Visible to the public Biblio

Filters: Keyword is adaptive filtering  [Clear All Filters]
2020-12-28
Quincozes, S. E., Passos, D., Albuquerque, C., Ochi, L. S., Mossé, D..  2020.  GRASP-based Feature Selection for Intrusion Detection in CPS Perception Layer. 2020 4th Conference on Cloud and Internet of Things (CIoT). :41—48.

Cyber-Physical Systems (CPS) will form the basis for the world's critical infrastructure and, thus, have the potential to significantly impact human lives in the near future. In recent years, there has been an increasing demand for connectivity in CPS, which has brought to attention the issue of cyber security. Aside from traditional information systems threats, CPS faces new challenges due to the heterogeneity of devices and protocols. In this paper, we investigate how Feature Selection may improve intrusion detection accuracy. In particular, we propose an adapted Greedy Randomized Adaptive Search Procedure (GRASP) metaheuristic to improve the classification performance in CPS perception layer. Our numerical results reveal that GRASP metaheuristic overcomes traditional filter-based feature selection methods for detecting four attack classes in CPSs.

Barni, M., Nowroozi, E., Tondi, B., Zhang, B..  2020.  Effectiveness of Random Deep Feature Selection for Securing Image Manipulation Detectors Against Adversarial Examples. ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2977—2981.

We investigate if the random feature selection approach proposed in [1] to improve the robustness of forensic detectors to targeted attacks, can be extended to detectors based on deep learning features. In particular, we study the transferability of adversarial examples targeting an original CNN image manipulation detector to other detectors (a fully connected neural network and a linear SVM) that rely on a random subset of the features extracted from the flatten layer of the original network. The results we got by considering three image manipulation detection tasks (resizing, median filtering and adaptive histogram equalization), two original network architectures and three classes of attacks, show that feature randomization helps to hinder attack transferability, even if, in some cases, simply changing the architecture of the detector, or even retraining the detector is enough to prevent the transferability of the attacks.

Abazar, T., Masjedi, P., Taheri, M..  2020.  A Binary Relevance Adaptive Model-Selection for Ensemble Steganalysis. 2020 17th International ISC Conference on Information Security and Cryptology (ISCISC). :77—81.

Steganalysis is an interesting classification problem in order to discriminate the images, including hidden messages from the clean ones. There are many methods, including deep CNN networks to extract fine features for this classification task. Nevertheless, a few researches have been conducted to improve the final classifier. Some state-of-the-art methods try to ensemble the networks by a voting strategy to achieve more stable performance. In this paper, a selection phase is proposed to filter improper networks before any voting. This filtering is done by a binary relevance multi-label classification approach. The Logistic Regression (LR) is chosen here as the last layer of network for classification. The large-margin Fisher’s linear discriminant (FLD) classifier is assigned to each one of the networks. It learns to discriminate the training instances which associated network is suitable for or not. Xu-Net, one of the most famous state-of-the-art Steganalysis models, is chosen as the base networks. The proposed method with different approaches is applied on the BOSSbase dataset and is compared with traditional voting and also some state-of-the-art related ensemble techniques. The results show significant accuracy improvement of the proposed method in comparison with others.

Raju, R. S., Lipasti, M..  2020.  BlurNet: Defense by Filtering the Feature Maps. 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W). :38—46.

Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations (RP2), generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP2 attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP2 algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this lowpass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.

Slavic, G., Campo, D., Baydoun, M., Marin, P., Martin, D., Marcenaro, L., Regazzoni, C..  2020.  Anomaly Detection in Video Data Based on Probabilistic Latent Space Models. 2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS). :1—8.

This paper proposes a method for detecting anomalies in video data. A Variational Autoencoder (VAE) is used for reducing the dimensionality of video frames, generating latent space information that is comparable to low-dimensional sensory data (e.g., positioning, steering angle), making feasible the development of a consistent multi-modal architecture for autonomous vehicles. An Adapted Markov Jump Particle Filter defined by discrete and continuous inference levels is employed to predict the following frames and detecting anomalies in new video sequences. Our method is evaluated on different video scenarios where a semi-autonomous vehicle performs a set of tasks in a closed environment.

