An MRI Coil-Mounted Multi-Probe Robotic Positioner for Cryoablation

Cryoablation is a percutaneous procedure for treating solid tumors using needle-like instruments. This paper presents an interventional guidance device for faster and more accurate alignment and insertion of multiple probes during cryoablation performed in closed bore magnetic resonance (MR) imaging systems. The device is compact and is intended to be mounted onto a Siemens 110 mm MR loop coil. A cable-driven two-degrees-of-freedom spherical mechanism mimics the wrist motion as it orients the intervention probes about a remote center of motion located 15 mm above the skin. A carriage interfaces with the probes via a thumbscrew-fastened latch to passively release the probes from their tracks, enabling them to be inserted sequentially and freeing them to move with respiration. Small actuator modules containing piezoelectric encoder-based motors are designed to be snap-fit into the device for ease of replacement and sterilization. The robot MRI compatibility was validated with standard cryoablation imaging sequences in 3T MR environment, yielding a maximum of 4% signal to noise ratio during actuator motion. Bench-level device characterization demonstrated a maximum error of 0.78° in the carriage movement. Needle-tip placement experiments for multiple targets in gelatin were performed using our image-guided navigation software, measuring an average targeting error of 2.0 mm.

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An autofocusing method for imaging the targets for TWI radar systems with correction of thickness and dielectric constant of wall

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078718001356 ◽

2018 ◽

Vol 11 (1) ◽

pp. 15-21 ◽

Cited By ~ 1

Author(s):

Sandeep Kaushal ◽

Bambam Kumar ◽

Dharmendra Singh

Keyword(s):

Genetic Algorithm ◽

Dielectric Constant ◽

Continuous Wave ◽

Signal To Noise Ratio ◽

Imaging Systems ◽

Signal To Noise ◽

Radar Systems ◽

Stepped Frequency ◽

Correct Image ◽

Wall Imaging

AbstractIn through the wall imaging systems, wall parameters like its thickness and dielectric constant play an important role in the true and correct image formation of an object behind the wall made of various materials like brick cement, wood, plastic, etc. Incorrect estimation of these parameters leads to dislocation of the object and smearing or blurriness of the image too. A new autofocusing technique for a stepped frequency continuous wave -based radar at the frequency of 1–3 Ghz has been developed that corrects the wall's parameters like its thickness and dielectric constant and provides a better focused image of the target. For this purpose, a peak signal to noise ratio -based autofocusing technique has been developed by using curve fitting and the genetic algorithm. It is observed that the proposed technique has capability to focus the image up to good extent.

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Performance Enhancement of Inexpensive Glow Discharge Detector Operating in Up-Conversion Mode in Millimeter Wave Detection for Focal Plane Arrays

Applied Sciences ◽

10.3390/app11209564 ◽

2021 ◽

Vol 11 (20) ◽

pp. 9564

Author(s):

Arun Ramachandra Kurup ◽

Daniel Rozban ◽

Lidor Kahana ◽

Amir Abramovich ◽

Yitzhak Yitzhaky ◽

...

Keyword(s):

Glow Discharge ◽

Millimeter Wave ◽

Performance Enhancement ◽

Signal To Noise Ratio ◽

Focal Plane Arrays ◽

Imaging Systems ◽

Pass Filter ◽

Detailed Derivation ◽

Glow Discharge Detector ◽

Thz Sensor

Performance enhancement of a very inexpensive millimeter-wave (MMW)/terahertz (THz) sensor for MMW/THz imaging systems is experimentally demonstrated in this study. The MMW sensor is composed of a glow discharge detector (GDD) and a light-to-frequency (LTF) converter combination. The experimental results given in this study show an improvement in the performance parameters of the detector element, such as the minimum detectable signal, as well as the signal to noise ratio (SNR) and the noise equivalent power (NEP), when a NIR long-pass filter was inserted between the GDD and the LTF combination. A detailed derivation of the NEP of this unique sensor is presented in this work. Based on this derivation and the experimental measurements, the NEP value was calculated.

