This document provides information about parameterization and lambda expressions in Java. It discusses how parameterization allows functions to be configured through parameters. Lambda expressions are described as anonymous functions that can be passed as arguments or stored in variables. The document outlines the benefits of streams in Java 8 for processing data in a declarative way through composable and parallelizable operations like filter, map, and reduce. It compares streams to collections and explains intermediate and terminal stream operations.
Java 8 introduced new features like default methods, lambda expressions, and stream API. Default methods allow interfaces to provide implementation without breaking existing classes. Lambda expressions enable functional programming by allowing blocks of code to be treated as values. The stream API allows functional-style processing of data such as filtering, mapping, and reducing collections. Some limitations of streams include that they are not reusable, can have worse performance than loops, and are less familiar to procedural programmers.
The document discusses new features in Java 8 including default methods in interfaces, lambda expressions, and streams. Default methods allow interfaces to include method implementations. Lambda expressions allow treating functionality as a method argument and code as data. Streams provide utilities for functional-style operations on collections of values and include intermediate and terminal operations.
Lambda expressions allow implementing anonymous functions more concisely. Interfaces can now contain default and static methods. Streams facilitate functional-style operations on collections of elements. Optional handles null references more gracefully. The Date/Time API replaces the previous Date and Calendar classes. Nashorn allows executing JavaScript code from Java. Various new tools like jdeps were introduced.
This document discusses Java 8 streams and how they are implemented under the hood. Streams use a pipeline concept where the output of one unit becomes the input of the next. Streams are built on functional interfaces and lambdas introduced in Java 8. Spliterators play an important role in enabling parallel processing of streams by splitting data sources into multiple chunks that can be processed independently in parallel. The reference pipeline implementation uses a linked list of processing stages to represent the stream operations and evaluates whether operations can be parallelized to take advantage of multiple threads or cores.
The document discusses Java 8 Streams and provides an overview of key concepts:
- It outlines the agenda which includes an overview of Java 8 features, why streams were introduced, how streams work internally, and a hands-on session.
- Streams were introduced to implement internal iteration for better framework code, provide parallelism support, and make lambda expressions work well with stream operations.
- Streams are lazy and perform computations on demand. They operate on source data and produce pipelined data for operations using functional interfaces.
- The document demonstrates creating and working with streams including intermediate operations like filter, map, and sorted.
Lambda expressions allow implementing functional interfaces using anonymous functions. Method references provide a shorthand syntax for referring to existing methods as lambda expressions. The Stream API allows functional-style operations on streams of values, including intermediate and terminal operations. The new Date/Time API provides a safer and more comprehensive replacement for the previous date/time classes in Java.
This document summarizes the agenda and details of the Belfast Java User Group meeting on October 23rd, 2013. The meeting will include introductions to the Belfast JUG, an overview of what a JUG is and how to get involved. There will also be presentations on keynotes from JavaOne and 20+ new features in JEE7. Attendees are encouraged to contribute ideas and get involved in the leadership team.
2015 01-17 Lambda Architecture with Apache Spark, NextML ConferenceDB Tsai
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. In Lambda architecture, the system involves three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries, and each comes with its own set of requirements.
In batch layer, it aims at perfect accuracy by being able to process the all available big dataset which is an immutable, append-only set of raw data using distributed processing system. Output will be typically stored in a read-only database with result completely replacing existing precomputed views. Apache Hadoop, Pig, and HIVE are
the de facto batch-processing system.
In speed layer, the data is processed in streaming fashion, and the real-time views are provided by the most recent data. As a result, the speed layer is responsible for filling the "gap" caused by the batch layer's lag in providing views based on the most recent data. This layer's views may not be as accurate as the views provided by batch layer's views created with full dataset, so they will be eventually replaced by the batch layer's views. Traditionally, Apache Storm is
used in this layer.
In serving layer, the result from batch layer and speed layer will be stored here, and it responds to queries in a low-latency and ad-hoc way.
One of the lambda architecture examples in machine learning context is building the fraud detection system. In speed layer, the incoming streaming data can be used for online learning to update the model learnt in batch layer to incorporate the recent events. After a while, the model can be rebuilt using the full dataset.
Why Spark for lambda architecture? Traditionally, different
technologies are used in batch layer and speed layer. If your batch system is implemented with Apache Pig, and your speed layer is implemented with Apache Storm, you have to write and maintain the same logics in SQL and in Java/Scala. This will very quickly becomes a maintenance nightmare. With Spark, we have an unified development framework for batch and speed layer at scale. In this talk, an end-to-end example implemented in Spark will be shown, and we will
discuss about the development, testing, maintenance, and deployment of lambda architecture system with Apache Spark.
