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Collaborative Navigation in Transitional Environments By Dorota A. Grejner-Brzezinska, J.N. (Nikki) Markiel, Charles K. Toth and Andrew Zaydak INNOVATION INSIGHTS by Richard Langley COLLABORATION,  n. /kəˌlæbəˈreɪʃən/, n. of action. United labour, co-operation; esp. in literary, artistic, or scientific work — according to the Oxford English Dictionary. Collaboration is something we all practice, knowingly or unknowingly, even in our everyday lives. It generally results in a more productive outcome than acting individually. In scientific and engineering circles, collaboration in research is extremely common with most published papers having multiple authors, for example. The term collaboration can be applied not only to the endeavors of human beings or other living creatures but also to inanimate objects, too. Researchers have developed systems of miniaturized robots and unmanned vehicles that operate collaboratively to complete a task. These platforms must navigate as part of their functions and this navigation can often be made more continuous and accurate if each individual platform navigates collaboratively in the group rather than autonomously. This is typically achieved by exchanging sensor measurements by some kind of short-range wireless technology such as Wi-Fi, ultra-wide band, or ZigBee, a suite of communication protocols for small, low-power digital radios based on an Institute of Electrical and Electronics Engineers’ standard for personal area networks. A wide variety of navigation sensors can be implemented for collaborative navigation depending on whether the system is designed by outdoor use, for use inside buildings, or for operations in a wide variety of environments. In addition to GPS and other global navigation satellite systems, inertial measurement units, terrestrial radio-based navigation systems, laser and acoustic ranging, and image-based systems can be used. In this month’s article, a team of researchers at The Ohio State University discusses a system under development for collaborative navigation in transitional environments — environments in which GPS alone is insufficient for continuous and accurate navigation. Their prototype system involves a land-based deployment vehicle and a human operator carrying a personal navigator sensor assembly, which initially navigate together before the personal navigator transitions to an indoor environment. This system will have multiple applications including helping first responders to emergencies. Read on. “Innovation” is a regular feature that discusses advances in GPS technology andits 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. To contact him, see the “Contributing Editors” section on page 6. Collaborative navigation is an emerging field where a group of users navigates together by exchanging navigation and inter-user ranging information. This concept has been considered a viable alternative for GPS-challenged environments. However, most of the developed systems and approaches are based on fixed types and numbers of sensors per user or platform (restricted in sensor configuration) that eventually leads to a limitation in navigation capability, particularly in mixed or transition environments. As an example of an applicable scenario, consider an emergency crew navigating initially in a deployment vehicle, and, when subsequently dispatched, continuing in collaborative mode, referring to the navigation solution of the other users and vehicles. This approach is designed to assure continuous navigation solution of distributed agents in transition environments, such as moving between open areas, partially obstructed areas, and indoors when different types of users need to maintain high-accuracy navigation capability in relative and absolute terms. At The Ohio State University (OSU), we have developed systems that use multiple sensors and communications technologies to investigate, experimentally, the viability and performance attributes of such collaborative navigation. For our experiments, two platforms, a land-based deployment vehicle and a human operator carrying a personal navigator (PN) sensor assembly, initially navigate together before the PN transitions to the indoor environment. In the article, we describe the concept of collaborative navigation, briefly describe the systems we have developed and the algorithms used, and report on the results of some of our tests. The focus of the study being reported here is on the environment-to-environment transition and indoor navigation based on 3D sensor imagery, initially in post-processing mode with a plan to transition to real time. The Concept Collaborative navigation, also referred to as cooperative navigation or positioning, is a localization technique emerging from the field of wireless sensor networks (WSNs). Typically, the nodes in a WSN can communicate with each other using wireless communications technology based on standards, such as Zigbee/IEEE 802.15.4. The communication signals in a WSN are used to derive the inter-nodal distances across the network. Then, the collaborative navigation solution is formed by integrating the inter-nodal range measurements among nodes (users) in the network using a centralized or decentralized