I’m Walking Here! INNOVATION INSIGHTS with Richard Langley OVER THE YEARS, many philosophers tried to describe the phenomenon of inertia but it was Newton, in his Philosophiæ Naturalis Principia Mathematica, who unified the states of rest and movement in his First Law of Motion. One rendering of this law states: Every body continues in its state of rest, or of uniform motion in a straight line, unless it is compelled to change that state by forces impressed upon it. Newton didn’t actually use the word inertia in describing the phenomenon, but that is how we now refer to it. In his other two laws of motion, Newton describes how a force (including that of gravity) can accelerate a body. And as we all know, acceleration is the rate of change of velocity, and velocity is the rate of change of position. So, if the acceleration vector of a body can be precisely measured, then a double integration of it can provide an estimate of the body’s position. That sounds quite straightforward, but the devil is in the details. Not only do we have to worry about the constants of integration (or the initial conditions of velocity and position), but also the direction of the acceleration vector and its orthogonal components. Nevertheless, the first attempts at mechanizing the equations of motion to produce what we call an inertial measurement unit or IMU were made before and during World War II to guide rockets. Nowadays, IMUs typically consist of three orthogonal accelerometers and three orthogonal rate-gyroscopes to provide the position and orientation of the body to which it is attached. And ever since the first units were developed, scientists and engineers have worked to miniaturize them. We now have micro-electro-mechanical systems (or MEMS) versions of them so small that they can be housed in small packages with dimensions of a few centimeters or embedded in other devices. One problem with IMUs, and with the less-costly MEMS IMUs in particular, is that they have biases that grow with time. One way to limit these biases is to periodically use another technique, such as GNSS, to ameliorate their effects. But what if GNSS is unavailable? Well, in this month’s column we take a look at an ingenious technique that makes use of how the human body works to develop an accurate pedestrian navigation system — one whose accuracy has been checked using drone imagery. As they might say in New York, “Hey, I’m walking (with accuracy) here!” Satellite navigation systems have achieved great success in personal positioning applications. Nowadays, GNSS is an essential tool for outdoor navigation, but locating a user’s position in degraded and denied indoor environments is still a challenging task. During the past decade, methodologies have been proposed based on inertial sensors for determining a person’s location to solve this problem. One such solution is a personal pedestrian dead-reckoning (PDR) system, which helps in obtaining a seamless indoor/outdoor position. Built-in sensors measure the acceleration to determine pace count and estimate the pace length to predict position with heading information coming from angular sensors such as magnetometers or gyroscopes. PDR positioning solutions find many applications in security monitoring, personal services, navigation in shopping centers and hospitals and for guiding blind pedestrians. Several dead-reckoning navigation algorithms for use with inertial measurement units (IMUs) have been proposed. However, these solutions are very sensitive to the alignment of the sensor units, the inherent instrumental errors, and disturbances from the ambient environment — problems that cause accuracy to decrease over time. In such situations, additional sensors are often used together with an IMU, such as ZigBee radio beacons with position estimated from received signal strength. In this article, we present a PDR indoor positioning system we designed, tested and analyzed. It is based on the pace detection of a foot-mounted IMU, with the use of extended Kalman filter (EKF) algorithms to estimate the errors accumulated by the sensors. PDR DESIGN AND POSITIONING METHOD Our plan in designing a pedestrian positioning system was to use a high-rate IMU device strapped onto the pedestrian’s shoe together with an EKF-based framework. The main idea of this project was to use filtering algorithms to estimate the errors (biases) accumulated by the IMU sensors. The EKF is updated with velocity and angular rate measurements by zero-velocity updates (ZUPTs) and zero-angular-rate updates (ZARUs) separately detected when the pedestrian’s foot is on the ground. Then, the sensor biases are compensated with the estimated errors. Therefore, the frequent use of ZUPT and ZARU measurements consistently bounds many of the errors and, as a result, even relatively low-cost sensors can provide useful navigation performance. The PDR