This document summarizes a research paper on solving three-dimensional bin packing problems using an elitism-based genetic algorithm. The paper addresses the bin packing problem, which involves packing objects of different volumes into bins to minimize the number of bins used. Previous work focused on one and two-dimensional cases. The paper presents a genetic algorithm approach to solve the three-dimensional bin packing problem. The algorithm uses a probability vector to model population distribution and elitism to guide the search toward optimal solutions.
This document discusses speech-based emotion recognition using Gaussian mixture models (GMM). GMMs are statistical models that are well-suited for developing emotion recognition systems from large feature datasets. The document proposes using GMMs trained on excitation features extracted from speech signals to classify emotions into categories like happy, angry, sad, and neutral. It describes extracting excitation source features through linear predictive coding analysis to capture information about a speaker's vocal excitation source. The goal is to develop a GMM-based emotion recognition system that can classify emotions in conversations.
This document compares the performance of three routing protocols for mobile ad hoc networks (MANETs) - DSDV, AODV, and an ant colony optimization (ACO) based protocol. It presents the results of simulations run using the NS-2 network simulator. The simulations varied the number of nodes and compared the end-to-end delay, packet delivery ratio, and packet delivery fraction of the three protocols. The results showed that as network complexity increased with more nodes, the ACO based protocol performed better than AODV and DSDV in terms of lower delay and higher delivery rates, particularly for larger network sizes.
Statistical analysis of network data and evolution on GPUs: High-performance ...Michael Stumpf
Talk given on the 25th of January 2012 at the GPU in Statistics workshop in Warwick.
The talk covers approximate Bayesian computation (ABC) on GPUs, how to use spectral graph theory in ABC, and how to generate good random numbers on GPUs.
The document discusses maximum power point tracking (MPPT) using the perturb and observe method. MPPT is a technique used in solar panel systems to extract the maximum available power from the panels by matching the panel voltage to the maximum power point on its power-voltage curve. The perturb and observe method works by periodically perturbing the duty cycle of the power converter connecting the panel to the battery and observing whether power increases or decreases, allowing it to track the changing maximum power point.
This document describes the design and implementation of an IEEE-754 floating point adder/subtractor. It presents the hardware architecture in a block diagram with 8 pipelined stages. The design takes two 64-bit IEEE-754 operands and performs decimal floating point addition or subtraction according to the specified standard. It first decodes the operands, equalizes the exponents, performs the addition or subtraction of the significands, and then rounds and normalizes the result while handling special cases such as overflow and underflow. The key stages include leading zero detection, shifting, control signal generation, decimal addition, post-correction, and rounding.
This document discusses using graphics processing units (GPUs) to perform approximate Bayesian computation (ABC) for parameter estimation of complex models. It describes how GPUs are well-suited for ABC due to their ability to perform linear computations on many threads in parallel. The document provides examples of applying ABC to GPUs for problems involving dynamical systems, network evolution models, and parameter estimation for protein interaction networks.
This document discusses techniques to enhance security in FPGA-based systems. It begins by describing the basic architecture of FPGAs, including their programmable logic blocks and interconnects. It then discusses the main security concern with FPGAs, which is the copying or cloning of the configuration bitstream. Several threat and defense models are proposed to address this issue. Finally, a new technique is proposed to enhance the security of FPGA-based systems against bitstream cloning attacks.
This document summarizes an implementation of the International Data Encryption Algorithm (IDEA) using a Field Programmable Gate Array (FPGA). The authors designed an efficient hardware implementation of the IDEA algorithm using a novel modulo (2n+1) multiplier module. They synthesized and implemented the VHDL code on a Xilinx FPGA. Experimental results showed that the proposed design was faster, smaller, and consumed less power than previous hardware implementations of IDEA. The key aspects of the proposed design were an optimized modulo multiplier and a pipelined architecture to improve processing speed and throughput for encryption.
This document discusses speech-based emotion recognition using Gaussian mixture models (GMM). GMMs are statistical models that are well-suited for developing emotion recognition systems from large feature datasets. The document proposes using GMMs trained on excitation features extracted from speech signals to classify emotions into categories like happy, angry, sad, and neutral. It describes extracting excitation source features through linear predictive coding analysis to capture information about a speaker's vocal excitation source. The goal is to develop a GMM-based emotion recognition system that can classify emotions in conversations.
