Radiation Detection and Detectors

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Chapter 18. Detection and Locking As I pointed out in Chapter 17 when discussing Oracle's implicit locking mechanism during a transaction, just because you lock a resource before updating it does not prevent someone else from corrupting your update with his or her own update. As a matter of fact, database locks unto themselves do not solve the problem of multiuser data access integrity. Instead, you as the programmer are responsible for employing a methodology that will prevent application users from overwriting each other's data. In this chapter, we'll look into the problem of multiuser update integrity and at how you can use locks with detection (a pessimistic approach) or update detection (an optimistic approach) to ensure the integrity of data in a multiuser application. First, we'll examine the locking options available when utilizing an Oracle database. Then we'll review the reasons why locks alone don't solve the update integrity problem. We'll continue by exploring detection techniques, that is, detecting that a change has taken place outside the current session and transaction. Next, we'll discuss several pessimistic, high-contention approaches to solving the problem of maintaining data integrity. Finally, we'll discuss an optimistic approach. Since there's a popular notion that locking alone ensures data integrity, let's start by examining Oracle's locking mechanisms in order to debunk this notion. 18.1 Oracle's Locking Mechanisms Oracle provides three locking mechanisms. The first is the implicit locking that automatically takes place when you execute an INSERT, UPDATE, or DELETE statement. The second is the ability to lock rows for an update by first selecting the desired rows using the FOR UPDATE clause in a SELECT statement. The third is the LOCK TABLE command. Let's review implicit locking first. 18.1.1 Implicit Locking As we discussed in Chapter 17, if you execute an INSERT, UPDATE, or DELETE statement for a particular row, then the database implicitly locks that row until you commit or roll back the current transaction. This means that if you perform DML on a table with a primary key constraint or unique index, and you are not in auto-commit mode, another user in another session with its own transaction can see the database as it existed before you started your transaction. You may be thinking to yourself, "Well, that's good, then they can't step all over my data." But you're wrong. All implicit locking does is prevent the second user from updating the row in question until your transaction ends. At that point, her update can overwrite any changes you made without her ever knowing that you've made them. If you, as the first user, insert a new row, a second user inserting, updating, or deleting a row with the same primary key or unique index value will wait indefinitely until you end your transaction. At that time, if the second user is inserting, her insert will fail with a primary key constraint violation. However, if the second user is updating or deleting, her update or delete will be successful. If you update a particular row instead of inserting a row, then a second user updating or deleting the same row will once again wait indefinitely until your transaction ends. And finally, if you delete a row, then a second user updating or deleting the same row will wait until your transaction ends, at which point, her update or delete will succeed, but it will succeed without affecting any rows. In none of these instances will the second user have any indication that your actions had changed the row between the time when your SQL statement's implicit lock took place and the time when the second user's statement executed. No detection at all! This lack of Radiation Detection and Detectors Radiation Detection and Detectors Bởi: OpenStaxCollege It is well known that ionizing radiation affects us but does not trigger nerve impulses Newspapers carry stories about unsuspecting victims of radiation poisoning who fall ill with radiation sickness, such as burns and blood count changes, but who never felt the radiation directly This makes the detection of radiation by instruments more than an important research tool This section is a brief overview of radiation detection and some of its applications Human Application The first direct detection of radiation was Becquerel’s fogged photographic plate Photographic film is still the most common detector of ionizing radiation, being used routinely in medical and dental x rays Nuclear radiation is also captured on film, such as seen in [link] The mechanism for film exposure by ionizing radiation is similar to that by photons A quantum of energy interacts with the emulsion and alters it chemically, thus exposing the film The quantum come from an α-particle, β-particle, or photon, provided it has more than the few eV of energy needed to induce the chemical change (as does all ionizing radiation) The process is not 100% efficient, since not all incident radiation interacts and not all interactions produce the chemical change The amount of film darkening is related to exposure, but the darkening also depends on the type of radiation, so that absorbers and other devices must be used to obtain energy, charge, and particle-identification information 1/5 Radiation Detection and Detectors Film badges contain film similar to that used in this dental x-ray film and is sandwiched between various absorbers to determine the penetrating ability of the radiation as well as the amount (credit: