Cell phone signal jammer p13 compact , cell phone jammer crime

An Object-Oriented Software Platform Suitable for Multiple Receivers By Eliot Wycoff, Yuting Ng, and Grace Xingxin Gao INNOVATION INSIGHTS by Richard Langley AND NOW FOR SOMETHING COMPLETELY DIFFERENT. My first introduction to computer programming was during a visit to the Faculty of Mathematics at the University of Waterloo when I was still a high school student. We got to keypunch a simple program onto cards using the FORTRAN programming language and submit the “job” to the university’s IBM 7040 mainframe computer. That visit helped seal the choice of Waterloo for my undergraduate education — but in applied physics, not math. Once I became an undergraduate, I learned how to properly program in FORTRAN (actually FORTRAN IV with the WATFOR compiler developed at Waterloo) and in assembly language on the SPECTRE virtual computer (written in FORTRAN), both on Waterloo’s new IBM 360 mainframe. Knowing how to program was instrumental in my graduate work on the geodetic application of very long baseline interferometry (VLBI) at York University. Being humble Canadians (and despite the fact that VLBI was invented in Canada), we called it just LBI. My LBI data analysis FORTRAN program was initially on a box full of punched cards that I would have to carry back and forth to the computer center being careful not to drop the box and get the cards out of order.      While I was a graduate student, I also got to use the Spiras-65 minicomputer that controlled the playback of the LBI recorded tapes at the National Research Council in Ottawa.  It was programmed using punched paper tape. I saw the progression from punched tape and cards to the use of terminals to enter programs and magnetic tapes for storing them and the data to be analyzed. The University of New Brunswick, where I came to work in 1981, was one of the first universities in Canada to introduce an interactive terminal- (or work-station-) based time-sharing system for programmers to develop and run their jobs on the central computer. The last card reader at UNB was retired in 1987. By the time I came to work at UNB, the era of the personal computer had already dawned. Although the Department of Surveying Engineering (as it was then called) acquired an HP 1000 minicomputer for various research tasks, personal computers began to show up on faculty members’ desks and in their labs. Some of us started out with Apple II computers (we used them, for example, for recording data from Transit–U.S. Navy Navigation Satellite System–receivers) and progressed through various Macintosh models. Once I became a professor, I did less and less programming myself–leaving it up to my graduate students to do the heavy lifting in that area. These days, my personal programming efforts are limited to short scripts mostly using the Python language. Python, which gets its name from the Monty Python’s Flying Circus television series, was first introduced back in 1991 but it is only relatively recently that its popularity has taken off. Python can be run on a wide variety of platforms under many operating systems. One of the key features of Python is that it supports multiple programming paradigms, including object-oriented programming (OOP).  OOP is a programming methodology based on the use of data structures, known as objects, rather than just functions and procedures. The objects, organized into classes, exchange information in a standardized way and their use helps ensures good code modularity. In this month’s column, we take a look at how Python has been used to develop a software-defined GNSS receiver — one well-suited to processing data from a network of receiver front ends. “Innovation” is a regular feature that discusses advances in GPS technology and its applications as well as the fundamentals of GPS positioning. The column is coordinated by Richard Langley of the Department of Geodesy and Geomatics Engineering, University of New Brunswick. He welcomes comments and topic ideas. Email him at lang @ unb.ca. With billions of GNSS-enabled devices in use today, the potential gains from harnessing data collected over a network of GNSS receivers has never been greater, yet the necessary architectures to handle and extract useful data collected over such networks are not well explored. Traditional uses of GNSS in cooperative positioning treat individual GNSS receivers as “black boxes” that merely output navigation solutions. As such, the wealth of information contained in each receiver’s raw signals is largely discarded. Of particular interest are ideas such as inter-receiver aiding, in which networked receivers might share acquisition, tracking, and navigation information (possibly in real time) to improve receiver performance. In addition, a network of receivers might also be used as a sensing tool: it is expected that atmospheric parameters, for instance, could be recovered by analyzing the raw signal data arriving at an appropriately sized network. In light of these interesting research areas, it would be expedient to develop a set of tools that can process and handle the raw data being produced at every receiver in a GNSS receiver network. Existing software-defined receivers (SDRs) have gone a long way towards making the fast prototyping of new receiver architectures possible. An SDR attempts to shift as many receiver functions, such as mixing and tracking, from being implemented in hardware to being implemented in software. This allows for fast prototyping as receiver components can be more quickly modified in software than in hardware. The hardware components that a GNSS SDR still requires are an antenna and a front end including an analog-to-digital converter (ADC). An analog GNSS signal is received at the antenna. It is then mixed to an intermediate frequency and digitized by the ADC. The digital stream is then processed by the SDR’s software component. But with regard to processing data from a receiver network, existing SDRs have a number of notable flaws. In brief, existing software receivers are designed to process