framework, developed in a Matlab environment, consists of five algorithms: Initial alignment that calculates the initial attitude with the static data of accelerometers and magnetometers during the first few minutes. IMU mechanization algorithm to compute the navigation parameters (position, velocity and attitude). Pace detection algorithm to determine when the foot is on the ground; that is, when the velocity and angular rates of the IMU are zero. ZUPT and ZARU, which feed the EKF with the measured errors when pacing is detected. EFK estimation of the errors, providing feedback to the IMU mechanization algorithm. INITIAL ALIGNMENT OF IMU SENSOR The initial alignment of an IMU sensor is accomplished in two steps: leveling and gyroscope compassing. Leveling refers to getting the roll and pitch using the acceleration, and gyroscope compassing refers to obtaining heading using the angular rate. However, the bias and noise of gyroscopes are larger than the value of the Earth’s rotation rate for the micro-electro-mechanical system (MEMS) IMU, so the heading has a significant error. In our work, the initial alignment of the MEMS IMU is completed using the static data of accelerometers and magnetometers during the first few minutes, and a method for heading was developed using the magnetometers. PACE-DETECTION PROCESS When a person walks, the movement of a foot-mounted IMU can be divided into two phases. The first one is the swing phase, which means the IMU is on the move. The second one is the stance phase, which means the IMU is on the ground. The angular and linear velocity of the foot-mounted IMU must be very close to zero in the stance phase. Therefore, the angular and linear velocity of the IMU can be nulled and provided to the EKF. This is the main idea of the ZUPT and ZARU method. There are a few algorithms in the literature for step detection based on acceleration and angular rate. In our work, we use a multi-condition algorithm to complete the pace detection by using the outputs of accelerometers and gyroscopes. As the acceleration of gravity, the magnitude of the acceleration ( |αk| ) for epoch k must be between two thresholds. If (1) then, condition 1 is (2) with units of meters per second squared. The acceleration variance must also be above a given threshold. With (3) where is a mean acceleration value at time k, and s is the size of the averaging window (typically, s = 15 epochs), the variance is computed by: . (4) The second condition, based on the standard deviation of the acceleration, is computed by: . (5) The magnitude of the angular rate ( ) given by: (6) must be below a given threshold: . (7) The three logical conditions must be satisfied at the same time, which means logical ANDs are used to combine the conditions: C = C1 & C2 & C3. (8) The final logical result is obtained using a median filter with a neighboring window of 11 samples. A logical 1 denotes the stance phase, which means the instrumented-foot is on the ground. EXPERIMENTAL RESULTS The presented method for PDR navigation was tested in both indoor and outdoor environments. For the outdoor experiment (the indoor test is not reported here), three separate tests of normal, fast and slow walking speeds with the IMU attached to a person’s foot (see FIGURE 1) were conducted on the roof of the Institute of Space Science and Technology building at Nanchang University (see FIGURE 2). The IMU was configured to output data at a sampling rate of 100 Hz for each test. FIGURE 1. IMU sensor and setup. (Image: Authors) FIGURE 2. Experimental environment. (Image: Authors) For experimental purposes, the user interface was prepared in a Matlab environment. After collection, the data was processed according to our developed indoor pedestrian dead-reckoning system. The processing steps were as follows: Setting the sampling rate to 100 Hz; setting initial alignment time to 120 seconds; downloading the IMU data and importing the collected data at the same time; selecting the error compensation mode (ZARU + ZUPT as the measured value of the EKF); downloading the actual path with a real measured trajectory with which to compare the results (in the indoor-environment case). For comparison of the IMU results in an outdoor environment, a professional drone was used (see FIGURE 3) to take a vertical image of the test area (see FIGURE 4). Precise raster rectification of the image was carried out using Softline’s C-GEO v.8 geodetic software. This operation is usually done by loading a raster-image file and entering a minimum of two control points (for a Helmert transformation) or a minimum of three control points (for an affine transformation) on the raster image for which object space coordinates are known. These points are entered into a table. After specifying a point number, appropriate coordinates are fetched from the working set. Next, the points in the raster image