## Video of Wild Cat in Northern Minnesota

Quote of the Day

Hope without a plan is denial.

— Time management expert

Like me, a number of my coworkers have cabins in Northern Minnesota and most of us have cameras that record activity on our properties. A coworker came in the other day with this video that shows a wild cat going by one of his cameras. I am not sure what kind of cat it is – probably a lynx. This site is not far from the Canadian border. Maybe one of you out there can identify it?

## Probability That An Old President Does Not Finish Their Term

Quote of the Day

A man always has two reasons for the things he does -- a good one and the real one.

— J.P. Morgan. I have frequently have found this statement to be true.

## Introduction

Figure 1: Ronald Reagan, The Oldest US President at Inauguration (Wikipedia).

Ronald Reagan (Figure 1) was our oldest president at the time of inauguration  – 69 years 349 days old. The 2016 US presidential election is giving us a choice of two candidates that will be relatively old at inauguration: Donald Trump (70 years, 220 days), and Hilary Clinton (69 years, 86 days). Since US presidents often serve 2 terms, it is conceivable they we may have a 77- to 78-year old president in 2024. This fact makes me curious as to what is the likelihood that a 70 year-old's natural life will be long enough for them to serve one or two terms.

We can easily compute this probability using an actuarial life table. In this post, I will compute the probability that a 70-year old male or female will complete one or two 4-year terms.

## Background

The basic math behind the life table is well covered in the Wikipedia, and I will refer you there the details.

## Analysis

I have included an actuarial life table in Appendix A. The actual calculations are easily performed using a filtered pivot table version of the life table, which I show in Figure 2.

Figure 2: Calculation of the probability a 70 -year old will not live to be 74 or 78-years old.

This data is interesting:

• A 70-year old male has a 10% chance of dying by 74 and a 23% chance of dying by 78.
• A 70-year old female has a 7% chance of dying by 74 and a 17% chance of dying by 78.

## Conclusion

There is a ~23% chance that a 70-year old male president would not be able to complete 2 terms, which is about what I would have expected. There is ~17% chance that a 70-year old female president would not be able to complete 2 terms, which is higher than I would have expected.

### Appendix A: US Social Security Administration Life Table.

Table 1 is the 2013 life table used by the Social Security Administration (SSA). The analysis in this post consists of filtered pivot table version of this date.

