Intelligent Audio Analysis techniques for identification of Music in Smart Devices

2021 ◽  
Author(s):  
Pragun Mangla ◽  
Shefali Arora ◽  
M.P.S Bhatia
Keyword(s):  
Smart Devices ◽  
Audio Analysis ◽  
Analysis Techniques

Applying Machine Learning and Audio Analysis Techniques to Insect Recognition in Intelligent Traps

2013 ◽  
Author(s):  
Diego F. Silva ◽  
Vinicius M.A. De Souza ◽  
Gustavo E.A.P.A. Batista ◽  
Eamonn Keogh ◽  
Daniel P.W. Ellis
Keyword(s):  
Machine Learning ◽  
Audio Analysis ◽  
Analysis Techniques

The application of cochlear audio analysis techniques to electro‐acoustic music

2004 ◽  
Vol 115 (5) ◽  
pp. 2450-2450
Author(s):  
John A. Mills
Keyword(s):  
Audio Analysis ◽  
Analysis Techniques

scholarly journals Malware threat analysis techniques and approaches for IoT applications: a review

2021 ◽  
Vol 10 (3) ◽  
Author(s):  
Chimeleze Collins Uchenna ◽  
Norziana Jamil ◽  
Roslan Ismail ◽  
Lam Kwok Yan ◽  
Mohamad Afendee Mohamed
Keyword(s):  
Smart Devices ◽  
Analysis Techniques ◽  
Threat Analysis ◽  
Iot Applications ◽  
Holistic Approaches ◽  
Business Efficiency ◽  
Application Protocols ◽  
Smart Factories ◽  
Share Information ◽  
Constrained Devices

Internet of things (IoT) is a concept that has been widely used to improve business efficiency and customer’s experience. It involves resource constrained devices connecting to each other with a capability of sending data, and some with receiving data at the same time. The IoT environment enhances user experience by giving room to a large number of smart devices to connect and share information. However, with the sophistication of technology has resulted in IoT applications facing with malware threat. Therefore, it becomes highly imperative to give an understanding of existing state-of-the-art techniques developed to address malware threat in IoT applications. In this paper, we studied extensively the adoption of static, dynamic and hybrid malware analyses in proffering solution to the security problems plaguing different IoT applications. The success of the reviewed analysis techniques were observed through case studies from smart homes, smart factories, smart gadgets and IoT application protocols. This study gives a better understanding of the holistic approaches to malware threats in IoT applications and the way forward for strengthening the protection defense in IoT applications.

scholarly journals IoT Anomaly Detection using Multivariate

2020 ◽  
Vol 9 (4) ◽  
pp. 1662-1669
Keyword(s):  
Multivariate Analysis ◽  
Anomaly Detection ◽  
Principal Component ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Smart Devices ◽  
Environmental Sensors ◽  
Computational Power ◽  
Analysis Techniques ◽  
Computationally Intensive

Devices associated with Internet of Things are typically constrained in their resources and do not have the computational power necessary to analyze their input and detect anomalies that occur. Smart devices or and environmental sensors that measure temperature, air quality, or seismic activity are all built for specific purposes with minimal resources and often do not have enough security in place to protect against infiltration or detect abnormal behavior. Additionally, because these devices and sensors are typically always connected and transmit constant data in near real-time, the high dimensionality of the raw readings are extremely computationally intensive to analyze. A possible solution to reduce the dimensionality of the data while also extracting the most significant features is to use multivariate analysis techniques such as Principal Component Analysis. PCA is a method of multivariate analysis meant to reduce the size of matrices while not only keeping the most significant variables but also learning the interactions between them. In this paper, we propose exploring anomaly detection in IoT using multivariate analysis techniques to reduce the dimensionality of sensor input to reduce the computational complexity of analysis and learning the most significant variables. While the normal conditions of sensor data are often readily available, the size of the data makes it difficult to precisely determine instances of targeted anomalies. In this study, PCA is used to analyze the available features of the data and from them can determine the sensors under normal conditions. Once the normal conditions are determined, outliers which constitute anomalies can be determined through techniques such as Mahalanobis distance to determine the variance of each observation from the normal distribution. Our work can also be expanded to use other methods of dimensionality reduction and feature extraction such as t-Distributed Stochastic Neighbor Embedding.

scholarly journals Friendly Fire

2021 ◽  
Vol 24 (3) ◽  
pp. 1-40
Author(s):  
Musard Balliu ◽  
Massimo Merro ◽  
Michele Pasqua ◽  
Mikhail Shcherbakov
Keyword(s):  
Program Analysis ◽  
Security Policy ◽  
Smart Devices ◽  
External Information ◽  
Semantic Framework ◽  
Analysis Techniques ◽  
Friendly Fire ◽  
Safety Risks ◽  
Iot Platforms ◽  
Enforcement Mechanisms

IoT platforms enable users to connect various smart devices and online services via reactive apps running on the cloud. These apps, often developed by third-parties, perform simple computations on data triggered by external information sources and actuate the results of computations on external information sinks. Recent research shows that unintended or malicious interactions between the different (even benign) apps of a user can cause severe security and safety risks. These works leverage program analysis techniques to build tools for unveiling unexpected interference across apps for specific use cases. Despite these initial efforts, we are still lacking a semantic framework for understanding interactions between IoT apps. The question of what security policy cross-app interference embodies remains largely unexplored. This article proposes a semantic framework capturing the essence of cross-app interactions in IoT platforms. The framework generalizes and connects syntactic enforcement mechanisms to bisimulation-based notions of security, thus providing a baseline for formulating soundness criteria of these enforcement mechanisms. Specifically, we present a calculus that models the behavioral semantics of a system of apps executing concurrently, and use it to define desirable semantic policies targeting the security and safety of IoT apps. To demonstrate the usefulness of our framework, we define and implement static analyses for enforcing cross-app security and safety, and prove them sound with respect to our semantic conditions. We also leverage real-world apps to validate the practical benefits of our tools based on the proposed enforcement mechanisms.

