RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Deep Learning Neural Networks in the CloudIJAEMSJORNAL
Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
RunPool: A Dynamic Pooling Layer for Convolution Neural NetworkPutra Wanda
Deep learning (DL) has achieved a significant performance in computer vision problems, mainly in automatic feature extraction and representation. However, it is not easy to determine the best pooling method in a different case study. For instance, experts can implement the best types of pooling in image processing cases, which might not be optimal for various tasks. Thus, it is
required to keep in line with the philosophy of DL. In dynamic neural network architecture, it is not practically possible to find
a proper pooling technique for the layers. It is the primary reason why various pooling cannot be applied in the dynamic and multidimensional dataset. To deal with the limitations, it needs to construct an optimal pooling method as a better option than max pooling and average pooling. Therefore, we introduce a dynamic pooling layer called RunPool to train the convolutional
neuralnetwork(CNN)architecture.RunPoolpoolingisproposedtoregularizetheneuralnetworkthatreplacesthedeterministic
pooling functions. In the final section, we test the proposed pooling layer to address classification problems with online social network (OSN) dataset
Applying convolutional neural networks for limited-memory applicationTELKOMNIKA JOURNAL
Currently, convolutional neural networks (CNN) are considered as the most effective tool in image diagnosis and processing techniques. In this paper, we studied and applied the modified SSDLite_MobileNetV2 and proposed a solution to always maintain the boundary of the total memory capacity in the following robust bound and applied on the bridge navigational watch & alarm system (BNWAS). The hardware was designed based on raspberry Pi-3, an embedded single board computer with CPU smartphone level, limited RAM without CUDA GPU. Experimental results showed that the deep learning model on an embedded single board computer brings us high effectiveness in application.
Deep Learning Neural Networks in the CloudIJAEMSJORNAL
Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
COST-EFFECTIVE LOW-DELAY DESIGN FOR MULTI-PARTY CLOUD VIDEO CONFERENCINGnexgentechnology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Towards internet of things iots integration of wireless sensor network to clo...IJCNCJournal
Cloud computing provides great benefits for applications hosted on the Web that also have special
computational and storage requirements. This paper proposes an extensible and flexible architecture for
integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an
interoperable application layer that can be directly integrated into other application domains for remote
monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof
of concept, we have implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been implemented to notify users via
email or tweets for monitoring data when they exceed values and events of interest.
ENHANCED PROTOCOL FOR WIRELESS CONTENT-CENTRIC NETWORK cscpconf
Recently, Content-Centric Networking (CCN) was introduced and is expected as a new concept
of future internet architecture. Even though CCN is initially studied for wired networks,
recently, it is also studied for wireless environment. In this paper, we discuss improvement
method for efficient content flooding over wireless CCNs. The proposed scheme of this paper
use MAC Address of nodes when Interest and Data Packet are forwarded in order to limit the
area of flooding of packets. The proposed protocol not only reduces the spread of Data packets,
but also offers priority of forwarding to nodes of shortest path. As a consequence, it reduce
content download time which is proved by extensive simulations.
Enhanced Protocol for Wireless Content-Centric Network csandit
Recently, Content-Centric Networking (CCN) was intr
oduced and is expected as a new concept
of future internet architecture. Even though CCN is
initially studied for wired networks,
recently, it is also studied for wireless environme
nt. In this paper, we discuss improvement
method for efficient content flooding over wireless
CCNs. The proposed scheme of this paper
use MAC Address of nodes when Interest and Data Pac
ket are forwarded in order to limit the
area of flooding of packets. The proposed protocol
not only reduces the spread of Data packets,
but also offers priority of forwarding to nodes of
shortest path. As a consequence, it reduce
content download time which is proved by extensive
simulations.
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
PROCEDURE OF EFFECTIVE USE OF CLOUDLETS IN WIRELESS METROPOLITAN AREA NETWORK...IJCNCJournal
The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading
necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.
Elastic neural network method for load prediction in cloud computing gridIJECEIAES
Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
A SURVEY ON AUTHENTICATION AND KEY AGREEMENT PROTOCOLS IN HETEROGENEOUS NETWORKSIJNSA Journal
Unlike current closed systems such as 2nd and 3rd generations where the core network is controlled by a sole network operator, multiple network operators will coexist and manage the core network in Next Generation Networks (NGNs). This open architecture and the collaboration between different network
operators will support ubiquitous connectivity and thus enhances users’ experience. However, this brings to the fore certain security issues which must be addressed, the most important of which is the initial Authentication and Key Agreement (AKA) to identify and authorize mobile nodes on these various networks. This paper looks at how existing research efforts the HOKEY WG, Mobile Ethernet and 3GPP
frameworks respond to this new environment and provide security mechanisms. The analysis shows that most of the research had realized the openness of the core network and tried to deal with it using different methods. These methods will be extensively analysed in order to highlight their strengths and weaknesses.
Elementary CurriculaBoth articles highlight the fact that middle.docxtoltonkendal
Elementary Curricula
Both articles highlight the fact that middle-class students seem to benefit more from summer reading programs than their lower-SES peers. While we would hope that summer reading programs would have the same positive impact on all students, this information did not totally surprise me. Differences in funding, materials, and ability to recruit enough high-quality teachers for summer programs could be more difficult in lower-socioeconomic areas. In addition, the articles did not dive into other factors in the students’ lives that may be contributing to their performance such as attendance, how well-rested they are, trauma they have experiences that impacts their ability to focus during instruction, and the impact of being taught by a teacher who the students may not know or have a relationship with. Additionally, there could be a mismatch between the instructional practices and the specific needs of the students. Even though summer reading programs are only for a short time, I would challenge teachers to put energy into getting to know the students and building trust with them. This is a key foundation that is needed for learning to take place.
In challenging teachers during summer program and the regular school year to ”break out of the mold” to create better outcomes for students classified with low SES, in addition to building relationships with students, I would encourage them to build connections with their families. This may involve thinking outside the box and leaving their comfort zone. It could entail holding a parent-teacher conference off campus, closer to their home or in their community. It could also include providing resources and instructional videos to parents so they can help support their children at home. There are many parents who want to support their children academically, but they do not know how and may be uncomfortable asking the teacher for assistance. In addition, I would urge teachers to capitalize on the strengths and interests of their students to engage them in learning activities and provide them with opportunities to shine. We do not have to, and should not, be satisfied with the idea that low SES students will automatically not be able to perform. These students are capable of learning and growth just as much as any other student. I think data from test scores that demonstrate a gap between the performance of students classified as economically disadvantaged and not economically disadvantaged has led some people to hold the belief that students classified as low SES will not perform well. I think the way that school “report card” grades are published also perpetuates this belief, as it shows the test scores, but does not provide an explanation of or include any solutions for the many larger societal factors that contribute to those scores including high teacher turn over, lack of resources, child trauma, lack of sleep, lack of nutrition, crime & safety, and education level of parents.
It w.
Elementary Statistics (MATH220)
Assignment:
Statistical Project & Presentation
Purpose:
The purpose of this project is to supplement lecture material by having the students to do a case study on collecting, analyzing, and interpreting data.
***The best way to understand something is to experience it for yourself.
Guideline for Analyzing Data and Writing a Report
Below is a general outline of the topics that should be included in your report.
1.
Introduction.
State the topic of your study.
2.
Define Population.
Define the population that you intend for your study to represent.
3.
Define Variable.
Define clearly the variable that you obtained during your data collection; this should include information on how the variable is measured and what possible values this variable has.
4.
Data Collection.
Describe your data collection process, including your data source, your sampling strategy, and what steps you took to avoid bias.
5.
Study Design.
Describe the procedures you followed to analyze your data.
6.
Results: Descriptive Statistics.
Give the relevant descriptive statistics for the sample you collected.
7.
Results: Statistical Analysis.
Describe the results of your statistical analysis.
8.
Findings.
Interpret the results of your analysis in the context of your original research question. Was your hypothesis supported by your statistical analyses? Explain.
9.
Discussion.
What conclusions, if any, do you believe you can draw as a result of your study? If the results were not what you expected, what factors might explain your results? What did you learn from the project about the population you studied? What did you learn about the research variable? What did you learn about the specific statistical test you conducted?
.
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COST-EFFECTIVE LOW-DELAY DESIGN FOR MULTI-PARTY CLOUD VIDEO CONFERENCINGnexgentechnology
GET IEEE BIG DATA,JAVA ,DOTNET,ANDROID ,NS2,MATLAB,EMBEDED AT LOW COST WITH BEST QUALITY PLEASE CONTACT BELOW NUMBER
FOR MORE INFORMATION PLEASE FIND THE BELOW DETAILS:
Nexgen Technology
No :66,4th cross,Venkata nagar,
Near SBI ATM,
Puducherry.
