常用類型的sense amplifier介紹
標簽： Amplifiers Review Random Static Acces Sense for of
上傳時間： 2016-06-23
上傳用戶：nainiu
Without conceding a blemish in the first edition, I think I had best come clean and admit that I embarked on a second edition largely to adopt a more geometric approach to the detection of signals in white Gaussian noise. Equally rigorous, yet more intuitive, this approach is not only student-friendly, but also extends more easily to the detection problem with random parameters and to the radar problem
標簽： Communication Foundation Digital in
上傳時間： 2020-05-26
上傳用戶：shancjb
Modeling and simulation of nonlinear systems provide communication system designers with a tool to predict and verify overall system performance under nonlinearity and complex communication signals. Traditionally, RF system designers use deterministic signals (discrete tones), which can be implemented in circuit simulators, to predict the performance of their nonlinear circuits/systems. However, RF system designers are usually faced with the problem of predicting system performance when the input to the system is real-world communication signals which have a random nature.
標簽： Nonlinear_Distortion_in_Wireless_ Systems
上傳時間： 2020-05-31
上傳用戶：shancjb
Before delving into the details of orthogonal frequency division multiplexing (OFDM), relevant background material must be presented first. The purpose of this chapter is to provide the necessary building blocks for the development of OFDM principles. Included in this chapter are reviews of stochastic and random process, discrete-time signals and systems, and the Discrete Fourier Transform (DFT). Tooled with the necessary mathematical foundation, we proceed with an overview of digital communication systems and OFDM communication systems. We conclude the chapter with summaries of the OFDM wireless LAN standards currently in existence and a high-level comparison of single carrier systems versus OFDM.
上傳時間： 2020-05-31
上傳用戶：shancjb
Part I provides a compact survey on classical stochastic geometry models. The basic models defined in this part will be used and extended throughout the whole monograph, and in particular to SINR based models. Note however that these classical stochastic models can be used in a variety of contexts which go far beyond the modeling of wireless networks. Chapter 1 reviews the definition and basic properties of Poisson point processes in Euclidean space. We review key operations on Poisson point processes (thinning, superposition, displacement) as well as key formulas like Campbell’s formula. Chapter 2 is focused on properties of the spatial shot-noise process: its continuity properties, its Laplace transform, its moments etc. Both additive and max shot-noise processes are studied. Chapter 3 bears on coverage processes, and in particular on the Boolean model. Its basic coverage characteristics are reviewed. We also give a brief account of its percolation properties. Chapter 4 studies random tessellations; the main focus is on Poisson–Voronoi tessellations and cells. We also discuss various random objects associated with bivariate point processes such as the set of points of the first point process that fall in a Voronoi cell w.r.t. the second point process.
標簽： Stochastic Geometry Networks Wireless Volume and
上傳時間： 2020-06-01
上傳用戶：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
標簽： Convolutional Networks Neural Guide to
上傳時間： 2020-06-10
上傳用戶：shancjb
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上傳時間： 2021-10-22
上傳用戶：
MT2625 DatasheetVersion: 1.2Release date: 31 January 2018NB-IoT transceiver? Compliant with 3GPP R13/R14 NB-IoT standard? Supports DL 200kHz bandwidth/UL single tone and multi-tone? Supported RF bands: B1/B2/B3/B5/B8/B11/B12/B13/B17/B18/B19/B20/B21/B25/B26/B28/B31/B66/B70/B71? Supports PSM and eDRX modeMicrocontroller subsystem? ARM? Cortex?-M4 with FPU and MPU? 14 DMA channels? One RTC timer, one 64-bit and five 32-bit general purpose timers? Development support: SWD, JTAG? Crypto engineo AES 128, 192, 256 bitso DES, 3DESo MD5, SHA-1, 224, 256, 384, 512? True random number generator? JTAG password protection
標簽： MT2625
上傳時間： 2022-07-04
上傳用戶：
實驗二、Python 運算符、內置函數實驗目的：1、熟練運用 Python 運算符。2、熟練運用 Python 內置函數。實驗內容：1、編寫程序，輸入任意大的自然數，輸出各位數字之和。2、編寫程序，輸入兩個集合 setA 和 setB，分別輸出它們的交集、并集和差集 setA-setB。3、編寫程序，輸入一個自然數，輸出它的二進制、八進制、十六進制表示形式。實驗三、使用蒙特·卡羅方法計算圓周率近似值實驗目的：1、理解蒙特·卡羅方法原理。2、理解 for 循環本質與工作原理。3、了解 random 模塊中常用函數。實驗內容：蒙特·卡羅方法是一種通過概率來得到問題近似解的方法，在很多領域都有重要的應用，其中就包括圓周率近似值的計算問題。假設有一塊邊長為 2 的正方形木板，上面畫一個單位圓，然后隨意往木板上扔飛鏢，落點坐標(x, y)必然在木板上（更多的時候是落在單位圓內），如果扔的次數足夠多，那么落在單位圓內的次數除以總次數再乘以 4，這個數字會無限逼近圓周率的值。這就是蒙特·卡羅發明的用于計算圓周率近似值的方法，如圖所示。
標簽： python
上傳時間： 2022-07-09
上傳用戶：