Table 1: 2013 Actuarial Life Table from the SSA.
Age Male Prob of Death Males Alive Male Life Exp. Female Prob of Death Female Alive Female Life Exp.
0 0.006519 100000 76.28 0.005377 100000 81.05
1 0.000462 99348 75.78 0.000379 99462 80.49
2 0.000291 99302 74.82 0.000221 99425 79.52
3 0.000209 99273 73.84 0.000162 99403 78.54
4 0.000176 99252 72.85 0.000133 99387 77.55
5 0.000159 99235 71.87 0.000119 99373 76.56
6 0.000146 99219 70.88 0.000109 99361 75.57
7 0.000133 99205 69.89 0.000101 99351 74.58
8 0.000118 99192 68.9 0.000096 99341 73.58
9 0.000102 99180 67.9 0.000093 99331 72.59
10 0.000091 99170 66.91 0.000094 99322 71.60
11 0.000096 99161 65.92 0.0001 99312 70.60
12 0.000128 99151 64.92 0.000112 99303 69.61
13 0.000195 99138 63.93 0.000134 99291 68.62
14 0.000288 99119 62.94 0.000162 99278 67.63
15 0.000389 99091 61.96 0.000194 99262 66.64
16 0.000492 99052 60.99 0.000226 99243 65.65
17 0.000607 99003 60.02 0.000261 99220 64.67
18 0.000735 98943 59.05 0.000297 99194 63.68
19 0.000869 98870 58.09 0.000334 99165 62.70
20 0.001011 98785 57.14 0.000373 99132 61.72
21 0.001145 98685 56.2 0.000412 99095 60.75
22 0.001246 98572 55.27 0.000446 99054 59.77
23 0.001301 98449 54.33 0.000472 99010 58.80
24 0.001321 98321 53.4 0.000493 98963 57.82
25 0.00133 98191 52.47 0.000513 98915 56.85
26 0.001345 98060 51.54 0.000537 98864 55.88
27 0.001363 97928 50.61 0.000563 98811 54.91
28 0.001391 97795 49.68 0.000593 98755 53.94
29 0.001427 97659 48.75 0.000627 98697 52.97
30 0.001467 97519 47.82 0.000664 98635 52.01
31 0.001505 97376 46.89 0.000705 98569 51.04
32 0.001541 97230 45.96 0.000748 98500 50.08
33 0.001573 97080 45.03 0.000794 98426 49.11
34 0.001606 96927 44.1 0.000845 98348 48.15
35 0.001648 96772 43.17 0.000903 98265 47.19
36 0.001704 96612 42.24 0.000968 98176 46.23
37 0.001774 96448 41.31 0.001038 98081 45.28
38 0.001861 96277 40.38 0.001113 97979 44.33
39 0.001967 96097 39.46 0.001196 97870 43.37
40 0.002092 95908 38.53 0.001287 97753 42.43
41 0.00224 95708 37.61 0.001393 97627 41.48
42 0.002418 95493 36.7 0.001517 97491 40.54
43 0.002629 95262 35.78 0.001662 97343 39.60
44 0.002873 95012 34.88 0.001827 97182 38.66
45 0.003146 94739 33.98 0.002005 97004 37.73
46 0.003447 94441 33.08 0.002198 96810 36.81
47 0.003787 94115 32.19 0.002412 96597 35.89
48 0.004167 93759 31.32 0.002648 96364 34.97
49 0.004586 93368 30.44 0.002904 96109 34.06
50 0.005038 92940 29.58 0.003182 95829 33.16
51 0.00552 92472 28.73 0.003473 95524 32.27
52 0.006036 91961 27.89 0.003767 95193 31.38
53 0.006587 91406 27.05 0.004058 94834 30.49
54 0.00717 90804 26.23 0.004352 94449 29.62
55 0.007801 90153 25.41 0.004681 94038 28.74
56 0.008466 89450 24.61 0.00504 93598 27.88
57 0.009133 88693 23.82 0.0054 93126 27.01
58 0.009792 87883 23.03 0.005756 92623 26.16
59 0.010462 87022 22.25 0.006128 92090 25.31
60 0.011197 86112 21.48 0.006545 91526 24.46
61 0.012009 85147 20.72 0.007034 90927 23.62
62 0.012867 84125 19.97 0.007607 90287 22.78
63 0.013772 83042 19.22 0.008281 89600 21.95
64 0.014749 81899 18.48 0.009057 88858 21.13
65 0.015852 80691 17.75 0.009953 88054 20.32
66 0.017097 79412 17.03 0.01095 87177 19.52
67 0.018463 78054 16.32 0.01201 86223 18.73
68 0.019959 76613 15.61 0.013124 85187 17.95
69 0.021616 75084 14.92 0.01433 84069 17.18
70 0.023528 73461 14.24 0.015728 82864 16.43
71 0.025693 71732 13.57 0.017338 81561 15.68
72 0.028041 69889 12.92 0.019108 80147 14.95
73 0.030567 67930 12.27 0.021041 78616 14.23
74 0.033347 65853 11.65 0.023191 76961 13.53
75 0.036572 63657 11.03 0.025713 75177 12.83
76 0.040276 61329 10.43 0.028609 73244 12.16
77 0.044348 58859 9.85 0.03176 71148 11.50
78 0.048797 56249 9.28 0.035157 68888 10.86
79 0.053739 53504 8.73 0.03892 66467 10.24
80 0.059403 50629 8.2 0.043289 63880 9.64
81 0.065873 47621 7.68 0.048356 61114 9.05
82 0.073082 44484 7.19 0.054041 58159 8.48
83 0.08107 41233 6.72 0.060384 55016 7.94
84 0.089947 37890 6.27 0.067498 51694 7.42
85 0.099842 34482 5.84 0.075516 48205 6.92
86 0.110863 31040 5.43 0.084556 44565 6.44
87 0.123088 27598 5.04 0.094703 40796 5.99
88 0.136563 24201 4.68 0.106014 36933 5.57
89 0.151299 20896 4.34 0.118513 33017 5.17
90 0.167291 17735 4.03 0.132206 29104 4.80
91 0.18452 14768 3.74 0.147092 25257 4.45
92 0.202954 12043 3.47 0.163154 21542 4.13
93 0.222555 9599 3.23 0.180371 18027 3.84
94 0.243272 7463 3.01 0.198714 14775 3.57
95 0.263821 5647 2.82 0.217264 11839 3.34
96 0.283833 4157 2.64 0.235735 9267 3.12
97 0.302916 2977 2.49 0.25381 7083 2.93
98 0.320672 2075 2.36 0.271155 5285 2.76
99 0.336706 1410 2.24 0.287424 3852 2.60
100 0.353541 935 2.12 0.30467 2745 2.45
101 0.371218 605 2.01 0.32295 1909 2.30
102 0.389779 380 1.9 0.342327 1292 2.17
103 0.409268 232 1.8 0.362867 850 2.03
104 0.429732 137 1.7 0.384639 541 1.91
105 0.451218 78 1.6 0.407717 333 1.78
106 0.473779 43 1.51 0.43218 197 1.67
107 0.497468 23 1.42 0.458111 112 1.56
108 0.522341 11 1.34 0.485597 61 1.45
109 0.548458 5 1.26 0.514733 31 1.35
110 0.575881 2 1.18 0.545617 15 1.26
111 0.604675 1 1.11 0.578354 7 1.17
112 0.634909 0 1.04 0.613055 3 1.08
113 0.666655 0 0.97 0.649839 1 1.00
114 0.699987 0 0.9 0.688829 0 0.92
115 0.734987 0 0.84 0.730159 0 0.85
116 0.771736 0 0.78 0.771736 0 0.78
117 0.810323 0 0.72 0.810323 0 0.72
118 0.850839 0 0.67 0.850839 0 0.67
119 0.893381 0 0.61 0.893381 0 0.61

## Quick Look at a High-Power PoE Graph

Quote of the Day

Whatever you are, be a good one.

