This document appears to be notes from lessons on machines and mechanical advantage. It includes:
1. Definitions and examples of simple machines like inclined planes, screws, levers, and pulleys.
2. Concepts related to work, efficiency, and power calculations. Formulas and examples of calculating work, efficiency of machines, and power are provided.
3. Explanations of mechanical advantage and how it relates to the efficiency of machines. Examples are given of calculating mechanical advantage.
Compressive Oversampling for Robust Data Transmission in Sensor Networks - P...Zainul Charbiwala
http://nesl.ee.ucla.edu/document/edit/324
Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive perspective. Since many physical signals of interest are known to be sparse or compressible, employing CS, not only compresses the data and reduces effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors. In this paper, we propose that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. We show that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally cheaper than conventional erasure coding. We support our proposal through extensive performance studies.
Graphical Method of solving Linear Programing problems PP.pptxHailemariam
he graphical method is used to optimize the two-variable linear programming. If the problem has two decision variables, a graphical method is the best method to find the optimal solution. In this method, the set of inequalities are subjected to constraints.
In Mathematics, linear programming is a method of optimising operations with some constraints. The main objective of linear programming is to maximize or minimize the numerical value. It consists of linear functions which are subjected to the constraints in the form of linear equations or in the form of inequalities. Linear programming is considered an important technique that is used to find the optimum resource utilisation. The term “linear programming” consists of two words as linear and programming. The word “linear” defines the relationship between multiple variables with degree one. The word “programming” defines the process of selecting the best solution from various alternatives.
Linear Programming is widely used in Mathematics and some other fields such as economics, business, telecommunication, and manufacturing fields. In this article, let us discuss the definition of linear programming, its components, and different methods to solve linear programming problems.
Leading Robotics Research: SMAC Direct Drive Servo Motor Robotic FingerJohn Miewald
At the Automate 2015 show in Chicago, SMAC Moving Coil Actuators introduced a prototype version of the world's first direct drive servo motor robotic finger, acknowledged as a technological breakthrough by a leading European technical university. It is shown here operating a Samsung Galaxy touchscreen phone. SMAC expects an early 2016 release to be shortly followed by a robotic thumb and ultimately the first true functional robotic hand capable of reproducing work done by human hands.
Compressive Oversampling for Robust Data Transmission in Sensor Networks - P...Zainul Charbiwala
http://nesl.ee.ucla.edu/document/edit/324
Data loss in wireless sensing applications is inevitable and while there have been many attempts at coping with this issue, recent developments in the area of Compressive Sensing (CS) provide a new and attractive perspective. Since many physical signals of interest are known to be sparse or compressible, employing CS, not only compresses the data and reduces effective transmission rate, but also improves the robustness of the system to channel erasures. This is possible because reconstruction algorithms for compressively sampled signals are not hampered by the stochastic nature of wireless link disturbances, which has traditionally plagued attempts at proactively handling the effects of these errors. In this paper, we propose that if CS is employed for source compression, then CS can further be exploited as an application layer erasure coding strategy for recovering missing data. We show that CS erasure encoding (CSEC) with random sampling is efficient for handling missing data in erasure channels, paralleling the performance of BCH codes, with the added benefit of graceful degradation of the reconstruction error even when the amount of missing data far exceeds the designed redundancy. Further, since CSEC is equivalent to nominal oversampling in the incoherent measurement basis, it is computationally cheaper than conventional erasure coding. We support our proposal through extensive performance studies.
Graphical Method of solving Linear Programing problems PP.pptxHailemariam
he graphical method is used to optimize the two-variable linear programming. If the problem has two decision variables, a graphical method is the best method to find the optimal solution. In this method, the set of inequalities are subjected to constraints.
In Mathematics, linear programming is a method of optimising operations with some constraints. The main objective of linear programming is to maximize or minimize the numerical value. It consists of linear functions which are subjected to the constraints in the form of linear equations or in the form of inequalities. Linear programming is considered an important technique that is used to find the optimum resource utilisation. The term “linear programming” consists of two words as linear and programming. The word “linear” defines the relationship between multiple variables with degree one. The word “programming” defines the process of selecting the best solution from various alternatives.
Linear Programming is widely used in Mathematics and some other fields such as economics, business, telecommunication, and manufacturing fields. In this article, let us discuss the definition of linear programming, its components, and different methods to solve linear programming problems.
Leading Robotics Research: SMAC Direct Drive Servo Motor Robotic FingerJohn Miewald
At the Automate 2015 show in Chicago, SMAC Moving Coil Actuators introduced a prototype version of the world's first direct drive servo motor robotic finger, acknowledged as a technological breakthrough by a leading European technical university. It is shown here operating a Samsung Galaxy touchscreen phone. SMAC expects an early 2016 release to be shortly followed by a robotic thumb and ultimately the first true functional robotic hand capable of reproducing work done by human hands.
some of the links from the first page are messed up. I recommend you just go through it in order
I also meant cm squared on the pressure units, slide 23
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Have you ever heard that user-generated content or video testimonials can take your brand to the next level? We will explore how you can effectively use video testimonials to leverage and boost your sales, content strategy, and increase your CRM data.🤯
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Putting the SPARK into Virtual Training.pptxCynthia Clay
This 60-minute webinar, sponsored by Adobe, was delivered for the Training Mag Network. It explored the five elements of SPARK: Storytelling, Purpose, Action, Relationships, and Kudos. Knowing how to tell a well-structured story is key to building long-term memory. Stating a clear purpose that doesn't take away from the discovery learning process is critical. Ensuring that people move from theory to practical application is imperative. Creating strong social learning is the key to commitment and engagement. Validating and affirming participants' comments is the way to create a positive learning environment.
Kseniya Leshchenko: Shared development support service model as the way to ma...Lviv Startup Club
Kseniya Leshchenko: Shared development support service model as the way to make small projects with small budgets profitable for the company (UA)
Kyiv PMDay 2024 Summer
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Youtube – https://www.youtube.com/startuplviv
FB – https://www.facebook.com/pmdayconference
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