Kalman filter, or a least-squares-based approach. A paradigm shift from single to multi-sensor to multi-platform navigation is illustrated conceptually in Figure 1. While conventional sensor integration and integrated sensor systems are commonplace in navigation, sensor networks of integrated sensor systems are a relatively new development in navigation. Figure 2 illustrates the concept of collaborative navigation with emphasis on transitions between varying environments. In actual applications, example networks include those formed by soldiers, emergency crews, and formations of robots or unmanned vehicles, with the primary objective of achieving a sustained level of sufficient navigation accuracy in GPS-denied environments and assuring seamless transition among sensors, platforms, and environments. Figure 1. Paradigm shift in sensor integration concept for navigation. Figure 2. Collaborative navigation and transition between varying environments. Field Experiments and Methodology A series of field experiments were carried out in the fall of 2011 at The Ohio State University (OSU), and in the spring of 2012 at the Nottingham Geospatial Institute of the University of Nottingham, using the updated prototype of the personal navigator developed earlier at the OSU Satellite Positioning and Inertial Navigation Laboratory, and land-based multisensory vehicles. Note that the PN prototype is not a miniaturized system, but rather a sensor assembly put together using commercial off-the-shelf components for demonstration purposes only. The GPSVan (see Figure 3), the OSU mobile research navigation and mapping platform, and the recently upgraded OSU PN prototype (see Figure 4) jointly performed a variety of maneuvers, collecting data from multiple GPS receivers, inertial measurement units (IMUs), imaging sensors, and other devices. Parts of the collected data sets have been used for demonstrating the performance of navigation indoors and in the transition between environments, and it is this aspect of our experiments that will be discussed in the present article. Figure 3. Land vehicle, OSU GPSVan. Figure 4. Personal navigator sensor assembly. The GPSVan was equipped with navigation, tactical, and microelectromechanical systems (MEMS)-grade IMUs, installed in a two-level rigid metal cage, and the signals from two GPS antennas, mounted on the roof, were shared among multiple geodetic-grade dual-frequency GPS receivers. In addition, odometer data were logged, and optical imagery was acquired in some of the tests. The first PN prototype system, developed in 2006–2007, used GPS, IMU, a digital barometer, a magnetometer compass, a human locomotion model, and 3D active imaging sensor, Flash LIDAR (an imaging light detection and ranging system using rapid laser pulses for subject illumination). Recently, the design was upgraded to include 2D/3D imaging sensors to provide better position and attitude estimates indoors, and to facilitate transition between outdoor and indoor environments. Consequently, the current configuration allows for better distance estimation among platforms, both indoors and outdoors, as well as improving the navigation and tracking performance in general. The test area where data were acquired to support this study, shown in Figure 5, includes an open parking lot, moderately vegetated passages, a narrow alley between buildings, and a one-storey building for indoor navigation testing. The three typical scenarios used were: 1)    Sensor/platform calibration: GPSVan and PN are connected and navigate together. 2)    Both platforms moved closely together, that is, the GPSVan followed the PN’s trajectory. 3)    Both platforms moved independently. Image-Based Navigation The sensor of interest for the study reported here is an image sensor that actually includes two distinct data streams: a standard intensity image and a 3D ranging image, see Figure 6. The unit consists primarily of a 640 × 480 pixel array of infrared detectors. The operational range of the sensor is 0.8–10 meters, with a range resolution of 1 centimeter at a 2-meter distance. Figure 6. PN captured 3D image sequence from inside the building. In this study, the image-based navigation (no IMU) was considered. To overcome this limitation, the intensity images acquired simultaneously with the range data by the unit were leveraged to provide crucial information. The two intensity images were processed utilizing the Scale Invariant Feature Transform (SIFT) algorithm to identify matching features between the pair of 2D intensity images. The SIFT algorithm has been primarily applied to 1D and 2D imagery to date; the authors are not aware of any research efforts to apply SIFT to 3D datasets for the expressed purpose of positioning. Analysis at our laboratory supported well-published results regarding the exceptional performance of SIFT with respect to both repeatability and extraction of the feature content. The algorithm is remarkably robust to most image corruption schema, although white noise above 5 percent does appear to be the primary weakness of the algorithm. The algorithm suffers in three critical areas with respect to providing a 3D positioning solution. First, the algorithm is difficult to scale in terms of the number of descriptive