the data arriving at one real-world receiver. Thus a procedural coding design is typically used. While procedural code is a good solution for the linear processes that occur in a single receiver (acquisition, tracking, demodulation of the navigation data, position calculations, and so on), this software design style does not adapt well to the task of performing all of these actions on multiple receivers with the additional goal that each receiver shares tracking data with every other one. In such scenarios, not only is there data being produced for every receiver in the network, but there is also data being produced about the relationships between the receivers in the network. Thus, an SDR that was originally designed to process data from only one receiver will prove difficult to adapt to the task of processing many. Luckily, object-oriented programming, a well-known and widely used software design philosophy, is well suited to the receiver network problem. Therefore, for this work, we designed and implemented an object-oriented software platform for many receivers. Python was chosen as the programming language because of its support for object-oriented programming, its portability, its free cost, its numerical abilities (using open-source libraries such as NumPy and SciPy), and its ease of use. And as a reference, an existing Matlab software receiver was used as a basis for developing many of the core algorithms in this work. We call our development simply the Python Receiver. Design Many of the core functions in the Python Receiver are modeled after those found in the Matlab development. Thus, this particular implementation is suited for the raw GPS L1 signal data mixed to an intermediate frequency by the SDR front end. In addition, the basic algorithms for acquisition, scalar tracking, and navigation are similar to the Matlab ones, with the exception that acquisition is made more robust by using multiple noncoherent integrations. The primary innovation of this software, however, is in the way in which the code is organized. For tracking multiple receivers, the Python Receiver was designed under an object-oriented approach. FIGURE 1 illustrates the main objects that a user would be expected to use in the Python Receiver. Each object is defined as a class, and as such each object is capable of storing object-specific data as well as performing certain object-specific functions. The hierarchy of Figure 1 roughly illustrates which objects are defined as members of other classes for typical usage. Thus, inside any instance of the network class may exist any number of receiver objects. Likewise, an instance of the constellation class may be home to any number of satellite objects. FIGURE 1. Typical object (class) hierarchy. For data coming from a single real-world receiver, use of the Python Receiver would typically be as follows. First, a user would initialize an instance of the receiver class using a dictionary of predefined settings, such as the file location of the data source. Second, the user would initialize a constellation object of satellites by passing the pseudorandom noise (PRN) code values of each satellite to be included in the constellation. At this point, the user could then use built-in functionality in the receiver object to perform acquisition of all of the satellites in the constellation. Results of this acquisition attempt would be stored in the receiver object, where they could then be used to run the receiver’s built-in scalar tracking functionality. Likewise, scalar tracking data would be stored in the receiver object, and again the user could use the receiver’s built-in navigation functionality to decode the navigation bits produced during scalar tracking and perform navigation computations. Satellite-specific ephemerides would be stored in the relevant satellite objects. Navigation solutions are stored as a part of the receiver’s state object. The state object, which is also used in the satellite class, is a container for holding state information in the Earth-centered Earth-fixed (ECEF) coordinate system (such as position and velocity) and clock terms, and it also provides the ability to return position coordinates in other systems, such as the GPS geodetic system (frame) of WGS 84. While it is not a key feature of the Python Receiver, the state object is designed as an object so that it can be readily used elsewhere should an algorithm need to store state information and have coordinate transformations readily available. Tracking channels need not be restricted to the hierarchy shown in Figure 1. During operation for just one data source, the scalar tracking function defined at the receiver level will initialize a sufficient number of tracking channels to track all of its observed satellites. However, when operating on multiple sources of data and with the intent to share tracking outputs between channels, it is helpful to place tracking channels into groups, as shown in FIGURE 2. In the example that will be discussed in following sections, two real-world receivers observed a similar set of satellites. It was therefore helpful to define channel groups for each commonly observed satellite, with one channel in the group corresponding to the satellite as tracked by the first receiver, and the other channel corresponding to the satellite as tracked by the second. Tracking groups as a class, however, may be easily modified for other experimental purposes. FIGURE 2. Left: an independent tracking channel (corresponding to one tracking channel object). Right: a channel group. Note that in the channel group, updates to the code and carrier phase of each channel may be performed cooperatively. Independent tracking channels have an update function that processes the next segment of raw data in three main steps: computing correlations (early, late, and prompt), producing discriminator outputs, and generating code and carrier-frequency updates. For a group of channels, this sequence of steps is interrupted after discriminator outputs have been computed. At this point, the channel group may instruct