This document compares the performance of three routing protocols for mobile ad hoc networks (MANETs) - DSDV, AODV, and an ant colony optimization (ACO) based protocol. It presents the results of simulations run using the NS-2 network simulator. The simulations varied the number of nodes and compared the end-to-end delay, packet delivery ratio, and packet delivery fraction of the three protocols. The results showed that as network complexity increased with more nodes, the ACO based protocol performed better than AODV and DSDV in terms of lower delay and higher delivery rates, particularly for larger network sizes.
Statistical analysis of network data and evolution on GPUs: High-performance ...Michael Stumpf
Talk given on the 25th of January 2012 at the GPU in Statistics workshop in Warwick.
The talk covers approximate Bayesian computation (ABC) on GPUs, how to use spectral graph theory in ABC, and how to generate good random numbers on GPUs.
The document discusses maximum power point tracking (MPPT) using the perturb and observe method. MPPT is a technique used in solar panel systems to extract the maximum available power from the panels by matching the panel voltage to the maximum power point on its power-voltage curve. The perturb and observe method works by periodically perturbing the duty cycle of the power converter connecting the panel to the battery and observing whether power increases or decreases, allowing it to track the changing maximum power point.
This document describes the design and implementation of an IEEE-754 floating point adder/subtractor. It presents the hardware architecture in a block diagram with 8 pipelined stages. The design takes two 64-bit IEEE-754 operands and performs decimal floating point addition or subtraction according to the specified standard. It first decodes the operands, equalizes the exponents, performs the addition or subtraction of the significands, and then rounds and normalizes the result while handling special cases such as overflow and underflow. The key stages include leading zero detection, shifting, control signal generation, decimal addition, post-correction, and rounding.
This document discusses using graphics processing units (GPUs) to perform approximate Bayesian computation (ABC) for parameter estimation of complex models. It describes how GPUs are well-suited for ABC due to their ability to perform linear computations on many threads in parallel. The document provides examples of applying ABC to GPUs for problems involving dynamical systems, network evolution models, and parameter estimation for protein interaction networks.
This document discusses techniques to enhance security in FPGA-based systems. It begins by describing the basic architecture of FPGAs, including their programmable logic blocks and interconnects. It then discusses the main security concern with FPGAs, which is the copying or cloning of the configuration bitstream. Several threat and defense models are proposed to address this issue. Finally, a new technique is proposed to enhance the security of FPGA-based systems against bitstream cloning attacks.
This document summarizes an implementation of the International Data Encryption Algorithm (IDEA) using a Field Programmable Gate Array (FPGA). The authors designed an efficient hardware implementation of the IDEA algorithm using a novel modulo (2n+1) multiplier module. They synthesized and implemented the VHDL code on a Xilinx FPGA. Experimental results showed that the proposed design was faster, smaller, and consumed less power than previous hardware implementations of IDEA. The key aspects of the proposed design were an optimized modulo multiplier and a pipelined architecture to improve processing speed and throughput for encryption.
This document summarizes research applying an ant colony optimization algorithm to solve the 3D constrained rectangular bin packing problem. The goal is to pack arbitrary sized 3D rectangular bins into standard sized containers in a way that minimizes empty space. The algorithm considers constraints like placement, overlapping, stability and develops a packing pattern represented by matrices. Experiments show the ant colony approach improves performance over other algorithms and is computationally efficient for solving this NP-hard bin packing optimization problem.
Ortmann [2010] Heuristics for Offline Rectangular Packing ProblemsFrank Ortmann
This document provides an abstract and introduction for Frank Gerald Ortmann's dissertation on heuristics for offline rectangular packing problems. The dissertation evaluates 218 new heuristics for the strip packing problem and proposes a new heuristic approach for the multiple bin size bin packing problem (MBSBPP). Key findings include that several newly proposed pseudolevel heuristics outperform known heuristics for strip packing, and that a modified plane-packing heuristic yields the best results for the MBSBPP in terms of packing density and time.
Decision Support For Packing In WarehousesGurdal Ertek
This document summarizes a research paper titled "Decision Support for Packing in Warehouses" by Ertek and Kilic. The paper proposes three algorithms - greedy, beam search, and tree search - to solve a packing problem for an automobile manufacturer's spare parts warehouse. The problem involves optimally packing items from customer orders into boxes to minimize costs. This is a novel 3D multiple bin size bin packing problem that has not been previously analyzed in literature. The paper compares the performance of the proposed algorithms in terms of cost and computation time.