Email Id: praveen@nexgenproject.com
Mobile: 9791938249
Telephone: 0413-2211159
www.nexgenproject.com
Towards internet of things iots integration of wireless sensor network to clo...IJCNCJournal
Cloud computing provides great benefits for applications hosted on the Web that also have special
computational and storage requirements. This paper proposes an extensible and flexible architecture for
integrating Wireless Sensor Networks with the Cloud. We have used REST based Web services as an
interoperable application layer that can be directly integrated into other application domains for remote
monitoring such as e-health care services, smart homes, or even vehicular area networks (VAN). For proof
of concept, we have implemented a REST based Web services on an IP based low power WSN test bed,
which enables data access from anywhere. The alert feature has also been implemented to notify users via
email or tweets for monitoring data when they exceed values and events of interest.
ENHANCED PROTOCOL FOR WIRELESS CONTENT-CENTRIC NETWORK cscpconf
Recently, Content-Centric Networking (CCN) was introduced and is expected as a new concept
of future internet architecture. Even though CCN is initially studied for wired networks,
recently, it is also studied for wireless environment. In this paper, we discuss improvement
method for efficient content flooding over wireless CCNs. The proposed scheme of this paper
use MAC Address of nodes when Interest and Data Packet are forwarded in order to limit the
area of flooding of packets. The proposed protocol not only reduces the spread of Data packets,
but also offers priority of forwarding to nodes of shortest path. As a consequence, it reduce
content download time which is proved by extensive simulations.
Enhanced Protocol for Wireless Content-Centric Network csandit
Recently, Content-Centric Networking (CCN) was intr
oduced and is expected as a new concept
of future internet architecture. Even though CCN is
initially studied for wired networks,
recently, it is also studied for wireless environme
nt. In this paper, we discuss improvement
method for efficient content flooding over wireless
CCNs. The proposed scheme of this paper
use MAC Address of nodes when Interest and Data Pac
ket are forwarded in order to limit the
area of flooding of packets. The proposed protocol
not only reduces the spread of Data packets,
but also offers priority of forwarding to nodes of
shortest path. As a consequence, it reduce
content download time which is proved by extensive
simulations.
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
CLOUD COMPUTING – PARTITIONING ALGORITHM AND LOAD BALANCING ALGORITHMijcseit
Tremendous usage of internet has made huge data on the network, without compromising on the
performance of network the end-users must obtain best service. As cloud provides different services on
leasing basis many companies are migrating from their own Infrastructure to cloud,This migration should
not compromise on performance of the cloud, The performance of the cloud can be improved by having
excellent load balancing strategy such that the end user is satisfied. The paper reveals the method by which
a cloud can be partitioned and a study of different algorithm with comparative study to balance the
dynamic load. The comparative study between Ant Colony and Honey Bee algorithm gives the result which
algorithm is optimal in normal load condition also the simplest round robin algorithm is applied when the
partition are in Idle state
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
PROCEDURE OF EFFECTIVE USE OF CLOUDLETS IN WIRELESS METROPOLITAN AREA NETWORK...IJCNCJournal
The article develops a method to ensure the efficient use of cloudlet resources by the mobile users. The article provides a solution to the problem of correct use of cloudlets located on the movement route of mobile users in Wireless Metropolitan Area Networks - WMAN environment. Conditions for downloading
necessary applications to the appropriate cloudlet using the possible values that determine the importance and coordinates of the cloudlets were studied. The article provides a model of the mobile user's route model in metropolitan environments and suggests a method for solving the problem.
Elastic neural network method for load prediction in cloud computing gridIJECEIAES
Cloud computing still has no standard definition, yet it is concerned with Internet or network on-demand delivery of resources and services. It has gained much popularity in last few years due to rapid growth in technology and the Internet. Many issues yet to be tackled within cloud computing technical challenges, such as Virtual Machine migration, server association, fault tolerance, scalability, and availability. The most we are concerned with in this research is balancing servers load; the way of spreading the load between various nodes exists in any distributed systems that help to utilize resource and job response time, enhance scalability, and user satisfaction. Load rebalancing algorithm with dynamic resource allocation is presented to adapt with changing needs of a cloud environment. This research presents a modified elastic adaptive neural network (EANN) with modified adaptive smoothing errors, to build an evolving system to predict Virtual Machine load. To evaluate the proposed balancing method, we conducted a series of simulation studies using cloud simulator and made comparisons with previously suggested approaches in the previous work. The experimental results show that suggested method betters present approaches significantly and all these approaches.
Residual balanced attention network for real-time traffic scene semantic segm...IJECEIAES
Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.
A SURVEY ON AUTHENTICATION AND KEY AGREEMENT PROTOCOLS IN HETEROGENEOUS NETWORKSIJNSA Journal
Unlike current closed systems such as 2nd and 3rd generations where the core network is controlled by a sole network operator, multiple network operators will coexist and manage the core network in Next Generation Networks (NGNs). This open architecture and the collaboration between different network
operators will support ubiquitous connectivity and thus enhances users’ experience. However, this brings to the fore certain security issues which must be addressed, the most important of which is the initial Authentication and Key Agreement (AKA) to identify and authorize mobile nodes on these various networks. This paper looks at how existing research efforts the HOKEY WG, Mobile Ethernet and 3GPP
frameworks respond to this new environment and provide security mechanisms. The analysis shows that most of the research had realized the openness of the core network and tried to deal with it using different methods. These methods will be extensively analysed in order to highlight their strengths and weaknesses.
Elementary CurriculaBoth articles highlight the fact that middle.docxtoltonkendal
Elementary Curricula
Both articles highlight the fact that middle-class students seem to benefit more from summer reading programs than their lower-SES peers. While we would hope that summer reading programs would have the same positive impact on all students, this information did not totally surprise me. Differences in funding, materials, and ability to recruit enough high-quality teachers for summer programs could be more difficult in lower-socioeconomic areas. In addition, the articles did not dive into other factors in the students’ lives that may be contributing to their performance such as attendance, how well-rested they are, trauma they have experiences that impacts their ability to focus during instruction, and the impact of being taught by a teacher who the students may not know or have a relationship with. Additionally, there could be a mismatch between the instructional practices and the specific needs of the students. Even though summer reading programs are only for a short time, I would challenge teachers to put energy into getting to know the students and building trust with them. This is a key foundation that is needed for learning to take place.
In challenging teachers during summer program and the regular school year to ”break out of the mold” to create better outcomes for students classified with low SES, in addition to building relationships with students, I would encourage them to build connections with their families. This may involve thinking outside the box and leaving their comfort zone. It could entail holding a parent-teacher conference off campus, closer to their home or in their community. It could also include providing resources and instructional videos to parents so they can help support their children at home. There are many parents who want to support their children academically, but they do not know how and may be uncomfortable asking the teacher for assistance. In addition, I would urge teachers to capitalize on the strengths and interests of their students to engage them in learning activities and provide them with opportunities to shine. We do not have to, and should not, be satisfied with the idea that low SES students will automatically not be able to perform. These students are capable of learning and growth just as much as any other student. I think data from test scores that demonstrate a gap between the performance of students classified as economically disadvantaged and not economically disadvantaged has led some people to hold the belief that students classified as low SES will not perform well. I think the way that school “report card” grades are published also perpetuates this belief, as it shows the test scores, but does not provide an explanation of or include any solutions for the many larger societal factors that contribute to those scores including high teacher turn over, lack of resources, child trauma, lack of sleep, lack of nutrition, crime & safety, and education level of parents.
It w.
Elementary Statistics (MATH220)
Assignment:
Statistical Project & Presentation
Purpose:
The purpose of this project is to supplement lecture material by having the students to do a case study on collecting, analyzing, and interpreting data.
***The best way to understand something is to experience it for yourself.
Guideline for Analyzing Data and Writing a Report
Below is a general outline of the topics that should be included in your report.
1.
Introduction.
State the topic of your study.
2.
Define Population.
Define the population that you intend for your study to represent.
3.
Define Variable.
Define clearly the variable that you obtained during your data collection; this should include information on how the variable is measured and what possible values this variable has.
4.
Data Collection.
Describe your data collection process, including your data source, your sampling strategy, and what steps you took to avoid bias.
5.
Study Design.
Describe the procedures you followed to analyze your data.
6.
Results: Descriptive Statistics.
Give the relevant descriptive statistics for the sample you collected.
7.
Results: Statistical Analysis.
Describe the results of your statistical analysis.
8.
Findings.
Interpret the results of your analysis in the context of your original research question. Was your hypothesis supported by your statistical analyses? Explain.
9.
Discussion.
What conclusions, if any, do you believe you can draw as a result of your study? If the results were not what you expected, what factors might explain your results? What did you learn from the project about the population you studied? What did you learn about the research variable? What did you learn about the specific statistical test you conducted?
.
Elements of Religious Traditions PaperWritea 700- to 1,050-word .docxtoltonkendal
Elements of Religious Traditions Paper
Write
a 700- to 1,050-word paper that does the following:
Describes these basic components of religious traditions and their relationship to the sacred
:
What a religious tradition says—its teachings, texts, doctrine, stories, myths, and others
What a religious tradition does—worship, prayer, pilgrimage, ritual, and so forth
How a religious tradition organizes—leadership, relationships among members, and so forth
Identifies key critical issues in the study of religion.