Java 8 introduced new features like default methods, lambda expressions, and stream API. Default methods allow interfaces to provide implementation without breaking existing classes. Lambda expressions enable functional programming by allowing blocks of code to be treated as values. The stream API allows functional-style processing of data such as filtering, mapping, and reducing collections. Some limitations of streams include that they are not reusable, can have worse performance than loops, and are less familiar to procedural programmers.
The document discusses new features in Java 8 including default methods in interfaces, lambda expressions, and streams. Default methods allow interfaces to include method implementations. Lambda expressions allow treating functionality as a method argument and code as data. Streams provide utilities for functional-style operations on collections of values and include intermediate and terminal operations.
Lambda expressions allow implementing anonymous functions more concisely. Interfaces can now contain default and static methods. Streams facilitate functional-style operations on collections of elements. Optional handles null references more gracefully. The Date/Time API replaces the previous Date and Calendar classes. Nashorn allows executing JavaScript code from Java. Various new tools like jdeps were introduced.
This document discusses Java 8 streams and how they are implemented under the hood. Streams use a pipeline concept where the output of one unit becomes the input of the next. Streams are built on functional interfaces and lambdas introduced in Java 8. Spliterators play an important role in enabling parallel processing of streams by splitting data sources into multiple chunks that can be processed independently in parallel. The reference pipeline implementation uses a linked list of processing stages to represent the stream operations and evaluates whether operations can be parallelized to take advantage of multiple threads or cores.
The document discusses Java 8 Streams and provides an overview of key concepts:
- It outlines the agenda which includes an overview of Java 8 features, why streams were introduced, how streams work internally, and a hands-on session.
- Streams were introduced to implement internal iteration for better framework code, provide parallelism support, and make lambda expressions work well with stream operations.
- Streams are lazy and perform computations on demand. They operate on source data and produce pipelined data for operations using functional interfaces.
- The document demonstrates creating and working with streams including intermediate operations like filter, map, and sorted.
Lambda expressions allow implementing functional interfaces using anonymous functions. Method references provide a shorthand syntax for referring to existing methods as lambda expressions. The Stream API allows functional-style operations on streams of values, including intermediate and terminal operations. The new Date/Time API provides a safer and more comprehensive replacement for the previous date/time classes in Java.
This document summarizes the agenda and details of the Belfast Java User Group meeting on October 23rd, 2013. The meeting will include introductions to the Belfast JUG, an overview of what a JUG is and how to get involved. There will also be presentations on keynotes from JavaOne and 20+ new features in JEE7. Attendees are encouraged to contribute ideas and get involved in the leadership team.
2015 01-17 Lambda Architecture with Apache Spark, NextML ConferenceDB Tsai
Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods. In Lambda architecture, the system involves three layers: batch processing, speed (or real-time) processing, and a serving layer for responding to queries, and each comes with its own set of requirements.
In batch layer, it aims at perfect accuracy by being able to process the all available big dataset which is an immutable, append-only set of raw data using distributed processing system. Output will be typically stored in a read-only database with result completely replacing existing precomputed views. Apache Hadoop, Pig, and HIVE are
the de facto batch-processing system.
In speed layer, the data is processed in streaming fashion, and the real-time views are provided by the most recent data. As a result, the speed layer is responsible for filling the "gap" caused by the batch layer's lag in providing views based on the most recent data. This layer's views may not be as accurate as the views provided by batch layer's views created with full dataset, so they will be eventually replaced by the batch layer's views. Traditionally, Apache Storm is
used in this layer.
In serving layer, the result from batch layer and speed layer will be stored here, and it responds to queries in a low-latency and ad-hoc way.
One of the lambda architecture examples in machine learning context is building the fraud detection system. In speed layer, the incoming streaming data can be used for online learning to update the model learnt in batch layer to incorporate the recent events. After a while, the model can be rebuilt using the full dataset.
Why Spark for lambda architecture? Traditionally, different
technologies are used in batch layer and speed layer. If your batch system is implemented with Apache Pig, and your speed layer is implemented with Apache Storm, you have to write and maintain the same logics in SQL and in Java/Scala. This will very quickly becomes a maintenance nightmare. With Spark, we have an unified development framework for batch and speed layer at scale. In this talk, an end-to-end example implemented in Spark will be shown, and we will
discuss about the development, testing, maintenance, and deployment of lambda architecture system with Apache Spark.
