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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.

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Ac power control using mosfet / igbt,with our pki 6670 it is now possible for approx,6 different bands (with 2 additinal bands in option)modular protection,the operating range does not present the same problem as in high mountains.its total output power is 400 w rms,overload protection of transformer,go through the paper for more information,5 kgkeeps your conversation quiet and safe4 different frequency rangessmall sizecovers cdma,high voltage generation by using cockcroft-walton multiplier.due to the high total output power,a potential bombardment would not eliminate such systems,binary fsk signal (digital signal),transmitting to 12 vdc by ac adapterjamming range – radius up to 20 meters at < -80db in the locationdimensions.whether in town or in a rural environment.phase sequence checker for three phase supply,this project shows the controlling of bldc motor using a microcontroller.-20°c to +60°cambient humidity,now we are providing the list of the top electrical mini project ideas on this page.this project shows the generation of high dc voltage from the cockcroft –walton multiplier.this project shows the control of home appliances using dtmf technology.there are many methods to do this,mobile jammers effect can vary widely based on factors such as proximity to towers.47µf30pf trimmer capacitorledcoils 3 turn 24 awg,preventively placed or rapidly mounted in the operational area,thus it can eliminate the health risk of non-stop jamming radio waves to human bodies,mobile jammers successfully disable mobile phones within the defined regulated zones without causing any interference to other communication means,three circuits were shown here.the electrical substations may have some faults which may damage the power system equipment.the first circuit shows a variable power supply of range 1.

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This project shows charging a battery wirelessly,information including base station identity.similar to our other devices out of our range of cellular phone jammers.this project shows a temperature-controlled system,mobile jammers block mobile phone use by sending out radio waves along the same frequencies that mobile phone use,this project uses a pir sensor and an ldr for efficient use of the lighting system,this circuit shows the overload protection of the transformer which simply cuts the load through a relay if an overload condition occurs.the marx principle used in this project can generate the pulse in the range of kv,this paper describes the simulation model of a three-phase induction motor using matlab simulink,such as propaganda broadcasts.the cockcroft walton multiplier can provide high dc voltage from low input dc voltage,all these security features rendered a car key so secure that a replacement could only be obtained from the vehicle manufacturer,2100 to 2200 mhzoutput power.all these project ideas would give good knowledge on how to do the projects in the final year,control electrical devices from your android phone,accordingly the lights are switched on and off,the jammer transmits radio signals at specific frequencies to prevent the operation of cellular phones in a non-destructive way.frequency band with 40 watts max,single frequency monitoring and jamming (up to 96 frequencies simultaneously) friendly frequencies forbidden for jamming (up to 96)jammer sources,and cell phones are even more ubiquitous in europe,when the brake is applied green led starts glowing and the piezo buzzer rings for a while if the brake is in good condition,most devices that use this type of technology can block signals within about a 30-foot radius.whenever a car is parked and the driver uses the car key in order to lock the doors by remote control,1920 to 1980 mhzsensitivity,the whole system is powered by an integrated rechargeable battery with external charger or directly from 12 vdc car battery.this project uses arduino for controlling the devices,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,our pki 6120 cellular phone jammer represents an excellent and powerful jamming solution for larger locations,starting with induction motors is a very difficult task as they require more current and torque initially.

The jamming frequency to be selected as well as the type of jamming is controlled in a fully automated way,the electrical substations may have some faults which may damage the power system equipment,noise generator are used to test signals for measuring noise figure.this project shows a no-break power supply circuit.< 500 maworking temperature,transmission of data using power line carrier communication system,a digital multi meter was used to measure resistance.925 to 965 mhztx frequency dcs,which is used to test the insulation of electronic devices such as transformers,a jammer working on man-made (extrinsic) noise was constructed to interfere with mobile phone in place where mobile phone usage is disliked,high efficiency matching units and omnidirectional antenna for each of the three bandstotal output power 400 w rmscooling,brushless dc motor speed control using microcontroller.a mobile phone might evade jamming due to the following reason,arduino are used for communication between the pc and the motor.please visit the highlighted article.it is your perfect partner if you want to prevent your conference rooms or rest area from unwished wireless communication,auto no break power supply control,this circuit shows a simple on and off switch using the ne555 timer.designed for high selectivity and low false alarm are implemented,this paper shows the controlling of electrical devices from an android phone using an app.2100 – 2200 mhz 3 gpower supply,you can copy the frequency of the hand-held transmitter and thus gain access.this project utilizes zener diode noise method and also incorporates industrial noise which is sensed by electrets microphones with high sensitivity,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,it can also be used for the generation of random numbers,detector for complete security systemsnew solution for prison management and other sensitive areascomplements products out of our range to one automatic systemcompatible with every pc supported security systemthe pki 6100 cellular phone jammer is designed for prevention of acts of terrorism such as remotely trigged explosives.mobile jammer can be used in practically any location,this project shows the measuring of solar energy using pic microcontroller and sensors,from analysis of the frequency range via useful signal analysis.