— Abraham Lincoln

## Introduction

Figure 1: Graphic From High-Power PoE Presentation.

I have been sitting in a meeting on a high power version of Power over Ethernet (PoE) known as IEEE 802.3bt. It supports 90 W of output power with a guarantee of 71 W at the load. During the talk, Figure 1 was discussed (my version of the chart). When I am given some mathematical information, I like to experiment with it to see if I understand what I am being told.

In this post, I am going to summarize some quick calculations that I did while sitting in the meeting that helped me understand how this proposed version of PoE transfers power over a data cable. The math shown is simple, but of a type that is very common for a working electrical engineer.

## Background

### Definitions

Power over Ethernet (PoE)
PoE is IEEE 802.3af/at which supports sending power and data over the same category 5e Ethernet cable, which contains four wire-pairs (i.e. 8 wires total). PoE is enormously popular because only one cable is required to network an Ethernet-fed device, which greatly reduces the cost and complexity of networking remote devices, like cameras. For the version of PoE discussed in this post, power is transmitted over two wire-pairs by applying a DC voltage between each pair (see Figure 1). Superimposing DC on the wire-pairs does not interfere with data transmission because Ethernet uses differential signaling.
Type 1 PoE
Type 1 PoE is an IEEE standard (802.3af) for transferring as much as 13 W over an Ethernet cable.
Type 2 PoE
Type 2 PoE is an IEEE standard (802.3at) for transferring as much as 25.5 W over an Ethernet cable. The standard is also known as  "PoE+".
Power Supplying Device (PSE)
A PSE is a device that provides power on an Ethernet cable.
Powered Device (PD)
A PD is a device powered by a PSE.
IEEE 803.3bt
A proposed version of PoE that can transmit as much as 71 W over an Ethernet cable. The extra power is achieved by using all four available wire pairs (8 wires total).

### Post Objective

My objective with this post is to show how a number of important features of the proposed PoE version can be derived from Figure 1.

## Analysis

### Key Graph Characteristics

Figure 1 is a simple graph:

• It is linear with a y-intercept of 90 W (the maximum allowed source power) and a slope of -0.19 W/m.
• The graph ends with a power of 71 W and a range of 100 m, which is the maximum reach guaranteed for 1000Base-T Ethernet.
• The power source have an output voltage that must be greater than 52 V with a load of 90 W.

We also need know that standard Category 5e (Cat 5e) Ethernet cable is composed of 4 pairs of 24 American Wire Gauge (AWG) wire.

### Calculations

#### Wire Resistance Utility Functions

Figure 2 shows the utility functions that I use to compute the resistance of annealed copper wire as a function of temperature, length, and gauge. I have used these functions for years  – they are based on curve fits of old Bellcore wire resistance data.

Figure 2: Utility Functions for Computing Wire Resistance as a Function of Temperature, Length, and Wire Gauge.

#### Resistance Model

Figure 3 shows the circuit model for this PoE circuit.

Figure 3: PoE Resistance Model. L is the total length of the wire, R is the total resistance of the wire, and λ is the 2-way resistance of the wire.

#### Maximum Supply Current (IMax) and Line Resistance Per Meter (λ)

Figure 4 shows how to use Ohm's law with Figure 1 to determine (a) the maximum current on the line, and (b) the two-way resistance of the line per meter, and (c) the wire gauge that meets this resistance requirement. As you would expect, 24 AWG wire meets the requirements for the PoE standard and is the wire gauge used in Cat 5e cable.

Figure 4: Resistivity Implied By Figure 1.

### Minimum Load Voltage

Figure 5 shows how we can compute the load voltage when the cable is carrying the maximum current over the maximum length cable.

Figure 5: Minimum Load Voltage Calculation.

This is the minimum voltage at which the load's power converter must operate.

### Cross-Check Calculations

Figure 6  shows some calculations that demonstrate that my results are internally consistent.

Figure 6: Cross-Check Calculations.

## Conclusion

I have always felt that the today's PoE power limits of 15.5 W (type 1) and 25.5 W (type 2) are not adequate for many applications involving MIMO wireless access points. This new standard will be a real boon to customers with wireless coverage issues.