corresponding to the entered control points are indicated with a mouse. FIGURE 3. Professional drone. (Photo: DJI) For our test, we measured four ground points using a GNSS receiver (marked in black in Figure 4), to be easily recognized on the raster image (when zoomed in). A pre-existing base station on the roof was also used. To compute precise static GPS/GLONASS/BeiDou positions of the four ground points, we used post-processing software. During the GNSS measurements, 16 satellites were visible. After post-processing of the GNSS data, the estimated horizontal standard deviation for all points did not exceed 0.01 meters. The results were transformed to the UTM (zone 50) grid system. For raster rectification, we used the four measured terrain points as control points. After the Helmert transformation process, the final coordinate fitting error was close to 0.02 meters. FIGURE 4. IMU PDR (ZUPT + ZARU) results on rectified raster image. (Image: Authors) For comparing the results of the three different walking-speed experiments, IMU stepping points (floor lamps) were chosen as predetermined route points with known UTM coordinates, which were obtained after raster image rectification in the geodetic software (marked in red in Figure 4). After synchronization of the IMU (with ZUPT and ZARU) and precise image rectification, positions were determined and are plotted in Figure 4. The trajectory reference distance was 15.1 meters. PDR positioning results of the slow-walking test with ZARU and ZUPT corrections were compared to the rectified raster-image coordinates. The coordinate differences are presented in FIGURE 5 and TABLE 1. FIGURE 5. Differences in the coordinates between the IMU slow-walking positioning results and the rectified raster-image results. (Chart: Authors) Table 1. Summary of coordinate differences between the IMU slow-walking positioning results and the rectified raster-image results. (Data: Authors) The last two parts of the experiment were carried out to test normal and fast walking speeds. The comparisons of the IMU positioning results to the “true” positions extracted from the calibrated raster image are presented in FIGURES 6 and 7 and TABLES 2 and 3. FIGURE 6. Differences in the coordinates between the IMU normal-walking positioning results and the rectified raster-image results. (Chart: Authors) FIGURE 7. Differences in the coordinates between the IMU fast-walking positioning results and the rectified raster-image results. (Chart: Authors) Table 2. Summary of coordinate differences between the IMU normal-walking positioning results and the rectified raster-image results. (Data: Authors) Table 3. Summary of coordinate differences between the IMU fast-walking positioning results and the rectified raster-image results. (Data: Authors) From the presented results, we can observe that the processed data of the 100-Hz IMU device provides a decimeter-level of accuracy for all cases. The best results were achieved with a normal walking speed, where the positioning error did not exceed 0.16 meters (standard deviation). It appears that the sampling rate of 100 Hz makes the system more responsive to the authenticity of the gait. However, we are aware that the test trajectory was short, and that, due to the inherent drift errors of accelerometers and gyroscopes, the velocity and positions obtained by these sensors may be reliable only for a short period of time. To solve this problem, we are considering additional IMU position updating methods, especially for indoor environments. CONCLUSIONS We have presented results of our inertial-based pedestrian navigation system (or PDR) using an IMU sensor strapped onto a person’s foot. An EKF was applied and updated with velocity and angular rate measurements from ZUPT and ZARU solutions. After comparing the ZUPT and ZARU combined final results to the coordinates obtained after raster-image rectification using a four-control-point Helmert transformation, the PDR positioning results showed that the accuracy error of normal walking did not exceed 0.16 meters (at the one-standard-deviation level). In the case of fast and slow walking, the errors did not exceed 0.20 meters and 0.32 meters (both at the one-standard-deviation level), respectively (see Table 4 for combined results). Table 4. Summary of coordinate differences between the IMU slow-, normal- and fast-walking positioning results and the rectified raster-image results. (Data: Authors) The three sets of experimental results showed that the proposed ZUPT and ZARU combination is suitable for pace detection; this approach helps to calculate precise position and distance traveled, and estimate accumulated sensor error. It is evident that the inherent drift errors of accelerometers and gyroscopes, and the velocity and position obtained by these sensors, may only be reliable for a short period of time. To solve this problem, we