This document summarizes key parts of Java 8 including lambda expressions, method references, default methods, streams API improvements, removal of PermGen space, and the new date/time API. It provides code examples and explanations of lambda syntax and functional interfaces. It also discusses advantages of the streams API like lazy evaluation and parallelization. Finally, it briefly outlines the motivation for removing PermGen and standardizing the date/time API in Java 8.
This document introduces lambda expressions in Java 8. It provides background on why lambda expressions were added to Java, including to allow for more functional programming and parallel processing. It covers the syntax of lambda expressions, when they should and should not be used, functional interfaces, method and constructor references, referencing external variables, debugging lambda expressions, and new lambda methods added in Java 8. The document also advertises exercises for the reader to complete to practice using lambda expressions.
This is a 3 part series on Java8 Features. Drop me an email for a discussion - singh.marut@gmail.com
Code is available at https://github.com/singhmarut/java8training
Videos available at my youtube channel https://www.youtube.com/channel/UCBM4yHwfjQ_syW6Lz8kYpmA
This document summarizes Simon Ritter's presentation on Java 8. The presentation introduced several new features in Java 8 including lambda expressions, streams, date/time API improvements, and others. It provided examples of how to use lambda expressions and streams to concisely represent operations on collections and other data sources in a more functional style.
Java Developers, make the database work for you (NLJUG JFall 2010)Lucas Jellema
The general consensus among Java developers has evolved from a dogmatic strive for database independence to a much more pragmatic wish to leverage the power of the database. This session demonstrates some of the (hidden) powers of the database and how these can be utilized from Java applications using either straight JDBC or working through JPA. The Oracle database is used as example: SQL for Aggregation and Analysis, Flashback Queries for historical comparison and trends, Virtual Private Database, complex validation, PL/SQL and collections for bulk data manipulation, view and instead-of triggers for data model morphing, server push of relevant data changes, edition based redefinition for release management.
- overview of role of database in JEE architecture (and a little history on how the database is perceived through the years)
- discussion on the development of database functionality
- demonstration of some powerful database features
- description of how we leveraged these features in our JSF (RichFaces)/JPA (Hibernate) application
- demo of web application based on these features
- discussion on how to approach the database
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
This document provides an overview of building data pipelines using Apache Airflow. It discusses what a data pipeline is, common components of data pipelines like data ingestion and processing, and issues with traditional data flows. It then introduces Apache Airflow, describing its features like being fault tolerant and supporting Python code. The core components of Airflow including the web server, scheduler, executor, and worker processes are explained. Key concepts like DAGs, operators, tasks, and workflows are defined. Finally, it demonstrates Airflow through an example DAG that extracts and cleanses tweets.
UCLA CS219 Course Project Report (Prof. George Varghese)
StateKeeper: Generalizing Reachability
Today there are many tools that are built using formal methods to
verify networks. The majority of these tools check network specifi-
cations and configurations to identify only a limited class of failures
such as Reachability, Forwarding Loops and Slicing. They don’t take
into account the rate-limiting rules that affect the throughput of the
system and also link delays. Both of these quantities are responsible
for the Quality of Service (QoS). We believe that without a decent
throughput and affordable delay, a network is pretty much in a
dead state even if it passes the tests for reachability, forwarding
detection, and other such failure classes. We developed StateKeeper,
a tool based on ideas from Atomic Predicates Verifier. StateKeeper
keeps track of both network performance and network reachability.
In our experiments, we factor in link delay and throughput as our
performance metrics. StateKeeper will allow network operators
to verify Quality of Service at each port along a route by showing
information about the state of each step.
The document discusses Java 8 Streams, which provide a way to process data in a functional style. Streams allow operations like filter, map, and reduce to be performed lazily on collections, arrays, or I/O sources. The key aspects of streams are that they are lazy, support both sequential and parallel processing, and represent a sequence of values rather than storing them. The document provides examples of using intermediate operations like filter and map and terminal operations like forEach and collect. It also discusses spliterators, which drive streams and allow parallelization, and functional interfaces which are used with lambda expressions in streams.
Sf rmr - Servicing Forwarding Remote Multiplexing RelayAlenMilincevic
This presentation shows an alternative and new aporoach to using only one port and multiple protocols. The protocols can be defined and will be recognised.
This can be done to use one internet socket for servising multiple applications that use internet sockets.
Java 8 includes new features such as lambda expressions for functional programming, streams API for bulk data operations, date and time API improvements, and miscellaneous enhancements. It also removes some deprecated features and improves performance.
