//CONFIG 1 #pragma config FCMEN=OFF,IESO=OFF,CLKOUTEN=OFF,BOREN=NSLEEP,CPD=OFF #pragma config CP=ON,MCLRE=OFF,PWRTE=OFF,WDTE=OFF,FOSC=INTOSC //CONFIG 2 #pragma config LVP=OFF,BORV=LO,STVREN=OFF,PLLEN=OFF #pragma config VCAPEN=OFF,WRT=HALF
標(biāo)簽： xc8
上傳時(shí)間： 2018-06-26
上傳用戶(hù)：HMHAZM
The SP2526A device is a dual +3.0V to +5.5V USB Supervisory Power Control Switch ideal for self-powered and bus-powered Universal Serial Bus (USB) applications. Each switch has low on-resistance (110mΩ typical) and can supply 500mA minimum. The fault currents are limited to 1.0A typical and the flag output pin for each switch is available to indicate fault conditions to the USB controller. The thermal shutdown feature will prevent damage to the device when subjected to excessive current loads. The undervoltage lockout feature will ensure that the device will remain off unless there is a valid input voltage present.
標(biāo)簽： High-Side Switch Power Dual USB
上傳時(shí)間： 2019-03-06
上傳用戶(hù)：bhitr
A decade ago, I first wrote that people moved, and networks needed to adapt to the reality that people worked on the go. Of course, in those days, wireless LANs came with a trade-off. Yes, you could use them while moving, but you had to trade a great deal of throughput to get the mobility. Although it was possible to get bits anywhere, even while in motion, those bits came slower. As one of the network engineers I worked with put it, “We’ve installed switched gigabit Ethernet everywhere on campus, so I don’t understand why you’d want to go back to what is a 25-megabit hub.” He un- derestimated the allure of working on the go.
標(biāo)簽： Survival 802.11 Guide
上傳時(shí)間： 2020-05-26
上傳用戶(hù)：shancjb
The core thrust of architecture has been to define core business requirements, and then construct the IT solution to meet those requirements, typically as instances of software. While this seems like a simple concept, many in enter- prise IT went way off course in the last 10 to 15 years.
標(biāo)簽： Architecture Revolution Agile
上傳時(shí)間： 2020-05-26
上傳用戶(hù)：shancjb
This research work aims at eliminating the off-chip RF SAW filters from fre- quency division duplexed (FDD) receivers. In the first approach, a monolithic passive RF filter was constructed using on-chip capacitors and bondwire inductors. The bond- wire characteristics were studied in details and the effect of mutual inductive coupling between the bondwires on the filter performance was analyzed. Based on that, a bond- wire configuration was proposed to improve the frequency response of the filter. The filter was implemented in 0.18 μm CMOS process for WCDMA applications.
標(biāo)簽： Interference Mitigation Techniques
上傳時(shí)間： 2020-05-27
上傳用戶(hù)：shancjb
his research aims at creating broadband tunable, fully integrated filters for the application of cognitive radio and signal classification receivers. The approach under study is the N-path filter technique which is capable of translating a baseband impedance to a reference frequency creating a tunable filter. The traditional N-path filter suffers from fundamental architectural limitations, namely : a trade-off between insertion loss and out-of-band rejection, reference clock feed- through, and jammer power handling limitations. In the first approach, the fundamental trade- off of the traditional N-path filter between insertion loss and out-of-band rejection is improved by a transmission line (T-line) N-path filter technique.
上傳時(shí)間： 2020-05-31
上傳用戶(hù)：shancjb
As we enter the next millennium, there are clear technological patterns. First, the electronic industry continues to scale microelectronic structures to achieve faster devices, new devices, or more per unit area. Secondly, electrostatic charge, electrostatic discharge (ESD), electrical overstress (EOS) and electromagnetic emissions (EMI) continue to be a threat to these scaled structures. This dichotomy presents a dilemma for the scaling of semiconductor technologies and a future threat to new technologies. Technological advancements, material changes, design techniques, and simulation can fend off this growing concern – but to maintain this ever-threatening challenge, one must continue to establish research and education in this issue.
標(biāo)簽： ESD-Phenomena-and-the-Reliability
上傳時(shí)間： 2020-06-05
上傳用戶(hù)：shancjb
The continuous progress in modern power device technology is increasingly supported by power-specific modeling methodologies and dedicated simulation tools. These enable the detailed analysis of operational principles on the the device and on the system level; in particular, they allow the designer to perform trade- off studies by investigating the operation of competing design variants in a very early stage of the development process. Furthermore, using predictive computer simulation makes it possible to analyze the device and system behavior not only under regularoperatingconditions, but also at the rim of the safe-operatingarea and beyond of it, where destructive processes occur that limit the lifetime of a power system.
標(biāo)簽： POWERHVMOS_Devices_Compact_Modeli ng
上傳時(shí)間： 2020-06-07
上傳用戶(hù)：shancjb
This book is about global navigation satellite systems (GNSS), their two main instru- ments, which are a receiver and a simulator, and their applications. The book is based on an operational off-the-shelf real-time software GNSS receiver and off-the-shelf GNSS signalsimulator.Theacademicversionsofthesetoolsarebundledwiththisbookandfree for readers to use for study and research.
標(biāo)簽： Navigation Geophysics Satellite Digital and
上傳時(shí)間： 2020-06-09
上傳用戶(hù)：shancjb
General paradigm in solving a computer vision problem is to represent a raw image using a more informative vector called feature vector and train a classifier on top of feature vectors collected from training set. From classification perspective, there are several off-the-shelf methods such as gradient boosting, random forest and support vector machines that are able to accurately model nonlinear decision boundaries. Hence, solving a computer vision problem mainly depends on the feature extraction algorithm
標(biāo)簽： Convolutional Networks Neural Guide to
上傳時(shí)間： 2020-06-10
上傳用戶(hù)：shancjb
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