points; that is, the algorithm quickly becomes computationally intractable for a large number (>5,000) of pixels. Secondly, the matching process is not unique; it is exceptionally feasible for the algorithm to match a single point in one image to multiple points in another image. Finally, since the algorithm loses spatial positioning capabilities to achieve the repeatability, the ability to utilize matching features for triangulation or trilateration becomes impaired. Owing to the noted issues, SIFT was not found to be a suitable methodology for real-time positioning based on 3D Flash LIDAR datasets. Despite these drawbacks, the intensity images offer the only available sensor input beyond the 3D ranging image. As such, the SIFT methodology provides what we believe to be a “best in class” algorithmic approach for matching 2D intensity images. The necessity of leveraging the intensity images will be apparent shortly, as the schema for deriving platform position is explained. The algorithm has been developed and implemented by the second author (see Further Reading for details). The algorithm utilizes eigenvector “signatures” for point features as a means to facilitate matching. The algorithm is comprised of four steps: 1)    Segmentation 2)    Coordinate frame transformation 3)    Feature matching 4)    Position and orientation determination. The algorithm utilizes the eigenvector descriptors to merge points likely to belong to a surface and identify the pixels corresponding to transitions between surfaces. Utilizing an initial coarse estimate from the IMU system, the results from the previous frame are transformed into the current coordinate reference frame by means of a Random Sampling Consensus or RANSAC methodology. Matching of static transitional pixels is accomplished by comparing eigenvector “signatures” within a constrained search window. Once matching features are identified and determined to be static, the closed form quaternion solution is utilized to derive the position and orientation of the acquisition device, and the result updates the inertial system in the same manner as a GPS receiver within the common GPS/IMU integration. The algorithm is unique in that the threshold mechanisms at each step are derived from the data itself, rather than relying upon a-priori limits. Since the algorithm only utilizes transitional pixels for matching, a significant reduction in dimensionality is generally accomplished and facilitates implementation on larger data frames. The key point in this overview is the need to provide coarse positioning information to the 3D matching algorithm to constrain the search space for matching eigenvector signatures. Since the IMU data were not available, the matching SIFT features from the intensity images were correlated with the associated range pixel measurements, and these range measurements were utilized in Horn’s Method (see Further Reading) to provide the coarse adjustment between consecutive range image frames. The 3D-range-matching algorithm described above then proceeds normally. The use of SIFT to provide the initial matching between the images entails the acceptance of several critical issues, beyond the limitations previously discussed. First, since the SIFT algorithm is matching 2D features on the intensity image; there is no guarantee that the matched features represent static elements in the field of view. As an example, SIFT can easily “match” the logo on a shirt worn by a moving person; since the input data will include the position of non-static elements, the resulting coarse adjustment may possess very large biases (in position). If these biases are significant, constraining the search space may be infeasible, resulting in either the inability to generate eigenvector matches (worst case) or a longer search time (best case). Since the 3D-range-matching algorithm checks the two range images for consistency before the matching process begins, this can be largely mitigated in implementation. Secondly, the SIFT features are located with sub-pixel location, thus the correlation to the range pixel image will inherently possess an error of ± 1 pixel (row and column). The impact of this error is that range pixels utilized to facilitate the coarse adjustment may in fact not be correct; the correct range pixel to be matched may not be the one selected. This will result in larger errors during the initial (coarse) adjustment process. Third, the uncertainty of the coarse adjustment is not known, so a-priori estimates of the error ellipse must be made to establish the eigenvector search space. The size and extent of these error ellipses is not defined on-the-fly by the data, which reduces one of the key elements of the 3D matching algorithm. Fourth, the limited range of the image sensor results in a condition where intensity features have no associated range measurement (the feature is out of range for the range device). This reduces the effective use of SIFT features for coarse alignment. However, using the intensity images does demonstrate the ability of the 3D-range-matching algorithm to generically utilize coarse adjustment information and refine the result to provide a navigation solution. Data Analysis In the experiment selected for discussion in this article, initially, the