Includes specific examples from the various religious traditions described in the Week One readings that honor the sacred—such as rituals of the Igbo to mark life events, the vision quest as a common ritual in many Native American societies, or the influence of the shaman as a leader. You may also include examples from your own religious tradition or another religious tradition with which you are familiar.
Format
your paper consistent with APA guidelines
.
Elements of MusicPitch- relative highness or lowness that we .docxtoltonkendal
Elements of Music
Pitch- relative highness or lowness that we hear in a sound.
Tone- sound that has a definite pitch.
(For example striking a bat against a ball does not produce a D# but striking a D#
on a piano does)
Dynamics- the degree of loudness or softness in music
pp pianissimo /very soft
p piano /soft
mp mezzo-piano /medium-soft
mf mezzo-forte /medium-loud
f forte /loud
ff fortissimo /very loud
When dynamics are altered in a piece of music, they are termed as follows:
decrescendo/ diminuendo gradually softer
crescendo gradually louder
Timbre/Tone Color- the character or quality of a sound.
dark, bright, mellow, cool, metallic, rich, brilliant, thin, etc.
Rhythm- a) the flow (or pattern) of music through time. b) the particular arrangement of
note lengths in a piece of music.
Syncopation- An accent placed on a beat where it is not normally expected.
Beat- the steady pulse in a piece of music.
Downbeat- the first or stressed beat of a measure.
Meter- the pattern in which beats are organized within a piece of music.
Examples:
3/4= three beats per measure
4/4= four beats per measure
6/8= six beats per measure
*In some musics, meter is not present- this is termed non-metric.
(Ex: Chant, some 20th century genres, world musics).
Melody- a series of single notes that add up to a recognizable whole.
*A melodic line has a shape -it ascends and descends in a series of continuous pitches.
Sequence- a repetition of a pattern at a higher or lower pitch.
Phrase- A short unit of music within a melodic line.
Cadence- The rest at the end of a musical phrase. Think of this as a musical period at the
end of a sentence.
Harmony- A) How chords are constructed and how they follow each other. B) The
relationship of tones when sounded in a group.
Chord- a combination of three or more tones sounded at once.
Consonance- a stable tone combination in a chord
Dissonance- and unstable tone combination in a chord; usually, an expected
and stable resolution will follow.
Tonic- a) the main key of a piece of music. b) the first note of a scale
Key- the central tone or scale in a piece of music.
(example: A major, b minor)
Modulation- a shift from one key to another within the same piece of music.
Texture- layering of musical sounds or instruments within a piece of music.
Monophonic- single, unaccompanied melodic line.
Homophonic- a melody with an accompaniment of chords.
Polyphonic- th.
Elevated Blood Lead Levels in Children AssociatedWith the Fl.docxtoltonkendal
Elevated Blood Lead Levels in Children Associated
With the Flint Drinking Water Crisis: A Spatial
Analysis of Risk and Public Health Response
Mona Hanna-Attisha, MD, MPH, Jenny LaChance, MS, Richard Casey Sadler, PhD, and Allison Champney Schnepp, MD
Objectives. We analyzed differences in pediatric elevated blood lead level incidence
before and after Flint, Michigan, introduced a more corrosive water source into an aging
water system without adequate corrosion control.
Methods. We reviewed blood lead levels for children younger than 5 years before
(2013) and after (2015) water source change in Greater Flint, Michigan. We assessed the
percentage of elevated blood lead levels in both time periods, and identified geo-
graphical locations through spatial analysis.
Results. Incidence of elevated blood lead levels increased from 2.4% to 4.9% (P < .05)
after water source change, and neighborhoods with the highest water lead levels ex-
perienced a 6.6% increase. No significant change was seen outside the city. Geospatial
analysis identified disadvantaged neighborhoods as having the greatest elevated blood
lead level increases and informed response prioritization during the now-declared public
health emergency.
Conclusions. The percentage of children with elevated blood lead levels increased
after water source change, particularly in socioeconomically disadvantaged neighbor-
hoods. Water is a growing source of childhood lead exposure because of aging infra-
structure. (Am J Public Health. 2016;106:283–290. doi:10.2105/AJPH.2015.303003)
See also Rosner, p. 200.
In April 2014, the postindustrial city ofFlint, Michigan, under state-appointed
emergency management, changed its water
supply from Detroit-supplied Lake Huron
water to the Flint River as a temporary
measure, awaiting a new pipeline to Lake
Huron in 2016. Intended to save money, the
change in source water severed a half-
century relationship with the Detroit Water
and Sewage Department. Shortly after the
switch to Flint River water, residents voiced
concerns regarding water color, taste, and
odor, and various health complaints in-
cluding skin rashes.1 Bacteria, including
Escherichia coli, were detected in the distri-
bution system, resulting in Safe Drinking
Water Act violations.2 Additional disinfec-
tion to control bacteria spurred formation of
disinfection byproducts including total tri-
halomethanes, resulting in Safe Drinking
Water Act violations for trihalomethane
levels.2
Water from the Detroit Water and
Sewage Department had very low corrosivity
for lead as indicated by low chloride, low
chloride-to-sulfate mass ratio, and presence
of an orthophosphate corrosion inhibitor.3,4
By contrast, Flint River water had high
chloride, high chloride-to-sulfate mass ratio,
and no corrosion inhibitor.5 Switching
from Detroit’s Lake Huron to Flint River
water created a perfect storm for lead leach-
ing into drinking water.6 The aging Flint
water distribution system contains a hig.
Elements of the Communication ProcessIn Chapter One, we learne.docxtoltonkendal
Elements of the Communication Process
In Chapter One, we learned communication is the process of creating or sharing meaning in informal conversation, group interaction, or public speaking. To understand how the process works, we described the essential elements in the process.
For the following interaction, identify the contexts, participants, channels. message, interference (noise), and feedback.
"Maria and Damien are meandering through the park, talking and drinking bottled water. Damien finishes his bottle, replaces the lid, and tosses the bottle into the bushes at the side of the path. Maria, who has been listening to Damien talk, comes to a stop, puts her hand on her hips, stares at Damien, and says angrily, " I can't believe what you just did! Damien blushes, averts his gaze, and mumbles, "Sorry, I'll get it- I just wasn't thinking." As the tension drains from Maria's face. she gives her head a playful toss, smiles, and says, Well, just see that it doesn't happen again.
1. Contexts
a. Physical
b. Social
c. Historical
d. Psychological
2. Participants
3. Channels
4. Message
5. Interference (Noise)
6. Feedback
.
Elements of Music #1 Handout1. Rhythm the flow of music in te.docxtoltonkendal
Elements of Music #1 Handout
1. Rhythm
the flow of music in terms of time
2. Beat
the pulse that recurs regularly in music
3. Meter
the regular pattern of stressed and unstressed beats
4. Tempo
the speed of the beats in a piece of music
5. Polyrhythm
two or more rhythm patterns occurring simultaneously
6. Pitch
the perceived highness or lowness of a musical sound
7. Melody
a series of consecutive pitches that form a cohesive musical entity
8. Counterpoint
two or more independent lines with melodic character occurring at the same time
9. Harmony
the simultaneous sounds of several pitches, usually in accompanying a melody
10. Dynamics
the amount of loudness in music
11. Timbre
tone quality or tone color in music
12. Form
the pattern or plan of a musical work
Framework for Improving
Critical Infrastructure Cybersecurity
Version 1.1
National Institute of Standards and Technology
April 16, 2018
April 16, 2018 Cybersecurity Framework Version 1.1
This publication is available free of charge from: https://doi.org/10.6028/NIST.CSWP.04162018 ii
No t e t o Rea d er s o n t h e U p d a t e
Version 1.1 of this Cybersecurity Framework refines, clarifies, and enhances Version 1.0, which
was issued in February 2014. It incorporates comments received on the two drafts of Version 1.1.
Version 1.1 is intended to be implemented by first-time and current Framework users. Current
users should be able to implement Version 1.1 with minimal or no disruption; compatibility with
Version 1.0 has been an explicit objective.
The following table summarizes the changes made between Version 1.0 and Version 1.1.
Table NTR-1 - Summary of changes between Framework Version 1.0 and Version 1.1.
Update Description of Update
Clarified that terms like
“compliance” can be
confusing and mean
something very different
to various Framework
stakeholders
Added clarity that the Framework has utility as a structure and
language for organizing and expressing compliance with an
organization’s own cybersecurity requirements. However, the
variety of ways in which the Framework can be used by an
organization means that phrases like “compliance with the
Framework” can be confusing.
A new section on self-
assessment
Added Section 4.0 Self-Assessing Cybersecurity Risk with the
Framework to explain how the Framework can be used by
organizations to understand and assess their cybersecurity risk,
including the use of measurements.
Greatly expanded
explanation of using
Framework for Cyber
Supply Chain Risk
Management purposes
An expanded Section 3.3 Communicating Cybersecurity
Requirements with Stakeholders helps users better understand
Cyber Supply Chain Risk Management (SCRM), while a new
Section 3.4 Buying Decisions highlights use of the Framework
in understanding risk associated with commercial off-the-shelf
products and services. Additional Cyber SCRM criteria we.