Heuristic Algorithm for Constrained 3D Container Loading Problem: A Genetic A...ijcoa
This paper presents an heuristic Genetic Algorithm for solving 3-Dimensional Single container packing optimization problem. The 3D container loading problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and inturn profit. Furthermore, various practical constraints like box orientation, stack priority, container stability, etc also applied. Boxes to be packed are of various sizes and of heterogeneous shapes. In this research work, several heuristic improvements were proposed over Genetic Algorithm (GA) to solve the container loading problem that significantly improves the search efficiency and to load most of heterogeneous boxes into a container along with the optimal position of loaded boxes, box orientation and boxes to be loaded by satisfying practical constraints. In this module, both the guillotine and non-guillotine moves were allowed. In general, these heuristic GA solutions being substantially better and satisfactory than those obtained by applying heuristics to the bin packing directly.
A Survey- Knapsack Problem Using Dynamic ProgrammingEditor IJCTER
A method for finding an optimal solution of mixed integer programming problems with one constraint is proposed. Initially, this method lessens the number of variables and the interval of their change; then, for the resulting problem one derives recurrent relations of dynamic programming that are used for computing. dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. Using a matrix for information storage, we can solve problems of a sufficiently large dimension. The computational experiments demonstrate that the method in question is highly efficient. In this paper shows study about Knapsack problem.
An efficient algorithm for 3D rectangular box packingmustafa sarac
The document summarizes an algorithm for efficiently packing 3D rectangular boxes into a container. It begins with background on the 3D packing problem and why it is important to shipping companies. It then:
1) Introduces the Largest Area First-Fit (LAFF) algorithm that places the largest boxes first by minimizing height.
2) Explains the inputs as the number and dimensions of boxes, and outputs as used space, wasted space, and time.
3) Details how LAFF works by first determining the container width/depth, then placing boxes in two methods - increasing height or fitting remaining space.
This document discusses bin packing algorithms in Golang. It begins with an introduction to bin packing and describes how it is an important problem in logistics and computing. It then provides details on implementing two bin packing algorithms (Best Fit and First Fit Decreasing) in Golang. It discusses using the Gota dataframe library for data analysis and filtering. The document also covers unit testing the bin packing algorithms and results, which showed successful testing of single and multiple SKUs. In conclusion, it was shown that the bin packing program could find solutions in a reasonable time frame using the implemented algorithms.
The document discusses algorithms for solving bin packing problems. It introduces bin packing as arranging items of different volumes into a finite number of bins to minimize the number of bins used. It then describes four key aspects: lower bounds, first-fit, first-fit decreasing, and full-bin packing. First-fit simply places items in the first bin they fit in. First-fit decreasing first sorts items by decreasing size, tending to produce better solutions. Full-bin packing aims to fully pack each bin, though it may be complex. The document provides overviews of each approach and notes first-fit decreasing generally performs better than first-fit, though not always optimally, in less time than full-bin packing.
This document summarizes a research paper that presents a new algorithm for solving the 0-1 Knapsack Problem. The algorithm combines previous approaches that generated valid inequalities and used surrogate relaxation. It is able to solve classical test instances with up to 10,000 variables in under 0.2 seconds. The key aspects of the new algorithm include:
1) Using a new initial "core" problem that is likely to produce a well-filled knapsack, rather than just items near the break item ratio.
2) Generating cardinality constraints if the number of states grows too large, relaxing them with the original weight constraint to obtain a good upper bound.
3) Attempting to improve the lower
Study of Different Multi-instance Learning kNN AlgorithmsEditor IJCATR
Because of it is applicability in various field, multi-instance learning or multi-instance problem becoming more popular in
machine learning research field. Different from supervised learning, multi-instance learning related to the problem of classifying an
unknown bag into positive or negative label such that labels of instances of bags are ambiguous. This paper uses and study three
different k-nearest neighbor algorithm namely Bayesian -kNN, citation -kNN and Bayesian Citation -kNN algorithm for solving multiinstance
problem. Similarity between two bags is measured using Hausdroff distance. To overcome the problem of false positive
instances constructive covering algorithm used. Also the problem definition, learning algorithm and experimental data sets related to
multi-instance learning framework are briefly reviewed in this paper
The document describes the design of the ¡bOxx!, a portable and compactable storage box created by students to address common problems encountered when carrying school supplies. The initial design of the ¡bOxx! included separate compartments and the ability to fold flat. Based on feedback, the design was improved to include a handle and durable materials. Pending tasks include sourcing cardboard, getting help to construct a prototype, and further refining the design based on surveys and identifying flaws.