Web services allow software components to communicate over the web through standardized interfaces. There are two main types - RESTful web services which use HTTP methods to manipulate resources, and SOAP-based services which use XML messages over HTTP. A WSDL contract describes the operations, messages, and data types of a web service. JAX-WS and JAX-RS are Java APIs for creating web services that map Java methods to WSDL operations and SOAP/HTTP messages. RESTful services follow architectural constraints like using URIs to identify resources and HTTP methods to manipulate them.
Apache Spark is an open-source unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. Some key components of Apache Spark include Resilient Distributed Datasets (RDDs), DataFrames, Datasets, and Spark SQL for structured data processing. Spark also supports streaming, machine learning via MLlib, and graph processing with GraphX.
In this Meetup Victor Perepelitsky - R&D Technical Leader at LivePerson leading the 'Real Time Event Processing Platform' team , talked about Java 8', 'Stream API', 'Lambda', and 'Method reference'.
Clarified what functional programming is and how can you use java 8 in order to create better software.
Victor will also covered some pain points that java 8 did not solve regarding functional and see how you can workaround it.
Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams ...Raffi Khatchadourian
Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ∼642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.
Java 8 Streams And Common Operations By Harmeet Singh(Taara)Harmeet Singh(Taara)
In this, we are discuss about Java 8 Streams. Common Operations . Java 8 Streams are huge topic, so i am not cover all the things, but try to cover the basics operations of Streams. Before this, please refer my previous presentation "Functional programming in java 8", because of clear some basic concept for functional programming. For the reference use Java 8 API docs.
Java 8 introduced several new features to the language including lambda expressions, default methods in interfaces, and static methods in interfaces. It also improved existing areas such as collections, date/time handling, concurrency, and I/O. Significant under-the-hood changes included replacing PermGen with Metaspace for class metadata and enabling AES encryption on newer CPUs.
This document provides an introduction and overview of Java lambda expressions and functional programming concepts in Java 8. It begins with getting started instructions and a review of pre-lambda approaches like anonymous inner classes. It then covers the basics of lambda expressions like syntax, type inference, implied returns, and effectively final variables. It discusses the @FunctionalInterface annotation and introduces Java's built-in java.util.function package for common functional interfaces. The document provides examples throughout and concludes with a wrap-up of lambda expressions in Java 8.
This document summarizes key parts of Java 8 including lambda expressions, method references, default methods, streams API improvements, removal of PermGen space, and the new date/time API. It provides code examples and explanations of lambda syntax and functional interfaces. It also discusses advantages of the streams API like lazy evaluation and parallelization. Finally, it briefly outlines the motivation for removing PermGen and standardizing the date/time API in Java 8.
This document introduces lambda expressions in Java 8. It provides background on why lambda expressions were added to Java, including to allow for more functional programming and parallel processing. It covers the syntax of lambda expressions, when they should and should not be used, functional interfaces, method and constructor references, referencing external variables, debugging lambda expressions, and new lambda methods added in Java 8. The document also advertises exercises for the reader to complete to practice using lambda expressions.
This is a 3 part series on Java8 Features. Drop me an email for a discussion - singh.marut@gmail.com
Code is available at https://github.com/singhmarut/java8training
Videos available at my youtube channel https://www.youtube.com/channel/UCBM4yHwfjQ_syW6Lz8kYpmA
This document summarizes Simon Ritter's presentation on Java 8. The presentation introduced several new features in Java 8 including lambda expressions, streams, date/time API improvements, and others. It provided examples of how to use lambda expressions and streams to concisely represent operations on collections and other data sources in a more functional style.
Java Developers, make the database work for you (NLJUG JFall 2010)Lucas Jellema
The general consensus among Java developers has evolved from a dogmatic strive for database independence to a much more pragmatic wish to leverage the power of the database. This session demonstrates some of the (hidden) powers of the database and how these can be utilized from Java applications using either straight JDBC or working through JPA. The Oracle database is used as example: SQL for Aggregation and Analysis, Flashback Queries for historical comparison and trends, Virtual Private Database, complex validation, PL/SQL and collections for bulk data manipulation, view and instead-of triggers for data model morphing, server push of relevant data changes, edition based redefinition for release management.