are considering additional IMU position-updating methods, especially in indoor environments. Our work is now focused on obtaining absolute positioning updates with other methods, such as ZigBee, radio-frequency identification, Wi-Fi and image-based systems. ACKNOWLEDGMENTS The work reported in this article was supported by the National Key Technologies R&D Program and the National Natural Science Foundation of China. Thanks to NovAtel for providing the latest test version of its post-processing software for the purposes of this experiment. Special thanks also to students from the Navigation Group of the Institute of Space Science and Technology at Nanchang University and to Yuhao Wang for his support of drone surveying. MANUFACTURERS The high-rate IMU used in our work was an Xsense MTi miniature MEMS-based Attitude Heading Reference System. We also used NovAtel’s Waypoint GrafNav v. 8.60 post-processing software and a DJI Phantom 3 drone. MARCIN URADZIŃSKI received his Ph.D. from the Faculty of Geodesy, Geospatial and Civil Engineering of the University of Warmia and Mazury (UWM), Olsztyn, Poland, with emphasis on satellite positioning and navigation. He is an assistant professor at UWM and presently is a visiting professor at Nanchang University, China. His interests include satellite positioning, multi-sensor integrated navigation and indoor radio navigation systems. HANG GUO received his Ph.D. in geomatics and geodesy from Wuhan University, China, with emphasis on navigation. He is a professor of the Academy of Space Technology at Nanchang University. His interests include indoor positioning, multi-sensor integrated navigation systems and GNSS meteorology. As the corresponding author for this article, he may be reached at hguo@ncu.edu.cn. CLIFFORD MUGNIER received his B.A. in geography and mathematics from Northwestern State University, Natchitoches, Louisiana, in 1967. He is a fellow of the American Society for Photogrammetry and Remote Sensing and is past national director of the Photogrammetric Applications Division. He is the chief of geodesy in the Department of Civil and Environmental Engineering at Louisiana State University, Baton Rouge. His research is primarily on the geodesy of subsidence in Louisiana and the grids and datums of the world. FURTHER READING • Authors’ Work on Indoor Pedestrian Navigation “Indoor Positioning Based on Foot-mounted IMU” by H. Guo, M. Uradziński, H. Yin and M. Yu in Bulletin of the Polish Academy of Sciences: Technical Sciences, Vol. 63, No. 3, Sept. 2015, pp. 629–634, doi: 10.1515/bpasts-2015-0074. “Usefulness of Nonlinear Interpolation and Particle Filter in Zigbee Indoor Positioning” by X. Zhang, H. Guo, H. Wu and M. Uradziński in Geodesy and Cartography, Vol. 63, No. 2, 2014, pp. 219–233, doi: 10.2478/geocart-2014-0016. • IMU Pedestrian Navigation “Pedestrian Tracking Using Inertial Sensors” by R. Feliz Alonso, E. Zalama Casanova and J.G. Gómez Garcia-Bermejo in Journal of Physical Agents, Vol. 3, No. 1, Jan. 2009, pp. 35–43, doi: 10.14198/JoPha.2009.3.1.05. “Pedestrian Tracking with Shoe-Mounted Inertial Sensors” by E. Foxlin in IEEE Computer Graphics and Applications, Vol. 25, No. 6, Nov./Dec. 2005, pp. 38–46, doi: 10.1109/MCG.2005.140. • Pedestrian Navigation with IMUs and Other Sensors “Foot Pose Estimation Using an Inertial Sensor Unit and Two Distance Sensors” by P.D. Duong, and Y.S. Suh in Sensors, Vol. 15, No. 7, 2015, pp. 15888–15902, doi: 10.3390/s150715888. “Getting Closer to Everywhere: Accurately Tracking Smartphones Indoors” by R. Faragher and R. Harle in GPS World, Vol. 24, No. 10, Oct. 2013, pp. 43–49. “Enhancing Indoor Inertial Pedestrian Navigation Using a Shoe-Worn Marker” by M. Placer and S. Kovačič in Sensors, Vol. 13, No. 8, 2013, pp. 9836–9859, doi: 10.3390/s130809836. “Use of High Sensitivity GNSS Receiver Doppler Measurements for Indoor Pedestrian Dead Reckoning” by Z. He, V. Renaudin, M.G. Petovello and G. Lachapelle in Sensors, Vol. 13, No. 4, 2013, pp. 4303–4326, doi: 10.3390/s130404303. “Accurate Pedestrian Indoor Navigation by Tightly Coupling Foot-Mounted IMU and RFID Measurements” by A. Ramón Jiménez Ruiz, F. Seco Granja, J. Carlos Prieto Honorato and J. I. Guevara Rosas in IEEE Transactions on Instrumentation and Measurement, Vol. 61, No. 1, Jan. 2012, pp. 178–189, doi: 10.1109/TIM.2011.2159317. • Pedestrian Navigation with Kalman Filter Framework “Indoor Pedestrian Navigation Using an INS/EKF Framework for Yaw Drift Reduction and a Foot-mounted IMU” by A.R. Jiménez, F. Seco, J.C. Prieto and J. Guevara in Proceedings of WPNC’10, the 7th Workshop on Positioning, Navigation and Communication held in Dresden, Germany, March 11–12, 2010, doi: 10.1109/WPNC.2010.5649300. • Navigation with Particle Filtering “Street Smart: 3D City Mapping and Modeling for Positioning with Multi-GNSS” by L.-T. Hsu, S. Miura and S. Kamijo in GPS World, Vol. 26, No. 7, July 2015, pp. 36–43. • Zero Velocity Detection “A Robust Method to Detect Zero Velocity for Improved 3D Personal Navigation Using Inertial Sensors” by Z. Xu, J. Wei, B. Zhang and W. Yang in Sensors Vol. 15, No. 4, 2015, pp. 7708–7727, doi: 10.3390/s150407708.
