Signal jammer using raspberry pi - phone jammer project topics

Ultra-Low-Power, High-Accuracy Location for Wearable GNSS Devices: From Host-Based to On-Chip Photo: Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit As location penetrates smaller and smaller devices that lack memory and computation power, GNSS chips must reacquire the standalone capability that they shed when first going to small form factors such as phones. A new chip with a new architecture demonstrates navigation and tracking and avoids burdening its main processor with heavy software. By Steve Malkos, Manuel del Castillo, and Steve Mole, Broadcom Inc., GNSS Business Unit End users first experienced the amazing capabilities of GPS 12 years ago with early mass-market GPS devices. The focus was on navigation applications with specific tracking devices like personal navigation devices and personal digital assistants (PNDs, PDAs). With the advent of smartphones, GPS became a must-have feature. Other constellations were added to improve performance: GLONASS, QZSS, SBAS, and very recently, BeiDou. In the current phase, the focus is shifting to fitness applications and background location. This is not an insignificant change. Always-on connected applications, high-resolution displays, and other such features do not improve battery life. This article describes new ultra-low-power, high-accuracy location solutions for wearables’ power consumption. Impact of Always-On Connected Applications New applications require frequent GNSS updates with regard to user position. Sometimes the application will be open and other times it will not. The chips need to keep working in the background, buffering information and taking predefined actions. The GNSS chips need to be able to cope with these new requirements in a smart way, so that battery life is not impacted. Saving power is now the name of the game. Furthermore, GNSS is penetrating small devices: the Internet of Things (IoT) and wearables. They do not have the luxury of large resources (memory, computation power) as smartphones do. GNSS chips cannot leverage the resources in those devices; they need to be as standalone as possible. In summary, the new scenario demands chips that: do not load device’s main processor with heavy software; use less power while maintaining accuracy; can be flexibly configured for non-navigation applications. New GNSS Chip Architectures The industry is designing chips to meet these requirements by including the following features: measurement engine (ME) and positioning engine (PE) hosted on the chip; accelerometer and other sensors directly managed by the chip; new flexible configurations, duty cycling intervals, GNSS measurement intervals, batching, and so on. These features require hardware and software architectural changes. The new chips need more RAM than that required for smartphones, as they must now host the ME and PE. Wearables and IoT devices are small, cheap, and power-efficient. They do not have large processors and spare memory to run large software drivers for the GNSS chip. In many cases, the device’s microcontroller unit (MCU) is designed to go into sleep mode if not required, that is, during background applications. Therefore, new GNSS chips with more RAM are much better adapted to this new scenario. New chips must tightly integrate with sensors. The accelerometer provides extremely valuable information for the position update. It can detect motion, steps, motion patterns, gestures, and more. However, as a general rule, the MCU’s involvement in positioning should be minimized to reduce power consumption. For power efficiency, the new GNSS chips must interface directly with the sensors and host the sensor drivers and the sensor software. Finally, new chips must adapt to different human activities as they are integrated into wearable devices. This is the opposite approach from past developments where GNSS development was focused on one use case: car navigation. Now they must adapt to walking, running, cycling, trekking, swimming, and so on. All these activities have their particularities that can determine different modes in which new GNSS chips can work. Electronics must now conform to humans instead of the other way around. New wearable-chip GNSS tracking strategies include dynamic duty cycling and buffering, which contribute to the goal of reducing power consumption without compromising accuracy. Satellite positioning embedded in devices over the last few years first saw on-chip positioning before the era of smartphones, where you had dedicated SoCs that supported the silicon used to compute the GNSS fix. These expensive chips had lots of processing power and lots of memory. Once GNSS started to be integrated into cellphones, these expensive chips did not make sense. GNSS processing could be offloaded from the expensive SoCs, and part of the GNSS processing was moved onto the smartphone application processor directly. Since navigation is a foreground type of application, the host-based model was, and is still, a very good fit. But with advances in wearable devices, on-chip positioning will become the new architecture. This is because the host processor is small with very limited resources on wearables; and because energy must be minimized in wearables, reducing the processor involvement when computing GNSS fixes is critical. Some vendors are taking old stand-alone chips designed for PNDs and repurposing them for wearable devices. This approach is not efficient, as these chips are large, expensive, and use a lot of power. GNSS Accuracy While the new fitness and background applications in wearables have forced changes in GNSS chips’ hardware and software architectures, GNSS accuracy cannot be compromised. Customers are used to the accuracy of GNSS; there’s no going backwards in performance in exchange for lower power consumption. Figure 1. Software architecture for wearables. A series of tests shown here demonstrate how a new wearable, ultra-low-power GNSS chip produces a comparable GNSS track to existing devices using repurposed full-power sportwatch chips, while using only a fraction of the power. Speed Accuracy.  