Unwanted Signals Research Articles

Multifrequency Nuclear Magnetic Resonance as an Efficient Tool To Investigate Heterospin Complexes in Solutions.

We report a multifrequency nuclear magnetic resonance (NMR) study of heterospin complexes [Eu(SQ)3Ln], where SQ is 3,6-di(tert-butyl)-1,2-semiquinone, L is tetrahydrofuran (THF), pyridine (Py), or 2,2'-dipyridyl (Dipy), and n is the number of diamagnetic ligands. Multifrequency NMR experiments allowed us to determine the effective paramagnetic shifts of the ligands (L = THF or Py) and the chemical equilibrium constant for [Eu(SQ)3(THF)2]. In addition, we have found a strong magnetic field effect on the NMR line broadening, giving rise to very broad NMR lines at high magnetic fields. We attribute this effect to broadening under fast exchange conditions when the NMR spectrum represents a homogeneously broadened line with a width proportional to the square of the NMR frequency difference of the free and bound forms of L. Consequently, the line width strongly increases with the magnetic field. This broadening effect allows one to determine relevant kinetic parameters, i.e., the effective exchange time. The strong broadening effect allows one to exploit the [Eu(SQ)3(THF)2] complex as an efficient shift reagent, which not only shifts unwanted NMR signals but also broadens them, notably, in high-field NMR experiments. We have also found that [Eu(SQ)3Dipy] is a thermodynamically stable complex; hence, one can study [Eu(SQ)3Dipy] solutions without special precautions. We report an X-ray structure of the [Eu(SQ)3Dipy]·C6D6 crystals that have been grown directly in an NMR tube. This shows that multifrequency NMR investigations of heterospin compound solutions not only provide thermodynamic and kinetic data for heterospin species but also can be useful for the rational design of stable heterospin complexes and optimization of synthetic approaches.

Development of an On-Board Measurement System for Railway Vehicle Wheel Flange Wear.

The maintenance of railway systems is critical for their safe operation. However some landscape geographical features force the track line to have sharp curves with small radii. Sharp curves are known to be the main source of wheel flange wear. The reduction of wheel flange thickness to an extreme level increases the probability of train accidents. To avoid the unsafe operation of a rail vehicle, it is important to stay continuously up to date on the status of the wheel flange thickness dimensions by using precise and accurate measurement tools. The wheel wear measurement tools that are based on laser and vision technology are quite expensive to implement in railway lines of developing countries. Alternatively significant measurement errors can result from using imprecise measurement tools such as the hand tools, which are currently utilized by the railway companies such as Addis Ababa Light Rail Transit Service (AALRTS). Thus, the objective of this research is to propose and test a new measurement tool that uses an inductive displacement sensor. The proposed system works in both static and dynamic state of the railway vehicle and it is able to save the historical records of the wheel flange thickness for further analysis. The measurement system is fixed on the bogie frame. The fixture was designed using dimensions of the bogie and wheelset structure of the trains currently used by AALRTS. Laboratory experiments and computer simulations for of the electronic system were carried out to assess the feasibility of the data acquisition and analysis method. The noises and unwanted signals due to the dynamics of the system are filtered out from the sensor readings. The results show that the implementation of the proposed measurement system can accurately measure the wheel flange wear. Also, the faulty track section can be identified using the system recorded data and the operational control center data.

A novel approach for high gain instrumentation amplifier in ΔΣ ADC

The power consumption, area on chip and signal accuracy plays a vital role in designing any sensor system. The output of the sensor system is often weak differential signals along with unwanted signals. The Instrumentation amplifier which has good CMRR amplifies the weak signals by eliminating the common mode voltages and has low power consumption. This paper proposes a new method for ultra-low power consumption. It explores the designing of Instrumentation amplifier based on Differential Difference Amplifier in Sigma Delta converter. The non-tailed bulk driven differential pairs circuits with positive feedback in Instrumentation amplifier has been used for ultra-low power consumption. The Instrumentation amplifier as an input structure for sigma delta convertor exhibits high input impedance and linearity. These properties made the direct use of sensor elements on converters without use of front end circuitry. The proposed work is simulated by using 180 nm CMOS process and the results will be compared with the conventional methods.

