Journal articlesshow all

The sensor hub for detecting the developmental characteristics in reading in children on a white vs. coloured background/coloured overlays, SENSORS (2021)
Deep Statistical Comparison for Multi-objective Stochastic Optimization Algorithms, SWARM EVOL COMPUT (2021)
Optimising an FFQ using a machine learning pipeline to learn an efficient nutrient intake predictive model, NUTRIENTS (2020)
P-NUT: Predicting NUTrient content from short text descriptions, MATHEMATICS (2020)
Simulation of Variational Gaussian Process NARX models with GPGPU, ISA T (2020)
DietHub: Dietary Habits Analysis through Understanding the Content of Recipes, TRENDS FOOD SCI TECH (2020)
MAKEDONKA: Applied Deep Learning Model for Text-to-Speech Synthesis in Macedonian Language, APPL SCI-BASEL (2020)
COVID-19 pandemic changes the food consumption patterns, TRENDS FOOD SCI TECH (2020)
Insights into Exploration and Exploitation Power of Optimization Algorithm Using DSCTool, MATHEMATICS (2020)
Improving the Maintenance of Railway Switches Through Proactive Approach, ELECTRONICS (2020)
Advanced sensor-based maintenance in real-world exemplary cases, TAUT (2020)
Fatty Acid and Stable Carbon Isotope Composition of Slovenian Milk: Year, Season, and Regional Variability, MOLECULES (2020)
Biomarkers of pre-existing risk of torsade de pointes under Sotalol treatment, J. ELECTROCARDIOL. (2020)
Geographical verification of Slovenian milk using stable isotope ratio, multi-element and multivariate modelling approaches, FOOD CHEM (2020)
Multi-objective optimization benchmarking using DSCTool, MATHEMATICS (2020)
Evaluating Missing Value Imputation Methods for Food Composition Databases, FOOD CHEM TOXICOL (2020)
Towards Managerial Support for Data Analytics Results, IND ENG MANAG (2020)
A survey of named-entity recognition methods for food information extraction, IEEE ACCESS (2020)
Dynamic features of cardiac vector as alternative markers of drug-induced spatial dispersion, J PHARMACOL TOX MET (2020)
FoodEx2vec: New foods’ representation for advanced food data analysis, FOOD CHEM TOXICOL (2020)
Understanding the problem space in single-objective numerical optimization using exploratory landscape analysis, APPL SOFT COMPUT (2020)
Multi-level information fusion for learning a blood pressure predictive model using sensor data, INFORM FUSION (2020)
DSCTool: a web services based framework for statistical comparison of stochastic optimization algorithms, APPL SOFT COMPUT (2020)
Selected elements and fatty acid composition in human milk as indicators of seafood dietary habits, ENVIRON RES (2020)
Data Transmission Efficiency in Bluetooth Low Energy Versions, SENSORS (2019)
Identifying Practical Significance Through Statistical Comparison of Meta-heuristic Stochastic Optimization Algorithms, APPL SOFT COMPUT (2019)
FoodBase corpus: a new resource of annotated food entities, DATABASE (2019)
MIGHT: Statistical Methodology for Missing-Data Imputation in Food Composition Databases, APPL SCI-BASEL (2019)
A multi-center survey on hospital malnutrition and cachexia in Slovenia, EUR J CLIN NUTR (2019)
Graphs with a given diameter that maximise the Wiener index, AMC (2019)
e-Learning Experiment: Web Conference Activities in Teaching at a Traditional University, InnovationLearning (2019)
Evaluation of harmonized EuroFIR documentation for macronutrient values in 26 European food composition databases, JFCA (2019)
Deep Learning-Based Channel Prediction in Realistic Vehicular Communications, IEEE ACCESS (2019)
Thermal phenomena in LTCC sensor structures, SENSOR ACTUAT A-PHYS (2019)
A Novel Statistical Approach for Comparing Meta-heuristic Stochastic Optimization Algorithms According to the Distribution of Solutions in the Search Space, INFORM SCIENCES (2019)
A comparison of models for forecasting the residential natural gas demand of an urban area, EGY (2019)
ISO-FOOD Ontology: A formal representation of the knowledge within the domain of Isotopes for Food Science, FOCH (2019)

Concurrent operation scheduling and unit allocation with an evolutionary technique in the process of integrated-circuit design

