Penn hardware catalog pdf download

Calibration -- 3. Capacitive Rain Gauge Calibration Example -- 3. Calibration Range -- 3. Measurement Calibration Process -- 3. Calibration Curve Variables -- 3. Difference between Calibration and Usage -- 3.

Data Modeling -- 3. Difference between Calibration and Data Modeling -- 3. Modeling as Filtering -- 3. Error Types -- 3. Systematic Errors -- 3.

Random Errors -- 3. Interference -- 3. Hysteresis Error -- 3. Dead Band Error -- 3. Statistical Concepts -- 3. Measurement Mathematical Model -- 3. Probability Concepts -- 3. Relative Frequency -- 3. Probability Density -- 3. Cumulative Distribution Function -- 3.

Electronic Noise -- 3. Mean, Variance, and Standard Deviation Estimates -- 3. Measurement Uncertainty -- 3. Uncertainty Definition -- 3. Confidence Intervals -- 3. Number of Measurements Required -- 3. Combined Uncertainty and Uncertainty Budget -- 3. Least Squares Fitting -- 3.

Least Squares Definition -- 3. Linear Least Squares-Statistical Basis -- 3. Quality of the Fit -- 3. Correlation Coefficients -- 3. Nonlinear Fits -- 3. Parametric Models -- 3. Power Series Models -- 3. Cautions with Least Squares -- 3. Model Selection -- 3. Outlying Points -- 3. Over-fitting the Model -- 3.

References -- 3. Introduction -- 4. Objectives -- 4. Computer Systems -- 4. Real-Time Computing Definition -- 4. Interrupt Characteristics -- 4. Software Characteristics -- 4. Computer Input-Output Interfaces -- 4. Serial Interfaces -- 4. Networks -- 4. User Interface Systems -- 4. Processing State Diagram -- 4. Telemetry Database -- 4.

Database Architecture -- 4. Data Timing -- 4. Database Storage -- 4. Telemetry Processing Levels and Unit Conversion -- 4. Telemetry Packet Processing -- 4. Telemetry Displays -- 4. Telemetry Data Partitioning -- 4.

Telemetry Status Indicators -- 4. Display Interaction with the Telemetry Database -- 4. Balloon Experiment Telemetry Display Example -- 4. Telecommand Interfaces -- 4. Command Dictionary -- 4. Command Data Input -- 4. Command Processing -- 4. Balloon Experiment Telecommand Interface Example -- 4. Payload Computer Systems -- 4. Payload Command Processing State Diagram -- 4. Payload Command Processing -- 4.

Payload Telemetry Processing -- 4. Payload Data Master Equipment List -- 4. Balloon Payload Computing System Example -- 4. Secure Communications -- 4. Operating Modes -- 4. Cloud Computing -- 4. Key Management -- 4. Communications Error Effects -- 4. Secure Hardware Systems -- 4. Secure Software Systems -- 4. References -- 4. Introduction -- 5. Objectives -- 5. Transmitting Sampled Versus Continuous Data -- 5. Continuous Analog Transmission -- 5.

Multiplexed Analog Transmission -- 5. Pulse Code Modulation Transmission -- 5. Signal Types -- 5. Pulse Code Modulation Signals -- 5. Digital Signals -- 5. Bi-level Signals -- 5. Discrete Signals -- 5. Bandlimiting -- 5. Fourier Transforms -- 5. Transform Definition -- 5. Magnitude and Phase Spectra -- 5. Signal Bandwidth -- 5. Bandlimited Signals -- 5. Essential Bandwidth Definition -- 5.

Signal Bandlimiting Architecture -- 5. Sampling -- 5. Sampling Theorem -- 5. Oversampling the Nyquist Rate -- 5. Aliasing -- 5. Filter Design -- 5. Reasons for Filtering -- 5. Filter Types and Parameters -- 5. Filter Transfer Functions -- 5.

Ideal Filters -- 5. Butterworth Filters -- 5. Chebyshev Filters -- 5. Bessel Filters -- 5. Analog Filter Design Method -- 5. Low Pass Building Block -- 5. Filter Type Determination -- 5. Filter Order Determination -- 5. Resistor and Capacitor Selection -- 5. Sample LPF Design -- 5. Conversion to High-Pass Design -- 5.