- overview of role of database in JEE architecture (and a little history on how the database is perceived through the years)
- discussion on the development of database functionality
- demonstration of some powerful database features
- description of how we leveraged these features in our JSF (RichFaces)/JPA (Hibernate) application
- demo of web application based on these features
- discussion on how to approach the database
The document discusses lambda expressions in Java 8. It provides background on the lambda calculus and functional programming. Lambda expressions allow anonymous functions and are implemented using functional interfaces in Java 8. This enables a more functional style of programming. Lambda expressions can access variables from their enclosing scope and method references provide a concise way to pass existing methods. The streams API allows functional-style operations on collections and supports sequential and parallel processing.
This document provides an overview of building data pipelines using Apache Airflow. It discusses what a data pipeline is, common components of data pipelines like data ingestion and processing, and issues with traditional data flows. It then introduces Apache Airflow, describing its features like being fault tolerant and supporting Python code. The core components of Airflow including the web server, scheduler, executor, and worker processes are explained. Key concepts like DAGs, operators, tasks, and workflows are defined. Finally, it demonstrates Airflow through an example DAG that extracts and cleanses tweets.
UCLA CS219 Course Project Report (Prof. George Varghese)
StateKeeper: Generalizing Reachability
Today there are many tools that are built using formal methods to
verify networks. The majority of these tools check network specifi-
cations and configurations to identify only a limited class of failures
such as Reachability, Forwarding Loops and Slicing. They don’t take
into account the rate-limiting rules that affect the throughput of the
system and also link delays. Both of these quantities are responsible
for the Quality of Service (QoS). We believe that without a decent
throughput and affordable delay, a network is pretty much in a
dead state even if it passes the tests for reachability, forwarding
detection, and other such failure classes. We developed StateKeeper,
a tool based on ideas from Atomic Predicates Verifier. StateKeeper
keeps track of both network performance and network reachability.
In our experiments, we factor in link delay and throughput as our
performance metrics. StateKeeper will allow network operators
to verify Quality of Service at each port along a route by showing
information about the state of each step.
The document discusses Java 8 Streams, which provide a way to process data in a functional style. Streams allow operations like filter, map, and reduce to be performed lazily on collections, arrays, or I/O sources. The key aspects of streams are that they are lazy, support both sequential and parallel processing, and represent a sequence of values rather than storing them. The document provides examples of using intermediate operations like filter and map and terminal operations like forEach and collect. It also discusses spliterators, which drive streams and allow parallelization, and functional interfaces which are used with lambda expressions in streams.
Sf rmr - Servicing Forwarding Remote Multiplexing RelayAlenMilincevic
This presentation shows an alternative and new aporoach to using only one port and multiple protocols. The protocols can be defined and will be recognised.
This can be done to use one internet socket for servising multiple applications that use internet sockets.
Java 8 includes new features such as lambda expressions for functional programming, streams API for bulk data operations, date and time API improvements, and miscellaneous enhancements. It also removes some deprecated features and improves performance.
Web services allow software components to communicate over the web through standardized interfaces. There are two main types - RESTful web services which use HTTP methods to manipulate resources, and SOAP-based services which use XML messages over HTTP. A WSDL contract describes the operations, messages, and data types of a web service. JAX-WS and JAX-RS are Java APIs for creating web services that map Java methods to WSDL operations and SOAP/HTTP messages. RESTful services follow architectural constraints like using URIs to identify resources and HTTP methods to manipulate them.
Apache Spark is an open-source unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. Some key components of Apache Spark include Resilient Distributed Datasets (RDDs), DataFrames, Datasets, and Spark SQL for structured data processing. Spark also supports streaming, machine learning via MLlib, and graph processing with GraphX.
In this Meetup Victor Perepelitsky - R&D Technical Leader at LivePerson leading the 'Real Time Event Processing Platform' team , talked about Java 8', 'Stream API', 'Lambda', and 'Method reference'.
Clarified what functional programming is and how can you use java 8 in order to create better software.
Victor will also covered some pain points that java 8 did not solve regarding functional and see how you can workaround it.
Safe Automated Refactoring for Intelligent Parallelization of Java 8 Streams ...Raffi Khatchadourian
Streaming APIs are becoming more pervasive in mainstream Object-Oriented programming languages. For example, the Stream API introduced in Java 8 allows for functional-like, MapReduce-style operations in processing both finite and infinite data structures. However, using this API efficiently involves subtle considerations like determining when it is best for stream operations to run in parallel, when running operations in parallel can be less efficient, and when it is safe to run in parallel due to possible lambda expression side-effects. In this paper, we present an automated refactoring approach that assists developers in writing efficient stream code in a semantics-preserving fashion. The approach, based on a novel data ordering and typestate analysis, consists of preconditions for automatically determining when it is safe and possibly advantageous to convert sequential streams to parallel and unorder or de-parallelize already parallel streams. The approach was implemented as a plug-in to the Eclipse IDE, uses the WALA and SAFE analysis frameworks, and was evaluated on 11 Java projects consisting of ∼642K lines of code. We found that 57 of 157 candidate streams (36.31%) were refactorable, and an average speedup of 3.49 on performance tests was observed. The results indicate that the approach is useful in optimizing stream code to their full potential.
