Bit Error Reduction in MIMO-OFDM with trellis Codes Using KALMN Filtering

The orthogonal frequency division multiplexing (OFDM) is used to transport data at a rapid rate. Due to the dynamic nature of the network, the bit error rate is the main problem with OFDM. The extra codes used in space-time trellis coding help to lower bit rate error on multipath fading channels. This study uses space-time trellis coding on a wireless channel to improve the bit error rates. In this work, space-time trellis codes with KALMAN filters are used to improve  the bit error rate over wireless channels. The proposed modal  is simulated in MATLAB software, and the results exhibit that the figure of bit error rate has decreased in network

Метод подавления акустического эха на основе рекуррентной нейронной сети и алгоритма кластеризации

The article solves the problem of acoustic echo suppression based on a neural network that evaluates an ideal binary mask IBM using features extracted from a mixture of near-end and far-end signals. The novelty of the proposed method lies in the use of the clustering algorithm in addition to the bidirectional recurrent neural network BLSTM. To evaluate the use of the EM, Mean-Shift, k-Means clustering algorithms, the models have been trained and tested on the TIMIT database. For each model, the ERLE, PESQ, STOI metrics have been calculated to characterize its quality. The use of the EM and Mean-Shift clustering algorithms appeared to be inefficient compared to the BLSTM algorithm at a signal-to-echo ratio of 10 dB. With a signal-to-echo ratio of 6 dB, BLSTM+Mean-Shift resulted in a marginal improvement in the PESQ metric compared to the BLSTM algorithm. The results of the experiments show the effectiveness of the proposed BLSTM model when using a network with the K-Means algorithm, compared to using a pure BLSTM for echo cancellation in double-talk scenarios. With a signal-to-echo ratio of 10 dB, the STOI metric, which characterizes speech intelligibility, has improved by 7%, and the PESQ metric, which characterizes the quality of speech restoration, by 18.8%.В статье решается задача подавления акустического эха на основе нейронной сети оценивающей идеальную двоичную маску IBM из признаков, извлеченных из смеси сигналов ближнего и дальнего конца. Новизна предложенного метода заключается в использовании алгоритма кластеризации дополнительно с двунаправленной рекуррентной нейронной сетью BLSTM. Для оценки использования алгоритмов кластеризации EM, Mean-Shift, k-Means, модели были обучены и протестированы на базе данных TIMIT. Для каждой модели были вычислены метрики ERLE, PESQ, STOI, характеризующие ее качество. Использование алгоритмов кластеризации EM, Mean-Shift оказалось неэффективным по сравнению с алгоритмом BLSTM при соотношении сигнал/эхо 10 дБ. При соотношении сигнал/эхо 6 дБ BLSTM+Mean-Shift привел к незначительному улучшению метрики PESQ по сравнению с алгоритмом BLSTM. Результаты экспериментов показали эффективность предложенной модели BLSTM при использовании сети с алгоритмом K-Means, по сравнению с использованием чистой BLSTM для подавления эха в сценариях с двойным разговором. При соотношении сигнал/эхо 10 дБ метрика STOI, характеризующая разборчивость речи, улучшилась на 7%, а метрика PESQ, характеризующая качество восстановления речи, на 18.8%

Information-based interactive services and support system

The fact that information-based interaction designed requires user involvement, Service Desk System (SDS) is necessary for collecting, tracking and processing requests for IT service support. The system can be dynamic, where many approaches can be used depending on the objective of the tasks at hand. Currently, Kulliyyah of Information and Communication Technology (KICT), International Islamic University Malaysia (IIUM) SDS do not cover a lot of evolving user's tasks. As a result, the main aim of this study is to propose interactive SDS for students and staffs at KICT, IIUM. The architectural framework of the system has been formulated to include all the necessary requirements that might have arisen over time. There are some drawbacks in the process of the current system which affects the whole management process of the organization. With the aid of the framework, a prototype has been implemented. This prototype is established using the Hypertext Markup Language (HTML), PHP with the support of Cascading Style Sheets (CSS3), Bootstrap and JavaScript along with MySQL as the database. The developed system is an online (web-based) system which allows users to request information in order for the administrator to respond promptly, which shows the efficiency and reliability of the syste