G. Papa

request for paper

abstract:
This work presents a method with an evolutionary approach to some of the tasks of integrated-circuit (IC) design. The first part consists of a short description of some essential characteristics of IC design. The discovery of the semiconducting properties of some materials and the production of transistors opened the door for digital technology. This technology allows for very high densities of transistor integration. The majority of today's ICs are built using CMOS technology, which results in low power losses and reduced noise while ensuring very symmetrical characteristics. This study is focused on application-specific integrated circuits (ASICs) that need an even more sophisticated design due to their specific use. The development of digital communications, multimedia, signal processing and networks in recent years has forced the development and widespread use of ASICs. High-level synthesis tools ensure a shorter design time with fewer iterations in order to achieve acceptable solutions. They also lead a designer through the design to reduce the probability of errors in the earlier design stages. Nowadays, these tools are able to build competitive and reliable ICs in an acceptable design time. A variety of design and optimization systems exist, these are mainly intended for use in small and specialized design groups. But there are also some commercial systems for a wide range of users. These systems do not allow users to influence the design-flow, but work according to built-in algorithms. Only a few research systems allow their tasks to be influenced, e.g. operation scheduling and unit allocation. This work presents a method with an evolutionary approach to some of the tasks of integrated-circuit (IC) design. The first part consists of a short description of some essential characteristics of IC design. The discovery of the semiconducting properties of some materials and the production of transistors opened the door for digital technology. This technology allows for very high densities of transistor integration. The majority of today's ICs are built using CMOS technology, which results in low power losses and reduced noise while ensuring very symmetrical characteristics. This study is focused on application-specific integrated circuits (ASICs) that need an even more sophisticated design due to their specific use. The development of digital communications, multimedia, signal processing and networks in recent years has forced the development and widespread use of ASICs. High-level synthesis tools ensure a shorter design time with fewer iterations in order to achieve acceptable solutions. They also lead a designer through the design to reduce the probability of errors in the earlier design stages. Nowadays, these tools are able to build competitive and reliable ICs in an acceptable design time. A variety of design and optimization systems exist, these are mainly intended for use in small and specialized design groups. But there are also some commercial systems for a wide range of users. These systems do not allow users to influence the design-flow, but work according to built-in algorithms. Only a few research systems allow their tasks to be influenced, e.g. operation scheduling and unit allocation...

