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Brief Review of Recent Researches in Speech Enhancement from Filters to Neural Networks

Brief Review of Recent Researches in Speech Enhancement from...

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computing and data Science (CDS), international conference on

作者： Fei Ge Information Science and Technology College Dalian Maritime University Dalian China

ISBN: (数字)9781728171067

ISBN: (纸本)9781728171074

According to the World Health Organization, more and more people will suffer from hearing loss in the future. Therefore, there will be greater demand for the output and technology of hearing aids. Under the help of artificial intelligence, the technology of smart hearing aids will also become more intelligent, so that the wearer can get a better experience. This paper mainly studies the related problems of speech signal processing in intelligent digital hearing aids. This article focuses on the speech enhancement in digital hearing aids. First, this work studies the application of filter in speech enhancement technology, mainly introduces the Wiener filter algorithm using the minimum mean square error criterion; the Kalman filter algorithm that can solve discrete signals; spectral subtraction based on multi-window spectrum estimation. Secondly, this paper studies some specific methods of deep learning technology in speech enhancement, mainly introduces DNN-based speech enhancement method, deep learning-based auditory cepstrum coefficient speech enhancement algorithm, and AE-CGAN-based speech enhancement algorithm. Finally, this paper studies the related problems of acoustic scene classification, divides the listening environment into Gaussian white noise, impact noise, and music noise, and explains the solutions for each listening environment.

关键词： FCC data science

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RepEL: A Utility-Preserving Privacy System for IoT-Based Energy Meters

RepEL: A Utility-Preserving Privacy System for IoT-Based Ene...

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IEEE international conference on Internet-of-Things Design and Implementation (IoTDI)

作者： Phuthipong Bovornkeeratiroj Srinivasan Iyengar Stephen Lee David Irwin Prashant Shenoy University of Massachusetts Amherst Microsoft Research India University of Pittsburgh

ISBN: (数字)9781728166025

ISBN: (纸本)9781728166032

Modern Internet of Things (IoT) applications transmit sensor data to the cloud where it is subjected to analytics to provide useful services to users. Unfortunately, IoT sensor data often embeds sensitive private information that is vulnerable to leakage when sent to the cloud. Prior work on preserving IoT data privacy, particularly in the energy domain, focuses on obfuscating data to prevent extraction of private information. However, unless done carefully, data obfuscation significantly reduces the ability to extract useful but non-private information from the data. As a result, these existing techniques also reduce much of the utility derived from deploying IoT devices. In this paper, we address this problem by designing RepEL, a new utility-preserving privacy technique, which intelligently obfuscates smart energy meter data to prevent leaking a home's private occupancy information, while retaining the ability to perform useful energy disaggregation analytics. To preserve energy data's utility, our approach creates a randomized permutation of actual device usage via load replay while suppressing private user behavior information (such as occupancy) in the original data. We implement our algorithm on an embedded gateway node to demonstrate its feasibility and empirically evaluate our approach using real energy traces from homes. Our results show that the privacy leak rate for nearly two-thirds of the homes is below 10%, with four homes having no privacy leak. At the same time, the change in device usage for these homes is less than 3%. Further, we also demonstrate that RepEL has the flexibility to randomly replay loads, which can prevent adversaries from inferring behavioral patterns from device usage or use the information to determine occupancy.

关键词： data privacy Cloud computing Privacy Meters Internet of Things Performance evaluation smart homes

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A Needle in a Haystack: Finding a Suitable Lightweight IoT Authentication Protocol and Improving Its Usability

A Needle in a Haystack: Finding a Suitable Lightweight IoT A...