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## Chromatic Dispersion with 10 Gigabit Optical Transports

Quote of the Day

The road of life is paved with flat squirrels who couldn’t make a decision.

— Source unknown. Indecision is fatal for squirrels. It is also the most destructive management impairment that I can think of.

## Introduction

Figure 1: Illustration of Chromatic Dispersion on a Glass Fiber. A single optical pulse is composed on a range of wavelengths. Because each color travels on the fiber with a slightly different speed, the wavelengths separate as the pulse travels down the fiber.

The Fiber-To-The-Home (FTTH) market is preparing to make the transition to a data rate of 10 Gigabits Per Second (Gbps) to and from the home. The current Gigabit PON (GPON) standard support 2.5 Gbps to the home and 1.25 Gbps from the home. One of the challenges we are facing with 10 Gbps is managing chromatic dispersion, which is an  important optical impairment (see Figure 1).

Transmitting information over a glass fiber requires that we use a laser to  modulate the power of the optical signal. To receive the optical signal with a minimum of errors, we need to provide the receiver with (a) an adequate amount of optical power and (2) most of the power for each bit must remain within its assigned bit time. Unfortunately, as the modulated signal travels on the fiber it encounters a number of impairments that reduce its power and limit its range. For PON systems, the most significant impairments are:

In this post, I will be showing how we model the effect of small amounts of dispersion as a power loss. We commonly refer to this power loss at the dispersion power penalty. I will also show how the need to limit the power penalty drives a critical laser parameter, the laser spectral width.

## Background

### My Quick Description of Chromatic Dispersion

For those who are looking for a quick description of dispersion, I will give you my "elevator pitch" on dispersion in fiber optic communications:

• Different wavelengths of light move at slightly different speeds along a fiber.
• Digital signals are sent on a fiber in the form of pulses of optical power.
• The pulses of optical power are generated by a laser, which produces produces optical power in a narrow range of wavelengths, usually on the order of 0.1 nm.
• As the pulses move down the fiber, the slower wavelengths of light eventually separate from the faster wavelengths of light.
• The separation of the wavelengths as they travel down the fiber causes the pulse to spread out. Eventually, the bits begin to merge together and become impossible to separate. Also, the difference in power between a logic 1 and logic 0 begins to reduce, making it impossible to accurately determine whether a time slot contains a 1 or 0.

### Definitions

Optical Impairment
Optical fiber is a very good transmission medium, but it is not perfect. It has a number of characteristics that limit its performance, which are referred to as impairments. The usually are broken down into linear and non-linear impairments. Our focus here is chromatic dispersion, which is a very important linear impairment. (Source)
Chromatic Dispersion
Dispersion is a physical phenomenon comprising the dependence of the phase or group velocity of a light wave in the medium on its propagation characteristics such as optical frequency (wavelength) or polarization mode. (Source: ITU G.989.2 standard)
Chromatic dispersion is the result of the different colors, or wavelengths, in a light beam arriving at their destination at slightly different times.  The result is a spreading in time of the on-off light pulses that convey digital information.  Chromatic dispersion is commonplace, as it is actually what causes rainbows – sunlight is dispersed by droplets of water in the air.  In fiber-based systems, an optical fiber, comprised of a core and cladding with differing refractive index materials, inevitably causes some wavelengths of light to travel slower or faster than others. Chromatic dispersion is usually modeled as a combination of material dispersion and waveguide dispersion. (Reference)
Laser Linewidth
A spectral line extends over a range of frequencies, not a single frequency (i.e., it has a nonzero linewidth). In addition, its center may be shifted from its nominal central wavelength. (Source)
Zero-Dispersion Wavelength
In a single-mode optical fiber, the zero-dispersion wavelength is the wavelength or wavelengths at which material dispersion and waveguide dispersion cancel one another. In all silica-based optical fibers, minimum material dispersion occurs naturally at a wavelength of approximately 1300 nm. (Source)

### Wavelengths Used

Figure 2 shows the wavelength plans for the various PON standards. The GPON (aka G-PON in Figure 2) is the most common PON type deployed in North America.

Figure 2: Good Summary of the Various PON Wavelength Plans. DS means downstream from the central office. US means upstream to the central office from the homes.  PtP means point-to-point and refers to wavelengths dedicated to specific customers. Video refers to the band reserved for NTSC RF video transmissions. (Source)

### Dispersion Power Penalty

There are a number of formulas that are used to model the loss of signal power caused by dispersion. For this post, I will be using Equation 1. I chose this formula because it is the most conservative of the commonly used models.