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MFNR: Multi-Frame Method for Complete Speckle Noise Removal in Ultrasound images Using KDE

10.20944/preprints202006.0117.v1 ◽

2020 ◽

Author(s):

Awais Nazir ◽

Muhammad Shahzad Younis ◽

Muhammad Khurram Shahzad

Keyword(s):

Signal To Noise Ratio ◽

Low Cost ◽

Speckle Noise ◽

Noise Removal ◽

General Purpose ◽

Imaging Systems ◽

Ultrasound Images ◽

Frame Method ◽

Probability Density Function Pdf ◽

Removal System

Speckle noise is one of the most difficult noises to remove especially in medical applications. It is a nuisance in ultrasound imaging systems which is used in about half of all medical screening systems. Thus, noise removal is an important step in these systems, thereby creating reliable, automated, and potentially low cost systems. Herein, a generalized approach MFNR (Multi-Frame Noise Removal) is used, which is a complete Noise Removal system using KDE (Kernal Density Estimation). Any given type of noise can be removed if its probability density function (PDF) is known. Herein, we extracted the PDF parameters using KDE. Noise removal and detail preservation are not contrary to each other as the case in single-frame noise removal methods. Our results showed practically complete noise removal using MFNR algorithm compared to standard noise removal tools. The Peak Signal to Noise Ratio (PSNR) performance was used as a comparison metric. This paper is an extension to our previous paper where MFNR Algorithm was showed as a general purpose complete noise removal tool for all types of noises

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EMG SIGNAL DENOISING USING ADAPTIVE FILTERS THROUGH HYBRID OPTIMIZATION ALGORITHMS

Biomedical Engineering Applications Basis and Communications ◽

10.4015/s1016237221500095 ◽

2020 ◽

pp. 2150009

Author(s):

Bhattiprolu Nagasirisha ◽

V. V. K. D. V. Prasad

Keyword(s):

Adaptive Filters ◽

Signal To Noise Ratio ◽

Optimization Algorithms ◽

Maximum Error ◽

Recursive Least Squares ◽

Signal Denoising ◽

Recording Equipment ◽

Advantages And Disadvantages ◽

Emg Signal ◽

Signal Features

Electromyography (EMG) signal recording equipment is comparatively modern. Still, there are enough restrictions in detection, recording, and characterization of EMG signals because of nonlinearity in the equipment, which leads to noise components. The most commonly affecting artifacts are Power Line Interference (PLI-Noise), Baseline Wander noise (BW-Noise), and Electrocardiogram noise (ECG-Noise). Adaptive filters are advanced and effective solutions for EMG signal denoising, but the improper tuning of filter coefficients leads to noise components in the denoised EMG signal. This defect in adaptive filters triggers or motivates us to optimize the filter coefficients with existing meta-heuristics optimization algorithms. In this paper, Least Mean Squares (LMS) filter and Recursive Least Squares (RLS) adaptive filter coefficients are optimized with a new Hybrid Firefly–Particle Swarm Optimization (HFPSO) by taking the advantages and disadvantages of both the algorithms. Experiments are conducted with the proposed HFPSO and it proved better in EMG signal denoising in terms of the measured parameters like signal-to-noise ratio (SNR) in dB, maximum error (ME), mean square error (MSE), etc. In the second part of the work, the denoised EMG signal features are extracted for the diagnosis of diseases related to myopathy and neuropathy as EMG signal reflects the neuromuscular function and EMG signal examination may contribute to the diagnosis of muscle disorder linked to myopathy and neuropathy.

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Degrees-Of-Freedom in Multi-Cloud Based Sectored Cellular Networks

Entropy ◽

10.3390/e22060668 ◽

2020 ◽

Vol 22 (6) ◽

pp. 668

Author(s):

Samet Gelincik ◽

Ghaya Rekaya-Ben Othman

Keyword(s):