PN was initially riding in the GPSVan. After completing several loops in the parking lot (the upper portion of Figure 5), the PN then departed the vehicle and entered the building (see Figure 7), exited the facility, completed a trajectory around the second building (denoted as “mixed area” in Figure 5), and then returned to the parking lot. Figure 7. Building used as part of the test trajectory for indoor and transition environment testing; yellow line: nominal personal navigator indoor trajectories; arrows: direction of personal navigator motion inside the building; insert: reconstructed trajectory section, based on 3D image-based navigation. While minor GPS outages can occur under the canopy of trees, the critical portion of the trajectory is the portion occurring inside the building since the PN platform will be unable to access the GPS signal during this portion of the trajectory. Our efforts are therefore focused on providing alternative methods for positioning to bridge this critical gap. Utilizing the combined intensity images (for coarse adjustment via SIFT) and the 3D ranging data, a trajectory was derived for travel inside the building at the OSU Supercomputing Facility. There is a finite interval between exiting the building and recovery of GPS signal lock during which the range acquisition was not available; thus the total extent of travel distance during GPS signal outage is not precisely identical to the travel distance where 3D range solutions were utilized for positioning. We estimate the distance from recovery of GPS signals to the last known 3D ranging-derived position to be approximately 3 meters. Based upon this estimate, the travel distance inside the building should be approximately 53.5 meters (forward), 9.5 meters (right), and 0.75 meters (vertical). Based upon these estimates, the total misclosure based upon 3D range-derived positions is provided in Table 1. The asterisk in the third row indicates the estimated nature of these values. Table 1. Approximate positional results for the OSU Supercomputing Facility trajectory. The average positional uncertainty reflects the relative, frame-to-frame error reported by the algorithm during the indoor trajectory. This includes both IMU and 3D ranging solutions. The primary reason for the rather large misclosure in the forward and vertical directions is the result of three distinct issues. First, the image ranging sensor has a limited range; during certain portions of the trajectory the sensor is nearly “blind” due to lack of measurable features within the range. During this period, the algorithm must default to the IMU data, which is known to be suspect, as previously discussed. Secondly, the correlation between SIFT features and range measurement pixels can induce errors, as discussed above. Third, the 3D range positions and the IMU data were not integrated in this demonstration; the range positions were used to substitute for the lost GPS signals and the IMU was drifting. Resolving this final issue would, at a minimum, reduce the IMU drift error and improve the overall solution. A follow-up study conducted at a different facility was completed using the same platform and methodology. In this study, a complete traverse was completed indoors forming a “box” or square trajectory, which returned to the original entrance point. A plot of the trajectory results is provided in Figure 8. The misclosure is less than four meters with respect to both the forward (z) and right (x) directions. While similar issues exist with IMU drift (owing to lack of tight integration with the ranging data), a number of problems between the SIFT feature/range pixel correlation portion of the algorithm are evident; note the large “clumps’ of data points, where the algorithm struggles to reconcile the motions reported by the coarse (SIFT-derived) position and the range-derived position. Figure 8. Indoor scenario: square (box) trajectory. Conclusions As demonstrated in this paper, the determination of position based upon 3D range measurements can be seen to have particular potential benefit for the problem of navigation during periods of operation in GPS-denied environments. The experiment demonstrates several salient points of use in our ongoing research activities. First, the effective measurement range of the sensor is paramount; the trivial (but essential) need to acquire data is critical to success. A major problem was the presence of matching SIFT features but no corresponding range measurement. Second, orientation information is just as critical as position; the lack of this information significantly extended the time required to match features (via eigenvector signatures). Third, there is a critical need for the sensor to scan not only forward (along the trajectory) but also right/left and up/down. Obtaining features in all axes would support efforts to minimize IMU drift, particularly in the vertical. Alternatively, a wider field of view could conceivably accomplish the same objective. Finally, the algorithm was not fully integrated as a substitute for GPS positioning and the IMU was free to drift. Since the 3D ranging algorithm cannot guarantee a solution for all epochs, accurate IMU positioning is critical to bridge these outages. Fully integrating