 Eq. 1 ${PP _D} = -5 \cdot \log \left( {1 - {{\left( {4 \cdot D \cdot L \cdot B \cdot {\sigma _\lambda }} \right)}^2}} \right)$

where

• PPD is the dispersion power penalty [dB].
• $\sigma _{\lambda}$ is the spectral standard deviation of the laser [nm].
• B is bandwidth of the signal [Hz].
• L is the length of the fiber [km].
• D is the dispersion constant of the fiber [ps/nm·km].

Equation 1 assumes that the engineer wishes to define the bit time as the period required to contain 95% of the pulse energy at the time of reception. There are lengths that will experience so much dispersion that you will not be able to contain 95% of the pulse energy within the assigned bit time. Mathematically, this means that the expression within Equation 1's logarithm will become zero or negative, which indicates no real solution.

I like to look at the terms in Equation 1 as shown in Figure 2. I think of dispersion as being a function of two terms: (1) a fiber plant dependent term and (2) a laser transport dependent term.

Figure 3: Breakdown of the Dispersion Power Penalty Formula.

Figure 3 shows a formula with five variables. In the case of G.989.2, the 10 Gbps portion of the standard specifies four of these numbers indirectly as follows:

• B = 10 Gbps

This is the transport data rate and is a fundamental system parameter.

• D · L  = 24 ps/nm · 20 km = 480 ps/km

I refer the product of the dispersion constant and the distance as the "dispersion load". It tells you how much delay variance per nm that your system can tolerate. For more information on the dispersion constant, see Appendix A.

• PPD = 0.25 dB (my allotment to dispersion from the total 1 dB power penalty)

There a number of optical impairments that reduce the effective power of the transport.

Given these values from the G.989.2 standard, we can determine our laser's maximum allowed spectral width – the objective of this post.

## Analysis

### Laser Spectra Width Requirement Derivation

Figure 4 shows how I can determine the required laser spectral width assuming the values for B, L · D, and PPD from the ITU specification. The laser spectral width is normally specified in terms of its Side-Mode Suppression Ratio (SMSR) value. We relate the SMSR value, Δλ,  to the σλ using the formula Δλ = 6.07 · σλ. Another common measure of spectral width, Full-Width at Half Maximum (FWHM), has a different conversion factor.

Figure 4: Laser Spectral Width Determination.

A laser with  Δλ=0.1 nm is readily obtainable.

### Variation of Power Penalty with Distance

Figure 5 shows how the power penalty for a laser with a 0.1 nm spectral width varies with distance.

Figure 5: Power Penalty Versus Distance.

## Conclusion

I was able to determine the required laser spectral width to meet the requirements in the NGPNO2 specification. I then used this spectral width to determine how the dispersion power penalty varies with distance.

## Appendix A: Dispersion Constant for SMF-28e Fiber

Figure 6 shows how to determine the dispersion constant SMF-28e using Sellmeier's formula. The model and its constants (S0 and λ0) are given in the SMF-28e datasheet.

Figure 6: Dispersion Constant Versus Wavelength for SMF-28e.

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## Using SUMPRODUCT to Evaluate Two-Variable Polynomial

Quote of the Day

Rub my knee coach – I just got kicked in the groin.

— Said by one of my brothers to a trainer while writhing in pain on a football field in front of thousands of people. He did not want everyone in the stadium to know what really happened.

## Introduction

Figure 1: Battery Capacity Chart from Innovative Battery Technologies. (Source)

I occasionally am asked to write Excel-based tools for customer use that have implementation restrictions because of security issues. The most common restriction is that macros cannot be used. This restriction makes sense because macros are an easy way for a hacker penetrate a system, however, macros also make complex algorithms available within Excel. In this post, I will show you how to use the SUMPRODUCT function to implement a polynomial curve fit and avoid the use of a macro.

One of the most common computation tasks that my customers face is estimating battery capacity based on the battery's temperature and discharge current. Figure 1 shows a example of the capacity curves for a typical lead-acid battery. Ten years ago, I chose to implement this function with an Excel spreadsheet that used a polynomial approximation for this function. An engineer today asked me to explain how my Excel implementation works, and I felt this would be a good topic for a post. This approach is implemented using SUMPRODUCT – no helper cells were required.

## Background

### Generating the Polynomial Approximation

For those curious about how I came up with the polynomial approximation, see this zip file with my Mathcad source, my Excel workbook example, and a PDF of the Mathcad source. The polynomial coefficient were generated using Mathcad's regression function.

### The Polynomial

Figure 2 shows the cubic, two-variable polynomial that I implemented in Excel. The x variable is temperature (°C) and the y variable is load current expressed in C-rate.

Figure 2: Cubic Polynomial Approximation for Battery Capacity Versus Temperature (x) and Load Current (y).

### SUMPRODUCT Implementation

Figure 3 shows how I evaluated the polynomial of Figure 2 using SUMPRODUCT. It may look complex, but I put each term on its own line to make understanding it easier. The key to understanding the implementation is to realize that you can (a) capture a range of values within braces, and (2) you can use the brace quantities as exponents.