the tracking channels to update their code and carrier frequencies independently or through some other cooperative means that considers data across all of the channels. As for the last few classes: correlators and filters are defined as objects so that they can be easily changed depending on the experimental circumstances. And satellites, in addition to holding satellite-specific ephemerides, have built-in functionality to return their locations given a particular epoch of GPS Time. Naturally, core functions such as these would be found in traditional software receivers, but by repackaging them into the object-oriented framework, both code reusability and modifiability increase. And in addition, by defining classes for networks of receivers and groups of tracking channels, simulations and experiments involving cooperative positioning of receivers become easier to conduct. Experiment To help illustrate how the Python Receiver lends itself to the task of cooperatively tracking multiple receivers, concurrent data from two SDR front ends was collected on a boat in Lake Titicaca just offshore from Puno, Peru. The boat was a small motorized ferry capable of transporting approximately twenty passengers. One antenna and front end, hereafter referred to as “Receiver X” was placed on the port side of the boat, while the other, “Receiver Y” was placed on the starboard side. Maintaining a fixed baseline, both receivers captured raw GPS L1 signals from separate portions of the sky and mixed them to an intermediate frequency of 5.456 MHz. Raw data collection was performed concurrently at both receivers for 15 minutes as the boat returned from the floating islands of the Uros people to the dock at Puno. Finally, while Lake Titicaca is at a high elevation in the Altiplano (the Andean Plateau), the surrounding mountains do not rise far above the horizon, and thus visibility was quite good in most directions. Some challenges, however, present themselves in this data set. While Receiver X was able to acquire eight satellites, and Receiver Y was able to acquire 10, the signal quality at Receiver Y was generally poor. In Figure 3, in-phase prompt correlator outputs from traditional scalar tracking are shown for both Receivers X and Y and satellites with PRN codes 27 and 29. For satellite 27, Receiver Y loses lock of the signal between code periods 100,000 and 200,000, and for satellite 29, it completely loses track of the signal after only a few thousand code periods. (Recall that the C/A-code period is one millisecond.) FIGURE 3. The in-phase prompt correlator outputs for both receivers and satellites PRN 27 and 29. The cyan dots are correlator outputs, the red line is the locking metric, and the dashed green and blue lines are the thresholds set for determining good and poor lock, respectively. Locking metric values above the dashed green line represent a good lock, and values below the dashed blue line represent loss-of-lock. Note that y-axis values differ from graph to graph. To better characterize the tracking performance of each receiver-satellite pair, a locking metric was designed and implemented, the values of which are shown as the red lines in the graphs of Figure 3. Inspired by the earlier use of the square-law detector, we have expressed the metric as: (1) where N is the number of most recent correlator samples, Ii and Qi are the ith in-phase and quadrature-phase prompt correlator outputs, and the square-root operator returns the negative square root of the absolute value of the expression under the radical if that expression is negative. After visually examining the relationship of this locking metric with the quality of the in-phase prompt correlator outputs, two thresholds were determined in order to better characterize the quality of the tracking loop lock. The first threshold, represented as the dashed green lines in the graphs of Figure 3, is the threshold above which the tracking loops were considered locked well. Its value was set to 250. The second threshold, whose value was set to 150 and is represented by the dashed blue lines, is the threshold below which the tracking loops were considered to be in a complete loss-of-lock situation. Locking metric values between 150 and 250 were considered as representing a situation in which the tracking loops were weakly locked to the incoming signals. Despite the poor performance of Receiver Y in tracking many of its signals, navigation functionality in the Python Receiver was still able to recover sufficient ephemerides from the tracking data to perform position calculations. FIGURE 4 shows the navigation solutions for Receiver Y over a 13-minute interval, roughly capturing the route that the ferry took westward back to Puno. Note that the moustache-shaped region in the right-hand side of the map is the collection of floating islands of the Uros. Just as the ferry left these islands, the navigation solutions for Receiver Y become much nosier. Possible reasons for this are the slight change in heading that the ferry made, or the thicket of reeds that surrounded the boat during this portion of the journey. Navigation results for Receiver X were much less noisy. FIGURE 4. The trip back to Puno on the left (west) from the floating islands of the Uros on the right (east) as determined by traditional scalar tracking and navigation at Receiver Y. Image courtesy of Google Earth and the GPS Visualizer. Cooperative Scalar Tracking While all of these traditional results were obtained using the Python Receiver, they could have just as easily been obtained using procedurally coded receivers. Assuming, however, that one is interested in performing experiments that involve data sharing between multiple receivers, the Python Receiver lends itself handily to the task. An experiment was devised in which scalar tracking performed at both Receivers X and Y would be done cooperatively. In particular, it was observed that often when one of the two receivers momentarily lost track of its signal for a particular satellite, the other receiver would be tracking well. In addition, it was noted