ADA Unit — 3 Dynamic Programming and Its Applications.pdfRGPV De Bunkers
Study Material: Analysis & Design of Algorithms - Semester 3
For RGPV Students of 4th Semester in Computer Science Engineering
Discover the power of algorithms with this comprehensive study material on "Analysis & Design of Algorithms" designed specifically for RGPV students in the 4th semester of Computer Science Engineering. Dive into the world of dynamic programming and its versatile applications, equipping yourself with essential problem-solving skills.
Unit Overview: Dynamic Programming and Its Applications
Learn the fundamental concepts of dynamic programming and its diverse applications. Dynamic programming is an algorithmic technique that efficiently solves complex problems by breaking them into smaller, overlapping subproblems. This unit explores key topics, including:
Concept of Dynamic Programming: Understand the significance of dynamic programming in algorithm design, leveraging overlapping subproblems and optimal substructure properties.
0/1 Knapsack Problem: Solve the classic optimization problem of 0/1 knapsack, maximizing value while respecting the knapsack's capacity.
Multistage Graph: Model decision-making processes with multistage graphs and use dynamic programming to find optimal paths.
Reliability Design: Optimize system reliability with dynamic programming, making smart decisions on redundancy and component selection.
Floyd-Warshall Algorithm: Determine shortest paths between vertices in a weighted graph using this versatile algorithm.
Why Choose This Study Material?
Tailored for RGPV Students: Specifically designed for 4th-semester Computer Science Engineering students at RGPV, aligning with the curriculum.
Comprehensive Coverage: Detailed explanations of each topic ensure a solid grasp of dynamic programming concepts.
Real-World Relevance: Apply your knowledge to project management, network design, manufacturing, and more.
Step-by-Step Approach: Understand problem-solving through step-by-step explanations.
Practical Examples: Numerous examples, including the knapsack problem and Floyd-Warshall algorithm, enrich your learning experience.
Study Smart, Excel in Algorithms!
Build a strong foundation in analysis and design of algorithms. Practice problem-solving and hands-on implementation. Mastering dynamic programming opens doors to innovation and efficient problem-solving in your future endeavors.
Equip yourself with the knowledge to design efficient algorithms, optimize solutions, and create reliable systems. Use this study material as your guide to success in "Analysis & Design of Algorithms" in your 4th semester at RGPV. Happy learning and best wishes for an exceptional academic journey!
A New Network Flow Model for Determining the Assortment of Roll Types in Pack...Gurdal Ertek
This paper reports work motivated by a real world assortment problem in packaging industry. A novel network flow model has been developed to solve the problem of selecting the optimal set of roll types for use in production. The model can incorporate fixed costs that depend on the number of elements in the assortment as well as the selected roll types. While the trade-off between inventory cost and cost of waste is resolved optimally through the model, graphical understanding of the trade-off can bring insights into the decision making process.
This graphical analysis has been demonstrated on a computational example.
http://research.sabanciuniv.edu.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document summarizes research applying an ant colony optimization algorithm to solve the 3D constrained rectangular bin packing problem. The goal is to pack arbitrary sized 3D rectangular bins into standard sized containers in a way that minimizes empty space. The algorithm considers constraints like placement, overlapping, stability and develops a packing pattern represented by matrices. Experiments show the ant colony approach improves performance over other algorithms and is computationally efficient for solving this NP-hard bin packing optimization problem.
Ortmann [2010] Heuristics for Offline Rectangular Packing ProblemsFrank Ortmann
This document provides an abstract and introduction for Frank Gerald Ortmann's dissertation on heuristics for offline rectangular packing problems. The dissertation evaluates 218 new heuristics for the strip packing problem and proposes a new heuristic approach for the multiple bin size bin packing problem (MBSBPP). Key findings include that several newly proposed pseudolevel heuristics outperform known heuristics for strip packing, and that a modified plane-packing heuristic yields the best results for the MBSBPP in terms of packing density and time.
Decision Support For Packing In WarehousesGurdal Ertek
This document summarizes a research paper titled "Decision Support for Packing in Warehouses" by Ertek and Kilic. The paper proposes three algorithms - greedy, beam search, and tree search - to solve a packing problem for an automobile manufacturer's spare parts warehouse. The problem involves optimally packing items from customer orders into boxes to minimize costs. This is a novel 3D multiple bin size bin packing problem that has not been previously analyzed in literature. The paper compares the performance of the proposed algorithms in terms of cost and computation time.