Java 8 Streams And Common Operations By Harmeet Singh(Taara)Harmeet Singh(Taara)
In this, we are discuss about Java 8 Streams. Common Operations . Java 8 Streams are huge topic, so i am not cover all the things, but try to cover the basics operations of Streams. Before this, please refer my previous presentation "Functional programming in java 8", because of clear some basic concept for functional programming. For the reference use Java 8 API docs.
Java 8 introduced several new features to the language including lambda expressions, default methods in interfaces, and static methods in interfaces. It also improved existing areas such as collections, date/time handling, concurrency, and I/O. Significant under-the-hood changes included replacing PermGen with Metaspace for class metadata and enabling AES encryption on newer CPUs.
This document provides an introduction and overview of Java lambda expressions and functional programming concepts in Java 8. It begins with getting started instructions and a review of pre-lambda approaches like anonymous inner classes. It then covers the basics of lambda expressions like syntax, type inference, implied returns, and effectively final variables. It discusses the @FunctionalInterface annotation and introduces Java's built-in java.util.function package for common functional interfaces. The document provides examples throughout and concludes with a wrap-up of lambda expressions in Java 8.
TIME DIVISION MULTIPLEXING TECHNIQUE FOR COMMUNICATION SYSTEMHODECEDSIET
Time Division Multiplexing (TDM) is a method of transmitting multiple signals over a single communication channel by dividing the signal into many segments, each having a very short duration of time. These time slots are then allocated to different data streams, allowing multiple signals to share the same transmission medium efficiently. TDM is widely used in telecommunications and data communication systems.
### How TDM Works
1. **Time Slots Allocation**: The core principle of TDM is to assign distinct time slots to each signal. During each time slot, the respective signal is transmitted, and then the process repeats cyclically. For example, if there are four signals to be transmitted, the TDM cycle will divide time into four slots, each assigned to one signal.
2. **Synchronization**: Synchronization is crucial in TDM systems to ensure that the signals are correctly aligned with their respective time slots. Both the transmitter and receiver must be synchronized to avoid any overlap or loss of data. This synchronization is typically maintained by a clock signal that ensures time slots are accurately aligned.
3. **Frame Structure**: TDM data is organized into frames, where each frame consists of a set of time slots. Each frame is repeated at regular intervals, ensuring continuous transmission of data streams. The frame structure helps in managing the data streams and maintaining the synchronization between the transmitter and receiver.
4. **Multiplexer and Demultiplexer**: At the transmitting end, a multiplexer combines multiple input signals into a single composite signal by assigning each signal to a specific time slot. At the receiving end, a demultiplexer separates the composite signal back into individual signals based on their respective time slots.
### Types of TDM
1. **Synchronous TDM**: In synchronous TDM, time slots are pre-assigned to each signal, regardless of whether the signal has data to transmit or not. This can lead to inefficiencies if some time slots remain empty due to the absence of data.
2. **Asynchronous TDM (or Statistical TDM)**: Asynchronous TDM addresses the inefficiencies of synchronous TDM by allocating time slots dynamically based on the presence of data. Time slots are assigned only when there is data to transmit, which optimizes the use of the communication channel.
### Applications of TDM
- **Telecommunications**: TDM is extensively used in telecommunication systems, such as in T1 and E1 lines, where multiple telephone calls are transmitted over a single line by assigning each call to a specific time slot.
- **Digital Audio and Video Broadcasting**: TDM is used in broadcasting systems to transmit multiple audio or video streams over a single channel, ensuring efficient use of bandwidth.
- **Computer Networks**: TDM is used in network protocols and systems to manage the transmission of data from multiple sources over a single network medium.
### Advantages of TDM
- **Efficient Use of Bandwidth**: TDM all
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
ACEP Magazine edition 4th launched on 05.06.2024Rahul
This document provides information about the third edition of the magazine "Sthapatya" published by the Association of Civil Engineers (Practicing) Aurangabad. It includes messages from current and past presidents of ACEP, memories and photos from past ACEP events, information on life time achievement awards given by ACEP, and a technical article on concrete maintenance, repairs and strengthening. The document highlights activities of ACEP and provides a technical educational article for members.