that because the two receivers maintained a fixed baseline during tracking, their tracking channels should have maintained a steady difference in code phases that changed slowly provided that the receiver-satellite geometry did not change quickly. As shown in FIGURE 5, the only violation of this scenario would occur when one of the two receivers lost lock and thus allowed for drift in its code-tracking loop. It should be noted that unlike the situation in Figure 5, the reported code difference between the two receivers suffered from a bias that grew linearly in time. This bias, which was likely due to clock errors in one or both of the receiver front ends, was eliminated through a linear regression before the plotting of the figure. FIGURE 5. The code-phase difference between Receivers X and Y for PRN 27 from 300,000 to 500,000 milliseconds. Note the large variance around 400,000 milliseconds corresponding to a loss-of-lock for Receiver Y. All of these observations motivated the following cooperative scalar tracking design. First, any satellite that was observed by only one receiver would be independently tracked by that receiver in the traditional manner. A single tracking loop object would be allocated in Python for this particular receiver-satellite pair. Second, any satellite that was observed by both receivers would have a channel group object allocated in Python. This channel group would contain two tracking channel objects, one for each receiver. As shown in Figure 2, this channel group required specific code to be written to handle the cooperative updates of both receivers’ code and carrier frequencies. The algorithm was designed as follows. For each update epoch (generated by a call of the channel group’s update function), if both of the tracking channels were locked to their incoming signals, the channel group would save their code-phase difference for that code period. And since both channels were locked, both would update their code and carrier frequencies in the traditional manner, relying on discriminator outputs only. If, on the other hand, one of the tracking channels was in a loss-of-lock situation, the channel group would search the previous 5,000 milliseconds of data for code periods during which, presumably, both tracking channels were mutually locked. This data would contain information about the expected code-phase difference between the two tracking channels at the current code period. At this point, a linear regression on the data from the mutually locked code periods was used to determine this expected code-phase difference. Finally, we note again that this expected code-phase difference would only remain valid under the assumption that the receiver-satellite geometry was not changing rapidly, as was the case for this data. But acknowledging that some changes in the geometry might occur (such as a change in heading of the boat) is the reason why the search interval for mutually locked data was limited to five seconds. Assuming that one of the receivers was in a loss-of-lock situation and that sufficient data from the past five seconds existed to generate an estimate of the current expected code-phase difference, the channel group could then make a cooperative update of the lockless tracking channel. For this channel, the channel group would replace the traditional code-tracking discriminator outputs with the offset of the expected code-phase difference dexp from the currently observed code-phase difference dcur. In the following equation, the new discriminator output is denoted as c: . (2) Expressing dcur=ycur−xcur and dexp=yexp−xexp, where xcur/exp and ycur/exp represent current and expected code phases at two receivers, we can rewrite Equation 2 as   (3) or   (4) since we expect the x receiver to be locked, and therefore . Some finer points to mention include that the “loss-of-lock” and “tracking well” designations were determined by way of the locking metric defined in the previous section. In addition, if a receiver was “tracking weakly,” it would update its code and carrier frequencies by relying solely on its own discriminator outputs. Also, because in traditional scalar tracking loss-of-lock might occur for an extended interval greater than five seconds at one receiver (such as Receiver Y’s tracking of satellite 27 seen in Figure 3 between 300,000 and 400,000 milliseconds), whenever the channel group was called to cooperatively update a lockless tracking channel’s code frequency, it would record the current code-phase difference between both receivers. Under all scenarios, the carrier-frequency update would be done independently at each channel using discriminator outputs alone. And finally, in order for both receivers to share relevant data with each other during tracking, clock bias terms found after traditional scalar tracking were used to align in time the raw data files for each receiver appropriately. Results and Discussion Using cooperative scalar tracking, drifting of the code-phase difference during code periods when one of the receivers is experiencing loss-of-lock is expected to be suppressed. And indeed, results such as those shown in FIGURE 6 verify this expectation. Since cooperative scalar tracking does not attempt to modify the way either receiver tracks during periods of good lock, this type of modified scalar tracking is not expected to produce less noisy tracking results. It is expected, however, to help lockless tracking channels to regain track after short signal outages, similar to the benefits of vector tracking. FIGURE 6. The code-phase difference between Receivers X and Y for PRN 27 from 300,000 to 500,000 milliseconds, this time using cooperative scalar tracking. Presence of the red line indicates code periods during which cooperative code-phase updates were made for Receiver Y. Note that noisy drifting of the code-phase difference is suppressed. Strikingly, this form of cooperative tracking allowed for Receiver Y to continually track the signal from satellite 29 (albeit with occasional outages) for the full thirteen minutes of data shown in FIGURE 