Most large modern buildings have active ventilation built in, meaning that electric fans drive the air through the building. The airflow direction is usually fixed at construction time. However, if the wind happens to blow from the opposite direction to the ventilation flow, then the fans require extra energy to counter the wind. On the other hand, if the wind agrees with the airflow in the building, then the fans may not need to be run at all. To save electricity, a building could have a wind direction sensor (a weather vane) on the roof connected to a switch that reverses the ventilation fans, so that the fans always pump air in approximately the same direction as the wind. If the wind is strong enough, a wind speed sensor (a small windmill or windsock) on the roof could stop the ventilation fans altogether.
The tradeoff for this adaptive ventilation system is the initial fixed construction cost and the ongoing maintenance of the weather vane, windsock and controller of the fans. All the extra components of the system (relative to the current unidirectional ventilation) are cheap and robust, so the both the fixed cost and the maintenance should be negligible.
Current ventilation systems have differently shaped air inlets and outlets in the rooms, which suggests that the system requires a particular airflow direction. In this case, adaptive ventilation may be much more expensive than the current ones, because the ventilation shafts and air vents need to be doubled. To avoid the need to build twice as many shafts and vents, have just the air inlets and outlets of the whole building switch roles with the wind direction. The rest of the system can remain unidirectional when the valves from the building’s inlet and outlet to the rest of the system switch appropriately. The air inside the building can then move in the opposite direction of the wind some of the time. In this case, the electricity saving is only realised if the building is sufficiently airtight, which is the case for modern highrises that have unopenable windows. If the air is allowed to move through the building independently of the ventilation and the wind is opposite the airflow in the system, then the fans have to overcome the air pressure difference like in the current systems. This wastes electricity.
My uninsulated apartment building went from too cold to too hot in about a week, which is normal in Canberra. People have started to open the windows in the stairwell in addition to their apartment windows. The timing of the opening seems a bit misguided – people open the windows in the morning. During daytime, the air outside is warmer than the air inside the stairwell, but during the night the outside air is colder. To state the obvious: to cool down the building, open the windows for the night and close them for the day. Currently the opposite seems to happen, although I counter this trend by closing the windows in the morning when I notice them open.
In general, if you want the building cooler and the outside air is colder than the inside, then open the windows, but if the outside is warmer, then close them. If you want the building warmer and the outside air is colder than the inside, then close the windows, but if the outside is warmer, then open them. This could easily be automated with temperature sensors outside and inside the building connected to a thermostat and small electric motors opening and closing the windows. Such a system would save some of the heating and cooling costs of the building.
There may be non-temperature reasons to open and close the windows, for example to let smell out of the stairwell or to keep insects from coming in. The second reason is not relevant for my building, because all windows have bugscreens and the exterior doors have a gap an inch wide under them, which the insects can easily use to get in.
Tethered balloons are cheaper to install than watchtowers of a similar height, although the day-to-day running cost of a balloon may be higher. Another benefit of a balloon is that its location can be changed much easier than a watchtower’s. A balloon’s advantage over flying drones is the cheaper initial price and running cost.
Cameras and sensors are sufficient for surveillance, so no people are needed to fly the balloon or even be near it (or at the top of a watchtower). The cameras can be powered from the ground, with the electrical cable doubling as one of the tethers, or by solar panels on the balloon, if these can be made light enough. Using infrared cameras, the balloons can help detect forest fires, allow farmers to watch their herd and see predators or large pests (kangaroos, wild donkeys, horses or camels) entering their land.
One possible limitation of a balloon is that in stormy regions it can be blown against the ground and rupture. The tethers can be made strong enough that the balloon does not fly away even in a hurricane, but the tethers are flexible, so cannot push the balloon away from the ground.