Borio, D., Gioia, C..  2020.  Mitigation of Frequency-Hopped Tick Jamming Signals. 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS). :624—630.

Global Navigation Satellite System (GNSS) jamming is an evolving technology where new modulations are progressively introduced in order to reduce the impact of interference mitigation techniques such as Adaptive Notch Filters (ANFs). The Standardisation of GNSS Threat reporting and Receiver testing through International Knowledge Exchange, Experimentation and Exploitation (STRIKE3) project recently described a new class of jamming signals, called tick signals, where a basic frequency tick is hopped over a large frequency range. In this way, discontinuities are introduced in the instantaneous frequency of the jamming signals. These discontinuities reduce the effectiveness of ANFs, which unable to track the jamming signal. This paper analyses the effectiveness of interference mitigation techniques with respect to frequency-hopped tick jamming signals. ANFs and Robust Interference Mitigation (RIM) techniques are analysed. From the analysis, it emerges that, despite the presence of frequency discontinuities, ANFs provide some margin against tick signals. However, frequency discontinuities prevent ANFs to remove all the jamming components and receiver operations are denied for moderate Jamming to Noise power ratio (J/N) values, RIM techniques are not affected by the presence of frequency discontinuities and significantly higher jamming power are sustained by the receiver when this type of techniques is adopted.

Hynek, K., Čejka, T., Žádník, M., Kubátová, H..  2020.  Evaluating Bad Hosts Using Adaptive Blacklist Filter. 2020 9th Mediterranean Conference on Embedded Computing (MECO). :1—5.

Publicly available blacklists are popular tools to capture and spread information about misbehaving entities on the Internet. In some cases, their straight-forward utilization leads to many false positives. In this work, we propose a system that combines blacklists with network flow data while introducing automated evaluation techniques to avoid reporting unreliable alerts. The core of the system is formed by an Adaptive Filter together with an Evaluator module. The assessment of the system was performed on data obtained from a national backbone network. The results show the contribution of such a system to the reduction of unreliable alerts.

Dove, R., Willett, K. D..  2020.  Contextually Aware Agile-Security in the Future of Systems Engineering. 2020 IEEE Systems Security Symposium (SSS). :1—8.

A recurring principle in consideration of the future of systems engineering is continual dynamic adaptation. Context drives change whether it be from potential loss (threats, vulnerabilities) or from potential gain (opportunity-driven). Contextual-awareness has great influence over the future of systems engineering and of systems security. Those contextual environments contain fitness functions that will naturally select compatible approaches and filter out the incompatible, with prejudice. We don't have to guess at what those environmental shaping forces will look like. William Gibson famously tells us why: “The future is already here, it's just not evenly distributed;” and, sometimes difficult to discern. This paper provides archetypes that 1) characterize general systems engineering for products, processes, and operations; 2) characterize the integration of security to systems engineering; and, 3) characterize contextually aware agile-security. This paper is more of a problem statement than a solution. Solution objectives and tactics for guiding the path forward have a broader range of options for subsequent treatment elsewhere. Our purpose here is to offer a short list of necessary considerations for effective contextually aware adaptive system security in the future of systems engineering.

Kulikov, G. V., Tien, D. T., Kulagin, V. P..  2020.  Adaptive filtering of non-fluctuation interference when receiving signals with multi-position phase shift keying. 2020 Moscow Workshop on Electronic and Networking Technologies (MWENT). :1—4.