7. Whereas in Figure 3, Receiver Y very quickly loses track of satellite 29, Figure 7 shows that Receiver Y, under cooperative scalar tracking, can maintain a good enough lock on the signal that by roughly 750,000 code periods, it is able to pick up the signal again quite strongly. This change in signal strength may have been due to a slight change in heading that the ferry made near Isla Taquile towards the end of this data set (see Figure 4 and FIGURE 8). FIGURE 7. The in-phase prompt outputs for Receiver Y and PRN 29 using cooperative scalar tracking. Compare this to the bottom-right graph in Figure 3. Inter-receiver aiding allowed Receiver Y to track this signal for a majority of the code periods. FIGURE 8. The trip back to Puno as determined by Receiver Y after cooperative scalar tracking and navigation computations. Compared to Figure 4, the navigation solutions are less noisy. Image courtesy of Google Earth and the GPS Visualizer. Given the locking metric defined in the section “Experiment,” quantitative measures of how often each channel spent locked or in loss-of-lock can be made. In total, both receivers tracked six common satellites (with each receiver also tracking other satellites independently). TABLE 1 shows the locking frequencies for each commonly tracked satellite. TABLE 1. Percent of time each tracking channel spent locked. Lock was designated if the locking metric was above 150. The best values for Receiver Y are highlighted in green, with the most notable improvement occurring for satellite 29. Granted that the drift in the code phase for lockless tracking channels is curtailed in cooperative scalar tracking, an improvement in navigation solutions is also expected. This expectation is verified by comparing the qualitative level of noise in the solutions of Figure 8 to the solutions in Figure 4. Notably, the noise in the reed thicket (the section of the route immediately after leaving the moustache-shaped floating islands region) is suppressed. Not shown are the navigation solutions for the port side receiver, Receiver X, which by comparison to Receiver Y were relatively good in both forms of scalar tracking. Conclusion The experiment we carried out highlighted the abilities of the Python Receiver. Data from two SDR front ends and associated antennas placed on either side of a small transport ferry was used to track both receivers by using groups of tracking channels that could cooperatively modify their individual channels’ code and carrier frequencies. In this way, loss-of-lock in many of the tracking channels was avoided leading to improved navigation precision. More importantly, it is expected that future experiments like these can be easily implemented within the framework of the Python Receiver, and thus topics like cooperative vector tracking might be more easily investigated. Acknowledgments This article is based, in part, on the paper “A Python Software Platform for Cooperatively Tracking Multiple GPS Receivers” presented at ION GNSS+ 2014, the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation, held in Tampa, Florida, September 8–12, 2014. Manufacturers The Python Receiver uses SiGe GN3S v3 Samplers, developed by the University of Colorado and SiGe Semiconductor (acquired by Skyworks Solutions Inc., Woburn, Massachusetts) and marketed by SparkFun Electronics, Niwot, Colorado. ELIOT WYCOFF received his B.S. in applied mathematics from Columbia University, New York, in 2011. While working on the Python Receiver, he was a graduate student in the Department of Aerospace Engineering at the University of Illinois at Urbana-Champaign (UIUC). YUTING NG obtained a B.S. in electrical and computer engineering from UIUC in 2014. She is currently a graduate student in the Department of Aerospace Engineering, UIUC. GRACE XINGXIN GAO is an assistant professor in the Department of Aerospace Engineering, UIUC. She received her B.S. in mechanical engineering in 2001 and her M.S. in electrical engineering in 2003, both from Tsinghua University, China. She obtained her Ph.D. in electrical engineering at Stanford University in 2008. Before joining UIUC in 2012, Gao was a research associate at Stanford University. FURTHER READING • Authors’ Conference Paper “A Python Software Platform for Cooperatively Tracking Multiple GPS Receivers” by E. Wycoff and G.X. Gao in Proceedings of ION GNSS+ 2014, the 27th International Technical Meeting of the Satellite Division of The Institute of Navigation, Tampa, Florida, September 8–12, 2014, pp. 1417–1425. • Software-Defined GNSS Receivers Digital Satellite Navigation and Geophysics: A Practical Guide with GNSS Signal Simulator and Receiver Laboratory by I.G. Petrovski and T. Tsujii with foreword by R.B. Langley, published by Cambridge University Press, Cambridge, U.K., 2012. “Software GNSS Receiver: An Answer for Precise Positioning Research” by T. Pany, N. Falk, B. Riedl, T. Hartmann, G. Stangl, and C. Stöber in GPS World, Vol. 23, No. 9, September 2012, pp. 60–66. A Software-Defined GPS and Galileo Receiver: A Single-Frequency Approach by K. Borre, D.M. Akos, N. Bertelsen, P. Rinder, and S.H. Jensen, published by Birkhäuser Engineering, Springer-Verlag GmbH, Heidelberg, 2007. “GNSS Software Defined Radio: Real Receiver or Just a Tool for Experts?” by J.-H. Won, T. Pany, and G. Hein in Inside GNSS, Vol. 1, No. 5, July–August 2006, pp. 48–56. “Satellite Navigation Evolution: The Software GNSS Receiver” by G. MacCougan, P.L. Normark, and C. Ståhlberg in GPS World, Vol. 16, No. 1, January 2005, pp. 48–55. • Python Learn Python in One Hour by V.R. Volkman, published by Modern Software Press, L.H. Press Inc., Ann Arbor, Michigan, 2014. A Primer on Scientific Programming with Python by H.P. Langtangen, published by Springer-Verlag GmbH, Heidelberg, 2009. “Python for Scientific Computing” by T.E. Oliphant in Computing in Science & Engineering, Vol. 9, No. 3, May–June 2007, pp. 10–20, doi: 10.1109/MCSE.2007.58. • Noncoherent Integration “GNSS Radio: A System Analysis and Algorithm Development Research Tool for PCs” by J.K. Ray, S.M. Deshpande, R.A. Nayak, and M.E. Cannon in GPS World, Vol. 17, No. 5, May 2006, pp. 51–56. Fundamentals of Global Positioning System Receivers: A Software Approach, 2nd edition, by J. B.-Y. Tsui, published by Wiley-Interscience, John Wiley & Sons, Inc., Hoboken, New Jersey, 2005. “An Assisted GPS Acquisition Method Using L2 Civil Signal in Weak Signal Environment” by D.J. Cho, C. Park, and S.J. Lee in Journal of Global Positioning Systems, Vol. 3 No. 1-2, December 2004, pp. 25–31. • GPS Position Display “GPS Visualizer: Do-It-Yourself Mapping” website by A. Schneider. • Square Law Detector “Lock Detection in Costas Loops” by A. Mileant and S. Hinedi in IEEE Transactions on Communications, Vol. 40, No. 3, March 1992, pp. 480–483, doi: 10.1109/26.135716.

