scholarly journals Lane Compression

2021 ◽  
Vol 20 (2) ◽  
pp. 1-26
Author(s):  
Yousun Ko ◽  
Alex Chadwick ◽  
Daniel Bates ◽  
Robert Mullins
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Huffman Coding ◽  
Statistical Characteristics ◽  
Learning Networks ◽  
Compression Technique ◽  
Wide Range ◽  
Run Time ◽  
On Chip ◽  
Learning Data

This article presents Lane Compression, a lightweight lossless compression technique for machine learning that is based on a detailed study of the statistical properties of machine learning data. The proposed technique profiles machine learning data gathered ahead of run-time and partitions values bit-wise into different lanes with more distinctive statistical characteristics. Then the most appropriate compression technique is chosen for each lane out of a small number of low-cost compression techniques. Lane Compression’s compute and memory requirements are very low and yet it achieves a compression rate comparable to or better than Huffman coding. We evaluate and analyse Lane Compression on a wide range of machine learning networks for both inference and re-training. We also demonstrate the profiling prior to run-time and the ability to configure the hardware based on the profiling guarantee robust performance across different models and datasets. Hardware implementations are described and the scheme’s simplicity makes it suitable for compressing both on-chip and off-chip traffic.

scholarly journals Developing Relative Humidity and Temperature Corrections for Low-Cost Sensors Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3338
Author(s):  
Ivan Vajs ◽  
Dejan Drajic ◽  
Nenad Gligoric ◽  
Ilija Radovanovic ◽  
Ivan Popovic
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
Relative Humidity ◽  
Low Cost ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Lower Accuracy ◽  
Wide Range ◽  
The Impact ◽  
Monitoring Stations

Existing government air quality monitoring networks consist of static measurement stations, which are highly reliable and accurately measure a wide range of air pollutants, but they are very large, expensive and require significant amounts of maintenance. As a promising solution, low-cost sensors are being introduced as complementary, air quality monitoring stations. These sensors are, however, not reliable due to the lower accuracy, short life cycle and corresponding calibration issues. Recent studies have shown that low-cost sensors are affected by relative humidity and temperature. In this paper, we explore methods to additionally improve the calibration algorithms with the aim to increase the measurement accuracy considering the impact of temperature and humidity on the readings, by using machine learning. A detailed comparative analysis of linear regression, artificial neural network and random forest algorithms are presented, analyzing their performance on the measurements of CO, NO2 and PM10 particles, with promising results and an achieved R2 of 0.93–0.97, 0.82–0.94 and 0.73–0.89 dependent on the observed period of the year, respectively, for each pollutant. A comprehensive analysis and recommendations on how low-cost sensors could be used as complementary monitoring stations to the reference ones, to increase spatial and temporal measurement resolution, is provided.

Big Data Models and the Public Sector

Web Services ◽  
2019 ◽  
pp. 105-126
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

scholarly journals Emerging Technologies Based on Artificial Intelligence to Assess the Quality and Consumer Preference of Beverages

Beverages ◽  
2019 ◽  
Vol 5 (4) ◽  
pp. 62 ◽  
Author(s):  
Claudia Gonzalez Viejo ◽  
Damir D. Torrico ◽  
Frank R. Dunshea ◽  
Sigfredo Fuentes
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Food Industry ◽  
Emerging Technologies ◽  
Low Cost ◽  
Consumer Preference ◽  
Beverage Industry ◽  
Wide Range ◽  
Alcoholic Drinks ◽  
Different Levels

Beverages is a broad and important category within the food industry, which is comprised of a wide range of sub-categories and types of drinks with different levels of complexity for their manufacturing and quality assessment. Traditional methods to evaluate the quality traits of beverages consist of tedious, time-consuming, and costly techniques, which do not allow researchers to procure results in real-time. Therefore, there is a need to test and implement emerging technologies in order to automate and facilitate those analyses within this industry. This paper aimed to present the most recent publications and trends regarding the use of low-cost, reliable, and accurate, remote or non-contact techniques using robotics, machine learning, computer vision, biometrics and the application of artificial intelligence, as well as to identify the research gaps within the beverage industry. It was found that there is a wide opportunity in the development and use of robotics and biometrics for all types of beverages, but especially for hot and non-alcoholic drinks. Furthermore, there is a lack of knowledge and clarity within the industry, and research about the concepts of artificial intelligence and machine learning, as well as that concerning the correct design and interpretation of modeling related to the lack of inclusion of relevant data, additional to presenting over- or under-fitted models.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals Surface-enhanced mid-infrared absorption spectroscopy using miniaturized-disc metasurface

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mitchell Semple ◽  
Ashwin K. Iyer
Keyword(s):  
Infrared Spectroscopy ◽  
Signal To Noise Ratio ◽  
Low Cost ◽  
Unit Cell ◽  
Mid Infrared ◽  
Wide Range ◽  
Surface Enhanced ◽  
Identification Measurements ◽  
Vibrational Bands ◽  
On Chip

AbstractSurface-enhanced infrared spectroscopy is an important technique for improving the signal-to-noise ratio of spectroscopic material identification measurements in the mid-infrared fingerprinting region. However, the lower bound of the fingerprinting region receives much less attention due to a scarcity of transparent materials, more expensive sources, and weaker plasmonic effects. In this paper, we present a miniaturized metasurface unit cell for surface-enhanced infrared spectroscopy of the 15-$$\upmu$$ μ m vibrational band of CO$$_{2}$$ 2 . The unit cell consists of a gold disc, patterned along the edge with fine gaps/wires to create a resonant metamaterial liner. In simulation, our plasmonic metamaterial-lined disc achieves greater than $$4\times$$ 4 × the average field intensity enhancement of a comparable dipole array and a miniaturized size of $$\lambda _0/5$$ λ 0 / 5 using complex, 100-nm features that are patterned using 100-kV electron-beam lithography. In a simple experiment, the metamaterial-lined disc metasurface shows a high tolerance to fabrication imperfections and enhances the absorption of CO$$_{2}$$ 2 at 15 $$\upmu$$ μ m. The resonant wavelength and reflection magnitude can be tuned over a wide range by adjusting the liner feature sizes and the metasurface array pitch to target other vibrational bands. This work is a step toward low-cost, more compact on-chip integrated gas sensors.