{The paper considers the efficiency of an adaptive non-recursive filter using the adjustment algorithm for weighting coefficients taking into account the constant envelope of the desired signal when receiving signals with multi-position phase shift keying against the background of noise and non-fluctuation interference. Two types of such interference are considered - harmonic and retranslated. The optimal filter parameters (adaptation coefficient and length) are determined by using simulation; the effect of the filter on the noise immunity of a quadrature coherent signal receiver with multi-position phase shift keying for different combinations of interference and their intensity is estimated. It is shown that such an adaptive filter can successfully deal with the most dangerous sighting harmonic interference}.

2020-08-03
Ferraris, Davide, Fernandez-Gago, Carmen, Daniel, Joshua, Lopez, Javier.  2019.  A Segregated Architecture for a Trust-based Network of Internet of Things. 2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC). :1–6.
With the ever-increasing number of smart home devices, the issues related to these environments are also growing. With an ever-growing attack surface, there is no standard way to protect homes and their inhabitants from new threats. The inhabitants are rarely aware of the increased security threats that they are exposed to and how to manage them. To tackle this problem, we propose a solution based on segmented architectures similar to the ones used in industrial systems. In this approach, the smart home is segmented into various levels, which can broadly be categorised into an inner level and external level. The external level is protected by a firewall that checks the communication from/to the Internet to/from the external devices. The internal level is protected by an additional firewall that filters the information and the communications between the external and the internal devices. This segmentation guarantees a trusted environment among the entities of the internal network. In this paper, we propose an adaptive trust model that checks the behaviour of the entities and in case the entities violate trust rules they can be put in quarantine or banned from the network.
Moradi, Ashkan, Venkategowda, Naveen K. D., Werner, Stefan.  2019.  Coordinated Data-Falsification Attacks in Consensus-based Distributed Kalman Filtering. 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP). :495–499.
This paper considers consensus-based distributed Kalman filtering subject to data-falsification attack, where Byzantine agents share manipulated data with their neighboring agents. The attack is assumed to be coordinated among the Byzantine agents and follows a linear model. The goal of the Byzantine agents is to maximize the network-wide estimation error while evading false-data detectors at honest agents. To that end, we propose a joint selection of Byzantine agents and covariance matrices of attack sequences to maximize the network-wide estimation error subject to constraints on stealthiness and the number of Byzantine agents. The attack strategy is then obtained by employing block-coordinate descent method via Boolean relaxation and backward stepwise based subset selection method. Numerical results show the efficiency of the proposed attack strategy in comparison with other naive and uncoordinated attacks.
Qin, Xinghong, Li, Bin, Huang, Jiwu.  2019.  A New Spatial Steganographic Scheme by Modeling Image Residuals with Multivariate Gaussian Model. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :2617–2621.
Embedding costs used in content-adaptive image steganographic schemes can be defined in a heuristic way or with a statistical model. Inspired by previous steganographic methods, i.e., MG (multivariate Gaussian model) and MiPOD (minimizing the power of optimal detector), we propose a model-driven scheme in this paper. Firstly, we model image residuals obtained by high-pass filtering with quantized multivariate Gaussian distribution. Then, we derive the approximated Fisher Information (FI). We show that FI is related to both Gaussian variance and filter coefficients. Lastly, by selecting the maximum FI value derived with various filters as the final FI, we obtain embedding costs. Experimental results show that the proposed scheme is comparable to existing steganographic methods in resisting steganalysis equipped with rich models and selection-channel-aware rich models. It is also computational efficient when compared to MiPOD, which is the state-of-the-art model-driven method.
Nakayama, Kiyoshi, Muralidhar, Nikhil, Jin, Chenrui, Sharma, Ratnesh.  2019.  Detection of False Data Injection Attacks in Cyber-Physical Systems using Dynamic Invariants. 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). :1023–1030.