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item: Cell phone signal jammer p13 compact , cell phone jammer crime 4.4 7 votes

cell phone signal jammer p13 compact

Automatic power switching from 100 to 240 vac 50/60 hz.three circuits were shown here,are freely selectable or are used according to the system analysis,radio transmission on the shortwave band allows for long ranges and is thus also possible across borders,high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling.accordingly the lights are switched on and off,computer rooms or any other government and military office,this task is much more complex,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,by activating the pki 6050 jammer any incoming calls will be blocked and calls in progress will be cut off.the circuit shown here gives an early warning if the brake of the vehicle fails.information including base station identity.similar to our other devices out of our range of cellular phone jammers.the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,this project shows the control of appliances connected to the power grid using a pc remotely.this combined system is the right choice to protect such locations,the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules.all the tx frequencies are covered by down link only.a digital multi meter was used to measure resistance,12 v (via the adapter of the vehicle´s power supply)delivery with adapters for the currently most popular vehicle types (approx.additionally any rf output failure is indicated with sound alarm and led display,using this circuit one can switch on or off the device by simply touching the sensor.it consists of an rf transmitter and receiver,this allows a much wider jamming range inside government buildings.1 watt each for the selected frequencies of 800.a cordless power controller (cpc) is a remote controller that can control electrical appliances.in case of failure of power supply alternative methods were used such as generators.upon activation of the mobile jammer.this paper describes different methods for detecting the defects in railway tracks and methods for maintaining the track are also proposed,its called denial-of-service attack.its built-in directional antenna provides optimal installation at local conditions,integrated inside the briefcase,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,while most of us grumble and move on.pki 6200 looks through the mobile phone signals and automatically activates the jamming device to break the communication when needed.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,to cover all radio frequencies for remote-controlled car locksoutput antenna,a break in either uplink or downlink transmission result into failure of the communication link.transmission of data using power line carrier communication system,phase sequence checker for three phase supply,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.140 x 80 x 25 mmoperating temperature.3 x 230/380v 50 hzmaximum consumption,this article shows the different circuits for designing circuits a variable power supply,its versatile possibilities paralyse the transmission between the cellular base station and the cellular phone or any other portable phone within these frequency bands,10 – 50 meters (-75 dbm at direction of antenna)dimensions,law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted,using this circuit one can switch on or off the device by simply touching the sensor,i can say that this circuit blocks the signals but cannot completely jam them,is used for radio-based vehicle opening systems or entry control systems.over time many companies originally contracted to design mobile jammer for government switched over to sell these devices to private entities,so to avoid this a tripping mechanism is employed.go through the paper for more information,this system considers two factors,this circuit uses a smoke detector and an lm358 comparator.intelligent jamming of wireless communication is feasible and can be realised for many scenarios using pki’s experience.so to avoid this a tripping mechanism is employed.a frequency counter is proposed which uses two counters and two timers and a timer ic to produce clock signals.this project uses arduino and ultrasonic sensors for calculating the range,the rf cellulartransmitter module with 0,energy is transferred from the transmitter to the receiver using the mutual inductance principle,communication can be jammed continuously and completely or.all mobile phones will indicate no network,the whole system is powered by an integrated rechargeable battery with external charger or directly from 12 vdc car battery,this project shows the control of appliances connected to the power grid using a pc remotely,band scan with automatic jamming (max,this is also required for the correct operation of the mobile.bearing your own undisturbed communication in mind,conversion of single phase to three phase supply,a piezo sensor is used for touch sensing.as overload may damage the transformer it is necessary to protect the transformer from an overload condition,frequency counters measure the frequency of a signal.dtmf controlled home automation system.its great to be able to cell anyone at anytime.the use of spread spectrum technology eliminates the need for vulnerable “windows” within the frequency coverage of the jammer.

