Edited Technical Paper, Border Security Using Wins

CHAPTER 1

INTRODUCTION

BORDER SECURITY USING WINS Page 1

INTRODUCTION

Wireless Integrated Network Sensors (WINS) provide distributed network and

Internet access to sensors, controls, and processors that are deeply embedded in

equipment, facilities, and the environment. The Wireless Integrated Network Sensors

(WINS) network is a new monitoring and control capability for applications in

transportation, manufacturing, health care, environmental monitoring, and safety and

security, border security. Wireless Integrated Network Sensors WINS networks provide

sensing, local control, and embedded intelligent systems in structures, materials, and

environments Wireless Integrated Network Sensors combine microsensor technology,

low power signal processing, low power computation, and low power, low cost wireless

networking capability in a compact system.

Wireless Integrated Network Sensors (WINS) combine sensing, signal processing,

decision capability, and wireless networking capability in a compact, low power system.

Compact geometry and low cost allows WINS to be embedded and distributed at a small

fraction of the cost of conventional wire line sensor and actuator systems. On a local,

wide-area scale, battlefield situational awareness will provide personnel health

monitoring and enhance security and efficiency. Also, on a metropolitan scale, new

traffic, security, emergency, and disaster recovery services will be enabled by WINS. On

a local, enterprise scale, WINS will create a manufacturing information service for cost

and quality control. WINS for biomedicine will connect patients in the clinic, ambulatory

outpatient services, and medical professionals to sensing, monitoring, and control. On a

local machine scale, WINS condition based maintenance devices will equip power plants,

appliances, vehicles, and energy systems for enhancements in reliability, reductions in

energy usage, and improvements in quality of service.

The opportunities for WINS depend on the development of scalable, low cost,

sensor network architecture. This requires that sensor information be conveyed to the

user at low bit rate with low power transceivers. Continuous sensor signal processing

must be provided to enable constant monitoring of events in an environment. Distributed

signal processing and decision making enable events to be identified at the remote sensor.


Thus, information in the form of decisions is conveyed in short message packets. Future

applications of distributed embedded processors and sensors will require massive

numbers of devices. Conventional methods for sensor networking would present

impractical demands on cable installation and network bandwidth. By eliminating the

requirements for transmission of all measured data, the burden on communication system

components, networks, and human resources are drastically reduced. In this paper we

have concentrated in the most important application, Border Security.


CHAPTER 2

WIRELESS INTEGRATED NETWORK SENSORS (WINS) SYSTEM

ARCHITECTURE


WINS SYSTEM ARCHITECTURE

The primary limitation on WINS node cost and volume arises from power

requirements and the need for battery energy sources. As will be described, low power

sensor interface and signal processing architecture and circuits enable continuous low

power monitoring. However, wireless communication energy requirements present

additional severe demands. Conventional wireless networks are supported by complex

protocols that are developed for voice and data transmission for handhelds and mobile

terminals. These networks are also developed to support communication over long range

(up to 1km or more) with link bit rate over 100kbps.

In contrast to conventional wireless networks, the WINS network must support

large numbers of sensors in a local area with short range and low average bit rate

communication (less than 1kbps). The network design must consider the requirement to

service dense sensor distributions with an emphasis on recovering environment

information. The WINS architecture, therefore, exploits the small separation between

WINS nodes to provide multihop communication.

Multihop communication (see Figure 2) yields large power and scalability

advantages for WINS networks. First, RF communication path loss has been a primary

limitation for wireless networking, with received power, PREC, decaying as transmission

range, R, as PREC R- (where varies from 3 – 5 in typical indoor and outdoor

environments). However, in a dense WINS network, multihop architectures may permit

N communication link hops between N+ 1 nodes. In the limit where communication

system power dissipation (receiver and transceiver power) exceeds that of other systems

within the WINS node, the introduction of N co-linear equal range hops between any

node pair reduces power by a factor of N-1 in comparison to a single hop system.