Werneuchen, Wikimedia Commons) Another very common radiation detector is the Geiger tube The clicking and buzzing sound we hear in dramatizations and documentaries, as well as in our own physics labs, is usually an audio output of events detected by a Geiger counter These relatively inexpensive radiation detectors are based on the simple and sturdy Geiger tube, shown schematically in [link](b) A conducting cylinder with a wire along its axis is filled with an insulating gas so that a voltage applied between the cylinder and wire produces almost no current Ionizing radiation passing through the tube produces free ion pairs that are attracted to the wire and cylinder, forming a current that is detected as a count The word count implies that there is no information on energy, charge, or type of radiation with a simple Geiger counter They not detect every particle, since some radiation can pass through without producing enough ionization to be detected However, Geiger counters are very useful in producing a prompt output that reveals the existence and relative intensity of ionizing radiation (a) Geiger counters such as this one are used for prompt monitoring of radiation levels, generally giving only relative intensity and not identifying the type or energy of the radiation 2/5 Radiation Detection and Detectors (credit: TimVickers, Wikimedia Commons) (b) Voltage applied between the cylinder and wire in a Geiger tube causes ions and electrons produced by radiation passing through the gas-filled cylinder to move towards them The resulting current is detected and registered as a count Another radiation detection method records light produced when radiation interacts with materials The energy of the radiation is sufficient to excite atoms in a material that may fluoresce, such as the phosphor used by Rutherford’s group Materials called scintillators use a more complex collaborative process to convert radiation energy into light Scintillators may be liquid or solid, and they can be very efficient Their light output can provide information about the energy, charge, and type of radiation Scintillator light flashes are very brief in duration, enabling the detection of a huge number of particles in short periods of time Scintillator detectors are used in a variety of research and diagnostic applications Among these are the detection by satellitemounted equipment of the radiation from distant galaxies, the analysis of radiation from a person indicating body burdens, and the detection of exotic particles in accelerator laboratories Light from a scintillator is converted into electrical signals by devices such as the photomultiplier tube shown schematically in [link] These tubes are based on the photoelectric effect, which is multiplied in stages into a cascade of electrons, hence the name photomultiplier Light entering the photomultiplier strikes a metal plate, ejecting an electron that is attracted by a positive potential difference to the next plate, giving it enough energy to eject two or more electrons, and so on The final output current can be made proportional to the energy of the light entering the tube, which is in turn proportional to the energy deposited ... Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown parameters such as signal amplitude, signal phase, or noise power. When the statistical distribution of x under a hypothesis depends on unknown (nuisance) parameters the hypothesis is called a composite hypothesis. To decide between the null and alternative hypotheses one might apply a high threshold to the sensor output x and make a decision that the signal is present if and only if the threshold is exceeded at some time within [0,T]. The engineer is Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown parameters such as signal amplitude, signal phase, or noise power. When the statistical distribution of x under a hypothesis depends on unknown (nuisance) parameters the hypothesis is called a composite hypothesis. To decide between the null and alternative hypotheses one might apply a high threshold to the sensor output x and make a decision that the signal is present if and only if the threshold is exceeded at some time within [0,T]. The engineer is Hero, A. “Signal Detection and Classification” Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton: CRC Press LLC, 1999 c  1999byCRCPressLLC 13 Signal Detection and Classification Alfred Hero University of Michigan 13.1 Introduction 13.2 Signal Detection TheROCCurve • DetectorDesignStrategies • LikelihoodRatio Test 13.3 Signal Classification 13.4 The Linear Multivariate Gaussian Model 13.5 Temporal Signals in Gaussian Noise Signal Detection: Known Gains • Signal Detection: Unknown Gains • Signal Detection: Random Gains • Signal Detection: Single Signal 13.6 Spatio-Temporal Signals Detection: Known Gains and Known Spatial Covariance • Detection: Unknown Gains andUnknown SpatialCovariance 13.7 Signal Classification Classifying Individual Signals • Classifying Presence of Multi- ple Signals References 13.1 Introduction Detection and classification arise in signal processing problems whenever a decision is to be made among a finite number of hypotheses concerning an observed waveform. Signal detection algo- rithms decide whether the waveform consists of “noise alone” or “signal masked by noise.” Signal classification algorithms decide whether a detected signal belongs to one or another of prespecified classes of signals. The objective of signal detection and classification theory is to specify systematic strategies for designing algorithms which minimize