Heuristic Algorithm for Constrained 3D Container Loading Problem: A Genetic A...ijcoa
This paper presents an heuristic Genetic Algorithm for solving 3-Dimensional Single container packing optimization problem. The 3D container loading problem consists of ‘n’ number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and inturn profit. Furthermore, various practical constraints like box orientation, stack priority, container stability, etc also applied. Boxes to be packed are of various sizes and of heterogeneous shapes. In this research work, several heuristic improvements were proposed over Genetic Algorithm (GA) to solve the container loading problem that significantly improves the search efficiency and to load most of heterogeneous boxes into a container along with the optimal position of loaded boxes, box orientation and boxes to be loaded by satisfying practical constraints. In this module, both the guillotine and non-guillotine moves were allowed. In general, these heuristic GA solutions being substantially better and satisfactory than those obtained by applying heuristics to the bin packing directly.
A Survey- Knapsack Problem Using Dynamic ProgrammingEditor IJCTER
A method for finding an optimal solution of mixed integer programming problems with one constraint is proposed. Initially, this method lessens the number of variables and the interval of their change; then, for the resulting problem one derives recurrent relations of dynamic programming that are used for computing. dynamic programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems, solving each of those subproblems just once, and storing their solutions. Using a matrix for information storage, we can solve problems of a sufficiently large dimension. The computational experiments demonstrate that the method in question is highly efficient. In this paper shows study about Knapsack problem.
An efficient algorithm for 3D rectangular box packingmustafa sarac
The document summarizes an algorithm for efficiently packing 3D rectangular boxes into a container. It begins with background on the 3D packing problem and why it is important to shipping companies. It then:
1) Introduces the Largest Area First-Fit (LAFF) algorithm that places the largest boxes first by minimizing height.
2) Explains the inputs as the number and dimensions of boxes, and outputs as used space, wasted space, and time.
3) Details how LAFF works by first determining the container width/depth, then placing boxes in two methods - increasing height or fitting remaining space.
This document discusses bin packing algorithms in Golang. It begins with an introduction to bin packing and describes how it is an important problem in logistics and computing. It then provides details on implementing two bin packing algorithms (Best Fit and First Fit Decreasing) in Golang. It discusses using the Gota dataframe library for data analysis and filtering. The document also covers unit testing the bin packing algorithms and results, which showed successful testing of single and multiple SKUs. In conclusion, it was shown that the bin packing program could find solutions in a reasonable time frame using the implemented algorithms.
The document discusses algorithms for solving bin packing problems. It introduces bin packing as arranging items of different volumes into a finite number of bins to minimize the number of bins used. It then describes four key aspects: lower bounds, first-fit, first-fit decreasing, and full-bin packing. First-fit simply places items in the first bin they fit in. First-fit decreasing first sorts items by decreasing size, tending to produce better solutions. Full-bin packing aims to fully pack each bin, though it may be complex. The document provides overviews of each approach and notes first-fit decreasing generally performs better than first-fit, though not always optimally, in less time than full-bin packing.
This document summarizes a research paper that presents a new algorithm for solving the 0-1 Knapsack Problem. The algorithm combines previous approaches that generated valid inequalities and used surrogate relaxation. It is able to solve classical test instances with up to 10,000 variables in under 0.2 seconds. The key aspects of the new algorithm include:
1) Using a new initial "core" problem that is likely to produce a well-filled knapsack, rather than just items near the break item ratio.
2) Generating cardinality constraints if the number of states grows too large, relaxing them with the original weight constraint to obtain a good upper bound.
3) Attempting to improve the lower
Study of Different Multi-instance Learning kNN AlgorithmsEditor IJCATR
Because of it is applicability in various field, multi-instance learning or multi-instance problem becoming more popular in
machine learning research field. Different from supervised learning, multi-instance learning related to the problem of classifying an
unknown bag into positive or negative label such that labels of instances of bags are ambiguous. This paper uses and study three
different k-nearest neighbor algorithm namely Bayesian -kNN, citation -kNN and Bayesian Citation -kNN algorithm for solving multiinstance
problem. Similarity between two bags is measured using Hausdroff distance. To overcome the problem of false positive
instances constructive covering algorithm used. Also the problem definition, learning algorithm and experimental data sets related to
multi-instance learning framework are briefly reviewed in this paper
The document describes the design of the ¡bOxx!, a portable and compactable storage box created by students to address common problems encountered when carrying school supplies. The initial design of the ¡bOxx! included separate compartments and the ability to fold flat. Based on feedback, the design was improved to include a handle and durable materials. Pending tasks include sourcing cardboard, getting help to construct a prototype, and further refining the design based on surveys and identifying flaws.