Modern cyber-physical systems are increasingly complex and vulnerable to attacks like false data injection aimed at destabilizing and confusing the systems. We develop and evaluate an attack-detection framework aimed at learning a dynamic invariant network, data-driven temporal causal relationships between components of cyber-physical systems. We evaluate the relative performance in attack detection of the proposed model relative to traditional anomaly detection approaches. In this paper, we introduce Granger Causality based Kalman Filter with Adaptive Robust Thresholding (G-KART) as a framework for anomaly detection based on data-driven functional relationships between components in cyber-physical systems. In particular, we select power systems as a critical infrastructure with complex cyber-physical systems whose protection is an essential facet of national security. The system presented is capable of learning with or without network topology the task of detection of false data injection attacks in power systems. Kalman filters are used to learn and update the dynamic state of each component in the power system and in-turn monitor the component for malicious activity. The ego network for each node in the invariant graph is treated as an ensemble model of Kalman filters, each of which captures a subset of the node's interactions with other parts of the network. We finally also introduce an alerting mechanism to surface alerts about compromised nodes.

Chowdhary, Ankur, Sengupta, Sailik, Alshamrani, Adel, Huang, Dijiang, Sabur, Abdulhakim.  2019.  Adaptive MTD Security using Markov Game Modeling. 2019 International Conference on Computing, Networking and Communications (ICNC). :577–581.
Large scale cloud networks consist of distributed networking and computing elements that process critical information and thus security is a key requirement for any environment. Unfortunately, assessing the security state of such networks is a challenging task and the tools used in the past by security experts such as packet filtering, firewall, Intrusion Detection Systems (IDS) etc., provide a reactive security mechanism. In this paper, we introduce a Moving Target Defense (MTD) based proactive security framework for monitoring attacks which lets us identify and reason about multi-stage attacks that target software vulnerabilities present in a cloud network. We formulate the multi-stage attack scenario as a two-player zero-sum Markov Game (between the attacker and the network administrator) on attack graphs. The rewards and transition probabilities are obtained by leveraging the expert knowledge present in the Common Vulnerability Scoring System (CVSS). Our framework identifies an attacker's optimal policy and places countermeasures to ensure that this attack policy is always detected, thus forcing the attacker to use a sub-optimal policy with higher cost.
Li, Guanyu, Zhang, Menghao, Liu, Chang, Kong, Xiao, Chen, Ang, Gu, Guofei, Duan, Haixin.  2019.  NETHCF: Enabling Line-rate and Adaptive Spoofed IP Traffic Filtering. 2019 IEEE 27th International Conference on Network Protocols (ICNP). :1–12.
In this paper, we design NETHCF, a line-rate in-network system for filtering spoofed traffic. NETHCF leverages the opportunity provided by programmable switches to design a novel defense against spoofed IP traffic, and it is highly efficient and adaptive. One key challenge stems from the restrictions of the computational model and memory resources of programmable switches. We address this by decomposing the HCF system into two complementary components-one component for the data plane and another for the control plane. We also aggregate the IP-to-Hop-Count (IP2HC) mapping table for efficient memory usage, and design adaptive mechanisms to handle end-to-end routing changes, IP popularity changes, and network activity dynamics. We have built a prototype on a hardware Tofino switch, and our evaluation demonstrates that NETHCF can achieve line-rate and adaptive traffic filtering with low overheads.
Liu, Meng, Wang, Longbiao, Dang, Jianwu, Nakagawa, Seiichi, Guan, Haotian, Li, Xiangang.  2019.  Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters. ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). :6201–6205.
Automatic Speech Verification (ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters (AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform (FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.
Xin, Le, Li, Yuanji, Shang, Shize, Li, Guangrui, Yang, Yuhao.  2019.  A Template Matching Background Filtering Method for Millimeter Wave Human Security Image. 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). :1–6.
In order to solve the interference of burrs, aliasing and other noises in the background area of millimeter wave human security inspection on the objects identification, an adaptive template matching filtering method is proposed. First, the preprocessed original image is segmented by level set algorithm, then the result is used as a template to filter the background of the original image. Finally, the image after background filtered is used as the input of bilateral filtering. The contrast experiments based on the actual millimeter wave image verifies the improvement of this algorithm compared with the traditional filtering method, and proves that this algorithm can filter the background noise of the human security image, retain the image details of the human body area, and is conducive to the object recognition and location in the millimeter wave security image.
LiPing, Yuan, Pin, Han.  2019.  Research of Low-Quality Laser Security Code Enhancement Technique. 2019 Chinese Automation Congress (CAC). :793–796.
The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
Saxena, Shubhankar, Jais, Rohan, Hota, Malaya Kumar.  2019.  Removal of Powerline Interference from ECG Signal using FIR, IIR, DWT and NLMS Adaptive Filter. 2019 International Conference on Communication and Signal Processing (ICCSP). :0012–0016.
ECG signals are often corrupted by 50 Hz noise, the frequency from the power supply. So it becomes quite necessary to remove Power Line Interference (PLI) from the ECG signal. The reference ECG signal data was taken from the MIT-BIH database. Different filtering techniques comprising of Discrete Wavelet Transform (DWT), Normalized Least Mean Square (NLMS) filter, Finite Impulse Response (FIR) filter and Infinite Impulse Response (IIR) filter were used in this paper for denoising the ECG signal which was corrupted by the PLI. Later, the comparison was made among the methods, to find the best methodology to denoise the corrupted ECG signal. The parameters that were used for the comparison are Mean Square Error (MSE), Mean Absolute Error (MAE), Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR). Higher values of SNR & PSNR and lower values of MSE & MAE define the best denoising algorithm.
2019-01-21
Wasilewski, Jacek, Hurley, Neil.  2018.  Intent-aware Item-based Collaborative Filtering for Personalised Diversification. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. :81–89.