Because the balloon is distant from people, it can be filled with cheap hydrogen, despite hydrogen being explosive when mixed with air. The only limitation is if a fire or explosion of the balloon while it is in the air would cause significant economic or environmental damage. Examples are using the balloon for forest fire surveillance in fire-prone regions (burning bits of balloon may fall to the ground), or for watching an oil refinery. The risk of the balloon’s explosion on or close to the ground can be minimised by having an emergency mechanism detect when the balloon loses altitude and vent or explode the balloon while it is still high in the air. For the initial launch and subsequent landing for repairs, the balloon can be temporarily filled with helium or hot air. A thin hose from the ground is needed anyway to replenish the gas in the balloon that is slowly but steadily leaking out. The hose allows replacing the gas in the balloon with a different one.
Electrolysis equipment is probably not light enough to float attached to the balloon, so the balloon cannot produce its own hydrogen from the water vapour collected from the air. If the balloon has a power cable from the ground, then it might as well have a gas hose also. Again, a hose can double as a tether.
Australia would benefit from an app or website for reporting parking and traffic violations (Singapore has such a website) and rating drivers. It would make police work easier, and the greater probability of getting caught would deter illegal parking and dangerous driving. To prevent frivolous reports from overloading the system, people should make the report under their own name, which requires proving their identity to get an account on the app. Proving identity online is easy in countries with a national ID system like Estonia, but may require more red tape in Australia.
The app should allow uploading proof of the violation, for example a photo of an illegally parked vehicle or a dashcam video of someone’s dangerous driving. There should also be an option of uploading a signed statutory declaration describing the crime. In summary, the app should make it as easy as possible to prosecute a violator, so it should follow legal procedure and standards of evidence as much as possible.
The current system of calling the police non-emergency number to report small infringements is slow and cumbersome. For example if the answerer of the call does not understand the address, or the problem does not have a clear address (e.g. a car parked in the middle of a nature park), then it takes time and frustration to explain the place at which the law is being broken. An app could easily solve the address issue by allowing automatic location tracking. The current system of reporting by phone also has no way for a caller to provide evidence that someone is breaking the law.
Privacy laws in Australia are sometimes unreasonably strict. Even emergency services cannot see the location of the mobile phone from which they receive a call (https://www.acma.gov.au/theACMA/emergency-call-service-faq-i-acma) Such draconian privacy laws may prevent the uploading of proofs of violations, e.g. photos of illegally parked vehicles. Statutory declarations testifying to someone’s lawbreaking probably do not infringe on the lawbreaker’s privacy, so do not bring legal trouble to the person reporting the violation. Uploading declarations could be used as a first step to make the app useful for prosecution.
The app could also allow positive feedback, i.e. praising polite drivers. If this feedback is verifiable, because the users of the app have proved their identity, then a person applying for a driving job (bus, taxi, lorry) could use a good rating on the app to prove being a safe driver. This would be a selling point in the job interview.
Philosophically, policing anything means that the community agrees to impose punishments for certain behaviours. This sanctioning may be delegated to specialised workers like police officers, judges, prison wardens. The app for reporting violations could be used for distributed policing instead, meaning that anyone in the community can use the app to check the past feedback on others who they interact with. Then the community members can respond in the interaction according to the feedback they see, for example avoid trusting someone with who has been repeatedly reported for lawbreaking. Such a verifiable feedback system then rewards good past behaviour and punishes the breaking of social norms.
Falls are a major cause of hospitalisation in the elderly and people with impaired balance or strength. A fall may cause a vicious cycle: the bad experience leads to a fear of falling, which makes people avoid exercise. Not exercising leads to worse balance and muscle condition. Weakness and a lack of balance cause more falls.
To prevent falls, people should train their sense of balance and their stabilising muscles, but in a way that does not risk injury via falls during training. One device that would allow practising balance while preventing falling over is a rigid wide-flared skirt attached above a person’s centre of gravity (the attachment could be almost under the armpits). The hem of the skirt would be above the ground when the body is upright, but its edge would touch the ground if the body tilts too much in any direction. Support from the rigid skirt would then prevent further tipping in that direction. The lack of support in a central position (and for slight tilts around it) allows practising balance, for example by standing on one leg and trying to stay upright. The principle is the same as for helper wheels (training wheels) on childrens’ bicycles, which are off the ground while the bike is in a central position, but touch the road and stop too great a tilt to the side once the bike tips away from the centre. Other analogies to the rigid skirt are hands-free crutches pointing in all directions simultaneously, or a walking frame that surrounds the body, as opposed to being pushed in front.