Multihop communication, therefore, provides an immediate advance in capability for the

WINS narrow bandwidth devices. Clearly, multihop communication raises system

complexity. However, WINS multihop communication networks permit large power

reduction and the implementation of dense node distribution.


Conventional wireless networks are supported by complex protocols that are

developed for voice and data transmission for handhelds and mobile terminals. These

networks are also developed to support communication over long range (up to 1km or

more) with link bit rate over 100kbps. In contrast to conventional wireless networks, the

WINS network must support large numbers of sensors in a local area with short range

and low average bit rate communication (less than 1kbps). The network design must

consider the requirement to service dense sensor distributions with an emphasis on

recovering environment information. Multihop communication yields large power and

scalability advantages for WINS networks. Multihop communication, therefore, provides

an immediate advance in capability for the WINS narrow Bandwidth devices. However,

WINS Multihop Communication networks permit large power reduction and the

implementation of dense node distribution. The multihop communication has been shown

in the figure 2. The figure 1 represents the general structure of the wireless integrated

network sensors (WINS) arrangement.

Continuous operation low duty cycle

Figure 1. The wireless integrated network sensor (WINS) architecture.

The wireless integrated network sensor (WINS) architecture includes sensor, data

converter, signal processing, and control functions. Micropower RF communication

provides bidirectional network access for low bit rate, short range communication. The

micropower components operate continuously for event recognition, while the network

interface operates at low duty cycle.


CHAPTER 3

WINS NODE ARCHITECTURE


WINS NODE ARCHITECTURE

WINS Node Architectures development was initiated in 1993 at the University of

California, Los Angeles; the first generation of field-ready WINS devices and software

was fielded there three years later. The DARPA-sponsored low-power wireless integrated

micro sensors (LWIM) project demonstrated the feasibility of multihop, self-assembled,

wireless networks. This first network also demonstrated the feasibility of algorithms for

operating wireless sensor nodes and networks at micropower levels. In another DARPA-

funded joint development program (involving UCLA and the Rockwell Science Center of

Thousand Oaks, Calif.), a modular development platform was devised to enable

evaluation of more sophisticated networking and signal-processing algorithms and to deal

with many types of sensors, though with less emphasis on power conservation than

LWIM. These experiments taught us to recognize the importance of separating the real-

time functions that have to be optimized for low power from the higher-level functions

requiring extensive software development but that are invoked with light-duty cycles.

The WINS NG node architecture was subsequently developed by Sensor.com,

founded by the authors in 1998 in Los Angeles, to enable continuous sensing, signal

processing for event detection, local control of actuators, event identification, and

communication at low power. Since the event-detection process is continuous, the sensor,

data converter, data buffer, and signal processing all have to operate at micropower

levels, using a real-time system. If an event is detected, a process may be alerted to

identify the event. Protocols for node operation then determine whether extra energy

should be expended for further processing and whether a remote user or neighboring

WINS node should be alerted. The actuator continuously vigilant operation low-duty

cycle operation WINS node then communicates an attribute of the identified event,

possibly the address of the event in an event look-up table stored in all network nodes.

These infrequent events can be managed by the higher-level processors in the first

version of WINS NG, a Windows CE-based device selected for the availability of low-

cost developer tools. By providing application programming interfaces enabling the

viewing and controlling of the lower-level functions, a developer is either shielded from


real-time functions or is allowed to delve into them as desired to improve applications

efficiency.

Future generations will also support plug-in Linux devices; other development

will include very small but limited sensing devices that interact with WINS NG nodes in

heterogeneous networks. These small devices might scavenge their energy from the

environment by means of photocells or piezoelectric materials, capturing energy from

vibrations and achieving perpetual life spans. A clear technical path exists today, offering

increased circuit integration and improved packaging. This path should produce very

low-cost and compact devices in the near future.