Conference papers
show all

Finding Potential Inhibitors of COVID-19, BIOINFORMATICS 2021
On Statistical Analysis of MOEAs with Multiple Performance Indicators, EMO2021
Dynamic Parameter Changing During the Run to Speed Up Evolutionary Algorithms: PPSN 2020 tutorial, PPSN 2020
APRICOT: A humAn-comPuteR InteraCtion tool for linking foOd wasTe streams across different semantic resources, BFNDMA2020 at IEEE BigData 2020
Comparison of Feature Selection Algorithms for Minimization of Target Specific FFQs, BFNDMA2020 at IEEE BigData 2020
Toward Robust Food Ontology Mapping, BFNDMA2020 at IEEE BigData 2020
Exploring Knowledge Domain Bias on a Prediction Task for Food and Nutrition Data, BFNDMA2020 at IEEE BigData 2020
BuTTER: BidirecTional LSTM for FoodNamed-Entity Recognition, BFNDMA2020 at IEEE BigData 2020
On Formulating the Ground Scheduling Problem as a Multi-objective Bilevel Problem, BIOMA 2020
Refining the CC-RDG3 Algorithm with Increasing Population Scheme and Persistent Covariance Matrix, BIOMA 2020
Ontology alignment using Named-Entity Recognition methods in the domain of food, IS 2020
Dynamic Parameter Choices in Evolutionary Computation: WCCI 2020 tutorial, WCCI 2020
Dynamic control parameter choices in evolutionary computation: GECCO 2020 tutorial, GECCO 2020
Solving min-max optimisation problems by means of bilevel evolutionary algorithms: a preliminary study, GECCO 2020
Colors and colored overlays in dyslexia treatment, IPSSC 2020
An Insight into Food Semantics: Review, Analysis, and Lessons Learnt over Food-related Studies, ICBO 2020
Using Exploratory Landscape Analysis to Visualize Single-objective Problems, GECCO 2020
In-depth Insights into Swarm Intelligence Algorithms, MDIS 2020
FoodViz: Visualization of Food Entities Linked Across Different Standards, LOD 2020
Linear Matrix Factorization Embeddings for Single-objective Optimization Landscape Space, IEEE SSCI 2020
Deep Statistics: More Robust Performance Statistics for Single-objective Optimization Benchmarking, GECCO 2020
performViz: A Machine Learning Approach to Visualize and Understand the Performance of Single-objective Optimization Algorithms, GECCO 2020
Performance2vec: A step further in explainable stochastic optimization algorithms performance, GECCO 2020
Is the statistical significance between evolutionary algorithms performance also significant in practice?, GECCO 2020
Food Data Integration by Using Heuristics based on Lexical and Semantic Similarities, HEALTHINF 2020
A Novel Approach for Modelling the Relationship between Blood Pressure and ECG by Using Time-series Feature Extraction, BIOSIGNALS 2020
Optimisation platform for remote collaboration of different teams, OSE5
Experimental evaluation of deep-learning applied on pendulum balancing, ERK 2019
Exploring Dietary Intake Data collected by FPQ using Unsupervised Learning, BFNDMA 2019 at IEEE BigData 2019
Food Waste Ontology: A Formal Description of Knowledge from the Domain of Food Waste, BFNDMA 2019 at IEEE BigData 2019
Comparing Semantic and Nutrient Value Similarities of Recipes, BFNDMA 2019 at IEEE BigData 2019
Extracting patient-reported functional status and disease activity information from electronic health records, TextXD 2019
A pilot method linking greenhouse gas emission databases to the FoodEx2 classification, LEAP 2019
An adaptive evolutionary surrogate-based approach for single-objective bilevel optimisation, UQOP 2019
Exploring a standardized language for describing foods using embedding techniques, BFNDMA 2019 at IEEE BigData 2019
Mix and Rank: A Framework for Benchmarking Recommender Systems, BPOD 2019 at IEEE BigData 2019
LOCALE: A rule-based location named-entity recognition method for Latin text, HistoInformatics 2019
Snomed2Vec: Random Walk and Poincaré Embeddings of a Clinical Knowledge Base for Healthcare Analytics, KDD 2019
Parameter Control in Evolutionary Bilevel Optimisation, IPSSC 2019
The sensor hub for detecting the influence of colors on reading in children with dyslexia, IPSSC 2019
A Knowledge Graph-based Approach for Exploring the U.S. Opioid Epidemic, ICLR 2019
Understanding Exploration and Exploitation Powers of Meta-heuristic Stochastic Optimization Algorithms through Statistical Analysis, GECCO 2019
FoodOntoMap: Linking Food Concepts across different Food Ontologies, KEOD 2019
GECCO Black-Box Optimization Competitions: Progress from 2009 to 2018, GECCO 2019
CEC Real-Parameter Optimization Competitions: Progress from 2013 to 2018, CEC 2019
UsabEU: online platform for translation, validation and native use of usability questionnaires with multilingual user groups, HCII 2019
FoodIE: a rule-based named-entity recognition for food information extraction, ICPRAM 2019
Data-driven Autism Biomarkers Selection by using Signal Processing and Machine Learning Techniques, BIOINFORMATICS 2019
Comparing different settings of parameters needed for pre-processing of ECG signals used for blood pressure classification, BIOSIGNALS 2019
Performance measures fusion for experimental comparison of methods for multi-label classificaiton, AAAI-MAKE 2019

Books & Chaptersshow all

Memetic Algorithm and Evolutionary Operators for Multi-Objective Matrix Tri-Factorization Problem, Heuristics for Optimization and Learning, Springer (2020)
ECGpp: A Framework for Selecting the Pre-processing Parameters of ECG Signals Used for Blood Pressure Classification, Biomedical Engineering Systems and Technologies Part of the Communications in Computer and Information Science book series (CCIS, volume 1211), Springer (2020)
Success Stories on Real Pilots, The MANTIS Book - Cyber Physical System Based Proactive Collaborative Maintenance, River Publishers (2019)
From KPI Dashboards to Advanced Visualization, The MANTIS Book - Cyber Physical System Based Proactive Collaborative Maintenance, River Publishers (2019)
The MANTIS Book - Cyber Physical System Based Proactive Collaborative Maintenance, The MANTIS Book - Cyber Physical System Based Proactive Collaborative Maintenance, River Publishers (2019)
Comparison of multi-objective approaches to the real-world production scheduling, Advances in evolutionary and deterministic methods for design, optimization and control in engineering and sciences, Springer (2019)
Statistical data analysis and natural language processing for nutrition science, Doctoral Dissertation, Jožef Stefan International Postgraduate School (2018)