This paper presents a new wavelet-domain technique for despeckling of medical ultrasound (US) images for improved clinical diagnosis. The method uses the generalized Gaussian distribution and generalized gamma distribution to model the image and the speckle, respectively, in the detailed sub-bands of wavelet decomposition of the log-transformed US image. Combining these, a priori  distributions with the Bayesian maximum a posteriori  criterion, shrinkage estimators are derived for processing the wavelet coefficients of the detail sub-bands. The visual comparison of despeckled US images and the higher values of quality metrics indicate that the new method suppresses the speckle noise well while preserving the texture and organ surfaces.

The interconnect architecture is essential to the performance of the interconnection network, so it is very significant to design a new cost-effective interconnect architecture. 2D meshes are the most popular interconnect architecture for parallel processing. However, the diameter and average distance of a 2D mesh are large enough to greatly influence performance of the network. This paper presents a novel interconnect architecture called TM, which combines the advantages of both a 2D torus and a 2D mesh. For an n  × n  network, number of links of a TM is the same as that of a mesh, while the diameter of a TM is close to that of a torus. Besides, the average distance of a TM is at the middle of that of a torus and a mesh. To prevent deadlocks in TMs, a novel deadlock avoidance scheme, called proxy policy, is proposed. Moreover, both the deterministic and fully adaptive routing techniques in TMs are proposed using proxy policy to prevent deadlocks. Sufficient simulation results are presented to show the effectiveness of the TM network and the new routing schemes.

Potentiality of Impact Avalanche Transit Time (IMPATT) devices based on different semiconductor materials such as InP, 4H-SiC, and Wurtzite-GaN (Wz-GaN) has been explored for operation at terahertz (THz) frequencies. Drift-diffusion model is used to design double-drift region (DDR) IMPATTs based on above mentioned materials at different millimeter-wave (mm-wave) and THz frequencies and the upper cut-off frequency limits of those devices are obtained from the avalanche response times at those mm-wave and THz frequencies. Results show that the upper cut-off frequency limits of both InP and 4H-SiC DDR IMPATTs are 1.0 THz; whereas, the same is 5.0 THz in Wz-GaN DDR IMPATTs. The Wz-GaN DDR IMPATTs emerge as the most suitable devices for generation of THz frequencies due to their small avalanche response time, high DC to RF conversion ratio, and sufficiently high RF power output at THz frequencies. But, it is observed that up to 1.0 THz, 4H-SiC DDR IMPATTs excel Wz-GaN DDR IMPATTs due to their higher output power densities. Thus, the wide bandgap semiconductors such as Wz-GaN and 4H-SiC are highly suitable materials for DDR IMPATTs at both mm-wave and THz frequency ranges.

This paper presents a high-conversion gain, low-power, folded CMOS mixer for ZigBee application in 2.4 GHz of user bandwidth. The proposed mixer adapts current reuse technique to increase the conversion gain while substantially reducing the DC power dissipation. The current from LO stage is reused at the transconductance stage to reduce the power consumption. This mixer is verified in 0.13 μm standard CMOS technology. The simulation result exhibits a high-conversion gain performance (CG) of 10 dB, 1 dB compression point (P1 dB) of -13.43 dBm, third-order intercept point (IIP3) of -4.3 dBm, and a noise figure of 16.67 dB. The circuit draws 675 μA current from the 1.2 V of supply voltage headroom.

In view of the recent paradigm shift from system-on-board to designs embracing embedded cores-based system-on-chips (SOCs), the complexity of digital circuits has enormously increased. This growing complexity has resulted in a huge challenge in developing their appropriate and efficient fault testing environment. Despite significant efforts directed toward implementing effective testing strategies of very large scale integrated circuit chips with reasonable cost, new frontiers emerged with advances in technology. The subject paper endeavors to develop method to test verify circuit architecture under hardware software co-design environment, targeting specifically embedded cores-based SOCs. The concept of design-for-testability is utilized in the paper together with ModelSim simulation and verification tool to test synthesize the entire design. Some simulation results on the International Symposium on Circuits and Systems (ISCAS) 85 combinational and ISCAS 89 full-scan sequential benchmark circuits are also included with a comparison of the results with some earlier works.