Figure 3: Description of SUMPRODUCT Implementation.

I also should explain why I divide the function by 97.164%. I normally work with batteries that are specified to operate for 20 hours with a 0.05 C-rate load. I needed to adjust the function to ensure that I would have 100% capacity at a 0.05 C-rate load.

### Generating a Table of Capacity Values

We can illustrate the use of this formula by generating a two-dimensional data table of battery capacity values. Figure 4 shows the data table that I generated using the Excel formula of Figure 3

Figure 4: Date Table Example.

Ideally, Figure 4 would have had the same values as Figure 5, which was generated using a cubic spline routine of the same data used for determining my regression equation (Figure 2). As you can see, my regression formula is not perfect but it is more than adequate for estimating battery capacity.

Figure 5: Capacity Table of Interpolated Raw Data.

Figure 6 shows another view of the difference between my regression equation and the capacity curve (Figure 1). My regression formula (colored surface)  roughly follows the data from Figure 1 (round dots).

Figure 6: 3D Plot of Regression Surface and Raw Data (points).

## Conclusion

I often use polynomial approximations to implement empirical functions. While not always very accurate, they are simple to use, quick to implement, and usually accurate enough. In the case of a lead-acid battery, the battery-to-battery variation in capacity is larger than the error in my polynomial approximation.

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Posted in Batteries, Electronics | 2 Comments

## Open-Drain Comparator Circuit With Settable Trigger and Output Levels

Quote of the Day

If you don't stick to your values when they are being tested, they're not values; they're hobbies.

— Jon Stewart

## Introduction

Figure 1: An Open-Drain Schmitt Trigger Circuit with Settable Trigger and Output Levels.

I received a circuit design question from a reader who was asking how to design a comparator circuit with controllable trigger points and output levels. I have never encountered this requirement before, but it is a straightforward extension of circuits that I have covered before (example and example).

Figure 1 shows the circuit we will be working with. While simple as circuits go, you will soon see that the algebra can get quite impressive.

For those who want to work with my source, see this zip.

## Background

Let's arbitrarily selected some circuit requirements to use for an example:

• high supply voltage: VCC = 10 V
• output high voltage: VH = 8 V
• output low voltage: VL = 2 V
• rising hysteresis trigger level: ${{V}_{{TH\uparrow }}}=4\ \text{V}+0.4\text{ V}$
• falling hysteresis trigger level:${{V}_{{TH\downarrow }}}=4\ \text{V}-0.4\text{ V}$

## Analysis

### Equation Setup

Figure 2 shows how I applied Kirchhoff's voltage law to two circuit states: output voltage high (VO=VH) and output voltage level low (VO=VL).

Figure 2: Apply Kirchhoff's Voltage Law to High and Low Output Voltage Cases.

### Formulas for Resistances

Figure 3 shows how to solve for three of the resistor values (i.e. R2, R3, and R4) in terms of VH, VL, ${{V}_{{TH\uparrow }}}$, and ${{V}_{{TH\downarrow }}}$, which are set by specifications. Mathcad was not able to solve for R5 in a displayable form (i.e. too big) using these parameters, but it was able to obtain a reasonably small formula for R5 in terms of R2, R3, R4, VOL, and VL.

Figure 3: Solve For Resistance Formulas.

### Determine Component Values

Figure 4 shows how to determine the component values for this example.

Figure 4: Determine the Comparator Circuit's Component Values.

### LTSpice Modeling

Figure 5 shows how I used LTSpice to capture the schematic and component values for the comparator circuit of Figure 1. The simulation results are exactly as I would have expected. For you schematic artists out there, I know I should have made a fancier symbol for the LM393 – this will have to do because I only had a couple of hours to do all this work.

Figure 5: LTSpice Simulation of the Comparator Circuit.

## Conclusion

This is a good example of how I usually do circuit design work. I work to develop an analysis model and then move to a simulator once I have an intuitive feel for how the circuit works. I hope this answers the reader's question.

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## Janitor Math

Quote of the Day

To achieve great things, two things are needed; a plan, and not quite enough time.

— Leonard Bernstein. Similarly, Duke Ellington used to say "I don't need time – I need a deadline."

Figure 1: Typical Janitorial Paper Products.

I was working on a Saturday morning when the cleaning crew came in. On this morning, the owner of the cleaning company was with his crew. He stopped by my cube and wanted to speak for a few minutes about some cleaning issues he was seeing. His concerns centered on their increase in workload caused by a recent in the number of workers at our site.

He began the conversation with a statement something like:

You have added quite a few people lately. You have doubled the number of women and increased the number of men by about 10.

We have added a number of people lately, and I was surprised how accurately he knew the numbers. I was a bit curious about how he knew our staffing level and asked him how he figured the numbers of men and women out.