The advantage of the skirt for fall prevention over crutches or a walking frame is that the skirt is hands-free. The advantage over a fixed training frame, or somewhat slack ropes tied to the upper body that also prevent a fall, is that the skirt moves with the person. This makes training easier by allowing walking and jogging.
The skirt can be home-made from many materials, such as tent poles or bamboo sticks tied or duct taped to a belt at the top and a hula hoop at the bottom. Using modern materials such as carbon fiber ski poles can make the skirt light, yet strong and rigid.
Of course the rigid skirt looks strange and attracts notice if not too many people are using it. On the one hand, the skirt does not have to be used in public if in-home training is enough. On the other hand, the first walking frame or the first crutches must also have looked strange to bystanders, but are now accepted mobility aids that almost nobody reacts negatively or even curiously to.
For using the skirt on the street, one problem is the wide-flared base (about 2m in diameter) that makes it difficult to pass other pedestrians. One (expensive) solution is to make the skirt out of sticks that can be moved independently and add a robotic controller that keeps the skirt narrow if the body is upright, but when the tilt angle becomes large enough, flares the skirt out in the direction of the tilt to stop the fall. Flaring the skirt means moving the sticks outward and lengthening them.
An electric car drives into a charging station. The driver pushes a button to unlock the battery compartment hatch on the rear bumper. The hatch springs open, which is detected by a camera of the station. A robotic arm swings into motion and, guided by cameras, radar or ultrasound, latches on to well-marked standardised handles on the rear of the battery. The arm pulls out the 300kg, 1×2 metre battery from underneath the floor of the car and slides it onto a conveyor belt. The belt moves the battery to one side and brings up a new battery, which the robotic arm picks up and slides back into the car’s battery compartment. The driver pushes a button to close and lock the battery compartment and drives off. The whole charging process takes less than a minute – significantly faster than filling up a gasoline-powered car.
Due to the weight and size of an electric car’s battery, a robotic arm is probably necessary. It is also faster and more precise than a human.
The usage history of the battery should be recorded securely, in order to make users pay for its depreciation, not just the electricity they used. Blockchain may be useful for keeping track of usage, which is needed to deter the moral hazard of using the battery inappropriately and not paying for the damage, or swapping it for a cheaper alternative before having it changed back to a standard one in a charging station.
The compartment in which the battery is has to be water-tight and locked (like the trunk or hood of a car) to prevent theft. The compartment should also be unlockable remotely by the owner or other authorised person, in case the car is self-driving and has no humans in it.
Current noise-cancelling headphones deal well with predictable noise that has a short period of repetition, for example engine rumbling or the hum of an air-conditioner or fan. Unpredictable noise is of course difficult to cancel – the headphones would have to detect the new sound and produce the opposite wave of air pressure quicker than the human ear can detect the new sound. This is theoretically possible, because the sound reaches the outside of the headphone before it reaches the inside of the ear, but may not be feasible at the current technology level.
What is possible, but not done, at least by the Sony MDR-1000x headphones I have, is cancelling predictable noise with a longer period of repetition. Specifically, the beeping sound of trucks reversing has a period of 1-2s and is very predictable, but the headphones do not cancel it at all. It seems that a tweak of the noise-prediction algorithm could fix this – no need to invoke machine learning or anything more complicated. The headphones would just have to keep track of the sounds reaching them in the last few seconds and look for simple repeating patterns. Then these patterns can be predicted and cancelled. Currently the headphones seem to predict only based on the last half-second or less, so any longer repetitions of sound are not taken into account.
Some birdsong is repeated beeping, similar to the signal of trucks reversing, but of course slightly less predictable. This bird-noise could conceivably also be cancelled, although if the gaps between the beeps vary, then the first small length of time during an unexpectedly early beep would be difficult. Similarly, if the length of the beeps varies, then a beep that stops unexpectedly early would be over-cancelled (headphones produce a sound that is detectable on the background of silence).