Batteries -Introduction – Types of Batteries – discharging and charging of battery - characteristics of battery –battery rating- various tests on battery- – Primary battery: silver button cell- Secondary battery :Ni-Cd battery-modern battery: lithium ion battery-maintenance of batteries-choices of batteries for electric vehicle applications.
Fuel Cells: Introduction- importance and classification of fuel cells - description, principle, components, applications of fuel cells: H2-O2 fuel cell, alkaline fuel cell, molten carbonate fuel cell and direct methanol fuel cells.
15. Lambda:Introduction
● A lambda expression can be understood as a concise representation of an
anonymous function that can be passed around: it doesn’t have a name, but it
has a list of parameters, a body, a return type, and also possibly a list of
exceptions that can be thrown. That’s one big definition;
– Anonymous— We say anonymous because it doesn’t have an explicit name
like a method would normally have: less to write and think about!
– Function— We say function because a lambda isn’t associated with a
particular class like a method is. But like a method, a lambda has a list of
parameters, a body, a return type, and a possible list of exceptions that can
be thrown.
– Passed around— A lambda expression can be passed as argument to a
method or stored in a variable.
– Concise— You don’t need to write a lot of boilerplate like you do for
anonymous classes.
19. Funtional Interfaces
● What can you do with functional interfaces?
– Lambda expressions let you provide the implementation of
the abstract method of a functional interface directly inline
and treat the whole expression as an instance of a functional
interface (more technically speaking, an instance of a
concrete implementation of the functional interface).
– You can achieve the same thing with an anonymous inner
class, although it’s clumsier
36. Streams:Software Engineering Benefits
● The code is written in a declarative way:
– you specify what you want to achieve (that is, filter dishes that are low in
calories) as opposed to specifying how to implement an operation (using
control-flow blocks such as loops and if conditions).
– Together with behavior parameterization, enables you to cope with
changing requirements: you could easily create an additional version of
your code to filter high-calorie dishes using a lambda expression, without
having to copy and paste code.
● You chain together several building-block operations to express a
complicated data processing pipeline while keeping your code readable and
its intent clear.
– The result of the filter is passed to the sorted method, which is then
passed to the map method and then to the collect method.
37. Streams:Software Engineering Benefits
● Because operations such as filter (or sorted, map, and collect)
are available as high-level building blocks that don’t depend on
a specific threading model, their internal implementation could
be single-threaded or potentially maximize your multicore
architecture transparently!
39. Java 8 Streams API
● Declarative— More concise and readable
● Composable— Greater flexibility
● Parallelizable— Better performance
40. Java 8 Streams API
● A short definition is “a sequence of elements from a source that
supports data processing operations.”
– Sequence of elements—
● Like a collection, a stream provides an interface to a
sequenced set of values of a specific element type.
● Because collections are data structures, they’re mostly about
storing and accessing elements with specific time/space
complexities (for example, an ArrayList vs. a LinkedList).
● But streams are about expressing computations such as filter,
sorted, and map .
● Collections are about data; streams are about
computations.
41. Java 8 Streams API
● A short definition is “a sequence of elements from a source that
supports data processing operations.”
– Source—
● Streams consume from a data-providing source such as
collections, arrays, or I/O resources.
● Note that generating a stream from an ordered collection
preserves the ordering.
● The elements of a stream coming from a list will have the
same order as the list.
42. Java 8 Streams API
● A short definition is “a sequence of elements from a source that
supports data processing operations.”
– Data processing operations—
● Streams support database-like operations and common
operations from functional programming languages to
manipulate data, such as filter, map, reduce, find, match,
sort, and so on.
● Stream operations can be executed either sequentially or
in parallel.
43. Java 8 Streams API
● A short definition is “a sequence of elements from a source that supports
data processing operations.”
– Pipelining—
● Many stream operations return a stream themselves, allowing
operations to be chained and form a larger pipeline.
● This enables certain optimizations , such as laziness and short-
circuiting.
● A pipeline of operations can be viewed as a database-like query
on the data source.
– Internal iteration—
● In contrast to collections, which are iterated explicitly using an
iterator, stream operations do the iteration behind the scenes.
44. Java 8 Streams API
The result is [chole,
bhature,chicken
51. Java 8 Streams API:Intermediate
Operations
● notice several optimizations due to the lazy nature of streams.
– First, despite the fact that many dishes have more than 300
calories, only the first three are selected! This is because of
the limit operation and a technique called short-circuiting.