Low power, reliable and efficient network operation is obtained with intelligent

sensor nodes that include sensor signal processing, control, and a wireless network

interface. The signal processor described here can supply a hierarchy of information to

the user ranging from single-bit event detection, to power spectral density (PSD) values,

to buffered, real time data. This programmable system matches its response to the power

and information requirements. Distributed network sensor devices must continuously

monitor multiple sensor systems, process sensor signals, and adapt to changing

environments and user requirements, while completing decisions on measured signals.

Clearly, for low power operation, network protocols must minimize the operation duty

cycle of the high power RF communication system.

Unique requirements for the WINS node appear for sensors and micropower

sensor interfaces. The WINS network supports multihop communication (see Figure 2)

with a wireless bridge connection to a conventional wire line network service.

While unique requirements exist for low power node operation, there is a

balancing set of unique operational characteristics that permit low power operation if

properly exploited. In particular, WINS applications are generally tolerant to latency.

Specifically, in contrast to conventional wireless network applications where latency is

not tolerated, the WINS node event recognition may be delayed by 10 – 100 msec, or

longer. This permits low clock rate signal processing and architecture design that


minimizes computation and communication power at the expense of latency. For

example, in the latency-tolerant WINS system, time division multiple access protocols

may be implemented to reduce communication power. Also, it is important to note that

sensor signals are generally narrowband signals (bandwidth less than 10kHz) that require

only low sample and processing rates.

Many of the primary WINS applications require sensor nodes powered by

compact battery cells. Total average system supply currents must be less than 30A to

provide long operating life from typical compact Li coin cells (2.5 cm diameter and 1 cm

thickness). In addition, these compact cells may provide a peak current of no greater than

about 1 mA (higher peak currents degrade the cell energy capacity through electrode

damage.) Both average and peak current requirements present unique challenges for

circuit design. In this paper, the requirements, architectures, and circuits for micropower

WINS systems will be described.

The WINS node architecture (Figure 1) is developed to enable continuous

sensing, event detection, and event identification at low power. Since the event detection

process must occur continuously, the sensor, data converter, data buffer, and spectrum

analyzer must all operate at micro power levels. In the event that an event is detected, the

spectrum analyzer output may trigger the microcontroller. The microcontroller may then

issue commands for additional signal processing operations for identification of the event

signal. Protocols for node operation then determine whether a remote user or neighboring

WINS node should be alerted. The WINS node then supplies an attribute of the identified

event, for example, the address of the event in an event look-up-table stored in all

network nodes. Total average system supply currents must be less than 30A. Distributed

network sensor devices must continuously monitor multiple sensor systems, process

sensor signals, and adapt to changing environments and user requirements, while

completing decisions on measured signals.


Figure 2. WINS nodes (shown as disks)

For the particular applications of military security, the WINS sensor systems must

operate at low power, sampling at low frequency and with environmental background

limited sensitivity. The micro power interface circuits must sample at dc or low

frequency where “1/f” noise in these CMOS interfaces is large. The micro power signal

processing system must be implemented at low power and with limited word length. In

particular, WINS applications are generally tolerant to latency. The WINS node event

recognition may be delayed by 10 – 100 msec, or longer.


CHAPTER 4

WINS MICRO SENSORS


WINS MICRO SENSORS

Many important WINS applications require the detection of signal sources in the

presence of environmental noise. Source signals (seismic, infrared, acoustic and others)

all decay in amplitude rapidly with radial distance from the source. To maximize

detection range, sensor sensitivity must be optimized. In addition, due to the fundamental

limits of background noise, a maximum detection range exists for any sensor. Thus, it is

critical to obtain the greatest sensitivity and to develop compact sensors that may be

widely distributed. Clearly, microelectromechanical systems (MEMS) technology

provides an ideal path for implementation of these highly distributed systems. WINS

sensor integration relies on structures that are flip-chip bonded to a low temperature, co-

fired ceramic substrate. The sensor-substrate “Sensorstrate” is then a platform for support

of interface, signal processing, and communication circuits. Examples of WINS Micro

Seismometer and infrared detector devices are shown in Figure 3. The detector shown is

the thermal detector. It just captures the harmonic signals produced by the foot-steps of

the stranger entering the border. These signals are then converted into their PSD values

and are then compared with the reference values set by the user.