Big Data Models and the Public Sector

2016 ◽  
pp. 245-266
Author(s):  
N. Nawin Sona
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Public Sector ◽  
Predictive Analytics ◽  
Data Types ◽  
The Public ◽  
Emerging Trends ◽  
Wide Range ◽  
Learning Data

This chapter aims to give an overview of the wide range of Big Data approaches and technologies today. The data features of Volume, Velocity, and Variety are examined against new database technologies. It explores the complexity of data types, methodologies of storage, access and computation, current and emerging trends of data analysis, and methods of extracting value from data. It aims to address the need for clarity regarding the future of RDBMS and the newer systems. And it highlights the methods in which Actionable Insights can be built into public sector domains, such as Machine Learning, Data Mining, Predictive Analytics and others.

System-on-Chip Integration

2002 ◽  
Vol 12 (02) ◽  
pp. 325-332
Author(s):  
ROBERT R. DOERING
Keyword(s):  
Low Cost ◽  
System On Chip ◽  
Foreseeable Future ◽  
Digital Cmos ◽  
Chip Area ◽  
Traditional System ◽  
Time Performance ◽  
Wide Range ◽  
On Chip ◽  
Process Complexity

Numerous "signal-processing products" are now driving the semiconductor market for SOC solutions enabling real-time performance, low-cost, low-power, portability, etc. A primary limit on the types of electronic (or other) functions that will be integrated into future SOCs is cost of integration, which tends to grow non-linearly with process complexity and chip area. A near-continuum of System-on/in-X solutions is emerging between traditional System-on-Chip and System-on-Board. These approaches span the tradeoff between bandwidth and cost. For the foreseeable future, digital CMOS will continue to serve as a "host platform" for integrating a wide range of mechanical, optical, biological, and, perhaps, even "quantum" technologies.

scholarly journals Recognition of traffic generated by WebRTC communication

2021 ◽  
Vol 1 (1) ◽  
pp. 15-20
Author(s):  
Nadina Ajdinović ◽  
Semina Nurkić ◽  
Jasmina Baraković Husić ◽  
Sabina Baraković
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Wide Range ◽  
Traffic Recognition ◽  
Audio Video ◽  
Future Work

Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.

scholarly journals Enhancing Security on IoT Devices via Machine Learning on Conditional Power Dissipation

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1799
Author(s):  
Dimitrios Myridakis ◽  
Stefanos Papafotikas ◽  
Konstantinos Kalovrektis ◽  
Athanasios Kakarountas
Keyword(s):  
Machine Learning ◽  
Power Dissipation ◽  
Low Cost ◽  
Rapid Development ◽  
Abnormal Behavior ◽  
Smart Devices ◽  
Side Channel ◽  
Sensitive Data ◽  
Wide Range ◽  
Iot Devices

The rapid development of connected devices and the sensitive data, which they produce, is a major challenge for manufacturers seeking to fully protect their devices from attack. Consumers expect their IoT devices and data to be adequately protected against a wide range of vulnerabilities and exploits. Successful attacks target IoT devices, cause security problems, and pose new challenges. Successful attacks from botnets residing on mastered IoT devices increase significantly in number and the severity of the damage they cause is similar to that of a war. The characteristics of attacks vary widely from attack to attack and from time to time. The warnings about the severity of the attacks indicate that there is a need for solutions to address the attacks from birth. In addition, there is a need to quarantine infected IoT devices, preventing the spread of the virus and thus the formation of the botnet. This work introduces the exploitation of side-channel attack techniques to protect the low-cost smart devices intuitively, and integrates a machine learning-based algorithm for Intrusion Detection, exploiting current supply characteristic dissipation. The results of this work showed successful detection of abnormal behavior of smart IoT devices.

scholarly journals Robust Detection of Hidden Material Damages Using Low-Cost External Sensors and Machine Learning

Proceedings ◽  
2019 ◽  
Vol 42 (1) ◽  
pp. 56 ◽  
Author(s):  
Stefan Bosse ◽  
Dirk Lehmhus
Keyword(s):  
Machine Learning ◽  
Low Cost ◽  
Tight Coupling ◽  
Destructive Testing ◽  
Robust Detection ◽  
Real Time Scale ◽  
Wide Range ◽  
Mass Spring ◽  
Multi Body ◽  
Non Destructive

Machine learning (ML) techniques are widely used in structural health monitoring (SHM) and non-destructive testing (NDT), but the learning process, the learned models, and the prediction consistency are poorly understood. This work investigates and compares a wide range of ML models and algorithms for the detection of hidden damage in materials monitored using low-cost strain sensors. The investigation is performed by means of a multi-domain simulator imposing a tight coupling of physical and sensor network simulation in the real-time scale. The device under test is approximated by using a mass-spring network and a multi-body physics solver.

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