Metformin Protects Renal Function in Type 2 Diabetic Nephropathy: Regulation of Insulin Signaling in the Rat Model

The present study was aimed to investigate the protective role of metformin in type 2 diabetic nephropathic rats and elucidate regulation of unwanted insulin signaling in the kidney as the underlying mechanism. Diabetes mellitus was established in male Sprague Dawley rats by a single intraperitoneal injection of 40 mg/kg streptozotocin. A sustained level of blood glucose over 16.7 mM was defined as type 2 diabetes mellitus in this study. Rats were randomly classified into the normal group, diabetes mellitus group, diabetes mellitus plus low-dose and high-dose metformin groups (100 and 200 mg/kg orally). Rats received treatment for 12 weeks after which all rats were sacrificed and kidney samples were collected. The samples were dissected and prepared for the observation of structural changes of renal glomerular podocytes between the groups microscopically. The expressions of IRS-1, Akt, nephrin, desmin, phosphatidylinositol-3-hydroxy kinase (PI3K)-p85 and extra cellular matrix were probed by western blot. Compared to the control group, the downregulated nephrin and elevated levels of desmin and collagen IV in the kidney were significantly reversed by metformin treatment. Furthermore, metformin recovered the atypical activities of insulin/PI3K-Akt signaling to the normal ranges. Metformin evidently reduced the renal damage in diabetic rats, which could be due to modulating lipid metabolism and insulin resistance.

Comparative study for Active Noise Cancellation using Adaptive filtering and Standing wave pattern

Noise pollution is one of the most fundamental challenges facing our environment, causes health problem, communication inefficiency and degrade the performance of works due to lack of concentration, thus, mitigating this impact becomes an unavoidable requirement of time to protect people's health and the environment. This noise may originate from several sources including industrial machinery, system parts wear out, and adjacent environmental acoustics. To mitigate this noise effect, an Active Noise Cancellation (ANC) headphone is achieved by two effective techniques; Adaptive filtering and Standing wave phenomenon. In this work, an ANC system is designed using both adaptive filtering and standing wave techniques, the former one basically utilizes single-channel feedforward whereas the latter one utilizes both single-channel feedforward and feedback control. LMS adaptive filter algorithm is the basic component of the designed ANC headphone. For simulation, a noise-free signal will be used as the desired audio signal and a gaussian distributed noise as the unwanted noise signal, these are combined to form noise corrupted speech signal. Propose algorithms performance were evaluated based on the ability to mitigate effects of different frequency broad-band noise signals and of different Noise to Signal ratio. Evaluation measures used are; convergence rate and noise reduction in dB. Result reveals ANC headphone using standing wave technique has better performance at mitigating noise frequency below 800Hz, with low SNR than Adaptive filtering. However, at higher frequencies above 1000Hz, ANC headphone using Adaptive filtering has good performance of masking high frequencies up to 22dB.

Space-Time Adaptive Processing Techniques (STAP) for Mitigation of Jammer Interference and Clutter Suppression in Airborne Radar Systems: A MATLAB Implementation-based Study

Radar Systems are used for the detection of objects by transmitting the electromagnetic waves in the free space. They operate in environments where there is a high possibility that the desired echo signal interferes with the signals from other sources. These signals include clutters and jammer signals. The jammer is a device that continuously emits the wideband radio signals in the radar environment, to saturate the receiver with noise or false information. Thus, the total received signal has three components- returns from target, clutter, and jammer combined i.e. it is a three dimensional signal. The use of conventional signal processing techniques is not desirable, as they cannot separate the desired echo signal from the other components, because the statistics of these components present in the received signal is not known. This problem needs to be accounted for, in airborne surveillance radars, as they have to identify and locate the targets in multiple interference environments. The Space-Time Adaptive Techniques (STAP) is a combination of spatial and temporal filtering that can nullify the jammer signal, and recognize the slow-moving targets. These techniques filter the signal in the angular and the Doppler domain for suppressing the unwanted signals. This paper presents a theoretical study of space-time adaptive coding techniques, and the MATLAB implementation of STAP algorithms; namely, SMI, DPCA, and ADPCA, to suppress clutter and jammer interference in the received pulse.