Figure 3. Thermal Infrared Detector


4.1 REMOTE BATTLE FIELD SENSOR SYSTEM (REMBASS):

REMBASS is a ground-based, all-weather, day-and-night, battlefield surveillance,

target development, and early warning system capable of remote operation under field

conditions. The basic purpose of REMBASS is to detect, locate, classify, and report

personnel and vehicular (wheeled and tracked) activities in real-time within the area of

deployment. With a meteorological sensor attached, it will also sense and collect weather

information. It uses remotely monitored sensors emplaced along likely enemy avenues of

approach. These sensors respond to seismic-acoustic energy, infrared energy, and

magnetic field changes to detect enemy activities. The sensors process the data and

provide detection of classification information which is incorporated into digital

messages and transmitted through short burst transmission to the system sensor monitor

programmer set. The messages are demodulated, decoded, displayed, and recorded to

provide a time-phased record of enemy activity.

Figure 4. REMBASS

This system complements other manned/unmanned surveillance systems such as

ground surveillance radar, unmanned aerial vehicles, and night observation devices. The

system provides division, brigade, and battalion commanders with information from

beyond the forward line of own troops (FLOT), and enhances rear area protection. It can

be deployed anywhere in the world in a tactical environment in support of

reconnaissance, surveillance, and target acquisition (RSTA) operations. The system


consists of eleven major components: a passive infrared (IR) sensor, magnetic (MAG)

sensor, seismic/acoustic (SA) sensor, radio repeater. Sensor Monitoring Set (SMS), radio

frequency monitor (referred to as portable monitoring set (PMS)) , code programmer,

antenna group, power supply, mounting rack, and Sensor Signal Simulator (SSS). A set

consists of eight IR sensors, eight MAG sensors, thirty-two SA sensors, eight radio

repeaters, one SMS, three PMS, two code programmers, one antenna group, one power

supply, one mounting rack, and one SSS.

(1) Magnetic Sensor: This is a hand-emplaced, MAG sensor. The MAG sensor detects

vehicles (tracked or wheeled) and personnel carrying ferrous metal. It also provides

information on which to base a count of objects passing through its detection zone and

reports their direction of travel relative to its location. The monitor uses two different

(MAG and IR) sensors and their identification codes to determine direction of travel.

(2) Seismic Acoustic Sensor: This is a hand-emplaced SA classifying sensor. It detects

targets and classifies them as unknown, wheeled vehicle, tracked vehicle, or personnel.

(3) Passive Infrared Sensor: This is a hand-emplaced, IR detecting sensor. The sensor

detects tracked or wheeled vehicles and personnel. It also provides information on which

to base a count of objects passing through its detection zone and reports their direction of

travel relative to its location. The monitor uses two different (MAG and IR) sensors and

their identification codes to determine direction of travel.

(4) Radio Repeater: This is an expendable/recoverable, digital/analog radio repeater

used to extend the broadcast range of radio messages from anti-intrusion sensors to a

monitoring set. It receives, processes and relays messages from either an anti-intrusion

sensor or another like radio repeater. Several repeaters may be used in a station-to-station

chain, one sending to another, to relay messages over a long distance.

(5) Sensor Monitoring Set: The SMS has a dual channel receiver with a permanent hard

copy recorder and a temporary visual display (TVD). The SMS receives processes,

displays, and records sensor information relating to 60 sensor ID codes. Detections and

classification are displayed as: dashes (-) for unknown targets, (T) for tracked vehicles,

(W) for wheeled vehicles, and (P) for personnel. The TVD can simultaneously display up

to ten sensor ID codes with detection or classification information. A keyboard allows the

operator to program the SMS operation: set radio frequency (RF) channels, establish hard


copy recorder format, initiate system operational checks or built in test (BIT), and

calculate target speed. A separate display shows the keyboard functions and calculations.