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IEEE international conference on Trust, Privacy and Security in intelligent Systems and Applications (TPS-ISA)

作者： Nan Li Dongxi Liu Surya Nepal Guangyu Pei Data61 CSIRO Marsfield NSW Australia School of Electrical Engineering and Computing The University of Newcastle Australia Boeing USA

ISBN: (数字)9781728185439

ISBN: (纸本)9781728185446

Internet of Things (IoT) comprises of networked computing devices that can sense and even actuate. It has been widely used in many applications such as smart cities, smart factories, supply chains, smart grid, and precision health. One of the key challenges in practice is to find a suitable lightweight authentication protocol with concerns of security, performance, compatibility, and usability for resource-constrained IoT devices. Due to the lack of security standards for such IoT devices, it is challenging to choose a suitable authentication protocol in practice. For example, many lightweight authentication protocols provide theoretical security analyses, but practical feasibility is not discussed. The integration between a new protocol and the existing standards is non-trivial. It is hence important to develop a novel paradigm for comparing and selecting lightweight authentication protocols considering both theoretical and practical factors. In this paper, we first show the importance and challenges of finding suitable lightweight authentication protocols. Then, we demonstrate how such a paradigm can be built with DTLS, describing a methodology of selecting a protocol and integrating with industry-standard libraries. In our demonstration, the protocol implementation is conducted not only on resource-constrained devices but also based on OpenSSL libraries. The result shows the feasibility, though with difficulties, to integrate a strong security protocol with the existing standards.

关键词： Protocols Security Authentication Internet of Things Standards Performance evaluation Elliptic curve cryptography

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A novel approach for high-velocity big geo-data handling using iterative and feature learning algorithms

A novel approach for high-velocity big geo-data handling usi...

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international conference on Fog and Mobile Edge computing (FMEC)

作者： Sana Rekik Sami Faiz Higher Institute of Management of Tunis LTSIRS Lab of the National Engineers School of Tunis Tunisia Higher Institute of Multimedia Art of la Manouba LTSIRS Lab of the National Engineers School of Tunis Tunisia

ISBN: (数字)9781728172163

ISBN: (纸本)9781728172170

Geospatial data were exclusively generated by official agencies. However, following the technological revolution in data collection and production, various sources have emerged for the massive production of geospatial data, resulting the phenomenon of big geo-data. Therefore, dealing with large amounts of these data sets, results in a high velocity as they change very quickly, is a challenging task. Hence, analysis become more complex and computation become prohibitively expensive. As a result, spatial computing technologies become limited in front of these complex data and operations. Accordingly, we aimed to refine complexity with simplicity by replacing traditional geospatial models with referring to the simplest intelligent and minimum resource requirement algorithms that can be applied against these constraints, while ensuring the criteria of performance and scalability. In this work, we focus on the high-velocity of this big geo-data through the use of an iterative approach applied to a feature learning algorithms to decrease the memory consumption and the time complexity of traditional machine learning algorithms. According to our knowledge, although they were widely applied in the 19th century as a solution to overcome the problems of limitation of memory and computing resources. Iterative methods were still not used for the big geo-data analytics and generally for the big data domain. Thus, this approach could be beneficial especially for real time applications such as the anomaly monitoring and detection.

关键词： Big data Iterative methods Complexity theory Artificial intelligence Geospatial analysis Real-time systems Tools

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smart Monitoring of Automatic Under Frequency Load Shedding (SMOAUFLS)

Smart Monitoring of Automatic Under Frequency Load Shedding ...

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Emerging Technology (INCET), international conference for

作者： Shubham Meharkure Nandkishor Kinhekar Dept. of Electrical Engineering Sardar Patel College of Engineering Mumbai India

ISBN: (数字)9781728162218

ISBN: (纸本)9781728162225

Today's world is the internet world, the Internet of Things (IoT) is expanding at a rapid rate increasing technology. A network of connected computers hidden in every corner of our life monitoring and controlling things with minimal IoT supports to connect hardware devices to the internet to process the data for monitoring and security. This system uses some controllers and some sensors help to monitor the real-time parameters like Frequency, Wattage, Voltage, Current etc. are accessed and monitored from remote area by cloud computing called *** and controls the system operation by computer or smartphone for Automatic under frequency load shedding (AUFLS).This paper presents an application of IoT for smart Monitoring of Automatic Under frequency Load shedding Scheme at a system coordination centre.