Active noise cancellation in the rectangular enclosure systems

The interior active noise control (ANC) is essential to be explore because it is significant for automobile manufacturer to design noise control systems and interior noise treatments in the automobiles system. In this research, experimental work undertaken for cancelling an active noise in the rectangular enclosure. The rectangular enclosure was fabricated with multiple speakers and microphones inside the enclosure. A software program using digital signal processing is implemented to evaluate the proposed method. Noise is generated by using multi-speaker inside the enclosure and microphones are used for noise measurements. At the end of this research, the result of output noise before and after cancellation are presented and discussed. On the basis of the findings presented in this research, an active noise cancellation in the rectangular enclosure is worth exploring in order to improve the noise control technologies

Evaluation of countermeasure against future malware evolution with deterministic modeling

Recently, machine learning technologies have dramatically evolved. Accordingly, the concept of self-evolving botnets has been introduced, which discover vulnerabilities of hosts by distributed machine learning using the computational resources of infected hosts, and infect other hosts by attacks using the discovered vulnerabilities. The infectability of the self-evolving botnets is too strong compared with conventional botnets, so that such new botnets will become the serious threat to future network society including 5G and IoT environments. In this paper, we consider a volunteer model that discovers unknown vulnerabilities earlier than self-evolving botnets by distributed computing using volunteer hosts’ resources and repairs the vulnerabilities. We propose deterministic modeling for the volunteer model. Through numerical calculations, we evaluate the performance of the volunteer model against self-evolving botnets.This is a product of research which was financially supported by the Kansai University Fund for Supporting Young Scholars, 2018, "Design of anti-malware systems against future malware evolution". This research was partially supported by The Telecommunications Advancement Foundation, Japan.Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC 2019), November 18-21, 2019, Lanzhou, Chin

Lossless Encoding of Time-Aggregated Neuromorphic Vision Sensor Data Based on Point-Cloud Compression

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Neuromorphic Vision Sensors (NVSs) are emerging sensors that acquire visual information asynchronously when changes occur in the scene. Their advantages versus synchronous capturing (frame-based video) include a low power consumption, a high dynamic range, an extremely high temporal resolution, and lower data rates. Although the acquisition strategy already results in much lower data rates than conventional video, NVS data can be further compressed. For this purpose, we recently proposed Time Aggregation-based Lossless Video Encoding for Neuromorphic Vision Sensor Data (TALVEN), consisting in the time aggregation of NVS events in the form of pixel-based event histograms, arrangement of the data in a specific format, and lossless compression inspired by video encoding. In this paper, we still leverage time aggregation but, rather than performing encoding inspired by frame-based video coding, we encode an appropriate representation of the time-aggregated data via point-cloud compression (similar to another one of our previous works, where time aggregation was not used). The proposed strategy, Time-Aggregated Lossless Encoding of Events based on Point-Cloud Compression (TALEN-PCC), outperforms the originally proposed TALVEN encoding strategy for the content in the considered dataset. The gain in terms of the compression ratio is the highest for low-event rate and low-complexity scenes, whereas the improvement is minimal for high-complexity and high-event rate scenes. According to experiments on outdoor and indoor spike event data, TALEN-PCC achieves higher compression gains for time aggregation intervals of more than 5 ms. However, the compression gains are lower when compared to state-of-the-art approaches for time aggregation intervals of less than 5 ms.Funder: EPSRC; Grant(s): EP/P022715/

A Review on Emotion Recognition Algorithms using Speech Analysis

In recent years, there is a growing interest in speech emotion recognition (SER) by analyzing input speech. SER can be considered as simply pattern recognition task which includes features extraction, classifier, and speech emotion database. The objective of this paper is to provide a comprehensive review on various literature available on SER. Several audio features are available, including linear predictive coding coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), and Teager energy based features. While for classifier, many algorithms are available including hidden Markov model (HMM), Gaussian mixture model (GMM), vector quantization (VQ), artificial neural networks (ANN), and deep neural networks (DNN). In this paper, we also reviewed various speech emotion database. Finally, recent related works on SER using DNN will be discussed

Neural Natural Language Inference Models Enhanced with External Knowledge

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.Comment: Accepted by ACL 201