Cellular Networks ◽

Degrees Of Freedom ◽

Signal To Noise Ratio ◽

Base Stations ◽

Processing Capacity ◽

Coding Schemes ◽

Minimum Number ◽

Cut Set ◽

Base Band ◽

Multi Cloud

This paper investigates the achievable per-user degrees-of-freedom (DoF) in multi-cloud based sectored hexagonal cellular networks (M-CRAN) at uplink. The network consists of N base stations (BS) and K ≤ N base band unit pools (BBUP), which function as independent cloud centers. The communication between BSs and BBUPs occurs by means of finite-capacity fronthaul links of capacities C F = μ F · 1 2 log ( 1 + P ) with P denoting transmit power. In the system model, BBUPs have limited processing capacity C BBU = μ BBU · 1 2 log ( 1 + P ) . We propose two different achievability schemes based on dividing the network into non-interfering parallelogram and hexagonal clusters, respectively. The minimum number of users in a cluster is determined by the ratio of BBUPs to BSs, r = K / N . Both of the parallelogram and hexagonal schemes are based on practically implementable beamforming and adapt the way of forming clusters to the sectorization of the cells. Proposed coding schemes improve the sum-rate over naive approaches that ignore cell sectorization, both at finite signal-to-noise ratio (SNR) and in the high-SNR limit. We derive a lower bound on per-user DoF which is a function of μ BBU , μ F , and r. We show that cut-set bound are attained for several cases, the achievability gap between lower and cut-set bounds decreases with the inverse of BBUP-BS ratio 1 r for μ F ≤ 2 M irrespective of μ BBU , and that per-user DoF achieved through hexagonal clustering can not exceed the per-user DoF of parallelogram clustering for any value of μ BBU and r as long as μ F ≤ 2 M . Since the achievability gap decreases with inverse of the BBUP-BS ratio for small and moderate fronthaul capacities, the cut-set bound is almost achieved even for small cluster sizes for this range of fronthaul capacities. For higher fronthaul capacities, the achievability gap is not always tight but decreases with processing capacity. However, the cut-set bound, e.g., at 5 M 6 , can be achieved with a moderate clustering size.

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Development of a Spherical Positioning Robot and Neuro-Navigation System for Precise and Repetitive Non-Invasive Brain Stimulation

Applied Sciences ◽

10.3390/app9214561 ◽

2019 ◽

Vol 9 (21) ◽

pp. 4561

Author(s):

Shin ◽

Ryu ◽

Cho ◽

Yang ◽

Lee

Keyword(s):

Brain Stimulation ◽

Navigation System ◽

Visual Servoing ◽

Degrees Of Freedom ◽

Human Head ◽

Non Invasive ◽

Serial Robot ◽

The Subject ◽

6 Degrees Of Freedom ◽

Spherical Mechanism

Although non-invasive brain stimulation techniques do not involve surgical procedures, the challenge remains in correctly locating the stimulator from outside the head. There is a limit to which one can manually and precisely position and orient the stimulator or repeatedly move the stimulator around the same position. Therefore, in this study, we developed a serial robot with 6 degrees-of-freedom to move the stimulator and a neuro-navigation system to determine the stimulus point from looking at the shape of the subject’s brain. The proposed robot applied a spherical mechanism while considering the safety of the subject, and the workspace of the robot was designed considering the shape of the human head. Position-based visual servoing was applied to compensate for unexpected movements during subject stimulation. We also developed a neuro-navigation system that allows us visually to check the focus of the stimulator and the human brain at the same time and command the robot to the desired point. To verify the system performance, we first performed repeatability and motion compensation experiments of the robot and then evaluated the repeated biosignal response experiments through transcranial magnetic stimulation, a representative technique of non-invasive brain stimulation.

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Interference Alignment for Cognitive Radio Communications and Networks: A Survey

Journal of Sensor and Actuator Networks ◽

10.3390/jsan8040050 ◽

2019 ◽

Vol 8 (4) ◽

pp. 50 ◽

Cited By ~ 1

Author(s):

Yusuf Abdulkadir ◽

Oluyomi Simpson ◽

Yichuang Sun

Keyword(s):

