WSNs being energy constrained systems, one major problem is to employ the sensor nodes in such a manner so as to ensure maximum coverage and connectivity with minimal or optimal number of nodes and furthermore elongate network lifetime with maximum energy utilization.
The problem addressed has been tackled for 1-D linear array and further extended to 2-Dimensions as stated in the next slides.
C++ by Dissection presents a thorough introduction to the programming process by
carefully developing working programs to illuminate key features of the C++ programming
language. Program code is explained in an easy-to-follow, careful manner throughout.
The code has been tested on several platforms and is found on the bundled CDrom
accompanying this text. The code in C++ By Dissection can be used with most C++
systems, including those found in operating systems such as MacOS, MS-DOS, OS/2,
UNIX, and Windows.
The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local memory-based models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
Two scripts are included here.
1. convsys.m - combines the state space representation of two systems connected in series.
[Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2)
This algorithm gives the convolution of two state space representations
| A1 B1 | | A2 B2 |
u ==> | | ==> | | ==> y
| C1 D1 | | C2 D2 |
The algorithm also accepts state space objects as inputs and gives out a state space object as output.
2. sysfeedbk.m
[Ao,Bo,Co,Do]=convsys(A1,B1,C1,D1,A2,B2,C2,D2)
Gives the closed loop state space representation for two systems connected with negative feedback in the following manner.
| A1 B1 |
u ==> | | ==> y
+ o | C1 D1 | |
- | |
| | A2 B2 | |
|= | |= |
| C2 D2 |
The zip file also contains checkcompatibility.m , which checks the compatibility of matrix dimensions in the system and cleanss.m which can be used to clean a state space representation.
Providing QoS while optimizing the LTE network in a cost efficient manner is
very challenging. Thus, radio scheduling is one of the most important functions
in mobile broadband networks. The design of a mobile network radio scheduler
holds several objectives that need to be satisfied, for example: the scheduler needs
to maximize the radio performance by efficiently distributing the limited radio re-
sources, since the operator’s revenue depends on it.
Public telephone operators and new independent wireless operators through-
out the world are deploying wireless access in an effort to drastically reduce
delivery costs in the most expensive part of the network?the local loop.
Available radio technology enables both existing and new entrants to access
subscribers in a rapid manner and deliver their basic telephony products and
broadband-enhanced services.
Once upon a time, cellular wireless networks provided two basic services: voice
telephony and low-rate text messaging. Users in the network were separated
by orthogonal multiple access schemes, and cells by generous frequency reuse
patterns [1]. Since then, the proliferation of wireless services, fierce competition,
andthe emergenceof new service classes such as wireless data and multimediahave
resulted in an ever increasing pressure on network operators to use resources in a
moreefficient manner.In the contextof wireless networks,two of the most common
resources are power and spectrum—and, due to regulations, these resources are
typically scarce. Hence, in contrast to wired networks, overprovisioning is not
feasible in wireless networks.
Much has been written concerning the manner in which healthcare is changing, with
a particular emphasis on how very large quantities of data are now being routinely
collected during the routine care of patients. The use of machine learning meth-
ods to turn these ever-growing quantities of data into interventions that can improve
patient outcomes seems as if it should be an obvious path to take. However, the
field of machine learning in healthcare is still in its infancy. This book, kindly
supported by the Institution of Engineering andTechnology, aims to provide a “snap-
shot” of the state of current research at the interface between machine learning and
healthcare.
基于藥物治療在臨床治療中的重要性,分析目前服藥提醒裝置存在的不足,以STM32F103VET6單片機為控制核心,設計了一種多功能電子藥箱。該系統包括顯示模塊、語音模塊和數據存儲模塊。顯示模塊通過觸摸屏電路和LED指示燈電路,與語音模塊相配合,實現了服藥提醒及指導的功能;數據存儲模塊通過EEPROM存儲電路,能夠實現掉電時服藥信息不丟失的功能。并且為了實現電子藥箱的智能化控制,開發了手機APP,兩者之間可通過WIFI進行數據通信。經測試,該藥箱能夠有效地幫助慢性病患者按時、定量、正確服用藥物,適合在家庭中推廣使用,具有較高的應用價值和實踐意義。Based on the importance of drug therapy in clinical treatment, this paper analyzes the shortcomings of current drug reminder devices, and designs a multi-function electronic medicine box with STM32 F103 VET6 microcontroller as the control core. The system includes a display module, a voice module, and a data storage module. The display module cooperates with the voice module through the touch screen circuit and the LED indicator circuit to realize the function of reminding and guiding the medicine;the data storage module can realize the function of not losing the medication information when the power is off through the EEPROM storage circuit.After testing, the medicine box can effectively help chronic diseases patients to take drugs on time, in a quantitative and correct manner,and is suitable for popularization in the family, and has high application value and practical significance.