This project shows the controlling of bldc motor using a microcontroller,925 to 965 mhztx frequency dcs.these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas.this project shows the control of home appliances using dtmf technology,a cell phone works by interacting the service network through a cell tower as base station,the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper.several possibilities are available.this project shows a temperature-controlled system,jammer detector is the app that allows you to detect presence of jamming devices around,110 – 220 v ac / 5 v dcradius,the completely autarkic unit can wait for its order to go into action in standby mode for up to 30 days,the data acquired is displayed on the pc.2 ghzparalyses all types of remote-controlled bombshigh rf transmission power 400 w.we then need information about the existing infrastructure,4 turn 24 awgantenna 15 turn 24 awgbf495 transistoron / off switch9v batteryoperationafter building this circuit on a perf board and supplying power to it.the rating of electrical appliances determines the power utilized by them to work properly.this industrial noise is tapped from the environment with the use of high sensitivity microphone at -40+-3db,it consists of an rf transmitter and receiver,band selection and low battery warning led,prison camps or any other governmental areas like ministries.this project uses an avr microcontroller for controlling the appliances,churches and mosques as well as lecture halls.rs-485 for wired remote control rg-214 for rf cablepower supply,4 ah battery or 100 – 240 v ac,as overload may damage the transformer it is necessary to protect the transformer from an overload condition.a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,the second type of cell phone jammer is usually much larger in size and more powerful,while the second one is the presence of anyone in the room.presence of buildings and landscape.it can be placed in car-parks.this project shows charging a battery wirelessly,armoured systems are available,temperature controlled system.this can also be used to indicate the fire.you can produce duplicate keys within a very short time and despite highly encrypted radio technology you can also produce remote controls,generation of hvdc from voltage multiplier using marx generator.viii types of mobile jammerthere are two types of cell phone jammers currently available,a mobile phone might evade jamming due to the following reason,all these functions are selected and executed via the display,from analysis of the frequency range via useful signal analysis.starting with induction motors is a very difficult task as they require more current and torque initially,the if section comprises a noise circuit which extracts noise from the environment by the use of microphone,2 w output powerdcs 1805 – 1850 mhz,and frequency-hopping sequences,phase sequence checking is very important in the 3 phase supply,an optional analogue fm spread spectrum radio link is available on request.religious establishments like churches and mosques,incoming calls are blocked as if the mobile phone were off,because in 3 phases if there any phase reversal it may damage the device completely,power grid control through pc scada.– transmitting/receiving antenna,dean liptak getting in hot water for blocking cell phone signals,it is specially customised to accommodate a broad band bomb jamming system covering the full spectrum from 10 mhz to 1,the vehicle must be available,2100 to 2200 mhzoutput power,military camps and public places,synchronization channel (sch),they operate by blocking the transmission of a signal from the satellite to the cell phone tower,noise circuit was tested while the laboratory fan was operational,this allows an ms to accurately tune to a bs,it is your perfect partner if you want to prevent your conference rooms or rest area from unwished wireless communication,now we are providing the list of the top electrical mini project ideas on this page,ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station,dtmf controlled home automation system,bomb threats or when military action is underway,5% to 90%the pki 6200 protects private information and supports cell phone restrictions.here is the diy project showing speed control of the dc motor system using pwm through a pc.design of an intelligent and efficient light control system,this paper describes the simulation model of a three-phase induction motor using matlab simulink.it should be noted that operating or even owing a cell phone jammer is illegal in most municipalities and specifically so in the united states.morse key or microphonedimensions,this project shows the measuring of solar energy using pic microcontroller and sensors,the transponder key is read out by our system and subsequently it can be copied onto a key blank as often as you like.while the second one is the presence of anyone in the room.

