物件導向軟體之架構(Object-Oriented Software Construction)探討

It is shown that, particularly for terrestrial cellular telephony, the interference-suppression feature of CDMA (code division multiple access) can result in a many-fold increase in capacity over analog and even over competing digital techniques. A single-cell system, such as a hubbed satellite network, is addressed, and the basic expression for capacity is developed. The corresponding expressions for a multiple-cell system are derived. and the distribution on the number of users supportable per cell is determined. It is concluded that properly augmented and power-controlled multiple-cell CDMA promises a quantum increase in current cellular capacity. >

On the capacity of a cellular CDMA system

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

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A Break in the Clouds: Towards a Cloud Definition

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

IEEE 802.11ac amendment enhances WLAN throughput by exploiting spatial diversity of antennas in MU-MIMO downlink transmission. However, in the existing framework ample network resources remain underutilized in uplink transmission due to single user communication. In this paper, we have proposed an efficient Access Point controlled MAC (A-MAC) protocol that enables concurrent multiple uplink transmissions for different priority traffic classes. Performance measurement shows that network throughput of A-MAC is 150\% higher than the throughput of single uplink transmission in existing IEEE 802.11ac protocol. We have achieve the highest payload throughput of 64\% when the number of nodes $N_{sum}=8$ and $\lambda=138$ packet/s at uniformly varying packet arrival rates for all STAs. As the number of nodes increases, the network throughput gradually decreases but the packet handling capacity of the network increases. The peak network capacity of 1200 packets/s is achieved with $N_{sum}=16$ and $\lambda=75$ packets/s for all STAs. Further increase of the number of nodes results in decreasing both network throughput and packet handling capacity of the network. We have also evaluated the performance metrics of our proposed A-MAC protocol under non-uniformly varying packet arrival rates for a fixed number of nodes $N_{sum}=24$. The analysis shows that when the network has higher intensity of low priority traffic, the aggregate packet handling capacity of the network is 1400 packets/s. However, as the intensity of high priority traffic increases, the aggregate packet handling capacity of the network decreases to 1000 packets/s. The results show that the more the higher priority packets arrive, the faster the network becomes unstable.

Access Point Controlled MAC (A-MAC) Protocol for Uplink Multi-User Transmission in IEEE 802.11ac

We describe a wireless sensor network with a medium access control (MAC) protocol that supports wireless recharging of nodes. The MAC protocol uses round-robin scheduling with 1-limited service for simplicity. Nodes that are running out of energy may request a recharge through a radio-frequency pulse. To reduce the likelihood of energy outage, we propose a collaborative protocol in which nodes that hear another node's recharge request will repeat it until the master node responds with a recharge pulse. Performance of the proposed protocol is evaluated using a probabilistic energy expenditure model. Our results indicate that the collaborative protocol can ensure uninterrupted network operation under a widely varying range of parameter values.

A polling MAC with reliable RF recharging of sensor nodes

Radio-frequency (RF) recharging can extend maintenance-free operation of wireless sensor networks However, the period between recharging is limited by the distance between the most distant sensor node and the master, which sends out recharging pulses To increase this period, we propose a zoning scheme in which nodes are logically grouped into circular zones centered at the master so that nodes in a given zone send their data to their neighbors in the next closer zone, which act as relays We describe and analyze a polling Medium Access Control (MAC) protocol that supports zoning and relaying through a probabilistic model of the energy depletion process and a queueing model of the packet transmission process Our results indicate that zoning extends the time interval between recharge pulses and leads to equalization of node lifetimes, but limits the available data transmission bandwidth as well

Zoning and Relaying-Based MAC Protocol With RF Recharging

Pair programming has received a lot of attention from both industry and academia, but most paper focus on its technical aspects, while its business value has received much less attention.  In this paper, we focus on the business aspects of pair programming, by using a number of software development related met rics, such as pair speed advantage, module breakdown structure  of the software and project value discount rate, and augmenting them by taking into account the cost of change after the initial product release and inherent non-linearity of the discount rate curves. The proposed model allows for a more realistic estimation of the final project value, and the results of System Dynamics simulations demonstrate some useful insights for software development management.

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Analyzing the Cost and Benefit of Pair Programming Revisited

We consider the problem of maintaining the prescribed event sensing reliability in a network formed by two 802.15.4 sensor clusters (child and parent). In order to maximize cluster lifetime, both clusters have redundant sensors which are periodically sent to sleep through distributed activity management. The clusters are connected through a bridge which also takes on the role of the coordinator in one of the clusters. In order to maximize the network lifetime, the lifetime of both clusters should be nearly the same, but the increased contention caused by the bridge reduces the lifetime of the parent cluster. We model this problem analytically and derive the probability distribution of the network lifetime. We also derive the expression for node count in the parent cluster that compensates for the interaction with the bridge. The use of this technique ensures that both clusters have nearly the same lifetime, with all the nodes dying within a short time period centered around the mean lifetime.

Vojislav B. Misic

Papers

Access Point Controlled MAC (A-MAC) Protocol for Uplink Multi-User Transmission in IEEE 802.11ac

A polling MAC with reliable RF recharging of sensor nodes

Zoning and Relaying-Based MAC Protocol With RF Recharging

Analyzing the Cost and Benefit of Pair Programming Revisited

Network lifetime equalization in interconnected 802.15.4 clusters