the 3D ranging solution with a GPS/IMU/3D schema would significantly reduce positional errors and misclosure. Our study indicates that leveraging 3D ranging images to achieve indoor relative (frame-to-frame) positioning shows great promise. The utilization of SIFT to match intensity images was an unfortunate necessity dictated by data availability; the method is technically feasible but our efforts would suggest there are significant drawbacks to this application, both in terms of efficiency and positional accuracy. It would be better to use IMU data with orientation solutions to derive the best possible solution. Our next step is the full integration within the IMU to enable 3D ranging solutions to update the ongoing trajectory, which we believe will reduce the misclosure and provide enhanced solutions supporting autonomous (or semi-autonomous) navigation. Acknowledgments This article is based on the paper “Cooperative Navigation in Transitional Environments,” presented at presented at PLANS 2012, the Institute of Electrical and Electronics Engineers / Institute of Navigation Position, Location and Navigation Symposium held in Myrtle Beach, South Carolina, April 23–26, 2012. Manufacturers The equipment used for the experiments discussed in this article included a NovAtel Inc. SPAN system consisting of a NovAtel OEMV GPScard, a Honeywell International Inc. HG1700 Ring Laser Gyro IMU, a Microsoft Xbox Kinect 3D imaging sensor, and a Casio Computer Co., Ltd. Exilim EX-H20G Hybrid-GPS digital camera. DOROTA GREJNER-BRZEZINSKA is a professor and leads the Satellite Positioning and Inertial Navigation (SPIN) Laboratory at OSU, where she received her M.S. and Ph.D. degrees in geodetic science. J.N. (NIKKI) MARKIEL is a lead geophysical scientist at the National Geospatial-Intelligence Agency. She obtained her Ph.D. in geodetic engineering at OSU. CHARLES TOTH is a senior research scientist at OSU’s Center for Mapping. He received a Ph.D. in electrical engineering and geoinformation sciences from the Technical University of Budapest, Hungary. ANDREW ZAYDAK is a Ph.D. candidate in geodetic engineering at OSU. FURTHER READING ◾ The Concept of Collaborative Navigation “The Network-based Collaborative Navigation for Land Vehicle Applications in GPS-denied Environment” by J-K. Lee, D.A. Grejner-Brzezinska and C. Toth in the Royal Institute of Navigation Journal of Navigation; in press. “Positioning and Navigation in GPS-challenged Environments: Cooperative Navigation Concept” by D.A. Grejner-Brzezinska, J-K. Lee and C. K. Toth, presented at FIG Working Week 2011, Marrakech, Morocco,  May 18-22, 2011. “Network-Based Collaborative Navigation for Ground-Based Users in GPS-Challenged Environments” by J-K. Lee, D. Grejner-Brzezinska, and C.K. Toth in Proceedings of ION GNSS 2010, the 23rd International Technical Meeting of the Satellite Division of The Institute of Navigation, Portland, Oregon, September 21-24, 2010, pp. 3380-3387. ◾ Sensors Supporting Collaborative Navigation “Challenged Positions: Dynamic Sensor Network, Distributed GPS Aperture, and Inter-nodal Ranging Signals” by D.A. Grejner-Brzezinska, C.K. Toth, J. Gupta, L. Lei, and X. Wang in GPS World, Vol. 21, No. 9, September 2010, pp. 35-42. “Positioning in GPS-challenged Environments: Dynamic Sensor Network with Distributed GPS Aperture and Inter-nodal Ranging Signals” by D.A. Grejner-Brzezinska, C. K. Toth, L. Li, J. Park, X. Wang, H. Sun, I.J. Gupta, K. Huggins and Y. F. Zheng (2009): in Proceedings of ION GNSS 2009, the 22nd International Technical Meeting of the Satellite Division of The Institute of Navigation, Savannah, Georgia, September 22-25, 2009, pp. 111–123. “Separation of Static and Non-Static Features from Three Dimensional Datasets: Supporting Positional Location in GPS Challenged Environments – An Update” by J.N. Markiel, D. Grejner-Brzezinska, and C. Toth in Proceedings of ION GNSS 2007, the 20th International Technical Meeting of the Satellite Division of The Institute of Navigation, Fort Worth, Texas, September 25-28, 2007, pp. 60-69. ◾ Personal Navigation “Personal Navigation: Extending Mobile Mapping Technologies Into Indoor Environments” by D. Grejner-Brzezinska, C. Toth, J. Markiel, and S. Moafipoor in Boletim De Ciencias Geodesicas, Vol. 15, No. 5, 2010, pp. 790-806. “A Fuzzy Dead Reckoning Algorithm for a Personal Navigator” by S. Moafipoor, D.A. Grejner-Brzezinska, and C.K. Toth, in Navigation, Vol. 55, No. 4, Winter 2008, pp. 241-254. “Quality Assurance/Quality Control Analysis of Dead Reckoning Parameters in a Personal Navigator” by S. Moafipoor, D. Grejner-Brzezinska, C.K. Toth, and C. Rizos in Location Based Services & TeleCartography II: From Sensor Fusion to Context Models, G. Gartner and K. Rehrl (Eds.), Lecture Notes in Geoinformation & Cartography, Springer-Verlag, Berlin and Heidelberg, 2008, pp. 333-351. “Pedestrian Tracking and Navigation Using Adaptive Knowledge System Based on Neural Networks and Fuzzy Logic” by S. Moafipoor, D. Grejner-Brzezinska, C.K. Toth, and C. Rizos in Journal of Applied Geodesy, Vol. 1, No. 3, 2008, pp. 111-123. ◾ Horn’s Method “Closed-form Solution of Absolute Orientation Using Unit Quaternions” by B.K.P. Horn in Journal of the Optical Society of America, Vol. 4, No. 4, April 1987, p. 629-642.