(6) Radio Frequency Monitor: This is a single-channel PMS with a TVD. The PMS

receives, processes, and displays sensor ID codes and detection/classification messages.

(7) Code Programmer: The programmer is a portable device used to program sensors

and repeaters to the desired operating channel, ID code, mission life, arm mode, and gain.

It is also used to condition newly installed batteries in sensors and repeaters. It has a built

in visual self test to ensure the proper information programmed into the sensor or

repeater.

(8) Antenna Group: The antenna group consists of an omni directional unity gain

antenna, a mast assembly, a pre-amplifier suitable for mast mounting and an RF

multicoupler. It is used with the SMS and the PMS. Up to four monitoring devices can

use the antenna group simultaneously.

(9) Power Supply: The power supply is a custom flyback-type switching regulator that

converts external power sources (24 volts direct current (dc), 115 or 220 volts alternating

current) to 12 volts dc nominal prime power. The power supply can be used to power the

SMS, repeater or SSS.

(10) Mounting Rack: The mounting rack is an aluminum angle shock mounted rack. It is

used to mount the repeaters in helicopters.

(11) Sensor Signal Simulator (SSS): The SSS is similar in appearance to the SMS. It

has the capability to receive, record, edit, copy, and retransmit an operational scenario

involving any two of the 599 REMBASS channels. It also has the capability to transmit

pre-recorded scenarios. These functions are accomplished without any additional support

equipment. The SSS allows institutional or unit sustainment training in either a classroom

or field environment without the use of REMBASS/IREMBASS sensors. The operator

can monitor the outputs of the SSS on the PMS or SMS.


CHAPTER 5

ROUTING BETWEEN NODES


ROUTING BETWEEN NODES

The sensed signals are then routed to the major node. This routing is done based

on the shortest distance. That is the distance between the nodes is not considered, but the

traffic between the nodes is considered. This has been depicted in the figure 5. In the

figure, the distance between the nodes and the traffic between the nodes has been clearly

shown. For example, if we want to route the signal from the node 2 to node 4, the shortest

distance route will be from node 2 via node 3 to node 4. But the traffic through this path

is higher than the path node 2 to node 4. Whereas this path is longer in distance.

Figure 5. Nodal distance and Traffic


CHAPTER 6

SHORTEST DISTANCE ALGORITHM


SHORTEST DISTANCE ALGORITHM

In this process we find mean packet delay, if the capacity and average flow are

known. From the mean delays on all the lines, we calculate a flow-weighted average to

get mean packet delay for the whole subnet. The weights on the arcs in the figure 6 give

capacities in each direction measured in kbps.

Figure 6. Subnet with line capacities Figure 7.s Routing Matrix

In fig 7 the routes and the number of packets/sec sent from source to destination

are shown. For example, the E-B traffic gives 2 packets/sec to the EF line and also 2

packets/sec to the FB line. The mean delay in each line is calculated using the formula

TTi i =1/(µc-=1/(µc-λλ))

Ti = Time delay in sec

C = Capacity of the path in Bps

µ = Mean packet size in bits

λ = Mean flow in packets/sec.

The mean delay time for the entire subnet is derived from weighted sum of all the

lines. There are different flows to get new average delay. But we find the path, which has

the smallest mean delay-using program. Then we calculate the Waiting factor for each

path. The path, which has low waiting factor, is the shortest path. The waiting factor is

calculated using


W = W = λλi / i / λλ

λλi = Mean packet flow in pathi = Mean packet flow in path

λλ = Mean packet flow in subnet = Mean packet flow in subnet

The tabular column listed below gives waiting factor for each path.