– Second, despite the fact that filter and map are two separate
operations, they were merged into the same pass (we call
this technique loop fusion).
53. Java 8 Streams API:Terminal
Operations
● Terminal operations produce a result from a stream pipeline. A
result is any nonstream value such as a List, an Integer, or even
void.
64. Java 8 Streams API:Operations:findFirst
vs FindAny
● The answer is parallelism. Finding the first element is more
constraining in parallel. If you don’t care about which element is
returned, use findAny because it’s less constraining when using
parallel streams.
67. Java 8 Streams API:Operations:reduce
vs step-by-step iterative summation
● The benefit of using reduce compared to the step-by-step iteration summation
is that the iteration is abstracted using internal iteration, which enables the
internal implementation to choose to perform the reduce operation in parallel.
– The iterative summation example involves shared updates to a sum
variable, which doesn’t parallelize gracefully. With needed synchronization,
the thread contention robs you of all the performance that parallelism was
supposed to give you!
– Parallelizing this computation requires a different approach: partition the
input, sum the partitions, and combine the sums.
– int sum = numbers.parallelStream().reduce(0, Integer::sum);
– But there’s a price to pay to execute this code in parallel, the lambda
passed to reduce can’t change state (for example, instance variables), and
the operation needs to be associative so it can be executed in any order.
69. Java 8 Streams
API:Operations:Stateless vs Stateful
● Operations like map and filter take each element from the input
stream and produce zero or one result in the output stream.
These operations are thus in general stateless: they don’t have
an internal state (assuming the user-supplied lambda or method
reference has no internal mutable state).
● But operations like reduce, sum, and max need to have
internal state to accumulate the result. In this case the internal
state is small. The internal state is of bounded size no matter
how many elements are in the stream being processed.
70. Java 8 Streams
API:Operations:Stateless vs Stateful
● Some operations such as Sorted or Distinct seem at first to
behave like filter or map—all take a stream and produce
another stream (an intermediate operation), but there’s a crucial
difference.
– Both sorting and removing duplicates from a stream require
knowing the previous history to do their job.
– For example, sorting requires all the elements to be buffered
before a single item can be added to the output stream; the
storage requirement of the operation is unbounded.
– This can be problematic if the data stream is large or infinite.
These are stateful operations.
91. Java 8 Streams
API:Collector:Predefined
● Predefined collectors provide 3 main functionalities
– Reducing and summarizing stream elements to a single
value
– Grouping elements
– Partitioning elements
100. Java 8 Streams
API:Collector:Predefined:Collectors.red
ucing(generic factory)
● It takes three arguments:
– The first argument is the starting value of the reduction
operation and will also be the value returned in the case of a
stream with no elements, so clearly 0 is the appropriate
value in the case of a numeric sum.
– The second argument is the same function to transform a
dish into an int representing its calorie content.
– The third argument is a BinaryOperator that aggregates two
items into a single value of the same type. Here, it just sums
two ints.
101. Java 8 Streams API:Collector:Collect vs
Reduce
● This solution has two problems:
– a semantic one and a practical one. The semantic problem lies in the
fact that the reduce method is meant to combine two values and produce
a new one; it’s an immutable reduction. In contrast, the collect method is
designed to mutate a container to accumulate the result it’s supposed to
produce.
– using the reduce method with the wrong semantic is also the cause of a
practical problem: this reduction process can’t work in parallel because
theconcurrent modification of the same data structure operated by
multiple threads can corrupt the List itself.
108. Java 8 Streams API:Collector:Grouping:reduction within a
reduction
109. Java 8 Streams API:Collector:Partitioning
Partitioning has the advantage of
keeping both lists of the stream
elements, for which the application
of the partitioning function returns
true or false.
115. Java 8 Streams API:Collector:Custom Collectors
● supplier() returns a function that creates an instance of
accumulator - mutable data structure that we will use to
accumulate input elements of type T
● accumulator() returns a function that will take accumulator and
one item of type T, mutating accumulator
● combiner() is used to join two accumulators together into one. It
is used when collector is executed in parallel, splitting input
Stream<T> and collecting parts independently first
● finisher() takes an accumulator A and turns it into a result value,
e.g. collection, of type R. All of this sounds quite abstract, so
let's do a simple example
119. Java 8 Streams API:Collector:Custom Collectors
One possible optimization is to test
only if the candidate number is
divisible by prime numbers.The problem
with the predefined collectors is that
during the collecting process you don’t
have access to the partial result.