ADA Unit — 3 Dynamic Programming and Its Applications.pdfRGPV De Bunkers
Study Material: Analysis & Design of Algorithms - Semester 3
For RGPV Students of 4th Semester in Computer Science Engineering
Discover the power of algorithms with this comprehensive study material on "Analysis & Design of Algorithms" designed specifically for RGPV students in the 4th semester of Computer Science Engineering. Dive into the world of dynamic programming and its versatile applications, equipping yourself with essential problem-solving skills.
Unit Overview: Dynamic Programming and Its Applications
Learn the fundamental concepts of dynamic programming and its diverse applications. Dynamic programming is an algorithmic technique that efficiently solves complex problems by breaking them into smaller, overlapping subproblems. This unit explores key topics, including:
Concept of Dynamic Programming: Understand the significance of dynamic programming in algorithm design, leveraging overlapping subproblems and optimal substructure properties.
0/1 Knapsack Problem: Solve the classic optimization problem of 0/1 knapsack, maximizing value while respecting the knapsack's capacity.
Multistage Graph: Model decision-making processes with multistage graphs and use dynamic programming to find optimal paths.
Reliability Design: Optimize system reliability with dynamic programming, making smart decisions on redundancy and component selection.
Floyd-Warshall Algorithm: Determine shortest paths between vertices in a weighted graph using this versatile algorithm.
Why Choose This Study Material?
Tailored for RGPV Students: Specifically designed for 4th-semester Computer Science Engineering students at RGPV, aligning with the curriculum.
Comprehensive Coverage: Detailed explanations of each topic ensure a solid grasp of dynamic programming concepts.
Real-World Relevance: Apply your knowledge to project management, network design, manufacturing, and more.
Step-by-Step Approach: Understand problem-solving through step-by-step explanations.
Practical Examples: Numerous examples, including the knapsack problem and Floyd-Warshall algorithm, enrich your learning experience.
Study Smart, Excel in Algorithms!
Build a strong foundation in analysis and design of algorithms. Practice problem-solving and hands-on implementation. Mastering dynamic programming opens doors to innovation and efficient problem-solving in your future endeavors.
Equip yourself with the knowledge to design efficient algorithms, optimize solutions, and create reliable systems. Use this study material as your guide to success in "Analysis & Design of Algorithms" in your 4th semester at RGPV. Happy learning and best wishes for an exceptional academic journey!
A New Network Flow Model for Determining the Assortment of Roll Types in Pack...Gurdal Ertek
This paper reports work motivated by a real world assortment problem in packaging industry. A novel network flow model has been developed to solve the problem of selecting the optimal set of roll types for use in production. The model can incorporate fixed costs that depend on the number of elements in the assortment as well as the selected roll types. While the trade-off between inventory cost and cost of waste is resolved optimally through the model, graphical understanding of the trade-off can bring insights into the decision making process.
This graphical analysis has been demonstrated on a computational example.
http://research.sabanciuniv.edu.
Electrically small antennas: The art of miniaturizationEditor IJARCET
We are living in the technological era, were we preferred to have the portable devices rather than unmovable devices. We are isolating our self rom the wires and we are becoming the habitual of wireless world what makes the device portable? I guess physical dimensions (mechanical) of that particular device, but along with this the electrical dimension is of the device is also of great importance. Reducing the physical dimension of the antenna would result in the small antenna but not electrically small antenna. We have different definition for the electrically small antenna but the one which is most appropriate is, where k is the wave number and is equal to and a is the radius of the imaginary sphere circumscribing the maximum dimension of the antenna. As the present day electronic devices progress to diminish in size, technocrats have become increasingly concentrated on electrically small antenna (ESA) designs to reduce the size of the antenna in the overall electronics system. Researchers in many fields, including RF and Microwave, biomedical technology and national intelligence, can benefit from electrically small antennas as long as the performance of the designed ESA meets the system requirement.
This document provides a comparative study of two-way finite automata and Turing machines. Some key points:
- Two-way finite automata are similar to read-only Turing machines in that they have a finite tape that can be read in both directions, but cannot write to the tape.
- Turing machines have an infinite tape that can be read from and written to, allowing them to recognize recursively enumerable languages.
- Both models are examined in their ability to accept the regular language L={anbm|m,n>0}.