Diversity has been identified as one of the key dimensions of recommendation utility that should be considered besides the overall accuracy of the system. A common diversification approach is to rerank results produced by a baseline recommendation engine according to a diversification criterion. The intent-aware framework is one of the frameworks that has been proposed for recommendations diversification. It assumes existence of a set of aspects associated with items, which also represent user intentions, and the framework promotes diversity across the aspects to address user expectations more accurately. In this paper we consider item-based collaborative filtering and suggest that the traditional view of item similarity is lacking a user perspective. We argue that user preferences towards different aspects should be reflected in recommendations produced by the system. We incorporate the intent-aware framework into the item-based recommendation algorithm by injecting personalised intent-aware covariance into the item similarity measure, and explore the impact of such change on the performance of the algorithm. Our experiments show that the proposed method improves both accuracy and diversity of recommendations, offering better accuracy/diversity tradeoff than existing solutions.

Fortes, Reinaldo Silva, Lacerda, Anisio, Freitas, Alan, Bruckner, Carlos, Coelho, Dayanne, Gonçalves, Marcos.  2018.  User-Oriented Objective Prioritization for Meta-Featured Multi-Objective Recommender Systems. Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. :311–316.

Multi-Objective Recommender Systems (MO-RS) consider several objectives to produce useful recommendations. Besides accuracy, other important quality metrics include novelty and diversity of recommended lists of items. Previous research up to this point focused on naive combinations of objectives. In this paper, we present a new and adaptable strategy for prioritizing objectives focused on users' preferences. Our proposed strategy is based on meta-features, i.e., characteristics of the input data that are influential in the final recommendation. We conducted a series of experiments on three real-world datasets, from which we show that: (i) the use of meta-features leads to the improvement of the Pareto solution set in the search process; (ii) the strategy is effective at making choices according to the specificities of the users' preferences; and (iii) our approach outperforms state-of-the-art methods in MO-RS.

Meng, Leilei, Su, Xin, Zhang, Xuewu, Choi, Chang, Choi, Dongmin.  2018.  Signal Reception for Successive Interference Cancellation in NOMA Downlink. Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems. :75–79.