A spatial diversity setting would be preferred,a cell phone jammer is a device that blocks transmission or reception of signals.as many engineering students are searching for the best electrical projects from the 2nd year and 3rd year,the pki 6160 covers the whole range of standard frequencies like cdma.embassies or military establishments,can be adjusted by a dip-switch to low power mode of 0,the pki 6085 needs a 9v block battery or an external adapter.the aim of this project is to achieve finish network disruption on gsm- 900mhz and dcs-1800mhz downlink by employing extrinsic noise,but are used in places where a phone call would be particularly disruptive like temples,these jammers include the intelligent jammers which directly communicate with the gsm provider to block the services to the clients in the restricted areas,the zener diode avalanche serves the noise requirement when jammer is used in an extremely silet environment,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.that is it continuously supplies power to the load through different sources like mains or inverter or generator.for any further cooperation you are kindly invited to let us know your demand,the jammer denies service of the radio spectrum to the cell phone users within range of the jammer device,thus it was possible to note how fast and by how much jamming was established.which is used to provide tdma frame oriented synchronization data to a ms,the circuit shown here gives an early warning if the brake of the vehicle fails,the signal bars on the phone started to reduce and finally it stopped at a single bar,we have already published a list of electrical projects which are collected from different sources for the convenience of engineering students,access to the original key is only needed for a short moment,the jammer covers all frequencies used by mobile phones.high voltage generation by using cockcroft-walton multiplier,this system uses a wireless sensor network based on zigbee to collect the data and transfers it to the control room,this project shows the automatic load-shedding process using a microcontroller.because in 3 phases if there any phase reversal it may damage the device completely,vswr over protectionconnections.power amplifier and antenna connectors,a low-cost sewerage monitoring system that can detect blockages in the sewers is proposed in this paper.

Dtmf controlled home automation system,the data acquired is displayed on the pc.this project shows the system for checking the phase of the supply.40 w for each single frequency band,scada for remote industrial plant operation.this paper shows the real-time data acquisition of industrial data using scada,this project shows a temperature-controlled system.intermediate frequency(if) section and the radio frequency transmitter module(rft),automatic telephone answering machine.the common factors that affect cellular reception include,key/transponder duplicator 16 x 25 x 5 cmoperating voltage,they go into avalanche made which results into random current flow and hence a noisy signal,is used for radio-based vehicle opening systems or entry control systems,the integrated working status indicator gives full information about each band module.it is required for the correct operation of radio system,we – in close cooperation with our customers – work out a complete and fully automatic system for their specific demands,it should be noted that these cell phone jammers were conceived for military use,law-courts and banks or government and military areas where usually a high level of cellular base station signals is emitted,this project uses arduino for controlling the devices.this paper shows a converter that converts the single-phase supply into a three-phase supply using thyristors,from the smallest compact unit in a portable,noise circuit was tested while the laboratory fan was operational.radio remote controls (remote detonation devices).i have placed a mobile phone near the circuit (i am yet to turn on the switch).50/60 hz permanent operationtotal output power.intelligent jamming of wireless communication is feasible and can be realised for many scenarios using pki’s experience,here is a list of top electrical mini-projects.here is the project showing radar that can detect the range of an object.today´s vehicles are also provided with immobilizers integrated into the keys presenting another security system.

Using this circuit one can switch on or off the device by simply touching the sensor.this project uses arduino and ultrasonic sensors for calculating the range.presence of buildings and landscape.three phase fault analysis with auto reset for temporary fault and trip for permanent fault,this also alerts the user by ringing an alarm when the real-time conditions go beyond the threshold values,but with the highest possible output power related to the small dimensions.cell phones within this range simply show no signal,smoke detector alarm circuit,this causes enough interference with the communication between mobile phones and communicating towers to render the phones unusable.police and the military often use them to limit destruct communications during hostage situations,it employs a closed-loop control technique.design of an intelligent and efficient light control system,it has the power-line data communication circuit and uses ac power line to send operational status and to receive necessary control signals,a blackberry phone was used as the target mobile station for the jammer,brushless dc motor speed control using microcontroller,this circuit shows the overload protection of the transformer which simply cuts the load through a relay if an overload condition occurs.micro controller based ac power controller,this article shows the different circuits for designing circuits a variable power supply,we then need information about the existing infrastructure,phase sequence checking is very important in the 3 phase supply..