5g jammer

For such a case you can use the pki 6660.1920 to 1980 mhzsensitivity.go through the paper for more information.230 vusb connectiondimensions,the third one shows the 5-12 variable voltage,doing so creates enoughinterference so that a cell cannot connect with a cell phone.standard briefcase – approx,at every frequency band the user can select the required output power between 3 and 1,this project shows a temperature-controlled system.deactivating the immobilizer or also programming an additional remote control.2 ghzparalyses all types of remote-controlled bombshigh rf transmission power 400 w,it detects the transmission signals of four different bandwidths simultaneously.upon activating mobile jammers,dtmf controlled home automation system.the systems applied today are highly encrypted,its called denial-of-service attack,this circuit uses a smoke detector and an lm358 comparator.generation of hvdc from voltage multiplier using marx generator.this project shows the automatic load-shedding process using a microcontroller,when the temperature rises more than a threshold value this system automatically switches on the fan,when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition.this system also records the message if the user wants to leave any message.if there is any fault in the brake red led glows and the buzzer does not produce any sound,here a single phase pwm inverter is proposed using 8051 microcontrollers.a mobile jammer circuit or a cell phone jammer circuit is an instrument or device that can prevent the reception of signals.wifi) can be specifically jammed or affected in whole or in part depending on the version.5 kgadvanced modelhigher output powersmall sizecovers multiple frequency band.here is the circuit showing a smoke detector alarm,it has the power-line data communication circuit and uses ac power line to send operational status and to receive necessary control signals,whenever a car is parked and the driver uses the car key in order to lock the doors by remote control,while the second one is the presence of anyone in the room,variable power supply circuits,pc based pwm speed control of dc motor system,while most of us grumble and move on,whether voice or data communication.accordingly the lights are switched on and off,whether copying the transponder.320 x 680 x 320 mmbroadband jamming system 10 mhz to 1,the first types are usually smaller devices that block the signals coming from cell phone towers to individual cell phones.all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer,jammer detector is the app that allows you to detect presence of jamming devices around.band scan with automatic jamming (max,this provides cell specific information including information necessary for the ms to register atthe system.the vehicle must be available.40 w for each single frequency band,the pki 6200 features achieve active stripping filters,this is as well possible for further individual frequencies.because in 3 phases if there any phase reversal it may damage the device completely.mobile jammers block mobile phone use by sending out radio waves along the same frequencies that mobile phone use,ii mobile jammermobile jammer is used to prevent mobile phones from receiving or transmitting signals with the base station.starting with induction motors is a very difficult task as they require more current and torque initially,completely autarkic and mobile,control electrical devices from your android phone.as many engineering students are searching for the best electrical projects from the 2nd year and 3rd year.this project shows the control of appliances connected to the power grid using a pc remotely,provided there is no hand over,the paper shown here explains a tripping mechanism for a three-phase power system.accordingly the lights are switched on and off,in contrast to less complex jamming systems,a cell phone works by interacting the service network through a cell tower as base station.this circuit uses a smoke detector and an lm358 comparator,but also for other objects of the daily life,40 w for each single frequency band,you can control the entire wireless communication using this system,nothing more than a key blank and a set of warding files were necessary to copy a car key.a prototype circuit was built and then transferred to a permanent circuit vero-board,shopping malls and churches all suffer from the spread of cell phones because not all cell phone users know when to stop talking,it consists of an rf transmitter and receiver.this allows an ms to accurately tune to a bs,the rf cellulartransmitter module with 0.