Figure 5. WINS Comparator response


CHAPTER 7

WINS DIGITAL SIGNAL PROCESSING


WINS DIGITAL SIGNAL PROCESSING

The WINS architecture relies on a low power spectrum analyzer to process all

ADC output data to identify an event in the physical input signal time series. If a stranger

enters the border, his foot-steps will generate harmonic signals. It can be detected as a

characteristic feature in a signal power spectrum. Thus, a spectrum analyzer must be

implemented in the WINS digital signal processing system. The spectrum analyzer

resolves the WINS input data into a low-resolution power spectrum. Power spectral

density (PSD) in each frequency “bins” is computed with adjustable band location and

width. Bandwidth and position for each power spectrum bin is matched to the specific

detection problem. The WINS spectrum analyzer must operate at W power level. So the

complete WINS system, containing controller and wireless network interface

components, achieves low power operation by maintaining only the micropower

components in continuous operation. The WINS spectrum analyzer system, shown in

Figure 8, contains a set of parallel filters. Mean square power for each frequency bin, is

computed at the output of each filter. Of course, the microcontroller may support

additional, more complex algorithms that provide capability (at higher power) for event

identification.

The WINS spectrum analyzer architecture includes a data buffer, shown in Figure

8. Buffered data is stored during continuous computation of the PSD spectrum. If an

event is detected, the input data time series, including that acquired prior to the event, are

available to the microcontroller. Low power operation of the spectrum analyzer is

achieved through selection of an architecture that provides the required performance and

function while requiring only limited word length. First, since high resolution

measurements of PSD are required (5 Hz bandwidth passbands at frequencies of 5 – 50

Hz with a 200 Hz input word rate) FIR filters would require an excessive number of taps

and corresponding power dissipation. In contrast, IIR filter architectures have provided

adequate resolution with limited word length.


Figure 8. WINS micro power spectrum analyzer architecture.

7.1 PSD COMPARISION:

Each filter is assigned a coefficient set for PSD computation. Finally, PSD values

are compared with background reference values. In the event that the measured PSD

spectrum values exceed that of the background reference values, the operation of a

microcontroller is triggered. Thus, only if an event appears, the micro controller operates.

Buffered data is stored during continuous computation of the PSD spectrum. If an event

is detected, the input data time series, including that acquired prior to the event, are

available to the micro controller. The micro controller sends a HIGH signal, if the

difference is high. It sends a LOW signal, if the difference is low. For a reference value

of 25db, the comparison of the DFT signals is shown in the figure 9.


Figure 9. Comparator plot


CHAPTER 8

WINS MICROPOWER EMBEDDED RADIO


WINS MICROPOWER EMBEDDED RADIO

WINS systems present novel requirements for low cost, low power, short range,

and low bit rate RF communication. Simulation and experimental verification in the field

indicate that the embedded radio network must include spread spectrum signaling,

channel coding, and time division multiple access (TDMA) network protocols. The

operating bands for the embedded radio are most conveniently the unlicensed bands at

902-928 MHz and near 2.4 GHz. These bands provide a compromise between the power

cost associated with high frequency operation and the penalty in antenna gain reduction

with decreasing frequency for compact antennas. The prototype, operational, WINS

networks are implemented with a self-assembling, multihop TDMA network protocol.

The WINS embedded radio development is directed to CMOS circuit technology

to permit low cost fabrication along with the additional WINS components. In addition,

WINS embedded radio design must address the peak current limitation of typical battery

sources, of 1mA. It is critical, therefore, to develop the methods for design of micro

power CMOS active elements. For LC oscillator phase noise power, S, at frequency

offset of away from the carrier at frequency with an input noise power, Snoise

and LC tank quality factor, Q, phase noise power is:

Now, phase noise power, Snoise, at the transistor input, is dominated by “1/f”

noise. Input referred thermal noise, in addition, increases with decreasing drain current

and power dissipation due to the resulting decrease in transistor transconductance. The

tunability of micropower CMOS systems has been tested by implementation of several

VCO systems to be discussed below. The embedded radio system requires narrow band

operation and must exploit high Q value components.