Elements of Music Report InstrumentsFor the assignment on the el.docxtoltonkendal
Elements of Music Report Instruments
For the assignment on the elements of music, students will write a report with a minimum of 300 words.
Students must select one element of music that they consider to be the most important element:
Melody
Rhythm
Harmony
Form
When writing the report, be sure you address the following questions:
Why did you select this element from among all the rest?
Do you think that all kinds of music could exist without your selected element? Elaborate on your view.
Describe a piece of music that highlights the use of your selected element.
I encourage students do research on their element of music in order to get ideas for their reports. All reports must be original works!
Do not quote any source or anybody’s thoughts. Quotes are not permitted in this Instruments Report. I am interested in your own personal thoughts, opinions, and the material you have learned from your research.
.
Elements of GenreAfter watching three of the five .docxtoltonkendal
Elements of Genre
After watching three of the five movie clips listed in the
Multimedia
section, above, describe how they fit into a specific genre (or subgenre) as explained in the text. What elements of the film are characteristic of that genre? How does it fulfill the expectations of that genre? How does it play against these expectations?
Your initial post should be at least 150 words in length. Support your claims with examples from required material(s) and/or other scholarly resources, and properly cite any references.
.
Elements of DesignDuring the process of envisioning and designing .docxtoltonkendal
Elements of Design
During the process of envisioning and designing a film, the director, production designer, and art director (in collaboration with the cinematographer) are concerned with several major spatial and temporal elements. These design elements punctuate and underscore the movement of figures within the frame, including the following: setting, lighting, costuming, makeup, and hairstyles. Choose a scene from movieclips.com. In a three to five page paper, (excluding the cover and reference pages) analyze the mise-en-scène.
Respond to the following prompts with at least one paragraph per bulleted topic:
Identify the names of the artists involved in the film’s production: the director, the production designer, and the art director. Describe in separate paragraphs each artist’s role in the overall design process. Conduct additional research if necessary, citing your book, film, and other external sources correctly in APA format.
Explain how the artists utilize lighting in the scene. How does the lighting affect our emotional understanding of certain characters? What sort of mood does the lighting evoke? How does lighting impact the overall story the filmmaker is attempting to tell?
Describe the setting, including the time period, location, and culture in which the film takes place.
Explain what costuming can tell us about a character. In what ways can costuming be used to reflect elements of the film's plot?
Explain how hairstyle and makeup can help tell the story. What might hairstyle and makeup reveal about the characters?
Discuss your opinion regarding the mise-en-scène. Do the elements appear to work together in a harmonious way? Does the scene seem discordant? Do you think the design elements are congruent with the filmmaker’s vision for the scene?
.
Elements of Critical Thinking [WLOs 2, 3, 4] [CLOs 2, 3, 4]P.docxtoltonkendal
Elements of Critical Thinking [WLOs: 2, 3, 4] [CLOs: 2, 3, 4]
Prepare:
Prior to beginning work on this discussion forum, in preparation for discussing the importance of critical thinking skills,
Read the articles
Common Misconceptions of Critical Thinking
Combating Fake News in the Digital Age
6 Critical Thinking Skills You Need to Master Now (Links to an external site.)
Teaching and Learning in a Post-Truth world: It’s Time for Schools to Upgrade and Reinvest in Media Literacy Lessons
Critical Thinking and the Challenges of Internet (Links to an external site.)
Watch the videos
Fake News: Part 1 (Links to an external site.)
Critical Thinking
(Links to an external site.)
Review the resources
Critical Thinking Skills (Links to an external site.)
Valuable Intellectual Traits (Links to an external site.)
Critical Thinking Web (Links to an external site.)
Reflect:
Reflect on the characteristics of a critical thinker. Critical thinking gets you involved in a dialogue with the ideas you read from others in this class. To be a critical thinker, you need to be able to summarize, analyze, hypothesize, and evaluate new information that you encounter.
Write:
For this discussion, you will address the following prompts. Keep in mind that the article or video you’ve chosen should not be about critical thinking, but should be about someone making a statement, claim, or argument related to your Final Paper topic. One source should demonstrate good critical thinking skills and the other source should demonstrate the lack or absence of critical thinking skills. Personal examples should not be used.
Explain at least five elements of critical thinking that you found in the reading material.
Search the Internet, media, or the Ashford University Library, and find an example in which good critical thinking skills are being demonstrated by the author or speaker. Summarize the content and explain why you think it demonstrates good critical thinking skills.
Search the Internet, media, or the Ashford University Library, and find an example in which the author or speaker lacks good critical thinking skills. Summarize the content and explain why you think it demonstrates the absence of good, critical thinking skills.
Your initial post should be at least 250 words in length, which should include a thorough response to each prompt. You are required to provide in-text citations of applicable required reading materials and/or any other outside sources you use to support your claims. Provide full reference entries of all sources cited at the end of your response. Please use correct APA format when writing in-text citations (see
In-Text Citation Helper (Links to an external site.)
) and references (see
Formatting Your References List (Links to an external site.)
).
Reflecting on General Education and Career [WLOs: 2, 3, 4] [CLOs: 2, 3, 4]
Prepare:
Prior to beginning work on this discussion forum, read the articles
Teaching Writing S.
Elements of DesignDuring the process of envisioning and design.docxtoltonkendal
Elements of Design
During the process of envisioning and designing a film, the director, production designer, and art director (in collaboration with the cinematographer) are concerned with several major spatial and temporal elements. These design elements punctuate and underscore the movement of figures within the frame, including the following: setting, lighting, costuming, makeup, and hairstyles. Choose a scene from movieclips.com. In a three to five page paper, (excluding the cover and reference pages) analyze the mise-en-scène.
Respond to the following prompts with at least one paragraph per bulleted topic:
Identify the names of the artists involved in the film’s production: the director, the production designer, and the art director. Describe in separate paragraphs each artist’s role in the overall design process. Conduct additional research if necessary, citing your book, film, and other external sources correctly in APA format.
Explain how the artists utilize lighting in the scene. How does the lighting affect our emotional understanding of certain characters? What sort of mood does the lighting evoke? How does lighting impact the overall story the filmmaker is attempting to tell?
Describe the setting, including the time period, location, and culture in which the film takes place.
Explain what costuming can tell us about a character. In what ways can costuming be used to reflect elements of the film's plot?
Explain how hairstyle and makeup can help tell the story. What might hairstyle and makeup reveal about the characters?
Discuss your opinion regarding the mise-en-scène. Do the elements appear to work together in a harmonious way? Does the scene seem discordant? Do you think the design elements are congruent with the filmmaker’s vision for the scene?
.
Elements of a contact due 16 OctRead the Case Campbell Soup Co. v..docxtoltonkendal
Elements of a contact due 16 Oct
Read the Case Campbell Soup Co. v. Wentz in the text. Answer the following questions:
1. What were the terms of the contract between Campbell and the Wentzes?
2. Did the Wentzes perform under the contract?
3. Did the court find specific performance to be an adequate legal remedy in this case?
4. Why did the court refuse to help Campbell in enforcing its legal contract?
5. How could Campbell change its contract in the future so as to avoid the unconsionability problem?
Facts:
Per
a
written
contract
between
Campbell
Soup
Company
(a
New
Jersey
company)
and
the
Wentzes
(carrot
farmers
in
Pennsylvania),
the
Wentzes
would
deliver
to
Campbell
all
the
Chantenay
red
cored
carrots
to
be
grown
on
the
Wentz
farm
during
the
1947
season.
The
contract
price
for
the
carrots
was
$30
per
ton.
The
contract
between
Campbell
Soup
and
all
sellers
of
carrots
was
drafted
by
Campbell
and
it
had
a
provision
that
prohibited
farmers/sellers
from
selling
their
carrots
to
anyone
else,
except
those
carrots
that
were
rejected
by
Campbell.
The
contract
also
had
a
liquidated
damages
provision
of
$50
per
ton
if
the
seller
breached,
but
it
had
no
similar
provision
in
the
event
Campbell
breached.
The
contract
not
only
allowed
Campbell
to
reject
nonconforming
carrots,
but
gave
Campbell
the
right
to
determine
who
could
buy
the
carrots
it
had
rejected.
The
Wentzes
harvested
100
tons
of
carrots,
but
because
the
market
price
at
the
time
of
harvesting
was
$90
per
ton
for
these
rare
carrots,
the
Wentzes
refused
to
deliver
them
to
Campbell
and
sold
62
tons
of
their
carrots
to
a
farmer
who
sold
some
of
those
carrots
to
Campbell.
Campbell
sued
the
Wentzes,
asking
for
the
court's
order
to
stop
further
sale
of
the
contracted
carrots
to
others
and
to
compel
specific
performance
of
the
contract.
The
trial
court
ruled
for
the
Wentzes
and
Campbell
appealed.
Issues:
Is
specific
performance
an
appropriate
legal
remedy
in
this
case
or
is
the
contract
unconscionable?