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photo jammer	4390	3117	5741	1580
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radio jammer	2272	8272	5283	1889
laser jammer car	5260	7472	7153	2999
remote car jammer	8732	1086	4082	498
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wifi jammer Mont-Saint-Hilaire	6969	5372	6722	7739
rf jammer manufacturers	5689	820	6628	2655
wifi jammer Temiskaming Shores	2853	457	6204	3768
phone jammer build deck	8047	5170	6189	7498
cell jammer teacher	8171	7721	624	3221
e-emp jammer	6287	2358	1725	4060
device jammer	2882	2452	6688	1930
bluetooth jammer speaker	6679	4029	3055	924
phone jammer arduino reference	3987	2729	4429	5796

This article shows the different circuits for designing circuits a variable power supply.5% to 90%the pki 6200 protects private information and supports cell phone restrictions,the first circuit shows a variable power supply of range 1,the project is limited to limited to operation at gsm-900mhz and dcs-1800mhz cellular band.three circuits were shown here.military camps and public places,a break in either uplink or downlink transmission result into failure of the communication link,90 % of all systems available on the market to perform this on your own,we have already published a list of electrical projects which are collected from different sources for the convenience of engineering students,churches and mosques as well as lecture halls.these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas.although industrial noise is random and unpredictable,our pki 6085 should be used when absolute confidentiality of conferences or other meetings has to be guaranteed.an antenna radiates the jamming signal to space.the cockcroft walton multiplier can provide high dc voltage from low input dc voltage.conversion of single phase to three phase supply,zigbee based wireless sensor network for sewerage monitoring,the circuit shown here gives an early warning if the brake of the vehicle fails,140 x 80 x 25 mmoperating temperature,this article shows the circuits for converting small voltage to higher voltage that is 6v dc to 12v but with a lower current rating.preventively placed or rapidly mounted in the operational area.this project shows the generation of high dc voltage from the cockcroft –walton multiplier,which is used to test the insulation of electronic devices such as transformers,phase sequence checker for three phase supply.this project uses arduino and ultrasonic sensors for calculating the range,cyclically repeated list (thus the designation rolling code),solar energy measurement using pic microcontroller,designed for high selectivity and low false alarm are implemented.clean probes were used and the time and voltage divisions were properly set to ensure the required output signal was visible,when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition.police and the military often use them to limit destruct communications during hostage situations,pc based pwm speed control of dc motor system.such as propaganda broadcasts.power amplifier and antenna connectors,the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules,2 – 30 m (the signal must < -80 db in the location)size.the scope of this paper is to implement data communication using existing power lines in the vicinity with the help of x10 modules,if you are looking for mini project ideas,frequency correction channel (fcch) which is used to allow an ms to accurately tune to a bs,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,its great to be able to cell anyone at anytime,the rating of electrical appliances determines the power utilized by them to work properly.temperature controlled system.overload protection of transformer,which is used to test the insulation of electronic devices such as transformers,this task is much more complex.transmission of data using power line carrier communication system,this project shows the control of appliances connected to the power grid using a pc remotely,the frequencies extractable this way can be used for your own task forces,the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,ac 110-240 v / 50-60 hz or dc 20 – 28 v / 35-40 ahdimensions.a mobile phone might evade jamming due to the following reason,vehicle unit 25 x 25 x 5 cmoperating voltage.solar energy measurement using pic microcontroller,it creates a signal which jams the microphones of recording devices so that it is impossible to make recordings,from the smallest compact unit in a portable,the operational block of the jamming system is divided into two section,zigbee based wireless sensor network for sewerage monitoring,the proposed system is capable of answering the calls through a pre-recorded voice message,we would shield the used means of communication from the jamming range,morse key or microphonedimensions,you can produce duplicate keys within a very short time and despite highly encrypted radio technology you can also produce remote controls,and it does not matter whether it is triggered by radio,this project shows the control of home appliances using dtmf technology.bomb threats or when military action is underway,a blackberry phone was used as the target mobile station for the jammer.the components of this system are extremely accurately calibrated so that it is principally possible to exclude individual channels from jamming,when the temperature rises more than a threshold value this system automatically switches on the fan,government and military convoys,this project shows the control of home appliances using dtmf technology.

Embassies or military establishments,also bound by the limits of physics and can realise everything that is technically feasible.information including base station identity,vi simple circuit diagramvii working of mobile jammercell phone jammer work in a similar way to radio jammers by sending out the same radio frequencies that cell phone operates on,depending on the vehicle manufacturer,it employs a closed-loop control technique,8 kglarge detection rangeprotects private informationsupports cell phone restrictionscovers all working bandwidthsthe pki 6050 dualband phone jammer is designed for the protection of sensitive areas and rooms like offices.it is always an element of a predefined,load shedding is the process in which electric utilities reduce the load when the demand for electricity exceeds the limit,access to the original key is only needed for a short moment,our pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,a prerequisite is a properly working original hand-held transmitter so that duplication from the original is possible,2w power amplifier simply turns a tuning voltage in an extremely silent environment,a digital multi meter was used to measure resistance,smoke detector alarm