He told me that the number of paper products used by men and women in the bathroom makes the number of people in the building very predictable. He said that every six people using a bathroom means one extra carton of paper towels each month. Additionally, the usage rates of the different types of products vary between men and women. Thus, he was able to accurately estimate how many men and how many women we have added recently. He even told me where the engineers were from based on their toilet seat cover usage. I must admit, I was impressed.

Sometimes, these "rule of thumb" estimates can be uncannily accurate. For example, I once saw a government contract official get wide-eyed when a senior engineer determined a classified number by just making a few routine observations of unclassified information. Of course, he was not supposed to know that number, but a little math and physics gave it to him. This sort of thing happens all the time in the defense business. They even have a name for it – classification of compilations.

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## Interstellar Radio Communication Misfire

Quote of the Day

Do not let what you cannot do interfere with what you can do.

— John Wooden

## Introduction

Figure 1: RATAN-600 Radio Telescope. (Source)

There were an interesting series of news articles recently about the detection of a possible radio signal from the star HD164595. The actual detection occurred about a year ago (Figure 1 shows the instrument), but it came to the public's attention after an astronomer mentioned it in a recent presentation. Close inspection of the results indicate that the transmission was either from a Russian military satellite or electronic noise sources down on Earth.

These misfires occur occasionally. However, it did provide some interesting calculations of the incredible power required transmit a signal over interstellar distances. I have discussed the power required for a transmitter to receive a low-bandwidth, noise-limited signal in a previous post. For the discussion here, I will confirm some these calculations using the relatively powerful signal detected in this case.

## Background

The basic facts of the detection were:

• From the region of the sky near HD164595, which is 94 light-years away.
• The signal received had a level of 0.75 Jy (Jansky), which is a fairly strong signal.
• The receiver has a bandwidth of 1 GHz.

The calculation results that I wish to duplicate are described in this quote about how the signal could have been transmitted from HD164595:

(1) They decide to broadcast in all directions. Then the required power is 1020 watts, or 100 billion billion watts. That’s hundreds of times more energy than all the sunlight falling on Earth, and would obviously require power sources far beyond any we have.

(2) They aim their transmission at us. This will reduce the power requirement, but even if they are using an antenna the size of the 1000-foot Arecibo instrument, they would still need to wield more than a trillion watts, which is comparable to the total energy consumption of all humankind.

## Analysis

Figure 2 shows my calculations for the transmit power required from a (a) isotropic radiator – broadcast at all angles, and (b) a beam directed at Earth. The beam is assumed to have the angular extent as the Arecibo parabolic antenna (0.028°).

My answers are (1) 0.75·1020 W, and (2) 1.8 TW. Both values are consistent with the calculation results presented in the quote.

Figure 2: My Analysis Required for the Transmit Power Needed from HD164595.

## Conclusion

I must admit that I was skeptical when I first saw the article. It did not take very long for a more prosaic explanation to be found. At least I found some calculations that were interesting.

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Posted in Astronomy | 1 Comment

## View of Jupiter From Satellite Metis

Quote of the Day

Successful ... politicians are insecure and intimidated men. They advance politically only as they placate, appease, bribe, seduce, bamboozle or otherwise manage to manipulate the demanding and threatening elements in their constituencies.

Walter Lippmann (1955). Hmm … no change in politicians over the last 61 years.

## Introduction

Figure 1: Artist's Conception of the View From a Base on Metis. (Source)

The arrival of the Juno spacecraft at Jupiter has motivated me to take a look a closer look at the Jovian system. I was surprised to see that we have cataloged 67 moons, sixteen of which have been discovered since 2003 and are not yet named. One moon that was new to me is called Metis (Figure 2), which is Jupiter's innermost moon. It is very tiny and resides within Jupiter's main ring.

Figure 2: Metis Photograph from Galileo Spacecraft. (Source)

The Wikipedia's article on this moon had an interesting statement that I thought I would try to verify.

Because Metis orbits very close to Jupiter, Jupiter appears as a gigantic sphere about 67.9° in diameter from Metis , the largest angular diameter as viewed from any of Jupiter's moons. For the same reason only 31% of Jupiter's surface is visible from Metis at any one time, the most limited view of Jupiter from any of its moons.

I was able to confirm the 67.9° angular diameter, but I believe the 31% value is in error and should be 22%. I have posted a comment to the Wikipedia requesting that those folks double-check that number.

P.S. The Wikipedia has removed this statement from the Metis article because it could not be corroborated with a peer-reviewed reference.

## Analysis

### Geometry

Figure 3 shows the basic Jupiter viewing geometry from Metis. For information on how to compute the viewing area on a sphere in terms of the viewing angle θ,  see the Wikipedia article on spherical caps.

Figure 3: Visual Geometry of Metis to Jupiter. All dimensions to scale except that of Metis, which would be invisible at this scale.