Cognitive Radio ◽

Frequency Spectrum ◽

Degrees Of Freedom ◽

Signal To Noise Ratio ◽

Interference Alignment ◽

Radio Spectrum ◽

High Signal ◽

Radio Communications ◽

Main Research ◽

Transmission Strategy

Interference alignment (IA) is an innovative wireless transmission strategy that has shown to be a promising technique for achieving optimal capacity scaling of a multiuser interference channel at asymptotically high-signal-to-noise ratio (SNR). Transmitters exploit the availability of multiple signaling dimensions in order to align their mutual interference at the receivers. Most of the research has focused on developing algorithms for determining alignment solutions as well as proving interference alignment’s theoretical ability to achieve the maximum degrees of freedom in a wireless network. Cognitive radio, on the other hand, is a technique used to improve the utilization of the radio spectrum by opportunistically sensing and accessing unused licensed frequency spectrum, without causing harmful interference to the licensed users. With the increased deployment of wireless services, the possibility of detecting unused frequency spectrum becomes diminished. Thus, the concept of introducing interference alignment in cognitive radio has become a very attractive proposition. This paper provides a survey of the implementation of IA in cognitive radio under the main research paradigms, along with a summary and analysis of results under each system model.

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Design of Shape Memory Alloy-Based Soft Wearable Robot for Assisting Wrist Motion

Applied Sciences ◽

10.3390/app9194025 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4025 ◽

Cited By ~ 1

Author(s):

Jaeyeon Jeong ◽

Ibrahim Bin Yasir ◽

Jungwoo Han ◽

Cheol Hoon Park ◽

Soo-Kyung Bok ◽

...

Keyword(s):

Shape Memory Alloy ◽

Shape Memory ◽

Degrees Of Freedom ◽

Wrist Flexion ◽

Wearable Robot ◽

Maximum Torque ◽

Wrist Motion ◽

Contraction Ratio ◽

Flexion Extension ◽

The Average Range

In this paper, we propose a shape memory alloy (SMA)-based wearable robot that assists the wrist motion for patients who have difficulties in manipulating the lower arm. Since SMA shows high contraction strain when it is designed as a form of coil spring shape, the proposed muscle-like actuator was designed after optimizing the spring parameters. The fabricated actuator shows a maximum force of 10 N and a maximum contraction ratio of 40%. The SMA-based wearable robot, named soft wrist assist (SWA), assists 2 degrees of freedom (DOF) wrist motions. In addition, the robot is totally flexible and weighs 151g for the wearable parts. A maximum torque of 1.32 Nm was measured for wrist flexion, and a torque of larger than 0.5 Nm was measured for the other motions. The robot showed the average range of motion (ROM) with 33.8, 30.4, 15.4, and 21.4 degrees for flexion, extension, ulnar, and radial deviation, respectively. Thanks to the soft feature of the SWA, time cost for wearing the device is shorter than 2 min as was also the case for patients when putting it on by themselves. From the experimental results, the SWA is expected to support wrist motion for diverse activities of daily living (ADL) routinely for patients.

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POSE ESTIMATION IN AUTOMATED VISUAL INSPECTION USING GENETIC ALGORITHM

International Journal of Neural Systems ◽

10.1142/s0129065706000664 ◽

2006 ◽

Vol 16 (04) ◽

pp. 255-269

Author(s):

S. HATI ◽

K. CHAUDHURY ◽

A. IBRAHIM

Keyword(s):

Genetic Algorithm ◽

Degrees Of Freedom ◽

Visual Inspection ◽

Maximum Error ◽

Least Square ◽

Automated Visual Inspection ◽

Object Space ◽

Rotational Error ◽

Gradient Based ◽

Three Degrees Of Freedom

In this paper, we propose a genetic algorithm based approach to determine the pose of an object in Automated Visual Inspection having three degrees of freedom. We have investigated the effect of noise at 20 dB SNR and also mismatch resulting from incorrect correspondences between the object space points and the image space points, on the estimation of pose parameters. The maximum error in translation parameters is less than 0.45 cm and rotational error is less than 0.2 degree at 20 dB SNR. The error in parameter estimation is insignificant upto 7 pairs of mismatched points out of 24 points in object space and the results skyrockets when 8 or more pairs of points are mismatched. We have compared our result with that obtained by least square technique and it shows that GA based method outperform the gradient based technique when the number of vertices of the object to be inspected is small. These results have clearly established the robustness of GA in estimating the pose of an object with small number of vertices in automated visual inspection.

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