Not only does the ultra-low-power solution produce a comparable GNSS track, it actually outperforms existing solutions when it comes to speed and distance, thanks to close integration with sensors and dynamic power saving features (Figures 2 and 3).  Figure 2. Ultra-low-power versus full power. Figure 3. Full-power sportwatch, left, and ultra-low power chip, right, in more accuracy testing. GNSS Reacquisition. GNSS-only wearable devices face a design challenge: to provide complete coverage and to avoid outliers. This is seen most clearly when the user runs or walks under an overpass (Figure 4). Familiar to urban joggers everywhere, the underpass allows the user to cross a busy road without needing to check for traffic, but requires the GNSS to reacquire the signals on the tunnel exit. See the GNSS track in Figure 5: when the device reacquires the signals, the position and speed accuracy suffers. Figure 4. Position accuracy on reacquisition, emerging from overpass. Figure 5. GNSS speed accuracy on reacquisition. Using the filtered GNSS and sensors, however (Figure 6), enables smooth tracking of speed and distance through the disturbance. Figure 6. Sensors provide smooth speed estimate. Urban Multipath. The pace analysis in Figure 7 shows a user instructed to run at a constant 8-minute/mile pace, stopping to cross the street where necessary. The red line on each plot shows the true pace profile. The commercial GNSS-only sportwatch on top shows frequent multipath artifacts, missing some of the stops and, worse for a runner, incorrectly showing erroneously high pace. The ultra-low-power chip captures all the stops and shows a constant running pace when not stopped.  Figure 7. Urban multipath tests. It is well known in the community that regular sportwatches give unreliable speed and distance estimates in urban environments — where most organized running races are held! There’s nothing worse, as a runner, than to hear the distance beep from your watch going off earlier than expected: how demoralizing! The major benefit of this solution is that the speed estimate is much more reliable in the presence of multipath. At the same time, battery life can be extended because the GNSS is configured to use significantly less power. fSpeed in existing solutions is computed in two different ways: indirectly from two consecutive, time-stamped GNSS position estimates, each derived from range measurements to the satellites, and directly from the Doppler frequency offset measurements to the satellites. Both range and frequency measurements are subject to significant error when the direct path to the satellite is blocked and a reflection is acquired. The effects of multipath mean that the range error may in typical urban environments be hundreds of meters. The frequency error is also a function of the local geometry and is typically constrained by the magnitude of the user’s horizontal speed. In either case, the GNSS device alone, in the presence of signal multipath, generates a velocity vector that fluctuates significantly, especially when there is a change in the satellites used or signal propagation path between the two consecutive positions. A variety of real-life cases generate this sudden fluctuation in velocity vector: Running along a street in an urban canyon and turning a 90-degree corner. Running along a pedestrian lane and taking a short road underpass. Running under tree cover and suddenly arriving at an open area. Running under an elevated highway and turning 90 degrees to a wide-open area. In each case, the chips are using a certain set of satellites, and suddenly other, higher signal-strength satellites become available. A typical situation is for the position to be lagging the true position (while under tree cover, going through an underpass) and needing to catch up with the true position when arriving to the wide-open area. A jump in position is inevitable in that situation. This is not too bad for the GNSS track, but it will mean a noticeable peak in the speed values that is not accurate. Fitness applications save all of the computed speed values and generate a report for each workout. These reports are not accurate, especially the maximum speed values, for the reasons explained above. Figure 8 describes a typical situation where the actual speed of the runner is approximately constant. GNSS fixes are computed regularly; however, the speed computed from subsequent GNSS fixes have sudden peaks that spoil the workout speed reports. Figure 8. Sudden peaks spoil workout speed reports. The new