### Calculations

Figure 4 shows my calculations that verify the Wikipedia's statement on Jupiter's viewing angle from Metis. I could not confirm their statement on percent viewable  area, but I believe that I understand where our calculations differ – I obtain their value if I fail to divide the viewing angle by half for the spherical cap area calculation.

I present an alternative form of the percent viewable area formula in Appendix A. You can find further discussion on this form on this web page.

Figure 4: Viewing Angle and Jupiter Viewing Area Calculations.

## Conclusion

I like to imagine looking up at night and seeing a sky full of a planet. It would be an incredible sight. Unfortunately, Metis is a tiny, cold world that is exposed to an enormous amount of radiation. I cannot imagine humans ever visiting there.

Figure 5 shows a beautiful rendering of the view of Jupiter from Metis.

Figure 5: View of Jupiter from Metis Using Space Engine. (Source)

## Appendix A: Alternative  Percent Viewable Area Formula

A Wikipedia editor commented that I could express the viewable area of a satellite using a simpler formula if I chose a different parameter than the angular diameter. Figure 6 shows how to derive a different form of the percent viewing area formula.

Figure 6: Alternative Form of the Percentage Viewable Area Formula.

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## Age of Presidents at Inauguration

Quote of the Day

Imagination is more important than knowledge. For knowledge is limited to all we now know and understand, while imagination embraces the entire world, and all there ever will be to know and understand.

— Albert Einstein

Figure 1: At 42, Teddy Roosevelt was the youngest person to serve as president. (Source)

While crawling around the Wikipedia looking for presidential information, I found a list of the ages at inauguration of the US presidents ordered from oldest to youngest. I threw Hillary Clinton and Donald Trump into the list (Table 1) to see where they would place – they are old by historic standards. In fact, Donald Trump would be the oldest ever.

As a check, I computed the inaugural ages of all the presidents using Excel and found that the Wikipedia was accurate, which I normally find true. The age calculation was complicated by the fact that the age system used in Excel does not work for ages before 1900. Fortunately, I found the excellent extended date add-in from Walkenbach, which allows you to work with the string representation of dates. For those of you who like to code,  Excel's VBA does not have the same date restrictions as its workbooks, so you can write your own macros that will work quite well.

Before I dive into the specifics of presidential ages at inauguration, let's look at a histogram of the inauguration ages of all the presidents (Figure 2). Observe that most presidents are between 50 and 60 years old.

Figure 2: Histogram of Presidential Ages.

Table 1 shows the age of all the presidents by their age in years and days, plus the ages of Hillary and Donald.

Table 1: List of the Inaugural Ages of the Presidents with Hillary and Donald Added.
President/Candidate Age at inauguration
Donald Trump 70 years, 220 days
Ronald Reagan 69 years, 349 days
Hillary Clinton 69 years, 86 days
William Henry Harrison 68 years, 23 days
James Buchanan 65 years, 315 days
George H. W. Bush 64 years, 222 days
Zachary Taylor 64 years, 100 days
Dwight D. Eisenhower 62 years, 98 days
Andrew Jackson 61 years, 354 days
John Adams 61 years, 125 days
Gerald Ford 61 years, 26 days
Harry S. Truman 60 years, 339 days
Grover Cleveland (2nd Inauguration) 59 years, 351 days
James Monroe 58 years, 310 days
James Madison 57 years, 353 days
Thomas Jefferson 57 years, 325 days
John Quincy Adams 57 years, 236 days
George Washington 57 years, 67 days
Andrew Johnson 56 years, 107 days
Woodrow Wilson 56 years, 66 days
Richard Nixon 56 years, 11 days
Benjamin Harrison 55 years, 196 days
Warren G. Harding 55 years, 122 days
Lyndon B. Johnson 55 years, 87 days
Herbert Hoover 54 years, 206 days
George W. Bush 54 years, 198 days
Rutherford B. Hayes 54 years, 151 days
Martin Van Buren 54 years, 89 days
William McKinley 54 years, 34 days
Jimmy Carter 52 years, 111 days
Abraham Lincoln 52 years, 20 days
Chester A. Arthur 51 years, 349 days
William Howard Taft 51 years, 170 days
Franklin D. Roosevelt 51 years, 33 days
Calvin Coolidge 51 years, 29 days
John Tyler 51 years, 6 days
Millard Fillmore 50 years, 183 days
James K. Polk 49 years, 122 days
James A. Garfield 49 years, 105 days
Franklin Pierce 48 years, 101 days
Grover Cleveland (1st Inauguration) 47 years, 351 days
Barack Obama 47 years, 169 days
Ulysses S. Grant 46 years, 311 days
Bill Clinton 46 years, 154 days
John F. Kennedy 43 years, 236 days
Theodore Roosevelt 42 years, 322 days

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Posted in Personal | 3 Comments