This paper deals with two different discontinuous conduction modes of a Power Factor Corrected (PFC) Single Ended Primary Inductor Converter (SEPIC) -fed Permanent Magnet Brushless DC Motor (PMBLDCM) Drive. A novel scheme of speed control of PFC-based SEPIC-fed PMBLDCM drive is proposed using a single voltage sensor. The SEPIC is operated in a Discontinuous Inductor Current Mode (DICM) and Discontinuous Capacitor Voltage Mode (DCVM) feeding PMBLDCM Drive. A Diode Bridge Rectifier (DBR) fed from a single-phase AC supply followed by a SEPIC DC-DC converter is used for power factor correction. A three-phase Voltage Source Inverter (VSI) is used for the electronic commutation of the PMBLDC motor. The speed control is achieved by controlling the DC link voltage of the VSI. A voltage follower approach is used to control the DC link voltage of the front-end SEPIC converter. The developed model of the system is used to simulate the performance of the drive system to achieve improved power quality at AC mains with a wide range of speed control.

A wideband endfire directional microstrip antenna with metamaterials is specifically designed in this paper. The antenna has isolated square gaps and crossed strip-line etched on the metal patch and ground plane, respectively. The measurements show that the antenna presents an impedance bandwidth of 70.5% for ǀS₁₁  ǀ < −10 dB. Due to the left-handed transmission characteristics, the wave propagation along the patch induces the strongest radiation in endfire direction instead of the vertical direction of the conventional patch antenna. Simulated and experimental results obtained for this antenna show that it exhibits good radiation behavior in the wideband frequency range.

Multipath is one of the prominent error sources in Global Positioning System (GPS), which adversely affects the accuracy of the user's position. This paper describes the estimation of frequency components of multipath error signal using spectral analysis and its effective mitigation using time-varying digital filters. Four types of 2 ^nd  order filters, namely, Butterworth, Type I and II Chebyshev and Elliptic filters, are examined for mitigation of multipath and their performance is compared. It is observed that by applying digital filters of different cut-off frequencies over the spectrum of the multipath, one can significantly reduce the multipath errors. The designed filters are applied to GPS data acquired from a typical urban environment site and found that Butterworth filter reduced the error most effectively. To extract the magnitude of error contribution in low and medium multipath frequency ranges, its mitigation is carried out with Butterworth filter. The performance of the filter is validated by the spectral analysis for multipath signals before and after filtering process. However, this spectral analysis followed by digital filtering is different from the approaches reported elsewhere in the open literature. This new approach is very useful in identifying least-affecting multipath sites for GPS applications.

This paper presents identification of artificial neural network model of a Binary Distillation Column (BDC). In this paper, the two most common topologies of artificial neural networks in the area of control are introduced: Feed forward neural network and recurrent neural networks. The training of neural network has been performed by the data set acquired from real 9-tray continuous BDC setup available in laboratory. The network model is composed of two layers. A hyperbolic tangent sigmoid function and a pure linear function have been utilized as activation functions in the first and the second layers, respectively. The developed neural network model has been validated by an extensive data set of practical data received from real BDC setup.

Wireless Sensor Networks (WSN) are growing in popularity and penetrating newer fields of applications more than ever. Gradually, we are relying on WSNs to perform more complex tasks with increasing cognitive abilities. At the core of the WSN research transpires, the need is to achieve accurate sensing at real-time. Unfortunately, confidence in sensors' readings decreases in harsh environments and as a result of normal reading errors, message loss, or even low battery operations. The complex problem of dealing with corrections and in some cases shredding the outcome of entire deployments leads to loss of effort, time, and money. Classic approaches for correcting laboratory experiments like curve fitting and least square are well known and have been established for decades. But little research attempts have been made to correct and recalibrate sensors observations in real-time. Furthermore, classic approaches for correcting sensor observations require higher interaction between sensors to a level we cannot afford in deployments where battery, network, and memory represent scarce resource. In addition, classical corrections lack the ability to contemplate the physical properties of the underlying sensor environment. In this article, we present a sensor correction model that relies on clustered WSN. Our approach employs autonomous selection mechanism to elect cluster heads by applying a stochastic competition between cluster members while maintaining the underlying physical properties as the bases to locate competition winner. Then, we perform the stochastic competitive correction at real-time by referencing the underlying physical properties of the environment represented by selected relation as suggested by the physics of the environment. Finally, sensors adapt the minor changes and maintain a relation to their surroundings by continuously monitoring and assimilating information received from surrounding sensors. We show that this approach has smaller footprint in terms of processing, communicating, and storage. We present our approach and apply it on an environment of known physical property.