scholarly journals AnScalable Matrix Computing Unit Architecture for FPGA,and SCUMO User Design Interface

Electronics ◽  
2019 ◽  
Vol 8 (1) ◽  
pp. 94 ◽  
Author(s):  
Asgar Abbaszadeh ◽  
Taras Iakymchuk ◽  
Manuel Bataller-Mompeán ◽  
Jose Francés-Villora ◽  
Alfredo Rosado-Muñoz
Keyword(s):  
Machine Learning Algorithms ◽  
Circulant Matrices ◽  
Clock Frequency ◽  
Design Environment ◽  
Data Movement ◽  
Computing Unit ◽  
Matrix Operations ◽  
User Design ◽  
The Individual ◽  
Matrix Vector

High dimensional matrix algebra is essential in numerous signal processing and machine learning algorithms. This work describes a scalable square matrix-computing unit designed on the basis of circulant matrices. It optimizes data flow for the computation of any sequence of matrix operations removing the need for data movement for intermediate results, together with the individual matrix operations’ performance in direct or transposed form (the transpose matrix operation only requires a data addressing modification). The allowed matrix operations are: matrix-by-matrix addition, subtraction, dot product and multiplication, matrix-by-vector multiplication, and matrix by scalar multiplication. The proposed architecture is fully scalable with the maximum matrix dimension limited by the available resources. In addition, a design environment is also developed, permitting assistance, through a friendly interface, from the customization of the hardware computing unit to the generation of the final synthesizable IP core. For N × N matrices, the architecture requires N ALU-RAM blocks and performs O ( N 2 ) , requiring N 2 + 7 and N + 7 clock cycles for matrix-matrix and matrix-vector operations, respectively. For the tested Virtex7 FPGA device, the computation for 500 × 500 matrices allows a maximum clock frequency of 346 MHz, achieving an overall performance of 173 GOPS. This architecture shows higher performance than other state-of-the-art matrix computing units.

scholarly journals Design Strategies and Architectures for Ultra-Low-Voltage Delta-Sigma ADCs

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1156
Author(s):  
Lorenzo Benvenuti ◽  
Alessandro Catania ◽  
Giuseppe Manfredini ◽  
Andrea Ria ◽  
Massimo Piotto ◽  
...  
Keyword(s):  
Low Voltage ◽  
Flicker Noise ◽  
Supply Voltage ◽  
Cmos Process ◽  
Clock Frequency ◽  
Design Environment ◽  
Ultra Low Power ◽  
Analog Cmos ◽  
Gain Error ◽  
Dc Gain

The design of ultra-low voltage analog CMOS integrated circuits requires ad hoc solutions to counteract the severe limitations introduced by the reduced voltage headroom. A popular approach is represented by inverter-based topologies, which however may suffer from reduced finite DC gain, thus limiting the accuracy and the resolutions of pivotal circuits like analog-to-digital converters. In this work, we discuss the effects of finite DC gain on ultra-low voltage ΔΣ modulators, focusing on the converter gain error. We propose an ultra-low voltage, ultra-low power, inverter-based ΔΣ modulator with reduced finite-DC-gain sensitivity. The modulator employs a two-stage, high DC-gain, switched-capacitor integrator that applies a correlated double sampling technique for offset cancellation and flicker noise reduction; it also makes use of an amplifier that implements a novel common-mode stabilization loop. The modulator was designed with the UMC 0.18 μm CMOS process to operate with a supply voltage of 0.3 V. It was validated by means of electrical simulations using the CadenceTM design environment. The achieved SNDR was 73 dB, with a bandwidth of 640 Hz, and a clock frequency of 164 kHz, consuming only 200.5 nW. It achieves a Schreier Figure of Merit of 168.1 dB. The proposed modulator is also able to work with lower supply voltages down to 0.15 V with the same resolution and a lower power consumption despite of a lower bandwidth. These characteristics make this design very appealing in sensor interfaces powered by energy harvesting sources.