Discussion:
In
January
1948,
it
was
virtually
impossible
to
obtain
Chantenay
carrots
in
the
open
market.
Campbell
used
Chantenay
carrots
(which
are
easier
to
process
for
soup
making
than
other
carrots)
in
large
quantities
and
furnishes
the
seeds
to
farmers
with
whom
it
contracts.
Campbell
contracted
for
carrots
long
ahead,
and
farmers
entered
into
the
contract
willingly.
If
the
facts
of
this
case
were
this
simple,
specific
performance
should
have
been
granted.
However,
the
problem
is
with
the
contract
itself,
which
was
one-sided.
According
to
the
appellate
court,
the
most
direct
example
of
unconscionability
was
the
provision
that,
under
certain
.
Elements for analyzing mise en sceneIdentify the components of.docxtoltonkendal
Elements for analyzing mise en scene
Identify the components of the shot, but explaining the meaning or significance behind those components and connecting the shot to the themes of the film
1. Dominant: Where is the eye attracted first? Why?
2. Lighting key: High key? Low key? High contrast? Some combination of these?
3. Shot and camera proxemics: What type of shot? How far away is the camera from the action?
4. Angle: Is the viewer (through the eye of the camera) looking up or down on the subject? Or is the camera neutral (eye level)?
5. Color values: What is the dominant color? Are there contrasting foils? Is there color symbolism?
6. Lens/filter/stock: How do these distort or comment on the
photographed materials?
7. Subsidiary contrasts: What are the main eye-stops after taking in the dominant?
8. Density: How much visual information is packed into the image? Is the texture stark, moderate, or highly detailed?
9. Composition: How is the two-dimensional space segmented and organized? What is the underlying design?
10. Form: Open or closed? Does the image suggest a window that arbitrarily isolates a fragment of the scene? Or a proscenium arch, in which the visual elements are carefully arranged and held in balance?
11. Framing: Tight or loose? Do characters have little to no room to move, or can they move freely without impediments?
12. Depth: On how many planes is the image composed? Does the background or foreground comment in any way on the midground?
13. Character placement: What part of the framed space do the characters occupy? Center? Top? Bottom? Edges? Why?
14. Staging positions: Which way do the characters look vis-à-vis the camera?
15. Character proxemics: How much space is between the
characters?
What are the 4 distinct formal elements that make up a film's mise en scene?
• staging of the action
• physical setting and decor
• the manner in which these materials are framed
• the manner in which they are photographed
.
Elements in the same row have the same number of () levelsWhi.docxtoltonkendal
Elements in the same row have the same number of (*) levels
Which elements in B O U L A N would be in the same family? Which would have the same number of energy levels? Highest mass? Lowest mass?
Which is more reactive? Uranium or Lithium
Will elements B and U lose electrons in a chemical reactor?
Will elements B and U form positive or negative ions?
Thanks so much (:
.
ELEG 421 Control Systems Transient and Steady State .docxtoltonkendal
ELEG 421
Control Systems
Transient and Steady State
Response Analyses
Dr. Ashraf A. Zaher
American University of Kuwait
College of Arts and Science
Department of Electrical and Computer Engineering
Layout
2
Objectives
This chapter introduces the analysis of the time response of different
control systems under different scenarios. Only first and second order
systems will be considered in details using analytical and numerical
methods. Extension to higher order systems will be developed. Both
transient and steady state responses will be evaluated. Stability analysis
will be analyzed for different kinds of feedback, while investigating the
effect of both proportional and derivative control actions on the
performance of the closed-loop system. Finally systems types and
steady state errors will be calculated for unity feedback.
Outcomes
By the end of this chapter, students will be able to:
evaluate both transient/steady state responses for control systems,
analyze the stability of closed-loop LTI systems,
investigate the effect of P and I control actions on performance, and
understand dominant dynamics of higher order systems.
Dr. Ashraf Zaher
Introduction
3
Test signals
Transient response
Steady state response
Analytical techniques, and
Numerical (simulation) techniques.
Stability (definition and analysis methods),
Relative stability, and
Effect of P/I control actions on stability and performance.
Summary of the used systems:
First order systems,
Second order systems, and
Higher order systems.
Dr. Ashraf Zaher
Test Signals
4 Dr. Ashraf Zaher
Impulse function:
Used to simulate shock inputs,
Laplace transform: 1.
Step function:
Used to simulate sudden disturbances,
Laplace transform: 1/s.
Ramp function:
Used to simulate gradually changing inputs,
Laplace transform: 1/s2.
Sinusoidal function(s):
Used to test response to a certain frequency,
Laplace transform: s/(s2+ω2) for cos(ωt) and ω/(s2+ω2) for sin(ωt).
White noise function:
Used to simulate random noise,
It is a stochastic signal that is easier to deal with in the time domain.
Total response:
C(s) = R(s)*TF(s) = Ctr(s) + Css(s) → c(t) = ctr(t) + css(t)
Fundamentals
5 Dr. Ashraf Zaher
Definitions:
Zeros (Z) of the TF
Poles (P) of the TF
Transient Response (Natural)
Steady State Response (Forced)
Total Response
Limits:
Initial values
Final values
Systems (?Zs):
First order (one P)
Second order (two Ps)
Higher order!
More:
Stability and relative stability
Steady state errors (unity feedback)
First Order Systems
6 Dr. Ashraf Zaher
TF:
T: time constant
Unit Step Response:
1
1
)(
)(
+
=
TssR
sC
)/1(
11
1
1
1
11
)(
TssTs
T
sTss
sC
+
−=
+
−=
+
=
Ttetc /1)( −−=
632.01)( 1 =−== −eTtc
T
e
Tdt
tdc Tt
t
11)( /
0
== −
=
01)0( 0 =−== etc
11)( =−=∞= −∞etc
First Order Systems.
Element 010 ASSIGNMENT 3000 WORDS (100)Task Individual assign.docxtoltonkendal
Element 010 ASSIGNMENT: 3000 WORDS (100%)
Task: Individual assignment (3000 words)
Weighting: 100%
Assessment Case Study:
Greenland Garden Centre
[1]
Jon Smith spread his arms widely as he surveyed his garden centre.
‘Of course the whole market for leisure products and services, especially garden-related products, has been expanding over the last few years. Even so, we have been particularly successful. Partly this is because we are conveniently located, but it is also because we have developed a reputation for excellent service. Customers like coming to us for advice. We have also been successful in attracting some of the ‘personality gardeners’ from television to make special appearances. My main ambition now is to fully develop all of our twelve hectares to make the centre a place people will want to visit in its own right. I envisage the centre developing into almost a mini gardening theme park with special gardens, beautiful grounds and special events.’
Greenland is a large village situated in the Cotswolds, a popular tourist area of the UK. It has an interesting range of shops and restaurants, mainly catering for the tourist trade. About half a mile outside the village is the Greenland Garden Centre. The garden centre is served by a good network of main roads but is inaccessible by public transport.
Growth over the last five years has been dramatic and the garden centre now sells many other goods as well as gardening requisites. It also has a restaurant. It is open seven days a week, only closing on Christmas Day. Its opening hours are Monday– Saturday 9 a.m. to 6 p.m. and Sunday 10 a.m. to 5 p.m. all year round.
Outside the centre
The centre has a large car park which can accommodate about 350 cars. Outside the entrance a map indicates the various areas in the garden centre. Most customers walk round the grounds before making their purchases. The length of time people spend in the centre varies but, according to a recent study, averages 53 minutes during the week and 73 minutes at weekends.
The same study shows the extent to which the number of customers arriving at the garden centre varies depending on the time of year, day of the week, and time of day. There are two peaks in customer numbers, one during the late spring/early summer period and another in the build up to Christmas, as Greenland puts on particularly good Christmas displays.
Indoor sales area
The range of goods has increased dramatically over the past few years and now includes items such as:
pets and aquatics
seeds
fertilisers
indoor pots and plants
gardening equipment
garden lighting
conservatory-style furniture
outdoor clothing
picture gallery
books and toys
delicatessen
wine
kitchen equipment
soft furnishing
outdoor eating equipment
gifts, stationery, cards, aromatherapy products
freshly cut flowers
dried flowers.
Outside sales area
In the open air and in large glasshouses there is a complete range of plants, shrubs and trees. Gre.
ELEG 320L – Signals & Systems Laboratory Dr. Jibran Khan Yous.docxtoltonkendal
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
1
LAB 4: CONVOLUTION
Background & Concepts
Convolution is denoted by:
𝑦[𝑛] = 𝑥[𝑛] ∗ ℎ[𝑛]
Your book has described the "flip and shift" method for performing convolution. First, we
set up two signals 𝑥[𝑘] and ℎ[𝑘]:
Flip one of the signals, say ℎ[𝑘], to form ℎ[−𝑘]:
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
2
Shift ℎ[−𝑘] by n to form ℎ[𝑛 − 𝑘]. For each value of 𝑛, form 𝑦[𝑛] by multiplying and
summing all the element of the product of𝑥[𝑘]ℎ[𝑛 − 𝑘], −∞ < 𝑘 < ∞. The figure
below shows an example of the calculation of𝑦[1]. The top panel shows𝑥[𝑘]. The
middle panel showsℎ[1 − 𝑘]. The lower panel shows𝑥[𝑘]𝑦[1 − 𝑘]. Note that this is a
sequence on a 𝑘 axis. The sum of the lower sequence over all k gives 𝑦[1] = 2.