关键词： Relays Mathematical model Monitoring Internet of Things Generators intelligent sensors

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Uncertainty-Aware Forecasting of Renewable Energy Sources

Uncertainty-Aware Forecasting of Renewable Energy Sources

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international conference on Big data and smart computing (BIGCOMP)

作者： Direnc Pekaslan Christian Wagner Jonathan M. Garibaldi Luis G. Marin Doris Sáez Intelligent Modelling and Analysis (IMA) Group and Lab for Uncertainty in Data and Decision Making (LUCID) School of Computer Science University of Nottingham Nottingham UK University of Nottingham Nottingham UK University of Chile Santiago Chile

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

smart grid systems are designed to enable the efficient capture and intelligent distribution of electricity across a distributed set of utilities. They are an essential component of increasingly important renewable energy sources, where it is vital to forecast the levels of energy being fed into and drawn from the grid. However, because of the high levels of uncertainty affecting real-world environments, accurate forecasting for example of wind power generation - being directly dependent on meteorological parameters and climatic conditions - is extremely challenging. Fuzzy Logic systems are frequently used in control systems to leverage their capacity for handling varying levels of uncertainty. In most cases, while uncertainty affecting the systems is captured in fuzzy sets (FSs), the final output of such systems is reduced to a crisp number (e.g. a control output). The latter process, while providing an efficient pathway to generating a specific control output, at the same time implies substantial information loss, as the uncertainty information captured in the FS outputs of these systems is effectively discarded. In this paper, we explore the potential of Mamdani fuzzy logic system based forecasting in order to generate not only a numeric forecast of the energy generated, but to also generate uncertainty intervals around said forecast indicating the level of uncertainty associated with the prediction. The proposed model is explored using both synthetic and smart-grid specific real-world (wind power) time series datasets. The results of the study indicate that utilising the 'complete' FS output can provide valuable additional information in terms of the reliability of the forecast without any extra computational cost. At a general level, the approach indicates strong potential for leveraging the uncertainty information in fuzzy system outputs - which is commonly discarded - in real world applications.

关键词： Uncertainty Frequency selective surfaces Forecasting Time series analysis Fuzzy logic Fuzzy sets smart grids

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A UAV-assisted Blockchain Based Secure Device-to-Device Communication in Internet of Military Things

A UAV-assisted Blockchain Based Secure Device-to-Device Comm...

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international conference on Information and Communication Technology Convergence (ICTC)

作者： Mohtasin Golam Jae-Min Lee Dong-Seong Kim Networked System Laboratory School of Electronics Engineering Kumoh Institute of Technology Gumi South Korea

ISBN: (数字)9781728167589

ISBN: (纸本)9781728199016

The significant advancement of blockchain technology in the field of industrial networks has created a meaningful impact and for this, it comes to the field of the military where blockchain can have a greater impact. In the military environment, a lot of machines and smart devices are connected. Unmanned aerial vehicle (UAV) combined with mobile edge computing (MEC) can offer real-time services. However, through the UAV, smart devices and intelligent machines, attackers can easily access. This leads to disruption on operations and revealing the data confidentiality to an adversary. To prevent this issue an architecture is proposed based on blockchain technology. The architecture is applied to provide a solution for malicious cyber-attacks and prevent data loss to ensure the security on device-to-device (D2D) communication in the military network to provide maximum security. Simulation results illustrate that how efficaciously the applied method can work for restorative the security on D2D communication. Finally, in conclusion, how the proposed framework can be enhanced by applying other feasible techniques along with future research directions is discussed.

关键词： Military computing Blockchain Computer architecture Unmanned aerial vehicles Device-to-device communication Security smart devices

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Monitoring Vital Parameters of Comatose Patients using smart Sensors Integrated with Cloud Storage

Monitoring Vital Parameters of Comatose Patients using Smart...