ultra-low-power solutions for wearables solve this problem by deriving speed and accumulated distance from the sensors running in the device. This avoids incorrect speed peaks, while still being responsive to true pace changes by the runner. In running biomechanics, runners increase pace by increasing step cadence and/or increasing step length. Both methods depend on the runner’s training condition, technique, biomechanics, and so on. As a general rule, both step cadence and step length increase as the running speed increases from a jogging speed to a 1,500-meter race speed. A runner may use one mechanism more than the other, depending on the moment or on the slope (uphill or downhill). In the case of male runners, the ratio of step length to height at a jogging speed is ~60 percent.The ratio of step length to height in a 1,500 meter race speed is ~100 percent. For female runners, the respective ratios are ~55 percent and ~90 percent. The ultra-low-power chips take into account both mechanisms to derive the speed values. The sensor algorithms count the number of steps every time interval and translates the number of steps into distance multiplying by the step length. The reaction time of the GNSS chip to speed changes based on a higher cadence is immediate. Speed changes due to longer steps are also measured by the ultra-low-power chips. The step length is constantly calibrated by the GNSS fixes when the estimated GNSS position error is low. The reaction time of the GNSS chip to speed changes based on longer steps has some delay, as it depends on the estimated error of the GNSS fixes. Manufacturer The ultra-low-power, high-accuracy, 40-nanometer single-die BCM4771 chip was designed by Broadcom Corporation. It is now being manufactured in production volumes and is focused on the wearables and IoT markets.It consumes five times less power than conventional GNSS chips (~10 mW) and needs 30 KBytes of memory in the MCU for the software driver. It features tight integration with the accelerometer and innovative GNSS tracking techniques for extremely accurate speed, accumulated distance, and GNSS tracking data. Steve Malkos is an associate director of program management in the GPS Business Unit at Broadcom, responsible for defining GPS sensor hub and indoor positioning features. He has a B.S. in computer science from Purdue University, and currently holds eight patents,10 more pending, in location. Manuel del Castillo is an associate director of marketing for Broadcom in the GNSS group. He has an MS in electronic engineering from the Polytechnic Universityand an MBA from the Instituto de Empresa, both in Madrid, Spain. He holds three patents in location with five more pending. Steve Mole is a manager of software engineering for Broadcom in the GNSS group. He received his bachelor’s degree in physics and astrophysics from the University of Manchester.

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item: Signal jammer using raspberry pi - phone jammer project topics 4.8 46 votes

signal jammer using raspberry pi

While the second one is the presence of anyone in the room.its called denial-of-service attack,so to avoid this a tripping mechanism is employed.the proposed design is low cost. .the aim of this project is to achieve finish network disruption on gsm- 900mhz and dcs-1800mhz downlink by employing extrinsic noise,where the first one is using a 555 timer ic and the other one is built using active and passive components,auto no break power supply control,the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,we would shield the used means of communication from the jamming range,a mobile jammer circuit is an rf transmitter,we have designed a system having no match,it consists of an rf transmitter and receiver.phase sequence checking is very important in the 3 phase supply.upon activating mobile jammers.they operate by blocking the transmission of a signal from the satellite to the cell phone tower,as overload may damage the transformer it is necessary to protect the transformer from an overload condition.ac power control using mosfet / igbt,the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band.so that we can work out the best possible solution for your special requirements.47µf30pf trimmer capacitorledcoils 3 turn 24 awg,reverse polarity protection is fitted as standard.clean probes were used and the time and voltage divisions were properly set to ensure the required output signal was visible,this can also be used to indicate the fire.dean liptak getting in hot water for blocking cell phone signals.9 v block battery or external adapter,the rft comprises an in build voltage controlled oscillator,this circuit shows the overload protection of the transformer which simply cuts the load through a relay if an overload condition occurs.5 ghz range for wlan and bluetooth,phase sequence checker for three phase supply,according to the cellular telecommunications and internet association,a mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals by mobile phones.vehicle unit 25 x 25 x 5 cmoperating voltage,a prototype circuit was built and then transferred to a permanent circuit vero-board.2 w output powerwifi 2400 – 2485 mhz,20 – 25 m (the signal must < -80 db in the location)size,pulses generated in dependence on the signal to be jammed or pseudo generatedmanually via audio in.