We repeat this shifting, multiplication and summing for all values of 𝑛 to get the
complete sequence 𝑦[𝑛]:
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
3
The conv Command
conv(x,h) performs a 1-D convolution of vectors 𝑥 and ℎ. The resulting vector 𝑦
has length length(𝑦) = length(𝑥) + length(ℎ) − 1. Imagine vector 𝑥 as being
stationary and the flipped version of ℎ is slid from left to right. Note that conv(x,h) =
conv(h,x). An example of the convolution of two signals and plotting the result is
below:
>> x = [0.5 0.5 0.5]; %define input signal x[n]
>> h = [3.0 2.0 1.0]; %unit-pulse response h[n]
>> y = conv(x,h); %compute output y[n] via convolution
>> n = 0:(length(y)-1); %for plotting y[n]
>> stem(n,y) % plot y[n]
>> grid;
>> xlabel('n');
>> ylabel('y[n]');
>> title('Output of System via Convolution');
ELEG 320L – Signals & Systems Laboratory /Dr. Jibran Khan Yousafzai Lab 4
4
Deconvolution
The command [q,r] = deconv(v,u), deconvolves vector u out of vector v, using long
division. The quotient is returned in vector q and the remainder in vector r such that
v = conv(u,q)+r. If u and v are vectors of polynomial coefficients, convolving them is
equivalent to multiplying the two polynomials, and deconvolution is polynomial
division. The result of dividing v by u is quotient q and remainder r. An examples is
below:
If
>> u = [1 2 3 4];
>> v = [10 20 30];
The convolution is:
>> c = conv(u,v)
c =
10 40 100 160 170 120
Use deconvolution to recover v.
>> [q,r] = deconv(c,u)
q =
10 20 30
r =
0 0 0 0 0 0
This gives a quotient equal to v and a zero remainder.
Structures
Structures in Matlab are just like structures in C. They are basically containers that
allow one
Electronic Media PresentationChoose two of the following.docxtoltonkendal
Electronic Media Presentation
Choose
two of the following types of electronic media:
Radio
Sound recording
Motion pictures
Broadcast television
Research
the history of the media types your team selected. Include the following information in your presentation:
Introduction
Notable founders and parent organizations of your electronic media types
Notable historical dates
Dates of mergers with other radio stations, record production companies, motion picture companies, or television networks to form a large media conglomerate
Date the media types launched their websites, became active on the Internet, or became active in social media integration
Identify past, present, and future challenges confronting these types of media. How has the digital era affected them? Which types are best suited to adapt to the future? Explain why
How do these challenges affect advertising in these organizations--outside companies advertising--and advertising for these media--companies promoting themselves to others? What are innovative advertising strategies these media have engaged in?
What are two similarities and two differences between the two media types?
Conclusion
Present your Electronic Media Presentation.
These are 10- to 12-slideMicrosoft
®
PowerPoint
®
presentations with notes.
.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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Home assignment II on Spectroscopy 2024 Answers.pdf
Efficient Mobile Implementation ofA CNN-based Object Recogni.docx
1. Efficient Mobile Implementation of
A CNN-based Object Recognition System
Keiji Yanai Ryosuke Tanno Koichi Okamoto
Department of Informatics,
The University of Electro-Communications, Tokyo
1-5-1 Chofugaoka, Chofu, Tokyo 182-8585 JAPAN
{yanai,tanno-r,okamoto-k}@mm.inf.uec.ac.jp
ABSTRACT
Because of the recent progress on deep learning studies, Con-
volutional Neural Network (CNN) based method have out-
performed conventional object recognition methods with a
large margin. However, it requires much more memory and
computational costs compared to the conventional methods.
Therefore, it is not easy to implement a CNN-based object
recognition system on a mobile device where memory and
computational power are limited.
In this paper, we examine CNN architectures which are
suitable for mobile implementation, and propose multi-scale
network-in-networks (NIN) in which users can adjust the
trade-off between recognition time and accuracy. We imple-
mented multi-threaded mobile applications on both iOS and
Android employing either NEON SIMD instructions or the
BLAS library for fast computation of convolutional layers,
and compared them in terms of recognition time on mobile
devices. As results, it has been revealed that BLAS is better
for iOS, while NEON is better for Android, and that reduc-
ing the size of an input image by resizing is very effective
2. for speedup of CNN-based recognition.
Keywords
convolutional neural network, mobile implementation, network-
in-network, iOS, Android
1. INTRODUCTION
Due to the recent progress of the studies on deep learning,
convolutional neural network (CNN) based methods have
outperformed conventional methods with a large margin.
Therefore, CNN-based recognition should be introduced into
mobile object recognition. However, since CNN computa-
tion is usually performed on GPU-equipped PCs, it is not
easy for mobile devices where memory and computational
power is limited.
In this paper, we explore the possibility of CNN-based
object recognition on mobile devices, especially on iOS and
Android devices including smartphones and tablets in terms
of processing speed and required memory.
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4. distributed as “contrib” modules experimentally has sup-
ported a DNN module in which the network trained in Caffe [4]
and Torch-7 [5] can be deployed. Since OpenCV supports
both iOS and Android, anyone can implement a mobile deep
learning system easily with the DNN module. However, no
special optimization on the DNN module was carried out
for mobile devices. To run AlexNet on iOS or Android it re-
quires more than one second and much storage to store the
trained weights. For example, 60 million weights are needed
for AlexNet.
An open-source mobile deep learning software, DeepBe-
liefSDK2 is optimized for Android and iOS where the BLAS
library is employed on iOS and NEON SIMD instruction
sets are used on Android. It took about 130 ms for one-time
recognition on iPad Pro. In addition, to reduce memory
to store the weights, one weight is represented in one byte
by quantization [6]. Its drawback is that it is difficult to
change CNN models from the built-in AlexNet to the oth-
ers, since training function or importing function from other
frameworks is not provided.
As academic studies, many researchers try to compress
weights and reduce computational costs. Gong et al. [7] pro-
posed to apply Product Quantization (PQ) [2] to convnets
1http://opencv.org/
2https://github.com/jetpacapp/DeepBeliefSDK
362
to compress their weights. However, they applied PQ-based
compression to only fully connected (FC) layers in AlexNet
and not to convolutional (conv) layers. Yang et al. [8] also
5. proposed Fastfood transform and applied it for compression
of FC layers in Alexnet, and Han et al. [9] proposed a method
which combines pruning, quantization and Huffman Encod-
ing, and applied it to both conv and FC layers in Alexnet
and VGG-16. Courbariaux et al. [10] proposed a method to
binarize network weights to reduce computational costs and
required memory.
However, most of the authors did not implement their
proposed method on actual mobile devices. Exceptionally,
Wu et al. [11] implemented and tested Compressed AlexNet
on an Android smartphone. However, it took 0.95 seconds
for one-time recognition, which was much slower than our
implementation.
In this paper, we aim to implement a practical CNN-based
object recognition engine on both iOS and Android in terms
of computational speed and required memory to store CNN
weights.
Before the deep learning era got started, several local-
feature-based mobile recognition systems were proposed. For
example, Kawano et al. [12] proposed a mobile food recog-
nition system which can classify 100 food classes in only
0.065 seconds employing Fisher Vector (FV) [13]. To real-
ize fast recognition, they employed weight compression tech-
nique [14] and multi-threading implementation. This system
was called“FoodCam”which was implemented as an interac-
tive application taking advantage of their very quick recog-
nition. One of our objectives in this paper is implementing
such a fast mobile recognition system using a state-of-the-
art CNN architecture. We propose“DeepFoodCam”which is
a CNN-version of “FoodCam” as an example of CNN-based
mobile recognition systems in the last part of this paper.
3. CNN ARCHITECTURE
6. As basic CNN architectures for object recognition, AlexNet
[15],
Network-in-Network (NIN) [1], GoogLeNet [16] and VGG-
16 [17] are commonly used. For comparison, we show the
basic characteristics of their CNN architecture regarding the
number of layers, weights and computation on convolutional
(conv) layers and fully-connected (FC) layers in Table 1 3.
AlexNet is the first large-scale object recognition CNN
which consists of 6 conv layers and 3 FC layers, while VGG-
16 is a deeper version of AlexNet which has 13 conv lay-
ers and 3 FC layers. FC layers need a large number of
weights, 59M for AlexNet and 129M for VGG-16. Although
GoogLeNet also has a deep architecture consisting of 21 conv
layers, it has only one FC layers with 1M parameters. While
VGG-16 is a deep and high-performance but simple archi-
tecture which uses only 3×3 conv layers, GoogLeNet is quite
complicated which consists of many “Inception modules”
each of which consists of 5x5, 3x3, 1x1 conv layers and pool-
ing layers.