Conversion to Band-Pass Design -- 5. Software Filter Design -- 5. Digital Filter Equivalents -- 5. Data Processing Filtering -- 5. Moving Average Filter -- 5. Moving Least Squares Filter -- 5. Quantization -- 5. Quantization Process -- 5. Commutation -- 5. Quantization Noise and Resolution -- 5. Quantization Signal-to-Noise Ratio -- 5. Total Transmitted Data -- 5. Sampling Hardware -- 5. Process Timing -- 5. Sample-and-Hold Amplifiers -- 5. Analog-to-Digital Converters -- 5.

Successive Approximation Converters -- 5. Flash Converters -- 5. Dual Conversion Flash Converters -- 5. Sigma-Delta Analog-to-Digital Conversion -- 5.

References -- 5. Introduction -- 6. Objectives -- 6. Background -- 6. Context -- 6. Data Link Layer Packaging -- 6. Commutation -- 6. Telemetry Frames -- 6. Minor Frame -- 6. Major Frame -- 6. Commutated Data -- 6. Supercommutated Data -- 6. Subframes and Subcommutated Data -- 6. Supersubcommutated Data -- 6. Frame Examples -- 6. Standard Parameters -- 6. Format Changes -- 6. Asynchronous Embedded Format -- 6. Tagged Data -- 6. Synchronization Codes -- 6. Telemetry Frame Design -- 6. General Factors -- 6.

Management and Accounting Information -- 6. Data Packaging -- 6. Packet Telemetry -- 6. Packet Assumptions -- 6. Protocol Data Unit Format -- 6. Packet Modes -- 6. Commutated Mode -- 6.

Entropy Mode -- 6. Virtual Channel Mode -- 6. Table Driven Format -- 6. Inter-Range Instrumentation Group Modifications -- 6. Data Networking Packets -- 6. Packet Formats -- 6. Data Servers -- 6. Data Throughput Issues -- 6. Telemetry Data Streaming -- 6. Command Processor Interface -- 6. Data Waveform Formatting For Transmission -- 6. General Structure -- 6. Data Randomizers -- 6. Data Format Specification -- 6. Data Format Generation -- 6.

Usage Characteristics -- 6. References -- 6. Introduction -- 7. Objectives -- 7. Synchronization Process -- 7. Carrier Synchronization -- 7. Bit Synchronization -- 7. The next phase of the course reviews the metal oxide semiconductor field effect transistor MOSFET and proceeds along the same path taken for the bipolar transistor circuits.

Computer semiconductor memory circuits are considered next. The cell array, memory addressing circuits, and sense amplifier designs are all examined in detail. This is followed by the related subject of programmable logic arrays, the final topic. The emphasis of the laboratory component of the course is to compare the performance of representatives of each class of circuits to computer simulations of the same circuits.

Parameters such as input-output voltage transfer characteristics, noise margins, and propagation delays are evaluated by building and measuring laboratory models. Most of the laboratory exercises require the student to evaluate a specified circuit, but the final exercise requires the student to design a circuit to meet a predefined set of specifications, then to prove that the design meets the requirements by measuring the circuit performance.

Students are required to write a formal engineering report detailing the results of each laboratory exercise. Field programmable device architectures and technologies; rapid prototyping using top down design techniques; quick response systems.

The objective of this course is to introduce the student to digital design using Field Programmable ICs, and to provide an understanding of the underlying technologies and architectures of these Integrated Circuits. The course begins by introducing design alternatives for modern electronic systems identifying and classifying alternative system solutions, and evaluating when particular design solutions are optimal.

A homework assignment requires the student to quantitatively evaluate the cost, complexity, packaging, and time-to-market issues for a complex system design specification. Next, the underlying Field Programmable Logic IC architectures and technologies are studied in detail.

The first is the Xilinx XCxl line, because of the target boards used in the CAD laboratory component for this class. The initial lab portions of the class help the students to specify their design using various forms of design entry tools and also allows them to see how their design map on to the underlying FPGA architecture. The students also learn the underlying algorithms used by the design software they use in their Labs.