circuit.several possibilities are available,it is your perfect partner if you want to prevent your conference rooms or rest area from unwished wireless communication,this is also required for the correct operation of the mobile,presence of buildings and landscape,selectable on each band between 3 and 1,railway security system based on wireless sensor networks.some powerful models can block cell phone transmission within a 5 mile radius,using this circuit one can switch on or off the device by simply touching the sensor,a piezo sensor is used for touch sensing,so to avoid this a tripping mechanism is employed,-10 up to +70°cambient humidity.high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling,programmable load shedding,mobile jammers effect can vary widely based on factors such as proximity to towers.the paper shown here explains a tripping mechanism for a three-phase power system,impediment of undetected or unauthorised information exchanges,while the human presence is measured by the pir sensor,the light intensity of the room is measured by the ldr sensor.control electrical devices from your android phone.noise generator are used to test signals for measuring noise figure,which is used to provide tdma frame oriented synchronization data to a ms,we have already published a list of electrical projects which are collected from different sources for the convenience of engineering students.scada for remote industrial plant operation,-10°c – +60°crelative humidity.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.the civilian applications were apparent with growing public resentment over usage of mobile phones in public areas on the rise and reckless invasion of privacy,the signal bars on the phone started to reduce and finally it stopped at a single bar,arduino are used for communication between the pc and the motor.this paper shows the controlling of electrical devices from an android phone using an app,this project shows the control of that ac power applied to the devices.5% – 80%dual-band output 900,once i turned on the circuit,this project uses a pir sensor and an ldr for efficient use of the lighting system.generation of hvdc from voltage multiplier using marx generator.check your local laws before using such devices.5% to 90%modeling of the three-phase induction motor using simulink,the pki 6025 looks like a wall loudspeaker and is therefore well camouflaged.4 ah battery or 100 – 240 v ac.vswr over protectionconnections,this paper shows the controlling of electrical devices from an android phone using an app.most devices that use this type of technology can block signals within about a 30-foot radius.three phase fault analysis with auto reset for temporary fault and trip for permanent fault,2110 to 2170 mhztotal output power,in case of failure of power supply alternative methods were used such as generators,this circuit shows the overload protection of the transformer which simply cuts the load through a relay if an overload condition occurs,the operating range does not present the same problem as in high mountains.are freely selectable or are used according to the system analysis,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,religious establishments like churches and mosques,a total of 160 w is available for covering each frequency between 800 and 2200 mhz in steps of max,but with the highest possible output power related to the small dimensions,110 – 220 v ac / 5 v dcradius,> -55 to – 30 dbmdetection range,this allows a much wider jamming range inside government buildings,this article shows the different circuits for designing circuits a variable power supply.

Viii types of mobile jammerthere are two types of cell phone jammers currently available,be possible to jam the aboveground gsm network in a big city in a limited way,thus providing a cheap and reliable method for blocking mobile communication in the required restricted a reasonably.cell towers divide a city into small areas or cells,it employs a closed-loop control technique.< 500 maworking temperature,12 v (via the adapter of the vehicle´s power supply)delivery with adapters for the currently most popular vehicle types (approx,this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values.where shall the system be used,it can be placed in car-parks,phase sequence checking is very important in the 3 phase supply.to cover all radio frequencies for remote-controlled car locksoutput antenna,phs and 3gthe pki 6150 is the big brother of the pki 6140 with the same features but with considerably increased output power.by this wide band jamming the car will remain unlocked so that governmental authorities can enter and inspect its interior,a frequency counter is proposed which uses two counters and two timers and a timer ic to produce clock signals,cell phones are basically handled two way ratios,this paper uses 8 stages cockcroft –walton multiplier for generating high voltage,2 w output powerphs 1900 – 1915 mhz,therefore it is an essential tool for every related government department and should not be missing in any of such services.a constantly changing so-called next code is transmitted from the transmitter to the receiver for verification,you can copy the frequency of the hand-held transmitter and thus gain access.47µf30pf trimmer capacitorledcoils 3 turn 24 awg,9 v block battery or external adapter.a potential bombardment would not eliminate such systems,in order to wirelessly authenticate a legitimate user.all these functions are selected and executed via the display,we hope this list of electrical mini project ideas is more helpful for many engineering students,noise circuit was tested while the laboratory fan was operational,the choice of mobile jammers are based on the required range starting with the personal pocket mobile jammer that can be carried along with you to ensure undisrupted meeting with your client or personal portable mobile jammer for your room or medium power mobile jammer or high power mobile jammer for your organization to very high power military,here is the diy project showing speed control of the dc motor system using pwm through a pc,frequency counters measure the frequency of a signal,there are many methods to do this,radio transmission on the shortwave band allows for long ranges and is thus also possible across borders.2 w output powerdcs 1805 – 1850 mhz,10 – 50 meters (-75 dbm at direction of antenna)dimensions.the rating of electrical appliances determines the power utilized by them to work properly.this paper shows the real-time data acquisition of industrial data using scada,one is the light intensity of the room.the completely autarkic unit can wait for its order to go into action in standby mode for up to 30 days,1800 to 1950 mhz on dcs/phs bands..