Malicious alteration in an IC design is generally referred to as hardware Trojans (HTs). The involvement of multiple entities in the VLSI design cycle has made the process of HT detection very challenging. This article presents a novel method for the detection of HT at the gate level of abstraction. A path retrace algorithm detects the nets added/deleted by an adversary along with its location. The netlists of the genuine circuit (design netlist) and the Trojan-inserted circuit are used by the algorithm to detect the malicious nets in the circuit. This method utilizes testability analysis as the metric for segregating malicious nets in the compromised circuit netlist. Netlist of the fabricated IC, which is obtained through reverse engineering and the design netlist of the original circuit are used to determine the testability parameters, such as controllability and observability of the nets. Based on the variation in testability metrics of a signal in the original circuit, the path retrace algorithm identifies the malicious gates inserted into the original circuit. In addition, the algorithm also helps to isolate the Trojan circuit and comprehend its functional implication on the original design. Using the list of malicious gates and compromised circuit netlist, it is possible to identify the Trojan nets inserted by the adversary. This technique is more effective in detecting functional Trojans as compared to techniques employing reverse engineered images as it is design parameter independent and impervious to noise.

Wind profilers (WPs) are ground-based pulsed Doppler radars operating in the UHF/very high frequency (VHF) bands. They use backscatter from clear-air turbulence as a tracer of background wind in the troposphere and up to mesosphere. Wind profiling systems range from large research radar systems to smaller operational units. In recent times, multiparameter cost function (MPCF) has emerged as a computationally efficient Doppler profile estimation method. It has been claimed that the MPCF method can be easily migrated to any vertically sounding clear-air wind profiling system that works in Doppler beam swinging mode. In order to investigate this claim, MPCF was applied to the following wind profiling systems: the middle and upper atmosphere (MU) radar, located at Shigaraki, Japan, which is an active phased array VHF radar system with 475 transceivers, and Doppler sodar, located at Pune, India, which uses acoustic frequency to obtain echoes in the planetary boundary layer. Both these systems are complementary in the sense that they cover ranges from ground to about 80 km. During the experimentation, the MPCF algorithm did not need any change of parameters except the matching of the data reading formats. The results of MPCF on the MU radar were validated with Radiosonde data. These results indicate that the MPCF works seamlessly on all types of WP systems irrespective of the carrier, range, and radar type.

Wind profilers (WPs) are coherent pulsed Doppler radars operating in the ultrahigh frequency and very high frequency bands. These atmospheric radars receive backscattered signals from the atmospheric and nonatmospheric targets. Atmospheric targets include clear air turbulence, precipitation, mesospheric turbulence, ionospheric D, E, F regions, and meteors, whereas nonatmospheric targets are objects like airplanes, birds, insects, hills, and so on. Modern WPs operate for long hours and often change radar operating parameters. Echoes from one beam direction are observed for 8 to 32 s and a set of Doppler power spectra is generated. WPs generate more than a hundred sets of Doppler power spectra per hour. These Doppler spectra often contain echoes from more than one target. The studies for atmospheric modeling and prediction require analysis of the backscattered signal. In order to facilitate systematic study, the data need to be classified and archived according to the atmospheric target type. Considering the large data volume consisting of echoes from different atmospheric targets, there is a need of an automated classification technique that segregates the Doppler power spectral data according to the types of atmospheric targets. The technique is expected to operate in real time with the data generation. This paper presents a spectral feature-based classification (SFBC) method for the classification of Doppler power spectra. This method associates three to four descriptor spectral features with each type of atmospheric target. The SFBC method classifies the data into a particular type of the atmospheric target if concurrent occurrence of descriptor features corresponding to that target type is observed. This paper presents the results indicating that the SFBC classifies the data from different WP radars with good accuracy. It also shows that the SFBC method is computationally simpler compared with other established classification methods.

Wind profilers (WPs) are a special category of ground-based radar instruments used to obtain 3-D profile of wind velocity along the altitude in atmosphere. They transmit radio wave pulses in very high frequency (VHF) or ultrahigh frequency (UHF) bands in vertical and near-vertical directions. The Doppler frequency shift and the time of flight of the echo signals from each beam are measured. These data are analyzed to get the radial velocities of atmospheric targets at all the ranges. The data must be processed from at least three noncoplanar beam directions together to estimate 3-D wind profile. The signal-to-noise ratio is very low for the higher range target echoes. Also, echoes are often contaminated by nonatmospheric signals like clutter, radio frequency interference, and so on. Determining accurate wind profile, especially at higher heights, becomes a challenging task due to these factors. This paper presents a novel algorithm for the estimation of radial velocity profile by processing the set of Doppler power spectra of all range bins. The algorithm identifies prospective atmospheric echoes components. Then it forms velocity profile trails connecting five range bins. The program rejects the profile trails that show velocity change higher than a predefined limit. The remaining trails are evaluated using a specially designed multiparameter cost function (MPCF). The trails with maximum cost are selected and then connected to construct the complete Doppler profile or the radial velocity profile. The key innovation in this method is an improvised function that is created by weighted addition of two terms. The first term is proportional to the signal power, and the second term is a nonlinear function of differential wind shear. This algorithm has been tried on multiple sets of the Indian mesosphere-stratosphere-troposphere radar data and the lower atmospheric WP radar data. The performance of this method is compared with other methods, namely, the adaptive moment estimation method and fuzzy logic approach. It has been observed that the new method shows excellent consistency in extracting the Doppler profile from the power spectral data. This method requires at least 30% lesser computations compared with other methods. The results of the MPCF method showed a very good match with the data obtained from concurrently operated Global Positioning System sonde, an independent wind profiling method. This paper also shows the results on the performance improvement using power spectra from the symmetrical beams. This algorithm shows a great promise as a tool for automatic Doppler profile tracing.

The aim of this paper is to propose a new method in fingerprint enhancement with application of wavelet transform which is more efficient than existing methods. At present the methods that are in use are the ones involving the use of Gabor filtering and Fourier filtering. But the accuracy of these techniques is far from satisfactory. A new technique is being proposed that incorporates wavelet transform and Gabor filtering. The performance of this technique is being discussed in the paper.