An example of spectrum imaging used for comparison of EELS quantitative analysis techniques on Al-Li

1991 ◽  
Vol 49 ◽  
pp. 726-727
Author(s):  
John A. Hunt
Keyword(s):  
Quantitative Analysis ◽  
Large Data ◽  
Difference Spectrum ◽  
Large Data Sets ◽  
Foil Thickness ◽  
Data Sets ◽  
Analysis Techniques ◽  
Spectrum Imaging ◽  
Normal Spectrum ◽  
Electron Energy Loss

Spectrum-imaging is a useful technique for comparing different processing methods on very large data sets which are identical for each method. This paper is concerned with comparing methods of electron energy-loss spectroscopy (EELS) quantitative analysis on the Al-Li system. The spectrum-image analyzed here was obtained from an Al-10at%Li foil aged to produce δ' precipitates that can span the foil thickness. Two 1024 channel EELS spectra offset in energy by 1 eV were recorded and stored at each pixel in the 80x80 spectrum-image (25 Mbytes). An energy range of 39-89eV (20 channels/eV) are represented. During processing the spectra are either subtracted to create an artifact corrected difference spectrum, or the energy offset is numerically removed and the spectra are added to create a normal spectrum. The spectrum-images are processed into 2D floating-point images using methods and software described in [1].

Dissociated Σ=27 boundaries in silicon

1988 ◽  
Vol 46 ◽  
pp. 624-625
Author(s):  
A. Garg ◽  
W.A.T. Clark ◽  
J.P. Hirth
Keyword(s):  
Electron Diffraction ◽  
Local Minimum ◽  
Boundary Energy ◽  
Coincidence Site Lattice ◽  
Boundary Plane ◽  
Low Energy ◽  
Theoretical Understanding ◽  
Site Lattice ◽  
Kikuchi Pattern ◽  
Analysis Techniques

In the last twenty years, a significant amount of work has been done in the theoretical understanding of grain boundaries. The various proposed grain boundary models suggest the existence of coincidence site lattice (CSL) boundaries at specific misorientations where a periodic structure representing a local minimum of energy exists between the two crystals. In general, the boundary energy depends not only upon the density of CSL sites but also upon the boundary plane, so that different facets of the same boundary have different energy. Here we describe TEM observations of the dissociation of a Σ=27 boundary in silicon in order to reduce its surface energy and attain a low energy configuration.The boundary was identified as near CSL Σ=27 {255} having a misorientation of (38.7±0.2)°/[011] by standard Kikuchi pattern, electron diffraction and trace analysis techniques. Although the boundary appeared planar, in the TEM it was found to be dissociated in some regions into a Σ=3 {111} and a Σ=9 {122} boundary, as shown in Fig. 1.

Material analysis techniques using the ion mill

1996 ◽  
Vol 54 ◽  
pp. 1034-1035
Author(s):  
J. P. Benedict ◽  
R. M. Anderson ◽  
S. J. Klepeis
Keyword(s):  
Polished Surface ◽  
Surface Material ◽  
Analysis Techniques ◽  
Material Analysis ◽  
Optical Inspection ◽  
Electron Microscopy Analysis ◽  
Microscopy Analysis ◽  
Argon Ions ◽  
The Impact ◽  
Scanning Electron

Ion mills equipped with flood guns can perform two important functions in material analysis; they can either remove material or deposit material. The ion mill holder shown in Fig. 1 is used to remove material from the polished surface of a sample for further optical inspection or SEM ( Scanning Electron Microscopy ) analysis. The sample is attached to a pohshing stud type SEM mount and placed in the ion mill holder with the polished surface of the sample pointing straight up, as shown in Fig 2. As the holder is rotating in the ion mill, Argon ions from the flood gun are directed down at the top of the sample. The impact of Argon ions against the surface of the sample causes some of the surface material to leave the sample at a material dependent, nonuniform rate. As a result, the polished surface will begin to develop topography during milling as fast sputtering materials leave behind depressions in the polished surface.

Developmental Sentence Scoring

1984 ◽  
Vol 15 (3) ◽  
pp. 154-168 ◽  
Author(s):  
Mary Ann Lively
Keyword(s):  
Expressive Language ◽  
Analysis Techniques ◽  
Language Analysis ◽  
Scoring Errors

Developmental Sentence Scoring (DSS) is a useful procedure for quantifying thegrammatic structure of children's expressive language. Like most language analysis techniques, however, DSS requires considerable study and practice to use it correctly and efficiently. Clinicians learning DSS tend to make many scoring errors at first and they display similar confusions and mistakes. This article identifies some of these common "problem" areas and provides scoring examples to assist clinicians in learning the DSS procedure.

Creative job analysis techniques of astronaut using archival data

2010 ◽  
Author(s):  
Kathryn Keeton ◽  
Holly Patterson ◽  
Lacey L. Schmidt ◽  
Kelley J. Slack ◽  
Camille Shea
Keyword(s):  
Job Analysis ◽  
Archival Data ◽  
Analysis Techniques
Sign in / Sign up

Export Citation Format

Share Document