To help the headphones recognise new noise patterns, the user can press a button when an undesirable sound is heard, and release the button when the sound stops. The algorithm can compare the button presses to its sound-recording in the same time interval, which would help it identify the start and end of the noise that needs to be cancelled. Sometimes humans are better at detecting complex patterns than a computer, in which case this user input to the headphones would speed up the identification of new forms of noise.
A restaurant chain can collect data on what food people like by examining the plates collected from the tables – the more leftovers given the size of the dish, the less popular the food. However, looking at the plates and entering the data takes time. It would be much faster to automate the process. For example, there could be a small conveyor belt for dirty dishes brought back from the eating area. The dishes would be weighed to record the amount of leftovers before scraping and washing. To detect which food was left over, one option is that a camera above the belt photographs the leftovers and then a computer tries to identify the food. This is a complicated machine vision and machine learning problem. A simpler option is to serve different dishes on plates with different shapes, or patterns such as lines and circles that are easily distinguished by computer. Then the plate identifies the dish for the camera, similarly to colour-coded plates identifying the price at sushi-train restaurants.
Even less costly in terms of computation (and without any camera requirement) would be to put RFID tags or other remote-id technology in plates. Each dish would have to be served on a plate with a dish-specific RFID, so the returned plates can be exactly matched to the food served on them. Each plate becomes more costly, but not by much, because RFID tags are cheap.
A single restaurant could also collect data on leftovers, but a chain of restaurants would get a larger dataset faster, thus useful information sooner on which dishes to keep and which to discontinue.
The accelerometers in phones can detect vibrations, such as when the car that the phone is in drives through a pothole. The GPS in the phone can detect the location and speed of the car. An app that connects the jolt, location and speed (and detects whether the phone is in a moving car based on its past speed and location) can automatically measure the quality of the road. The resulting data can be automatically uploaded to a database to create an almost real-time map of road quality. The same detection and reporting would work for bike paths.
Perhaps such an app has already been created, but if not, then it would complement map software nicely. Drivers and cyclists are interested in the quality of the roads as well as the route, time and distance of getting to the destination. Map software already provides congestion data and takes traffic density into account when predicting arrival time at a destination. Road quality data would help drivers select routes to minimise damage to vehicles (and the resulting maintenance cost) and to sensitive cargo. This would be useful to trucking and delivery companies, and ambulances.
A less direct use of data on road quality collected by the app is in evaluating the level of local public services provided (one aspect of the quality of local government). Municipalities with the same climate, soil and traffic density with worse roads are probably less well run. For developing countries where data on governance quality and spending is difficult to get, road quality may be a useful proxy. The public services are correlated with the wealth of a region, so road quality is also a proxy for poverty.
Some of the downsides of automatic transmission in cars are that it does not anticipate hills or overtaking, and does not respond to slippery conditions appropriately. The technology that could enable the transmission to anticipate hills or overtaking is already available and incorporated in some cars, namely GPS, maps and sensors that look ahead of the car. If the map data includes altitude, then the location and movement direction of the car on the map predicts the slope that the car will be on in the near future. This information could be sent to the automatic transmission to enable it to shift gears in anticipation of a hill. A forward-looking sensor that has a range of a few hundred metres can also see a hill if the road does not curve too much. The sensor data could also be sent to the transmission. Similarly, a sensor could detect the nearing of the car in front and shift to a lower gear to prepare to accelerate for overtaking.
Slippery conditions can be predicted using the car’s thermometer, perhaps with the addition of a humidity sensor, or detected using a wheel slip sensor. This information could also be sent to the computer controlling the automatic transmission, to prevent it from spinning the wheels too fast when there is little grip. The GPS or forward-looking sensor could also tell whether the car is moving relative to the landscape. Comparing the movement data with the wheel spinning speed reveals whether the wheels are slipping.