Clinical Score and Machine Learning-Based Model to Predict Diagnosis of Primary Aldosteronism in Arterial Hypertension

Hypertension ◽  
2021 ◽  
Vol 78 (5) ◽  
pp. 1595-1604
Author(s):  
Fabrizio Buffolo ◽  
Jacopo Burrello ◽  
Alessio Burrello ◽  
Daniel Heinrich ◽  
Christian Adolf ◽  
...  
Keyword(s):  
Machine Learning ◽  
Arterial Hypertension ◽  
Primary Aldosteronism ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Clinical Score ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Individual Risk ◽  
The Individual

Primary aldosteronism (PA) is the cause of arterial hypertension in 4% to 6% of patients, and 30% of patients with PA are affected by unilateral and surgically curable forms. Current guidelines recommend screening for PA ≈50% of patients with hypertension on the basis of individual factors, while some experts suggest screening all patients with hypertension. To define the risk of PA and tailor the diagnostic workup to the individual risk of each patient, we developed a conventional scoring system and supervised machine learning algorithms using a retrospective cohort of 4059 patients with hypertension. On the basis of 6 widely available parameters, we developed a numerical score and 308 machine learning-based models, selecting the one with the highest diagnostic performance. After validation, we obtained high predictive performance with our score (optimized sensitivity of 90.7% for PA and 92.3% for unilateral PA [UPA]). The machine learning-based model provided the highest performance, with an area under the curve of 0.834 for PA and 0.905 for diagnosis of UPA, with optimized sensitivity of 96.6% for PA, and 100.0% for UPA, at validation. The application of the predicting tools allowed the identification of a subgroup of patients with very low risk of PA (0.6% for both models) and null probability of having UPA. In conclusion, this score and the machine learning algorithm can accurately predict the individual pretest probability of PA in patients with hypertension and circumvent screening in up to 32.7% of patients using a machine learning-based model, without omitting patients with surgically curable UPA.

Association of Snoring Characteristics with Predominant Site of Collapse of Upper Airway in Obstructive Sleep Apnoea Patients

SLEEP ◽  
2021 ◽  
Author(s):  
Arun Sebastian ◽  
Peter A Cistulli ◽  
Gary Cohen ◽  
Philip de Chazal
Keyword(s):  
Acoustic Analysis ◽  
Audio Signal ◽  
Upper Airway ◽  
Lateral Wall ◽  
Sleep Apnoea ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Individual Site ◽  
Obstructive Sleep ◽  
The Individual

Abstract Study objectives Acoustic analysis of isolated events and snoring by previous researchers suggests a correlation between individual acoustic features and individual site of collapse events. In this study, we hypothesised that multi-parameter evaluation of snore sounds during natural sleep would provide a robust prediction of the predominant site of airway collapse. Methods The audio signals of 58 OSA patients were recorded simultaneously with full night polysomnography. The site of collapse was determined by manual analysis of the shape of the airflow signal during hypopnoea events and corresponding audio signal segments containing snore were manually extracted and processed. Machine learning algorithms were developed to automatically annotate the site of collapse of each hypopnoea event into three classes (lateral wall, palate and tongue-base). The predominant site of collapse for a sleep period was determined from the individual hypopnoea annotations and compared to the manually determined annotations. This was a retrospective study that used cross-validation to estimate performance. Results Cluster analysis showed that the data fits well in two clusters with a mean silhouette coefficient of 0.79 and an accuracy of 68% for classifying tongue/non-tongue collapse. A classification model using linear discriminants achieved an overall accuracy of 81% for discriminating tongue/non-tongue predominant site of collapse and accuracy of 64% for all site of collapse classes. Conclusions Our results reveal that the snore signal during hypopnoea can provide information regarding the predominant site of collapse in the upper airway. Therefore, the audio signal recorded during sleep could potentially be used as a new tool in identifying the predominant site of collapse and consequently improving the treatment selection and outcome.