Next, the systematic top-down method for specifying complex designs using VHDL is introduced. Students are given a supporting homework assignment to develop high-level behavioral models for a simple digital system to reinforce this segment of the course. VHDL behavioral synthesis is now introduced as a preferred path to go from high-level system behavior to actual implementation on the FPGA. The strengths and weaknesses of synthesis are discussed, as are the emerging CAD tool trends.

The final segment of the class covers special topics that identify current trends in digital system architecture and programmable logic design. These include such topics as partially reconfigurable architectures and dynamic reconfiguration techniques, system design for testability, and field programmable analog arrays. Applications of FPGAs in special purpose computing environments such as signal processing, Java acceleration and image processing are also introduced.

In the laboratory, student design project assignments explore larger and more complete system specifications of such things as controllers, CPU and memory design, and signal processing blocks. These assignments reinforce the lecture content as the students model, synthesize and implement their digital designs on the target Xilinx FPGA boards.

Introduction to computer architecture. Memory hierarchy and design, CPU design, pipelining, multiprocessor architecture. They will apply their knowledge of digital logic design to explore the high-level interaction of the individual computer system hardware components. Concepts of sequential and parallel architecture including the interaction of different memory components, their layout and placement, communication among multiple processors, effects of pipelining, and performance issues, will be covered.

Students will apply these concepts by studying and evaluating the merits and demerits of selected computer system architectures. Resource management in computer systems. Introduction to topics such as image formation, segmentation, feature extraction, matching, shape recovery, object recognition, and dynamic scene analysis.

The goal of computer vision is to make computers understand and interpret visual information. Computer vision systems bring together imaging devices, computers, and sophisticated algorithms for solving problems in areas such as industrial inspection, medicine, document analysis, autonomous navigation, and remote sensing.

The course involves both pedagogical written assignments and computer projects. The beginning of the course gives an overview of computer vision and introduces low level image analysis techniques for binary images. Binary vision systems are useful when the silhouette of imaged objects convey enough information to recognize them. Examples can be found in optical character recognition, chromosome analysis and recognition of industrial parts.

Moreover, many techniques developed for binary systems can be applied to gray level or color images. Next, the course covers image segmentation and contours. These topics are the foundation of most computer vision techniques. For an image to be correctly interpreted, it must be partitioned into regions that correspond to distinct objects or parts of objects.

First, region based techniques such as thresholding, split and merge, region growing and texture analysis are introduced. Next, edge based techniques using gradient and Laplacian operators are discussed. Finally, contour representations and curve approximations linking edges into region boundaries are studied. Next, depth from vision, with emphasis in stereo vision, is considered. Calculating distances to and among various points in the scene is important in many computer vision tasks such as inspection, robot manipulation, and autonomous navigation.

In this part of the course the geometry of stereo systems and how to obtain depth maps from stereo image pairs is studied. Also, alternative 3D imaging sensors such as laser based range finders and radars are discussed. Following stereo, the topic of computer vision is broaden to understand sequences of images over time. In this section techniques using information on spatial and temporal changes are used to design computer vision systems capable of coping with moving and changing objects, changing illumination and changing viewpoints.

Visual motion is important primarily for two reasons. First, motion is a very important cue to understand the scene structure. Second, biological systems do use motion to infer properties of the surrounding world with very little a priori knowledge. Finally, the topic of 3D object recognition is discussed. Object recognition entails two main issues: object identification and object localization.

Identification determines the objects being imaged while localization determines their position in the world and with respect to the sensors. This topic builds upon all the different techniques discussed until this point. Overview of digital image processing techniques and their applications; image sampling, enhancement, restoration, and analysis; computer projects. The beginning of the course gives an overview of digital image processing systems and digital image fundamentals.

During this unit, important elements of human visual perception are reviewed; these ideas help motivate many of the computer-based techniques described in subsequent units.

Also, the standard model for a digital image, in addition to the concepts of sampling and quantization, are described. Finally, basic topological concepts between digital image pixel are discussed. The next unit considers image transform analysis, with a primary focus on Fourier-based techniques. The one-dimensional Fourier transform is reviewed, and then two-dimensional Fourier transform analysis is discussed.