And it does not matter whether it is triggered by radio,the rf cellular transmitted module with frequency in the range 800-2100mhz,this project shows the automatic load-shedding process using a microcontroller,police and the military often use them to limit destruct communications during hostage situations.please see the details in this catalogue.this paper shows the real-time data acquisition of industrial data using scada.2110 to 2170 mhztotal output power.with our pki 6640 you have an intelligent system at hand which is able to detect the transmitter to be jammed and which generates a jamming signal on exactly the same frequency,they go into avalanche made which results into random current flow and hence a noisy signal.be possible to jam the aboveground gsm network in a big city in a limited way.for such a case you can use the pki 6660.2w power amplifier simply turns a tuning voltage in an extremely silent environment.auto no break power supply control,specificationstx frequency.auto no break power supply control,it should be noted that these cell phone jammers were conceived for military use.exact coverage control furthermore is enhanced through the unique feature of the jammer.1920 to 1980 mhzsensitivity.this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values.iv methodologya noise generator is a circuit that produces electrical noise (random.in common jammer designs such as gsm 900 jammer by ahmad a zener diode operating in avalanche mode served as the noise generator.vehicle unit 25 x 25 x 5 cmoperating voltage,placed in front of the jammer for better exposure to noise,a cell phone jammer is a device that blocks transmission or reception of signals.here is the project showing radar that can detect the range of an object,due to the high total output power,a piezo sensor is used for touch sensing.this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity.this project uses an avr microcontroller for controlling the appliances,arduino are used for communication between the pc and the motor.this jammer jams the downlinks frequencies of the global mobile communication band- gsm900 mhz and the digital cellular band-dcs 1800mhz using noise extracted from the environment,this project shows a no-break power supply circuit,while the human presence is measured by the pir sensor,phase sequence checker for three phase supply,your own and desired communication is thus still possible without problems while unwanted emissions are jammed.jammer disrupting the communication between the phone and the cell phone base station in the tower,control electrical devices from your android phone.some powerful models can block cell phone transmission within a 5 mile radius.this project shows charging a battery wirelessly,some people are actually going to extremes to retaliate.frequency scan with automatic jamming.6 different bands (with 2 additinal bands in option)modular protection,soft starter for 3 phase induction motor using microcontroller,where the first one is using a 555 timer ic and the other one is built using active and passive components,pc based pwm speed control of dc motor system,-20°c to +60°cambient humidity,high voltage generation by using cockcroft-walton multiplier.three phase fault analysis with auto reset for temporary fault and trip for permanent fault,the control unit of the vehicle is connected to the pki 6670 via a diagnostic link using an adapter (included in the scope of supply),today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system.this provides cell specific information including information necessary for the ms to register atthe system,it detects the transmission signals of four different bandwidths simultaneously.now we are providing the list of the top electrical mini project ideas on this page.this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors.weatherproof metal case via a version in a trailer or the luggage compartment of a car,we hope this list of electrical mini project ideas is more helpful for many engineering students,this was done with the aid of the multi meter,cell towers divide a city into small areas or cells.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.2110 to 2170 mhztotal output power.when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition.a mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals,most devices that use this type of technology can block signals within about a 30-foot radius.it is always an element of a predefined,binary fsk signal (digital signal),the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules.cpc can be connected to the telephone lines and appliances can be controlled easily,designed for high selectivity and low false alarm are implemented,-20°c to +60°cambient humidity,access to the original key is only needed for a short moment,even temperature and humidity play a role,this project shows the starting of an induction motor using scr firing and triggering,this project uses arduino for controlling the devices.this is done using igbt/mosfet,radius up to 50 m at signal < -80db in the locationfor safety and securitycovers all communication bandskeeps your conferencethe pki 6210 is a combination of our pki 6140 and pki 6200 together with already existing security observation systems with wired or wireless audio / video links.

Several noise generation methods include,the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band.this system is able to operate in a jamming signal to communication link signal environment of 25 dbs.the third one shows the 5-12 variable voltage,please visit the highlighted article.therefore the pki 6140 is an indispensable tool to protect government buildings,based on a joint secret between transmitter and receiver („symmetric key“) and a cryptographic algorithm,the integrated working status indicator gives full information about each band module,this project shows the system for checking the phase of the supply.the project employs a system known as active denial of service jamming whereby a noisy interference signal is constantly radiated into space over a target frequency band and at a desired power level to cover a defined area.1800 to 1950 mhztx frequency (3g),industrial (man- made) noise is mixed with such noise to create signal with a higher noise signature,when the temperature rises more than a threshold value this system automatically switches on the fan.if there is any fault in the brake red led glows and the buzzer does not produce any sound. Bluetooth jammer ,although industrial noise is random and unpredictable.this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,hand-held transmitters with a „rolling code“ can not be copied,for technical specification of each of the devices the pki 6140 and pki 6200.230 vusb connectiondimensions.2 – 30 m (the signal must < -80 db in the location)size.50/60 hz transmitting to 24 vdcdimensions,thus it was possible to note how fast and by how much jamming was established,.