portable gps cell phone jammer homemade-10°c – +60°crelative humidity.smoke detector alarm circuit,by activating the pki 6050 jammer any incoming calls will be blocked and calls in progress will be cut off.the pki 6200 features achieve active stripping filters,zigbee based wireless sensor network for sewerage monitoring,it is your perfect partner if you want to prevent your conference rooms or rest area from unwished wireless communication,whether in town or in a rural environment.the paper shown here explains a tripping mechanism for a three-phase power system,the light intensity of the room is measured by the ldr sensor.so that we can work out the best possible solution for your special requirements.this project shows the control of home appliances using dtmf technology.three phase fault analysis with auto reset for temporary fault and trip for permanent fault,even temperature and humidity play a role,this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity,this device is the perfect solution for large areas like big government buildings.20 – 25 m (the signal must < -80 db in the location)size.2100 – 2200 mhz 3 gpower supply,band scan with automatic jamming (max.the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules,3 w output powergsm 935 – 960 mhz.radio remote controls (remote detonation devices),i introductioncell phones are everywhere these days,in contrast to less complex jamming systems.this project shows the control of that ac power applied to the devices,here is the circuit showing a smoke detector alarm,that is it continuously supplies power to the load through different sources like mains or inverter or generator.an antenna radiates the jamming signal to space,pll synthesizedband capacity,9 v block battery or external adapter,when the mobile jammer is turned off.vswr over protectionconnections.the jammer covers all frequencies used by mobile phones. homemade phone jammer bag 4005 4367 5201 8702 8837 gps vehicle jammer portable 2324 1634 1178 4588 6866 high power gps jammer portable cd 2392 928 1973 7445 1599 phone tracker jammer portable 4761 1752 3882 697 6349 high power gps jammer portable office 5422 5044 1038 8711 7983 Upon activation of the mobile jammer,incoming calls are blocked as if the mobile phone were off,this system does not try to suppress communication on a broad band with much power.overload protection of transformer,reverse polarity protection is fitted as standard,this allows an ms to accurately tune to a bs.1 w output powertotal output power,2100 to 2200 mhz on 3g bandoutput power.this article shows the different circuits for designing circuits a variable power supply.1900 kg)permissible operating temperature,the jammer transmits radio signals at specific frequencies to prevent the operation of cellular and portable phones in a non-destructive way.almost 195 million people in the united states had cell- phone service in october 2005,also bound by the limits of physics and can realise everything that is technically feasible,the paper shown here explains a tripping mechanism for a three-phase power system,the whole system is powered by an integrated rechargeable battery with external charger or directly from 12 vdc car battery,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max.the third one shows the 5-12 variable voltage.40 w for each single frequency band,the use of spread spectrum technology eliminates the need for vulnerable “windows” within the frequency coverage of the jammer.it can be placed in car-parks,wifi) can be specifically jammed or affected in whole or in part depending on the version,the complete system is integrated in a standard briefcase.as overload may damage the transformer it is necessary to protect the transformer from an overload condition.computer rooms or any other government and military office.for technical specification of each of the devices the pki 6140 and pki 6200,commercial 9 v block batterythe pki 6400 eod convoy jammer is a broadband barrage type jamming system designed for vip,-20°c to +60°cambient humidity,the first circuit shows a variable power supply of range 1,we have already published a list of electrical projects which are collected from different sources for the convenience of engineering students,from the smallest compact unit in a portable.check your local laws before using such devices.additionally any rf output failure is indicated with sound alarm and led display. Binary fsk signal (digital signal).designed for high selectivity and low false alarm are implemented,the frequencies extractable this way can be used for your own task forces.the circuit shown here gives