A new classification system for autism based on machine learning of artificial intelligence

2021 ◽  
pp. 1-18
Author(s):  
Seyed Reza Shahamiri ◽  
Fadi Thabtah ◽  
Neda Abdelhamid
Keyword(s):  
Machine Learning ◽  
Scoring Function ◽  
Autistic Traits ◽  
Well Being ◽  
Learning Technologies ◽  
Machine Learning Algorithms ◽  
The Social ◽  
Hidden Patterns ◽  
The Individual ◽  
Fold Cross Validation

BACKGROUND: Autistic Spectrum Disorder (ASD) is a neurodevelopment condition that is normally linked with substantial healthcare costs. Typical ASD screening techniques are time consuming, so the early detection of ASD could reduce such costs and help limit the development of the condition. OBJECTIVE: We propose an automated approach to detect autistic traits that replaces the scoring function used in current ASD screening with a more intelligent and less subjective approach. METHODS: The proposed approach employs deep neural networks (DNNs) to detect hidden patterns from previously labelled cases and controls, then applies the knowledge derived to classify the individual being screened. Specificity, sensitivity, and accuracy of the proposed approach are evaluated using ten-fold cross-validation. A comparative analysis has also been conducted to compare the DNNs’ performance with other prominent machine learning algorithms. RESULTS: Results indicate that deep learning technologies can be embedded within existing ASD screening to assist the stakeholders in the early identification of ASD traits. CONCLUSION: The proposed system will facilitate access to needed support for the social, physical, and educational well-being of the patient and family by making ASD screening more intelligent and accurate.

scholarly journals Machine Learning in Football Betting: Prediction of Match Results Based on Player Characteristics

2019 ◽  
Vol 10 (1) ◽  
pp. 46 ◽  
Author(s):  
Johannes Stübinger ◽  
Benedikt Mangold ◽  
Julian Knoll
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Home Team ◽  
Ensemble Strategy ◽  
The World ◽  
European Football ◽  
The Individual ◽  
Football Betting ◽  
Betting Odds

In recent times, football (soccer) has aroused an increasing amount of attention across continents and entered unexpected dimensions. In this course, the number of bookmakers, who offer the opportunity to bet on the outcome of football games, expanded enormously, which was further strengthened by the development of the world wide web. In this context, one could generate positive returns over time by betting based on a strategy which successfully identifies overvalued betting odds. Due to the large number of matches around the globe, football matches in particular have great potential for such a betting strategy. This paper utilizes machine learning to forecast the outcome of football games based on match and player attributes. A simulation study which includes all matches of the five greatest European football leagues and the corresponding second leagues between 2006 and 2018 revealed that an ensemble strategy achieves statistically and economically significant returns of 1.58% per match. Furthermore, the combination of different machine learning algorithms could neither be outperformed by the individual machine learning approaches nor by a linear regression model or naive betting strategies, such as always betting on the victory of the home team.

AN EXPERIMENTAL MULTI-AGENT ENVIRONMENT FOR ENGINEERING DESIGN

1996 ◽  
Vol 05 (02n03) ◽  
pp. 131-151 ◽  
Author(s):  
WEIMING SHEN ◽  
JEAN-PAUL A. BARTHES
Keyword(s):  
Engineering Design ◽  
Mechanical Design ◽  
Multidisciplinary Design ◽  
Design Teams ◽  
Efficient Design ◽  
Design Environment ◽  
Open Design ◽  
Long Time ◽  
Design Activities ◽  
The Individual

Real world engineering design projects require the cooperation of multidisciplinary design teams using sophisticated and powerful engineering tools. The individuals or the individual groups of the multidisciplinary design teams work in parallel and independently often for quite a long time with different tools located on various sites. In order to ensure the coordination of design activities in the different groups or the cooperation among the different tools, it is necessary to develop an efficient design environment. This paper discusses a distributed architecture for integrating such engineering tools in an open design environment, organized as a population of asynchronous cognitive agents. Before introducing the general architecture and the communication protocol, issues about an agent architecture and inter-agent communications are discussed. A prototype of such an environment with seven independent agents located in several workstations and microcomputers is then presented and demonstrated on an example of a small mechanical design.