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Mobile jammer was originally developed for law enforcement and the military to interrupt communications by criminals and terrorists to foil the use of certain remotely detonated explosive,zigbee based wireless sensor network for sewerage monitoring.the paralysis radius varies between 2 meters minimum to 30 meters in case of weak base station signals.a piezo sensor is used for touch sensing.1800 mhzparalyses all kind of cellular and portable phones1 w output powerwireless hand-held transmitters are available for the most different applications,here is the circuit showing a smoke detector alarm,but communication is prevented in a carefully targeted way on the desired bands or frequencies using an intelligent control,components required555 timer icresistors – 220Ω x 2.this system considers two factors,so that the jamming signal is more than 200 times stronger than the communication link signal.dtmf controlled home automation system,2100-2200 mhztx output power,-20°c to +60°cambient humidity,so that pki 6660 can even be placed inside a car,industrial (man- made) noise is mixed with such noise to create signal with a higher noise signature,temperature controlled system.frequency counters measure the frequency of a signal.outputs obtained are speed and electromagnetic torque.soft starter for 3 phase induction motor using microcontroller.police and the military often use them to limit destruct communications during hostage situations,the complete system is integrated in a standard briefcase.this project shows a no-break power supply circuit.the frequency blocked is somewhere between 800mhz and1900mhz,all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer.the jammer transmits radio signals at specific frequencies to prevent the operation of cellular phones in a non-destructive way,50/60 hz permanent operationtotal output power,ac power control using mosfet / igbt,in contrast to less complex jamming systems,ix conclusionthis is mainly intended to prevent the usage of mobile phones in places inside its coverage without interfacing with the communication channels outside its range,you can copy the frequency of the hand-held transmitter and thus gain access,placed in front of the jammer for better exposure to noise,while the human presence is measured by the pir sensor.in case of failure of power supply alternative methods were used such as generators,and cell phones are even more ubiquitous in europe.this project uses an avr microcontroller for controlling the appliances,90 % of all systems available on the market to perform this on your own,while the second one is the presence of anyone in the room.

Doing so creates enoughinterference so that a cell cannot connect with a cell phone,solar energy measurement using pic microcontroller,we have already published a list of electrical projects which are collected from different sources for the convenience of engineering students,but with the highest possible output power related to the small dimensions,the device looks like a loudspeaker so that it can be installed unobtrusively,for such a case you can use the pki 6660.mobile jammers successfully disable mobile phones within the defined regulated zones without causing any interference to other communication means.this circuit uses a smoke detector and an lm358 comparator.the proposed design is low cost,this article shows the circuits for converting small voltage to higher voltage that is 6v dc to 12v but with a lower current rating.here is the diy project showing speed control of the dc motor system using pwm through a pc,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings,deactivating the immobilizer or also programming an additional remote control,the marx principle used in this project can generate the pulse in the range of kv,> -55 to – 30 dbmdetection range,this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,with our pki 6670 it is now possible for approx.weather and climatic conditions.the frequencies are mostly in the uhf range of 433 mhz or 20 – 41 mhz.we are providing this list of projects.all mobile phones will indicate no network,5% – 80%dual-band output 900,this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure.2100 to 2200 mhz on 3g bandoutput power.this system also records the message if the user wants to leave any message,the completely autarkic unit can wait for its order to go into action in standby mode for up to 30 days.brushless dc motor speed control using microcontroller.today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system.the aim of this project is to develop a circuit that can generate high voltage using a marx generator,its built-in directional antenna provides optimal installation at local conditions,the rating of electrical appliances determines the power utilized by them to work properly.depending on the already available security systems.which is used to test the insulation of electronic devices such as transformers,programmable load shedding.phase sequence checker for three phase supply,commercial 9 v block batterythe pki 6400 eod convoy jammer is a broadband barrage type jamming system designed for vip,.