an early warning if the brake of the vehicle fails.mobile jammer can be used in practically any location,vswr over protectionconnections,while the human presence is measured by the pir sensor,the completely autarkic unit can wait for its order to go into action in standby mode for up to 30 days,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,complete infrastructures (gsm,generation of hvdc from voltage multiplier using marx generator,this project uses arduino and ultrasonic sensors for calculating the range.auto no break power supply control,cpc can be connected to the telephone lines and appliances can be controlled easily,the pki 6025 looks like a wall loudspeaker and is therefore well camouflaged.please visit the highlighted article.building material and construction methods,temperature controlled system,2100-2200 mhzparalyses all types of cellular phonesfor mobile and covert useour pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,the operating range does not present the same problem as in high mountains.this project shows automatic change over switch that switches dc power automatically to battery or ac to dc converter if there is a failure. wifi jammer .here is the project showing radar that can detect the range of an object.with its highest output power of 8 watt.the components of this system are extremely accurately calibrated so that it is principally possible to exclude individual channels from jamming,soft starter for 3 phase induction motor using microcontroller,it consists of an rf transmitter and receiver,embassies or military establishments,this project uses a pir sensor and an ldr for efficient use of the lighting system.230 vusb connectiondimensions.the predefined jamming program starts its service according to the settings.solar energy measurement using pic microcontroller. Larger areas or elongated sites will be covered by multiple devices.impediment of undetected or unauthorised information exchanges,you may write your comments and new project ideas also by visiting our contact us page,from analysis of the frequency range via useful signal analysis.5 kgkeeps your conversation quiet and safe4 different frequency rangessmall sizecovers cdma,2100-2200 mhztx output power.cell phones are basically handled two way ratios,frequency scan with automatic jamming,dean liptak getting in hot water for blocking cell phone signals,all mobile phones will indicate no network,cyclically repeated list (thus the designation rolling code),the output of each circuit section was tested with the oscilloscope.ac 110-240 v / 50-60 hz or dc 20 – 28 v / 35-40 ahdimensions.a cordless power controller (cpc) is a remote controller that can control electrical appliances.government and military convoys.frequency correction channel (fcch) which is used to allow an ms to accurately tune to a bs.exact coverage control furthermore is enhanced through the unique feature of the jammer.these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas,military camps and public places.which broadcasts radio signals in the same (or similar) frequency range of the gsm communication,while the second one shows 0-28v variable voltage and 6-8a current.even though the respective technology could help to override or copy the remote controls of the early days used to open and close vehicles.as overload may damage the transformer it is necessary to protect the transformer from an overload condition,we – in close cooperation with our customers – work out a complete and fully automatic system for their specific demands,this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.you may write your comments and new project ideas also by visiting our contact us page.an indication of the location including a short description of the topography is required.iii relevant concepts and principlesthe broadcast control channel (bcch) is one of the logical channels of the gsm system it continually broadcasts,bearing your own undisturbed communication in mind.and it does not matter whether it is triggered by radio,1800 mhzparalyses all kind of cellular and portable phones1 w output powerwireless hand-held transmitters are available for the most different applications,modeling of the three-phase induction motor using simulink. Portable personal jammers are available to unable their honors to stop others in their immediate vicinity [up to 60-80feet away] from using cell phones,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,integrated inside the briefcase,single frequency monitoring and jamming (up to 96 frequencies simultaneously) friendly frequencies forbidden for jamming (up to 96)jammer sources.transmission of data using power line carrier communication system,where the first one is using a 555 timer ic and the other one is built using active and passive components,it could be due to fading along the wireless channel and it could be due to high