scholarly journals Vehicle Price Prediction using SVM Techniques

2020 ◽  
Vol 9 (8) ◽  
pp. 398-401
Keyword(s):  
Machine Learning ◽  
Research Area ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Data Set ◽  
Network Support ◽  
Java Application ◽  
Learning Techniques ◽  
The Individual

The prediction of price for a vehicle has been more popular in research area, and it needs predominant effort and information about the experts of this particular field. The number of different attributes is measured and also it has been considerable to predict the result in more reliable and accurate. To find the price of used vehicles a well defined model has been developed with the help of three machine learning techniques such as Artificial Neural Network, Support Vector Machine and Random Forest. These techniques were used not on the individual items but for the whole group of data items. This data group has been taken from some web portal and that same has been used for the prediction. The data must be collected using web scraper that was written in PHP programming language. Distinct machine learning algorithms of varying performances had been compared to get the best result of the given data set. The final prediction model was integrated into Java application

scholarly journals User Authentication by Keystroke Dynamics Using Machine Learning Algorithms

2019 ◽  
pp. 400-407
Author(s):  
Najla Alavi ◽  
Kasim K
Keyword(s):  
Cyber Security ◽  
User Authentication ◽  
Machine Learning Algorithms ◽  
Identification Problems ◽  
Keystroke Dynamics ◽  
Cyber Forensics ◽  
Coupling Technique ◽  
Cyber Intelligence ◽  
Individual Personality ◽  
The Individual

Due to the expanding vulnerabilities in cyber forensics, security alone is not sufficient to forestall a rupture; however, cyber security is additionally required to anticipate future assaults or to distinguish the potential aggressor. Keystroke Dynamics has high use in cyber intelligence. The paper examines the helpfulness of keystroke dynamics to build up the individual personality. Three schemes are proposed for recognizing an individual while typing on keyboard. Lib SVM and binary SVM are proposed and their performance are shown. Lib SVM is showing a better performance when comparing with binary SVM. As the number of samples are increased it shows an increase in the accuracy. Pair wise user coupling technique is proposed. The proposed procedures are approved by utilizing keystroke information. In any case, these systems could similarly well be connected to other examples of pattern identification problems. This system is applicable in highly confidential areas like military.

scholarly journals SARCASM DETECTION IN PERSIAN

2020 ◽  
Vol 20 ◽  
Author(s):  
Zahra Bokaee Nezhad ◽  
Mohammad Ali Deihimi
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Network Architecture ◽  
Machine Learning Algorithms ◽  
Added Value ◽  
Part Of Speech ◽  
Preparation Step ◽  
Sentiment Dictionary ◽  
The Individual

Sarcasm is a form of communication where the individual states the opposite of what is implied. Therefore, detecting a sarcastic tone is somewhat complicated due to its ambiguous nature. On the other hand, identification of sarcasm is vital to various natural language processing tasks such as sentiment analysis and text summarisation. However, research on sarcasm detection in Persian is very limited. This paper investigated the sarcasm detection technique on Persian tweets by combining deep learning-based and machine learning-based approaches. Four sets of features that cover different types of sarcasm were proposed, namely deep polarity, sentiment, part of speech, and punctuation features. These features were utilised to classify the tweets as sarcastic and nonsarcastic. In this study, the deep polarity feature was proposed by conducting a sentiment analysis using deep neural network architecture. In addition, to extract the sentiment feature, a Persian sentiment dictionary was developed, which consisted of four sentiment categories. The study also used a new Persian proverb dictionary in the preparation step to enhance the accuracy of the proposed model. The performance of the model is analysed using several standard machine learning algorithms. The results of the experiment showed that the method outperformed the baseline method and reached an accuracy of 80.82%. The study also examined the importance of each proposed feature set and evaluated its added value to the classification.

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