Successive interference cancellation (SIC) receiver is adopted by power domain non-orthogonal multiple access (NOMA) at the receiver side as the baseline receiver scheme taking the forthcoming expected mobile device evolution into account. Development technologies and advanced techniques are boldly being considered in order to achieve power saving in many networks, to reach sustainability and reliability in communication due to envisioned huge amount of data delivery. In this paper, we propose a novel scheme of NOMA-SIC for the sake of balancing the trade-off between system performance and complexity. In the proposed scheme, each SIC level is comprised by a matching filter (MF), a MF detector and a regenerator. In simulations, the proposed scheme demonstrates the best performance on power saving, of which energy efficiency increases with an increase in the number of NOMA device pairs.

Sovilj, Dusan, Sanner, Scott, Soh, Harold, Li, Hanze.  2018.  Collaborative Filtering with Behavioral Models. Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization. :91–99.

Collaborative filtering (CF) has made it possible to build personalized recommendation models leveraging the collective data of large user groups, albeit with prescribed models that cannot easily leverage the existence of known behavioral models in particular settings. In this paper, we facilitate the combination of CF with existing behavioral models by introducing Bayesian Behavioral Collaborative Filtering (BBCF). BBCF works by embedding arbitrary (black-box) probabilistic models of human behavior in a latent variable Bayesian framework capable of collectively leveraging behavioral models trained on all users for personalized recommendation. There are three key advantages of BBCF compared to traditional CF and non-CF methods: (1) BBCF can leverage highly specialized behavioral models for specific CF use cases that may outperform existing generic models used in standard CF, (2) the behavioral models used in BBCF may offer enhanced intepretability and explainability compared to generic CF methods, and (3) compared to non-CF methods that would train a behavioral model per specific user and thus may suffer when individual user data is limited, BBCF leverages the data of all users thus enabling strong performance across the data availability spectrum including the near cold-start case. Experimentally, we compare BBCF to individual and global behavioral models as well as CF techniques; our evaluation domains span sequential and non-sequential tasks with a range of behavioral models for individual users, tasks, or goal-oriented behavior. Our results demonstrate that BBCF is competitive if not better than existing methods while still offering the interpretability and explainability benefits intrinsic to many behavioral models.

Schneider, Jens, Bläser, Max, Wien, Mathias.  2018.  Sparse Coding Based Frequency Adaptive Loop Filtering for Video Coding. Proceedings of the 23rd Packet Video Workshop. :48–53.

In-loop filtering is an important task in video coding, as it refines both the reconstructed signal for display and the pictures used for inter-prediction. In order to remove coding artifacts, machine learning based methods are assumed to be beneficial, as they utilize some prior knowledge on the characteristics of raw images. In this contribution, a dictionary learning / sparse coding based inloop filter and a frequency adaptation model based on the lp-ballenergy in the spectral domain is proposed. Thereby the dictionary is trained on raw data and the algorithms are controlled mainly by the parameter for the sparsity. The frequency adaption model results in further improvement of the sparse coding based loop filter. Experimental results show that the proposed method results in coding gains up to l-4.6 % at peak and -1.74 % on average against HEVC in a Random Access coding configuration.

Martinek, Radek, Kahankova, Radana, Bilik, Petr, Nedoma, Jan, Fajkus, Marcel, Blaha, Petr.  2018.  Speech Quality Assessment Based on Virtual Instrumentation. Proceedings of the 10th International Conference on Computer Modeling and Simulation. :49–53.

This paper introduces a program for objective and subjective evaluation of speech quality. Using this environment, a lot of speech recordings and various indoor and outdoor noises were processed. As a subjective speech evaluation method, the Dynamic time warping (DTW) method was selected, with PARCOR coefficients being chosen as symptom vectors. For the filtration of the noise in the recording, adaptive filtering based on LMS and RLS algorithms was used and the performance of the adaptive filtering was assessed. Similarity ranged from 70% to 95% for both algorithms. In terms of signal to noise ratio, the RLS algorithm ranged from 36 dB to 42 dB, while the LMS algorithm only varied from 20 dB to 29 dB.