To bridge the gap from the continuous world to the digital world, the sampling theorem is introduced. Next, the Discrete Fourier Transform and its properties are described. Fourier-based filtering techniques, such as the ideal low-pass and Butterworth filters are then introduced. The Fast Fourier Transform is also discussed. The next unit discusses techniques for image enhancement and segmentation.

These techniques include point-based techniques based on histogram analysis. They also involve linear and nonlinear mask-based methods for noise reduction and region sharpening. Further, techniques of mathematical morphology, which involve an application of set-theoretic concepts to image processing, are described.

Finally, image segmentation methods, based on edge detection and thresholding, are described. The final unit considers the concept of image compression. Techniques for image encoding and decoding are discussed. A brief model of the encoding-decoding process is described. Next, compression techniques, such as run-length encoding and Huffman coding, are described.

Data transmission, encoding, link control techniques, network architecture, design, protocols, and multiple access. CMPEN Communication Networks 3 This course introduces students to fundamental concepts and principles underlying data communication networks, with an emphasis on the Internet and its five-layer architecture: the application, transport, network, link, and physical layers. The student learning these principles will gain knowledge that lasts long after today's network standards and protocols have become obsolete.

This course explores the fundamental concepts and engineering processes of wireless communication systems, sensors, and security algorithms through the design, implementation, and evaluation of next generation wireless network architectures, and network and cryptographic protocols. This course is intended as a senior level course for computational majors such as computer science and computer engineering since it covers hardware and software design concepts associated with wireless access, data transmission, and computational security, security models, and privacy in a broad range of settings.

The first part of the course studies programmatic, computational, and engineering issues associated with wireless systems and sensors at the physical protocol layer. Hardware, software, and engineering design considerations associated with MIMO, low latency, high reliability, and high data rate constraints will be analyzed. The next part of this course will introduce virtual machines, function virtualization, and network-slicing for constraint matching, resource scheduling, and mobility management at the data link and network protocol layers.

The final component of the course focuses on the security and privacy for wireless systems and sensors including models and algorithms. Upon completion of the course students will be able to critically analyze the design, implementation, and protocols associated with wireless systems and sensors and assess the computational security and privacy vulnerabilities associated with these systems.

Basic switching theory and design of digital circuits, including combinational, synchronous sequential, and asynchronous sequential circuits. Microprocessors: architecture, design, assembly language, programming, interfacing, bus structure, and interface circuits and their use in embedded systems. CMPEN Microprocessors and Embedded Systems 3 In this course students should learn about the operation and design of microprocessor-based systems, including both hardware and software aspects with an emphasis on real time control environments and embedded systems.

After completing the course, students should be able to develop, write and debug programs in a microprocessor's assembly language and use standard assembly language program development tools.

They should also be able to interpret and analyze basic microprocessor system hardware. This course is a senior level elective for students in computer engineering and computer science. The course requires the use of general department computing facilities consisting of UNIX workstations running the appropriate program development tools.

Design of digital systems using microprocessors. CMPEN Microcomputer Laboratory 3 This laboratory course provides senior students with both theory and practice in designing, implementing, and debugging microprocessor-based systems. Students are guided through a series of projects in which they design, develop, and implement all of the components in a microprocessor based single-board system.

After completing the course students will be able to design microprocessor based systems, including both software and hardware design. Students will also be able to use standard system design tools including standard laboratory equipment.

This course is a senior level elective for computer engineering majors. CMPEN is a prerequisite for this course. The course requires the use of a design laboratory including standard test equipment such as an oscilloscope, logic analyzer and signal generator as well as a PC with appropriate design software and a microprocessor or EPROM emulation system.

Introduce concepts, methods, and technology for effective functional verification of modern electronic systems. CMPEN Functional Verification 3 Verifying design correctness of increasingly complex system-on-chip designs poses a major challenge to the semiconductor industry. Functional or logic errors in a chip design that are not identified early in the design phase can dramatically increase a project's overall cost and schedule.

Further, design verification is consuming an ever-increasing portion of IC development time and cost.