CHAPTER 9

CHARACTERISTICS AND APPLICATIONS


CHARACTERISTICS

The following are the characteristics of the wireless integrated network sensors

(WINS)

It supports large numbers of sensor.

Dense sensor distributions.

Low-power signal processing, computation, and low-cost wireless networking.

These sensors are also developed to support short distance RF communication.

Internet access to sensors, controls and processor is easy.

APPLICATIONS

Wireless integrated network sensors (WINS) provide distributed network and Internet

access to sensors, controls, and processors deeply embedded in equipment, facilities, and

the environment.

The WINS network represents a new monitoring and control capability for

applications in such industries as transportation, manufacturing, health care,

environmental oversight, and safety and security.

WINS combine micro sensor technology and low-power signal processing,

computation, and low-cost wireless networking in a compact system.

Recent advances in integrated circuit technology have enabled construction of far

more capable yet inexpensive sensors, radios, and processors, allowing mass

production of sophisticated systems linking the physical world to digital data

networks.


CHAPTER 10

CONCLUSION


CONCLUSION

A series of interface, signal processing, and communication systems have been

implemented in micropower CMOS circuits. A micropower spectrum analyzer has been

developed to enable low power operation of the entire WINS system. Thus WINS require

a Microwatt of power. But it is very cheaper when compared to other security systems

such as RADAR under use. It is even used for short distance communication less than 1

Km. It produces a less amount of delay. Hence it is reasonably faster. On a global scale,

WINS will permit monitoring of land, water, and air resources for environmental

monitoring. On a national scale, transportation systems, and borders will be monitored

for efficiency, safety, and security.


CHAPTER 11

REFERENCES


REFERENCES

[1] K. Bult, A. Burstein, D. Chang, M. Dong, M. Fielding, E. Kruglick, J. Ho, F. Lin, T.

H. Lin, W. J. Kaiser, H. Marcy, R. Mukai, P. Nelson, F. Newberg, K. S. J. Pister, G.

Pottie, H. Sanchez, O. M Stafsudd, K. B. Tan, C. M. Ward, S. Xue, J. Yao, “Low Power

Systems for Wireless Microsensors”, 1996 International Symposium on Low Power

Electronics and Design, Digest of Technical Papers, (1996), pp. 17-21.

[2] E. Vittoz, Design of Analog-Digital VLSI Circuits for Telecommunications & Signal

Processing, Prentice Hall, New York, 1994.

[3] M. J. Dong, G. Yung, and W. J. Kaiser, “Low Power Signal Processing Architectures

for Network Microsensors”, 1997 International Symposium on Low Power Electronics

and Design, Digest of Technical Papers (1997), pp. 173-177.

[4] A. A. Abidi, “Low-power radio-frequency ICs for portable communications”,

Proceedings of the IEEE, 83, (1995), pp. 544-69.

[5] D. B. Leeson, “A simple model of feedback oscillator noise spectra”, Proc. IEEE, 54,

(1966), pp. 329-330.

[6] J. Craninckx, M. S. J. Steyaert, “A 1.8-GHz low-phasenoise CMOS VCO using

optimized hollow spiral inductors” IEEE Journal of Solid-State Circuits, 32, (1997),

pp.736-44.

[7] B. Razavi, “A Study of Phase Noise in CMOS Oscillators”, IEEE J. of Solid-State

Circuits, 31, (1996), pp. 331-343.

[8] S. Vasudevan, A. Shaikh, “Microwave Characterization of Low Temperature Cofired

Tape Ceramic System”, Advancing Microelectronics, (1995), pp.16-25.


Edited Technical Paper, Border Security Using Wins

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