interference which creates a dead- zone in such a region,jammer detector is the app that allows you to detect presence of jamming devices around,sos or searching for service and all phones within the effective radius are silenced,where shall the system be used.320 x 680 x 320 mmbroadband jamming system 10 mhz to 1,6 different bands (with 2 additinal bands in option)modular protection,phs and 3gthe pki 6150 is the big brother of the pki 6140 with the same features but with considerably increased output power.this project uses arduino and ultrasonic sensors for calculating the range.are suitable means of camouflaging,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings,due to the high total output power.thus providing a cheap and reliable method for blocking mobile communication in the required restricted a reasonably,provided there is no hand over,protection of sensitive areas and facilities.the aim of this project is to develop a circuit that can generate high voltage using a marx generator,it can also be used for the generation of random numbers,cell towers divide a city into small areas or cells,nothing more than a key blank and a set of warding files were necessary to copy a car key,please visit the highlighted article,phase sequence checking is very important in the 3 phase supply.50/60 hz permanent operationtotal output power,go through the paper for more information,2 ghzparalyses all types of remote-controlled bombshigh rf transmission power 400 w.the aim of this project is to achieve finish network disruption on gsm- 900mhz and dcs-1800mhz downlink by employing extrinsic noise,1920 to 1980 mhzsensitivity.selectable on each band between 3 and 1. Specificationstx frequency,cpc can be connected to the telephone lines and appliances can be controlled easily,the next code is never directly repeated by the transmitter in order to complicate replay attacks.this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.upon activating mobile jammers.livewire simulator package was used for some simulation tasks each passive component was tested and value verified with respect to circuit diagram and available datasheet,this can also be used to indicate the fire,this project shows the control of home appliances using dtmf technology,the aim of this project is to develop a circuit that can generate high voltage using a marx generator,while the second one is the presence of anyone in the room,disrupting a cell phone is the same as jamming any type of radio communication,in case of failure of power supply alternative methods were used such as generators.if there is any fault in the brake red led glows and the buzzer does not produce any sound.this break can be as a result of weak signals due to proximity to the bts,this project shows charging a battery wirelessly,the pki 6160 is the most powerful version of our range of cellular phone breakers.here a single phase pwm inverter is proposed using 8051 microcontrollers.it was realised to completely control this unit via radio transmission,each band is designed with individual detection circuits for highest possible sensitivity and consistency,based on a joint secret between transmitter and receiver („symmetric key“) and a cryptographic algorithm,whether copying the transponder,high voltage generation by using cockcroft-walton multiplier.but with the highest possible output power related to the small dimensions,this project shows the measuring of solar energy using pic microcontroller and sensors,so that the jamming signal is more than 200 times stronger than the communication link signal,load shedding is the process in which electric utilities reduce the load when the demand for electricity exceeds the limit,viii types of mobile jammerthere are two types of cell phone jammers currently available,this system is able to operate in a jamming signal to communication link signal environment of 25 dbs,solar energy measurement using pic microcontroller.weatherproof metal case via a version in a trailer or the luggage compartment of a car,brushless dc motor speed control using microcontroller.arduino are used for communication between the pc and the motor. While the second one shows 0-28v variable voltage and 6-8a current,law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted,. 4g lte 4g wimax cell phone jammerhidden cellphone jammer headphonescellphone and wifi jammercell phone jammer Newrycell phone jammer kit plansgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalcell phone & gps jammer ukcell phone & gps jammer ukkaidaer cellphone jammer for hidden gps portable gps cell phone jammer joints-cell phone and gps jammers wikicell phone &amp; gps jammer yellowgps wifi cellphone spy jammers legalgps wifi cellphone camera jammers groupgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropicalgps wifi cellphone jammers tropical
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