the average number of decision errors. This theory is grounded in the mathematical discipline of statistical decision theory where detection and classification are respectively called binary and M-ary hypothesis testing [1, 2]. However, signal pro- cessing engineers must also contend with the exceedingly large size of signal processing datasets, the absence of reliable and tractible signal models, the associated requirement of fast algorithms, and the requirement for real-time imbedding of unsupervised algorithms into specialized software or hardware. While ad hoc statistical detection algorithms were implemented by engineers before 1950, the systematic development of signal detection theory was first undertaken by radar and radio engineers in the early 1950s [3, 4]. This chapter provides a brief and limited overview of some of the theory and practice of signal detection and classification. The focus will be on the Gaussian observation model. For more details and examples see the cited references. c  1999 by CRC Press LLC 13.2 Signal Detection Assume that for some physical measurement a sensor produces an output waveform x ={x(t) : t ∈ [0,T]} over a time interval [0,T]. Assume that the waveform may have been produced by ambient noise alone or by an impinging signal of known form plus the noise. These two possibilities are called the null hypothesis H and the alternative hypothesis K, respectively, and are commonly written in the compact notation: H : x = noise alone K : x = signal + noise. The hypotheses H and K are called simple hypotheses when the statistical distributions of x under H and K involve no unknown NANO EXPRESS Open Access Effective harvesting, detection, and conversion of IR radiation due to quantum dots with built-in charge Kimberly Sablon 1 , Andrei Sergeev 2 , Nizami Vagidov 2 , Andrei Antipov 2 , John Little 1 and Vladimir Mitin 2* Abstract We analyze the effect of doping on photoelectron kinetics in quantum dot [QD] structures and find two strong effects of the built-in-dot charge. First, the built-in-dot charge enhances the infrared [IR] transitions in QD structures. This effect significantly increases electron coupling to IR radiation and improves harvesting of the IR power in QD solar cells. Second, the built-in charge creates potential barriers around dots, and these barriers strongly suppress capture processes for photocarriers of the same sign as the built-in-dot charge. The second effect exponentially increases the photoelectron lifetime in unipolar devices, such as IR photodetectors. In bipolar devices, such as solar cells, the solar radiation creates the built-in-dot charge that equates the electron and hole capture rates. By providing additional charge to QDs, the appropriate doping can significantly suppress the capture and recombination processes via QDs. These improvements of IR absorption and photocarrier kinetic s radically increase the responsivity of IR photodetectors and photovoltaic efficiency of QD solar cells. Keywords: quantum dot, infrared photodetector, solar cell, photoresponse, doping, potential barrier, capture processes Introduction One of the main goals for the next generation of infrared [IR] imaging systems and solar cell photovoltaic devices is to increase the photoresponse to IR radiation [1]. To enhance the IR photoresponse, it is necessary to (1) improve electron coupling to IR radiation and (2) increase the photocarrier lifetime, i.e., to suppress recombination losses. However, it is not easy to increase IR absorption without enhancement of recombination losses because by introducing electron levels, which provide strong IR tran- sitions, we inevitably create additional channels for inverse processes that increase recombination losses. This trade-off between IR absorption and recombination processes are well understood for a number of technolo- gies and corresponding materials. For example, in the early 1960s, semiconductors with impurities which provide electron levels inside a semiconductor bandgap and induce IR transitions from localized impurity states to conducting states re ceived significant attention. However, midgap impurities drastically enhance the recombination processes, i.e., the Shockley-Read-Hall recombination, and deteriorate the photovoltaic conversion efficiency [2,3]. To accommodate the solar spectrum and utilize its IR portion, modern photovoltaic technology mainly employs multi-junction cells with dif ferent bandgaps [4]. In these devices, each p-n junction cell is designed to effectively harvest solar energy within a certain spectral window close to the bandgap. According to theoretical modeling, in a multi-junction solar cell with five or more junctions, the ultimate photovoltaic efficiency may exceed 70%. How- ever, current technology enables only triple-junction cells (Ge-substrate junction-InGaAs-AlInGaP) with the maxi- mum conversion efficiency of approximately 42% for con- centrator cells. Strong technological limitations are caused by the need for lattice m atch, thermal expansion match, ... energy of the radiation 2/5 Radiation Detection and Detectors (credit: TimVickers, Wikimedia Commons) (b) Voltage applied between the cylinder and wire in a Geiger tube causes ions and electrons... the object 4/5 Radiation Detection and Detectors Radioactive Dating Game Section Summary • Radiation detectors are based directly or indirectly upon the ionization created by radiation, as are.. .Radiation Detection and Detectors Film badges contain film similar to that used in this dental x-ray film and is sandwiched between various absorbers to