- The time complexity of a two-way finite automaton for this language is O(n2) due to making two passes over the
This document analyzes and compares the performance of the AODV and DSDV routing protocols in a vehicular ad hoc network (VANET) simulation. Simulations were conducted using NS-2, SUMO, and MOVE simulators for a grid map scenario with varying numbers of nodes. The results show that AODV performed better than DSDV in terms of throughput and packet delivery fraction, while DSDV had lower end-to-end delays. However, neither protocol was found to be fully suitable for the highly dynamic VANET environment. The document concludes that further work is needed to develop improved routing protocols optimized for VANETs.
This document discusses the digital circuit layout problem and approaches to solving it using graph partitioning techniques. It begins by introducing the digital circuit layout problem and how it has become more complex with increasing circuit sizes. It then discusses how the problem can be decomposed into subproblems using graph partitioning to assign geometric coordinates to circuit components. The document reviews several traditional approaches to solve the problem, such as the Kernighan-Lin algorithm, and discusses their limitations for larger circuit sizes. It also discusses more recent approaches using evolutionary algorithms and concludes by analyzing the contributions of various approaches.
This document summarizes various data mining techniques that have been used for intrusion detection systems. It first describes the architecture of a data mining-based IDS, including sensors to collect data, detectors to evaluate the data using detection models, a data warehouse for storage, and a model generator. It then discusses supervised and unsupervised learning approaches that have been applied, including neural networks, support vector machines, K-means clustering, and self-organizing maps. Finally, it reviews several related works applying these techniques and compares their results, finding that combinations of approaches can improve detection rates while reducing false alarms.
This document provides an overview of speech recognition systems and recent progress in the field. It discusses different types of speech recognition including isolated word, connected word, continuous speech, and spontaneous speech. Various techniques used in speech recognition are also summarized, such as simulated evolutionary computation, artificial neural networks, fuzzy logic, Kalman filters, and Hidden Markov Models. The document reviews several papers published between 2004-2012 that studied speech recognition methods including using dynamic spectral subband centroids, Kalman filters, biomimetic computing techniques, noise estimation, and modulation filtering. It concludes that Hidden Markov Models combined with MFCC features provide good recognition results for large vocabulary, speaker-independent, continuous speech recognition.
This document discusses integrating two assembly lines, Line A and Line B, based on lean line design concepts to reduce space and operators. It analyzes the current state of the lines using tools like takt time analysis and MTM/UAS studies. Improvements are identified to eliminate waste, including methods improvements, workplace rearrangement, ergonomic changes, and outsourcing. Paper kaizen is conducted and work elements are retimed. The goal is to integrate the lines to better utilize space and manpower while meeting manufacturing standards.
This document summarizes research on the exposure of microwaves from cellular networks. It describes how microwaves interact with biological systems and discusses measurement techniques and safety standards regarding microwave exposure. While some studies have alleged health hazards from microwaves, independent reviews by health organizations have found no evidence that exposure to microwaves below international safety limits causes harm. The document concludes that with precautions like limiting exposure time and using phones with lower SAR ratings, microwaves from cell phones pose minimal health risks.
This document summarizes a research paper that examines the effect of feature reduction in sentiment analysis of online reviews. It uses principle component analysis to reduce the number of features (product attributes) from a dataset of 500 camera reviews labeled as positive or negative. Two models are developed - one using the original set of 95 product attributes, and one using the reduced set. Support vector machines and naive Bayes classifiers are applied to both models and their performance is evaluated to determine if classification accuracy can be maintained while using fewer features. The results show it is possible to achieve similar accuracy levels with less features, improving computational efficiency.
This document provides a review of multispectral palm image fusion techniques. It begins with an introduction to biometrics and palm print identification. Different palm print images capture different spectral information about the palm. The document then reviews several pixel-level fusion methods for combining multispectral palm images, finding that Curvelet transform performs best at preserving discriminative patterns. It also discusses hardware for capturing multispectral palm images and the process of region of interest extraction and localization. Common fusion methods like wavelet transform and Curvelet transform are also summarized.
This document describes a vehicle theft detection system that uses radio frequency identification (RFID) technology. The system involves embedding an RFID chip in each vehicle that continuously transmits a unique identification signal. When a vehicle is stolen, the owner reports it to the police, who upload the vehicle's information to a central database. Police vehicles are equipped with RFID receivers. If a stolen vehicle passes within range of a receiver, the receiver detects the vehicle's ID signal and displays its details on a tablet. This allows police to quickly identify and recover stolen vehicles. The system aims to make it difficult for thieves to hide a vehicle's identity and allows vehicles to be tracked globally wherever the detection system is implemented.
