses to any failures encountered. In this letter, the architecture and components of underground facility management system supporting heterogeneous duplex communication is suggested, and relevant work flow is presented.

Heterogeneous network (HetNet) is now considered to be a promising technique for enhancing the coverage and reducing the transmit power consumption of the next 5G system. Deploying small cells such as femtocells in the current macrocell networks achieves great spatial reuse at the cost of severe cross-tier interference from concurrent transmission. In this situation, two novel energy efficient power control and resource allocation schemes in terms of energy efficiency (EE)-fairness and EE-maximum, respectively, are investigated in this paper. In the EE-fairness scheme, we aim to maximize the minimum EE of the femtocell base stations (FBSs). Generalized Dinkelbach's algorithm (GDA) is utilized to tackle this optimization problem and a distributed algorithm is proposed to solve the subproblem in GDA with limited intercell coordination, in which only a few scalars are shared among FBSs. In the EE-maximum scheme, we aim to maximize the global EE of all femtocells which is defined as the aggregate capacity over the aggregate power consumption in the femtocell networks. Leveraged by means of the lower-bound of logarithmic function, a centralized algorithm with limited computational complexity is proposed to solve the global EE maximization problem. Simulation results show that the proposed algorithms outperform previous schemes in terms of the minimum EE, fairness and global EE.

In most existing centralized lighting control systems, the lighting control problem (LCP) is reformulated as a constrained minimization problem and solved by linear programming (LP). However, in real-world applications, LCP is actually discrete and non-linear, which means that more accurate algorithm may be applied to achieve improvements in energy saving. In this paper, particle swarm optimization (PSO) is successfully applied for office lighting control and a linear programming guided particle swarm optimization (LPPSO) algorithm is developed to achieve considerable energy saving while satisfying users' lighting preference. Simulations in DIALux office models (one with small number of lamps and one with large number of lamps) are made and analyzed using the proposed control algorithms. Comparison with other widely used methods including LP shows that LPPSO can always achieve higher energy saving than other lighting control methods.

Designing secure revocable storage systems for a large number of users in a cloud-based environment is important. Cloud storage systems should allow its users to dynamically join and leave the storage service. Further, the rights of the users to access the data should be changed accordingly. Recently, Liang et al. proposed a cloud-based revocable identity-based proxy re-encryption (CR-IB-PRE) scheme that supports user revocation and delegation of decryption rights. Moreover, to reduce the size of the key update token, they employed a public key broadcast encryption system as a building block. In this paper, we show that the CR-IB-PRE scheme with the reduced key update token size is not secure against collusion attacks.

The software birthmarking technique has conventionally been studied in fields such as software piracy, code theft, and copyright infringement. The most recent API-based software birthmarking method (Han et al., 2014) extracts API call sequences in entire code sections of a program. Additionally, it is generated as a birthmark using a cryptographic hash function (MD5). It was reported that different application types can be categorized in a program through pre-filtering based on DLL/API numbers/names. However, similarity cannot be measured owing to the cryptographic hash function, occurrence of false negatives, and it is difficult to functionally categorize applications using only DLL/API numbers/names. In this paper, we propose an API-based software birthmarking method using fuzzy hashing. For the native code of a program, our software birthmarking technique extracts API call sequences in the segmented procedures and then generates them using a fuzzy hash function. Unlike the conventional cryptographic hash function, the fuzzy hash is used for the similarity measurement of data. Our method using a fuzzy hash function achieved a high reduction ratio (about 41% on average) more than an original birthmark that is generated with only the API call sequences. In our experiments, when threshold ε is 0.35, the results show that our method is an effective birthmarking system to measure similarities of the software. Moreover, our correlation analysis with top 50 API call frequencies proves that it is difficult to functionally categorize applications using only DLL/API numbers/names. Compared to prior work, our method significantly improves the properties of resilience and credibility.

The separation of signals with temporal structure from mixed sources is a challenging problem in signal processing. For this problem, blind source extraction (BSE) is more suitable than blind source separation (BSS) because it has lower computation cost. Nowadays many BSE algorithms can be used to extract signals with temporal structure. However, some of them are not robust because they are too dependent on the estimation precision of time delay; some others need to choose parameters before extracting, which means that arbitrariness can't be avoided. In order to solve the above problems, we propose a robust source extraction algorithm whose performance doesn't rely on the choice of parameters. The algorithm is realized by maximizing the objective function that we develop based on the non-Gaussianity and the temporal structure of source signals. Furthermore, we analyze the stability of the algorithm. Simulation results show that the algorithm can extract the desired signal from large numbers of observed sensor signals and is very robust to error in the estimation of time delay.

The high-availability seamless redundancy (HSR) protocol is a representative protocol that fulfills the reliability requirements of the IEC61850-based substation automation system (SAS). However, it has the drawback of creating unnecessary traffic in a network. To solve this problem, a dual virtual path (DVP) algorithm based on HSR was recently presented. Although this algorithm dramatically reduces network traffic, it does not consider the substation timing requirements of messages in an SAS. To reduce unnecessary network traffic in an HSR ring network, we introduced a novel packet transmission (NPT) algorithm in a previous work that considers IEC61850 message types. To further reduce unnecessary network traffic, we propose an extended dual virtual paths (EDVP) algorithm in this paper that considers the timing requirements of IEC61850 message types. We also include sending delay (SD), delay queue (DQ), and traffic flow latency (TFL) features in our proposal. The source node sends data frames without SDs on the primary paths, and it transmits the duplicate data frames with SDs on the secondary paths. Since the EDVP algorithm discards all of the delayed data frames in DQs when there is no link or node failure, unnecessary network traffic can be reduced. We demonstrate the principle of the EDVP algorithm and its performance in terms of network traffic compared to the standard HSR, NPT, and DVP algorithm using the OPNET network simulator. Throughout the simulation results, the EDVP algorithm shows better traffic performance than the other algorithms, while guaranteeing the timing requirements of IEC61850 message types. Most importantly, when the source node transmits heavy data traffic, the EDVP algorithm shows greater than 80% and 40% network traffic reduction compared to the HSR and DVP approaches, respectively.

This paper presents analysis and simple design guideline for ThruChip Interface (TCI) as located by LC-VCO which is used in high-speed SoC. The electromagnetic interference (EMI) from TCI channels to LC-VCO is analyzed and evaluated. The accuracy of the analysis and design guidelines is verified through the test-chip verification.

Connection Service Providers (CSP) are wishing to increase their Return on Investment (ROI) by utilizing the data assets generated by tracking subscriber behaviors. This results in the ability to apply personalized policies, monitor and control the service traffic to subscribers and gain more revenue through the usage of subscriber data with ad networks. In this paper, a system is proposed to monitor and analyze the Internet access of the subscribers of a regional SP in order to classify the subscribers into interest categories from the Interactive Advertising Bureau (IAB) categories. The study employs the categorization engine to build category vectors for all individuals using Internet services through the subscription. The proposal makes it easy to detect changes in the interests of individuals/subscribers over time.