For mobile implementation, these three architectures are
not appropriate, because AlexNet and VGG-16 have too
much parameters and GoogLeNet is a complicated archi-
tecture which is difficult for effective parallel implementa-
tion for mobile devices. Unlike these three architectures,
Network-in-Network (NIN) has a simple architecture con-
sisting of no FC layers and 12 conv layers. Instead of FC lay-
ers, NIN adopts Cascaded Cross Channel Parametric Pool-
ing (CCCP Pooling) which is implemented by two consec-
3We referred to the open models at Caffe Model Zoo
(https://github.com/BVLC/caffe/wiki/Model-Zoo).
Table 1: Comparison of CNN Architectures regard-
7. ing the number of layers, weights and computation
(multiplication). “ImageNet” means the top-5 error
rate for the ImageNet Challenge data with a single
model.
model Alex VGG-16 GoogLeNet NIN
layer 5 13 21 12
conv weights 3.8M 15M 5.8M 7.6M
comp. 1.1B 15.3B 1.5B 1.1B
layer 3 3 1 0
FC weights 59M 124M 1M 0
comp. 59M 124M 1M 0
TOTAL weights 62M 138M 6.8M 7.6M
comp. 1.1B 15.5B 1.5B 1.1B
ImageNet top-5 err. 17.0% 7.3% 7.9% 10.9%
utive 1 conv layers just after 3 × 3 or larger conv layers.
The number of the total parameters of NIN are 7.6 million,
and the number of the multiplication is 1.1 billion, which is
relatively smaller than the other architectures.
Given these characteristics of NIN, we have decided to
adopt NIN as a basic architecture of mobile implementation
in this work. In addition, NIN can accept an image of any
size as an input of the network by using global pooling in-
stead of fixed-size pooling in the last pooling layer, because
NIN is a CNN without FC layers. This is very helpful for
mobile implementation, because users can adjust the balance
between recognition speed and accuracy by their preference
without changing the network weights. We will refer this
point later.
8. 4. COMPRESSION OF NIN WEIGHTS
In this paper, we adopt Network-in-Network (NIN) [1] as
a basic CNN architecture, because both the amount of the
parameters and computational costs are relatively smaller.
However, 30 MByte at least is still needed to store 7.6 million
weights in 32-bit single floating point representation. Then
we introduce weight compression.
Inspired by Gong et al. [7], we apply product quantiza-
tion (PQ) [2] for weight compression of conv layers of NIN,
although Gong et al. applied PQ-based compression not to
conv layers but only to FC layers in AlexNet. By the ex-
periments, we confirm the effectiveness of PQ-based weight
compression for conv layers of NIN.
5. FAST IMPLEMENTATION ON MOBILE
DEVICES
In this work, we focus on iOS and Android devices in-
cluding smartphones and tablets as target devices for mobile
implementation.
For fast mobile implementation, it is essential to use (1)
Multi-threading, (2) SIMD instruction sets and (3) iOS Ac-
celerate Framework (only for iOS). In addition, it is possible
to reduce computational costs by devising CNN architec-
tures, computation algorithms, and introducing approxima-
tion into CNN computation. As the other easier way to
reduce computational costs, we can reduce the size of an
input image.
In this section, we describe (1), (2) and (3) which we con-
sider are prerequisite for fast implementation of CNNs on
mobile devices. Since so many works have been published
regarding reducing computational costs of CNNs as men-
9. tioned in Sec.2, we like to keep introducing them for our
future work. Instead, to verify the effectiveness of reducing
363
Figure 1: Computation of conv layers with “im2col”
and “matrix multiplication”.
of the size of an input image for speedup, we will exam-
ine the relations between input image sizes and recognition
accuracy in the next section.
Because NIN has only conv layers as the layers having
trained parameters, we need efficient mobile implementation
of feed-forward computation of convolutional layers. In gen-
eral, computation of conv layers is decomposed into“im2col”
operation and matrix multiplications. In im2col operation,
each patch to apply a conv kernel to is extracted from a
given feature map, and place them in one column of a patch
matrix, while each conv kernel is also placed in one row of
a kernel matrix (See Figure 1). After that, we can obtain
the output feature maps of the target conv layer by mul-
tiplying the kernel matrix by the im2col patch matrix. In
general, this generic matrix multiplication (GEMM) is very
time-consuming, since both matrices are large (sometime
larger than 1000 × 1000). Although we can use GPU for
fast GEMM calculation in case of PC or servers, for mobile
devices we need to use CPU as effective as possible.
(1) Multi-threading: Most of the recent CPU on mobile
devices have four CPU cores. By using four cores effectively,
four times speedup can be expected. In our implementation,
to use four CPU cores by multi-threading, we divide a kernel
matrix into four sub-matrices along the row. As shown in
Table 1, the number of conv kernels are usually multiples
10. of four. Four GEMM operations are carried out in parallel.
Note that most of the CPUs on iOS devices including the
latest APPLE CPU, A9X, have only two cores. Therefore,
only twice speedup is expected with multi-threading for iOS
devices at most.
(2) SIMD instruction: Most of the recent mobile devices
are equipped with ARM-based CPUs except for some de-
vices having Intel Atom. Therefore, we focus only on NEON
which is a SIMD instruction set of ARM processors. Among
the NEON instruction set, we use “vmla.f32” which can ex-
ecute four multiplications and accumulating calculations at
once. This operations are frequently used for GEMM cal-
culation. NEON instructions can be carried out in each
CPU core independently. Therefore, by combining multi-
threading and NEON, 16 times speedup for Android and 8
times for iOS are expected at most.
(3) iOS Accelerate Framework: In case of iOS, iOS Ac-
celerate Framework which includes highly optimized BLAS
(Basic Linear Algebra Subprograms) library is available. The
GEMM function in the BLAS library (“cblas sgemm”) can
be used for GEMM operations. BLAS in iOS probably uses
NEON instructions inside the library. However, the detail is
not disclosed. (2) and (3) are alternative to each other. We
will compare them in the experiments.
Table 2: The original and variants of the NIN archi-
tectures.
layer no. (1) original NIN (2) 4layers + BN (3) 5layers + BN
1 11x11x96 conv1 11x11x96 conv1 11x11x96 conv1
2 1x1x96 cccp1 1 1x1x96 cccp1 1 1x1x96 cccp1 1
3 1x1x96 cccp1 2 1x1x96 cccp1 2 1x1x96 cccp1 1
4 5x5x256 conv2 3x3x256 conv2 1 3x3x256 conv2 1
5 1x1x256 cccp2 1 3x3x256 conv2 2 3x3x256 conv2 2
6 1x1x256 cccp2 2 1x1x256 cccp2 1 1x1x256 cccp2 1
7 3x3x384 conv3 1x1x256 cccp2 2 1x1x256 cccp2 2
11. 8 1x1x384 cccp3 1 3x3x384 conv3 3x3x384 conv3
9 1x1x384 cccp3 2 1x1x384 cccp3 1 1x1x384 cccp3 1
10 3x3x1024 conv4 1x1x384 cccp3 2 1x1x384 cccp3 2
11 1x1x1024 cccp4 1 3x3x768 conv4 3x3x768 conv4
12 1x1xN cccp4 2 1x1x768 cccp4 1 1x1x768 cccp4 1
13 avg. pool 1x1xN cccp4 2 1x1x768 cccp4 2
14 sofmax avg. pool 3x3x1024 conv5
15 softmax 1x1x1024 cccp5 1
16 1x1x1024 cccp4 2
17 avg. poling
18 softmax
weights 7.6Million 5.5Million 15.8Million
computation 1.1Billion 1.2Billion 1.7Billion
6. MULTI-SCALE RECOGNITION FOR AD-
JUSTING TIME AND ACCURACY
The original NIN is designed so that the size of an input
image of NIN is 227 × 227. However, by using global aver-
age pooling which accepts feature maps of any size in the
final pooling layer, NIN can accept input images of any size,
since NIN has no FC layers. This is a helpful characteristics
for mobile implementation, since users can adjust processing
speed by changing the size of an input image without chang-
ing CNN weights. The smaller the size of an input image
becomes, the faster the NIN can recognize it. Instead, the
accuracy will be degraded.
In the experiments, we examine the trade-off between
recognition time and accuracy with input images of several
sizes and two kinds of methods to generate smaller images.
One is cropping smaller images from the original one without
resizing, while the other is resizing original one to smaller
images.
12. 7. EXTENSION OF THE NIN ARCHITEC-
TURE
The original NIN consists of four repetitive small struc-
tures each of which includes one conv layer and Cascaded
Cross Channel Parametric Pooling (CCCP) layers which are
equivalent to two consecutive 1×1 conv layers. When train-
ing, we added drop-out [18] to the two last 3 × 3 conv layers
to make training easier.