This document discusses and compares two techniques for image denoising using wavelet transforms: Dual-Tree Complex DWT and Double-Density Dual-Tree Complex DWT. Both techniques decompose an image corrupted by noise using filter banks, apply thresholding to the wavelet coefficients, and reconstruct the image. The Double-Density Dual-Tree Complex DWT yields better denoising results than the Dual-Tree Complex DWT as it produces more directional wavelets and is less sensitive to shifts and noise variance. Experimental results on test images demonstrate that the Double-Density method achieves higher peak signal-to-noise ratios, especially at higher noise levels.
This document compares the k-means and grid density clustering algorithms. It summarizes that grid density clustering determines dense grids based on the densities of neighboring grids, and is able to handle different shaped clusters in multi-density environments. The grid density algorithm does not require distance computation and is not dependent on the number of clusters being known in advance like k-means. The document concludes that grid density clustering is better than k-means clustering as it can handle noise and outliers, find arbitrary shaped clusters, and has lower time complexity.
This document proposes a method for detecting, localizing, and extracting text from videos with complex backgrounds. It involves three main steps:
1. Text detection uses corner metric and Laplacian filtering techniques independently to detect text regions. Corner metric identifies regions with high curvature, while Laplacian filtering highlights intensity discontinuities. The results are combined through multiplication to reduce noise.
2. Text localization then determines the accurate boundaries of detected text strings.
3. Text binarization filters background pixels to extract text pixels for recognition. Thresholding techniques are used to convert localized text regions to binary images.
The method exploits different text properties to detect text using corner metric and Laplacian filtering. Combining the results improves
This document describes the design and implementation of a low power 16-bit arithmetic logic unit (ALU) using clock gating techniques. A variable block length carry skip adder is used in the arithmetic unit to reduce power consumption and improve performance. The ALU uses a clock gating circuit to selectively clock only the active arithmetic or logic unit, reducing dynamic power dissipation from unnecessary clock charging/discharging. The ALU was simulated in VHDL and synthesized for a Xilinx Spartan 3E FPGA, achieving a maximum frequency of 65.19MHz at 1.98mW power dissipation, demonstrating improved performance over a conventional ALU design.
This document describes using particle swarm optimization (PSO) and genetic algorithms (GA) to tune the parameters of a proportional-integral-derivative (PID) controller for an automatic voltage regulator (AVR) system. PSO and GA are used to minimize the objective function by adjusting the PID parameters to achieve optimal step response with minimal overshoot, settling time, and rise time. The results show that PSO provides high-quality solutions within a shorter calculation time than other stochastic methods.
This document discusses implementing trust negotiations in multisession transactions. It proposes a framework that supports voluntary and unexpected interruptions, allowing negotiating parties to complete negotiations despite temporary unavailability of resources. The Trust-x protocol addresses issues related to validity, temporary loss of data, and extended unavailability of one negotiator. It allows a peer to suspend an ongoing negotiation and resume it with another authenticated peer. Negotiation portions and intermediate states can be safely and privately passed among peers to guarantee stability for continued suspended negotiations. An ontology is also proposed to provide formal specification of concepts and relationships, which is essential in complex web service environments for sharing credential information needed to establish trust.
This document discusses and compares various nature-inspired optimization algorithms for resolving the mixed pixel problem in remote sensing imagery, including Biogeography-Based Optimization (BBO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). It provides an overview of each algorithm, explaining key concepts like migration and mutation in BBO. The document aims to prove that BBO is the best algorithm for resolving the mixed pixel problem by comparing it to other evolutionary algorithms. It also includes figures illustrating concepts like the species model and habitat in BBO.
This document discusses principal component analysis (PCA) for face recognition. It begins with an introduction to face recognition and PCA. PCA works by calculating eigenvectors from a set of face images, which represent the principal components that account for the most variance in the image data. These eigenvectors are called "eigenfaces" and can be used to reconstruct the face images. The document then discusses how the system is implemented, including preparing a face database, normalizing the training images, calculating the eigenfaces/principal components, projecting the face images into this reduced space, and recognizing faces by calculating distances between projected test images and training images.
This document summarizes research on using wireless sensor networks to detect mobile targets. It discusses two optimization problems: 1) maximizing the exposure of the least exposed path within a sensor budget, and 2) minimizing sensor installation costs while ensuring all paths have exposure above a threshold. It proposes using tabu search heuristics to provide near-optimal solutions. The research also addresses extending the models to consider wireless connectivity, heterogeneous sensors, and intrusion detection using a game theory approach. Experimental results show the proposed mobile replica detection scheme can rapidly detect replicas with no false positives or negatives.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.