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international conference on I-SMAC (IoT in Social, Mobile, analytics and Cloud) (I-SMAC)

作者： P. Sinthia Malathi. M Sripriya. T Rahul Krishnan Gurumoorthy. G K. Jalaldeen Dept. of Biomedical Engineering Saveetha Engineering College Chennai India Dept. of ECE Rajalakshmi Institute of Technology Chennai India Dept. of ECE Jeppiaar Institute of Technology Chennai India Dept. of ECE SRM Institute of Science and Technology Chennai India Dept. of Medical Electronics Saveetha Engineering College Chennai India Dept. of ECE Solamalai College of Engineering Chennai India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The monitoring of comatose patients is critical for identifying adverse changes in their physiological state and ensuring timely medical intervention. This research presents a novel IoT-based system for remotely monitoring and assessing vital signs in comatose patients. The proposed system utilizes various IoT sensors to measure pulse, respiratory rate, and eye movement, transmitting data to a centralized database and connected devices for real-time monitoring by healthcare professionals and family members. This approach offers a significant improvement over traditional observation methods, which can be time-consuming and prone to human error. By providing up-to-date information about the patient's vital signs and body movements, the IoT-based system enhances the quality of care and improves patient outcomes. This paper discusses the system's characteristics, building blocks, and implementation challenges based on existing research in this area.

关键词： Temperature sensors Cloud computing Pulse measurements Medical services Sensor systems Real-time systems Reliability smart devices Monitoring intelligent sensors

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Towards intelligent and Dynamic Road Speed Adaptation Model in smart Cities

Towards Intelligent and Dynamic Road Speed Adaptation Model ...

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IEEE international conference on Wireless and Mobile computing, Networking And Communications (WiMob)

作者： Pierre E. Abi-Char Abduladhim Ashtaiwi College of Engineering and Technology American University of the Middle East Kuwait

ISBN: (数字)9781728197227

ISBN: (纸本)9781728197234

smart cities integrate data collection and communication technologies to operate more efficiently in order to provide better services to citizens, improved road traffic situation, better economic development, etc. However, safety still a main concern of road traffic as road crashes pose serious threats to road users causing death and injury all over the world. Road traffic safety is the process of implementing measures to reduce the number of crashes, death and severe injuries. In this paper, using Machine Learning (ML) algorithms, Off-line, we train and test the intelligent and Dynamic Adaptation Model (IDAM) on the road traffic data collected for 14 years of car crashes in United Kingdom (UK). IDAM then on-line determines the optimal speed limit for each road segment based on many predictors such as weather, road type, light condition, and many others, stated later. We train and test IDAM using three different machine learning algorithms: Artificial Neural Network (ANN), Decision Tree (DT), and Linear Regression (LR) with Stochastic Gradient Descent (SGD). Using the testing dataset, IDAM achieves prediction accuracy of around 96%. Implemented in an operation algorithm, IDAM can continuously adapt the road speed limit to an optimal value as the predictors change on-line.

关键词： Roads Accidents Mathematical model Automobiles Adaptation models Prediction algorithms Machine learning algorithms

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Integrated IoT and Machine Learning Solutions for Precision Farming: Crop Recommendation and Leaf Disease Diagnosis

Integrated IoT and Machine Learning Solutions for Precision ...

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international conference on I-SMAC (IoT in Social, Mobile, analytics and Cloud) (I-SMAC)

作者： Harish M J Vimala Devi Dept. of Computer Science and Engineering Dayananda Sagar College of Engineering(of Aff.VTU) Bengaluru India

ISBN: (数字)9798350376425

ISBN: (纸本)9798350376432

The digital convergence of the Internet of Things and machine learning has therefore redefined precision agriculture with respect to productivity and sustainability. This work therefore proposes a smart agriculture system via the following IoT sensors: Soil Moisture, DHT, and NPK sensors, along with data storage in ThingSpeak cloud *** Similarly, farmers learn which plants are best to grow based on their projected data from machine learning algorithms. A convolutional neural network named ResNet was used for its control to instantly and correctly identify plant diseases in live mode. For the system’s front end, HTML, CSS, and JavaScript were used while Python facilitated creation of the back end that provides users with an easy-to-use interface. It has achieved high accuracy levels in crop recommendation through models like Decision Tree, Naive Bayes, SVM, Logistic Regression, Random Forest and XGBoost among others have attained an accuracy of 90%, 99.09%, 97.95%, 95.27%, 99.09% and 99.31% respectively in crop recommendation. By ResNet model in the identification of leaf disease, a density of 99.2% was achieved.

关键词： Support vector machines Cloud computing Accuracy Soil moisture Crops Internet of Things Convolutional neural networks Sustainable development Random forests intelligent sensors

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