Recently batch normalization (BN) [3] has been proposed,
which can accelerate training without drop-out and make
training of deeper networks possible. Then, we build two
variant models of NIN by adding BN layers just after all the
conv/cccp layers and one repetitive structures. One is “4-
layers + BN” and the other is “5-layers + BN”. In addition,
we replaced 5x5 conv with two 3x3 conv layers, and reduced
the number of kernels in conv 4 from 1024 to 768. The de-
tails of the original NIN and two variants are shown in Table
2. We will compare them regarding recognition performance
in the experiments
Note that ReLUs (Rectified Linear Unit) exists just after
all the conv/cccp layers, and in case of (2) and (3), BN
(batch normalization) layers also exists just after all the
conv/cccp layers and before ReLU, although they are not
shown in the table.
364
Table 3: Recognition time [ms] on mobile devices.
NIN(BLAS) NIN(NEON) NIN4 NIN5 D-Belief
13. iPad Pro 66.0 221.4 66.6 103.5 131.9
iPhone SE 79.9 251.8 77.6 116.6 137.7
Galaxy Note 3 1652 251.1 - - -
Table 4: Recognition accuracy (top-1/5) [%] of the
trained models.
ImageNet2000 UEC-FOOD
model top-1 top-5 top-1 top-5 weights
FV(HOG+color)[12] - - 65.3 86.7 5.6M
AlexNet 44.5 67.8 78.8 95.2 62M
NIN 41.9 65.9 75.0 93.7 7.6M
NIN(4layers+BN) 39.8 65.0 77.9 94.6 5.5M
NIN(5layers+BN) 45.8 70.5 80.8 95.4 15.8M
8. EXPERIMENTS
8.1 Implementation and Training
We have implemented a mobile deep learning framework
which works on both iOS and Android. The framework sup-
ports only deployment of trained CNN models. We used
Caffe [4] for training of CNN models on a PC with two
Titan-X GPUs, and converted trained model files for our
framework.
In the experiments, we mainly used the augmented UEC-
FOOD100 dataset [19, 20] which is a 100-class food cate-
gories dataset containing at most 1000 food photos for each
food class, because our objective is implementing practi-
cal CNN-based recognition engines. Following [20], before
training with the food dataset, we pre-trained CNNs with
ImageNet 2000 category images (totally 2.1 million images)
14. which consisted of ILSVRC2012 1000 category images and
1000 food-related categories selected from all the 21,000 Im-
ageNet categories.
For evaluation, we used 20% of ImageNet 2000 images
and the images in the fold no.0 of the official split of UEC-
FOOD100 as test images of ImageNet 2000 and augmented
UEC-FOOD100, respectively. In the experiments, we mea-
sured processing time on mobile devices, while we evaluated
recognition accuracy of the trained CNNs on a PC.
8.2 Recognition Time on Mobile Devices
We measured recognition times on iPad Pro 9.7inch (iOS
9.3), iPhone SE (iOS 9.3) and Galaxy Note3 (Android 5.0).
We executed recognition more than twenty times and calcu-
lated the median of all the measured times for evaluation.
Table 3 shows the processing time for one-time recognition
with NIN with BLAS, NIN with NEON, NIN-4layers with
BLAS, NIN-5layers with BLAS, and DeepBeliefSDK with
BLAS (for comparison).
From these results, the BLAS library on iOS which was
implemented as a part of iOS Accelerate Framework was
very effective for speedup, while the OpenBLAS library which
we used as BLAS implementation for Android was too slow.
NEON implementation was moderate, although it was three
times as slow as iOS BLAS implementation. For more speedup
on Android, improvement of CNN models and computation
such as reducing weights and approximation of CNN com-
putation will be needed. This is one of our future works.
Although DeepBeliefSDK also employs BLAS, our NIN(BLAS)
was twice as fast as it. One of the possible reason is that we
used multi-threading, while they did not use.
For reference, we show the recognition accuracy for Ima-
15. geNet2000 and UEC-FOOD with Fisher Vector (FV) based
conventional method, AlexNet, NIN and two variants in Ta-
Table 5: Time [ms] and top-1/5 accuracy [%] with
images of various size.
(A) 4-layer + BN
Time 227x227 200x200 180x180 160x160
iPad Pro 66.6 49.7 44.0 32.6
iPhone SE 77.6 56.0 50.2 37.2
Accuracy top-1 top-5 top-1 top-5 top-1 top-5 top-1 top-5
resize 78.8 95.2 77.3 95.1 76.0 94.1 69.3 91.5
crop 78.8 95.2 75.8 93.9 72.0 92.1 63.0 87.7
multi-resize 74.7 93.9 74.0 94.6 74.4 94.7 71.5 93.7
multi-crop 74.7 93.9 70.8 92.2 69.8 92.2 61.4 87.2
(B) 5-layer + BN
Time 227x227 200x200 180x180 160x160
iPad Pro 103.5 71.9 61.1 46.6
iPhone SE 116.6 82.9 68.6 53.4
Accuracy top-1 top-5 top-1 top-5 top-1 top-5 top-1 top-5
resize 81.5 96.2 80.2 95.7 78.4 94.9 72.0 91.4
crop 81.5 96.2 78.3 95.1 75.1 93.6 65.3 87.3
multi-resize 78.2 95.3 78.2 95.1 78.2 95.6 75.1 93.8
multi-crop 78.2 95.3 75.8 93.2 73.1 92.2 66.3 88.3
Table 6: Accuracy (top-1/5) [%] with PQ-based
weight compression.
16. raw(32bit) 8bit 4bit 4bit(pair) 2bit(pair)
memory 30.4MB 7.6MB 3.8MB 3.8MB 1.9MB
top-1 75.0 74.5 66.8 72.9 50.3
top-5 93.7 93.5 89.7 92.9 78.1
ble 4. All the CNN based method outperformed the FV-
based result [12] with a large margin. Both of two variants of
NIN outperformed the original NIN except for NIN-4layers
on ImageNet2000. Reducing the number of the kernels in
the last conv layers for NIN-4layers is one of the possible
reasons of this performance loss. NIN-5layers outperformed
AlexNet, which showed that making a CNN deeper was ef-
fective for performance improvement.
8.3 Multi-scale Recognition
The advantage of NIN is that it can accept images of any
size as input images. Table 5 shows relation between im-
age size and time/accuracy. We used two methods to ob-
tain smaller images, resizing and cropping. In addition, we
trained NIN-4layers and NIN-5layers in multi-scale training
where we resized training images with random magnification
rate from 0.7 to 1.0 during training of the NIN models.
For the results, the processing time was proportional to
the number of the pixels of an image. For example, in case of
160x160, the time was reduced by half. Although reducing
the size from 227x227 to 180x180 brought only 2.8 point
and 3.1 point accuracy loss for NIN-4layers and NIN-5layers,
respectively, the processing time was reduced by two third.
As a method to reduce the size of an input image, resizing
is much better than cropping in terms of accuracy.
The multi-scale trained models were not as effective as
the normal models trained with 227x227 images except for
17. 160x160 input images. The results by the multi-scale trained
models are relatively uniform regardless the size of an input
image, although the accuracy for the full-sized image was
degraded compared to the normal models.
Until 180x180, reducing the size of an input image is effec-
tive and easy way to adjust the trade-off between accuracy
and processing time.
8.4 Weight Compression
We applied Product Quantization (PQ) [2] to compress
CNN weights for NIN to reduce required memory on mo-
bile devices. Table 6 shows accuracy from no compres-
365
Figure 2: Screen-shots of food recognition app (left)
and 2000-class recognition app (right).
sion to 1/16 compression. For “8bit” and “4bit”, we applied
quantization to each single element, while “4bit(pair)“ and
“2bit(pair)” means that we applied quantization to each pair
of elements. Performance loss in case of“4bit(pair)”was only
2.1 point, although it brought 1/8 compression. From these
results, PQ-based compression is helpful for NIN as well.
8.5 Mobile Applications
We have implemented two kinds of mobile CNN-based im-
age recognition apps on iOS, a food recognition app, “Deep-
FoodCam”, and a 2000-class object recognition app, which
employ NIN trained with the augmented UEC-FOOD100
dataset including a additional non-food class, and NIN trained
18. with 2000 ImageNet Food dataset, respectively. Both can
recognize a 227x227 photo in 66ms and a 160x160 in 33ms.
We have released the iOS-version food recognition appli-
cation on Apple App Store. Please search for “DeepFood-
Cam”. The movies where two apps are working on iOS can
be seen at our project page, http://foodcam.mobi/.
9. CONCLUSIONS
We proposed a mobile implementation of CNN which is
based on Network-in-Network (NIN) [1]. For reducing the
weights we examined PQ-based compression, while for fast
recognition we adopted multi-threading and SIMD instruc-
tion or highly-optimized iOS Accelerate Framework for com-
putation of convolutional layers. In addition, we examined
NIN-based multi-scale recognition which enabled us to ad-
just the trade-off between time and speed. As results, it
was turned out that reducing the size of input images was
very effective. In addition, for a 160 × 160 input image, we
achieved 32.6 ms recognition on iPad Pro, which is equiva-
lent to the “real” real-time speed.
For future work, we plan to apply our mobile framework
into real-time CNN-based mobile image processing such as
super-resolution, semantic segmentation and neural style-
transfer [21].
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