Opposite Partial Response Filter for Shingled Magnetic Recording Systems

Shingled magnetic recording (SMR) is regarded as the most influential technology for the next-generation magnetic recording systems. The SMR tracks are partly overlapped by each other, and the SMR systems can obtain higher area densities by increasing track per inch density. As a result, this brings more interference from the adjacent sidetracks, i.e., intertrack interference (ITI) while reading the home track. In this letter, we are interested in applying the filtering process to the squeezed tracks before recording to improve the quality of retrieved data. The preprocessing is to reduce the effect of unwanted signals from the sidetracks on the home track. The results show that the SMR system's performance is vastly improved even under the extremely severe effects of the ITI.

A Dynamic Filtering DF-RNN Deep-Learning-Based Approach for EEG-Based Neurological Disorders Diagnosis

Filtering of unwanted signals has a great impact on the performance of EEG signal processing applied to neurological disorders diagnosis. It is so difficult to remove undesirable noises using static filtering approaches as the performance of such techniques is strongly relying on specific EEG signal sub-bands, whose locations differ from one subject to another. In this paper, we present a novel dynamic filtering approach, which makes use of Finite and Infinite Impulse Response (FIR and IIR) filters along with a Recurrent Neural Networks using a Gated-Recurrent Unit (RNN-GRU), to identify and preprocess the most informative sub-bands pertaining to a particular neurological disorder. This combination of RNN with GRU requires more hidden layers than for conventional NN structures, and therefore offers much higher capacity to learn fitting and extract features from highly complex EEG data recording to afford better harmonization of the diagnosis process. Followed by an Independent Component Analysis (ICA) algorithm, all extracted features become independent to facilitate classification of clinical disorders using Convolutional Neural Network (CNN). The proposed diagnosis system achieves an average of 100% classification accuracy for epilepsy according to an offline diagnosis process using Bonn and MIT datasets, and when the same system is applied to autism provides an average accuracy of 99.5% using KAU dataset. The presented dynamic deep-learning approach applied to EEG classification pipeline, which includes artifact removal, feature extraction and classification, leads to significant improvements in the accuracy of the diagnosis classification regarding the targeted neurological pathologies.

Implementation of Low Cost Remote Primary Healthcare Services through Telemedicine: Bangladesh Perspectives

In this paper, we have implemented a low cost primary healthcare service for the remote rural people of Bangladesh. These services were delivered through our developed advanced telemedicine model. The main aim of this paper is to provide basic healthcare service through the developed low cost hardware. We have developed Arduino based low cost hardware’s to be used for this telemedicine services. Remote patients of Bangladesh can get the expert doctors opinion without going to the urban areas. We have collected nine vital signs such as electrocardiogram (ECG), oxygen saturation (SPO2), blood pressure, temperature, body position, glucose level, airflow, height, and weight of patients to be used in our model. We have removed unwanted signals from the collected vital signs through several filtering algorithms. Our system was successfully tested with the patients of Marie Stopes Bangladesh Hospital. From our developed model, rural patients can get primary healthcare services from the pharmacy of any remote village of Bangladesh with the assistance of local doctor by using Raspberry PI. Finally, we can say that the deployment of the developed healthcare service will reduce the cost of the telemedicine services and advances the healthcare facilities for the remote people of Bangladesh.

Band-selective NMR experiments for suppression of unwanted signals in complex mixtures.

Band-selective NMR experiments are presented that allow selective suppression of unwanted signals (SUN) from the spectra of complex metabolite mixtures. As a result, spectral overlap and dynamic range problems are substantially reduced and low-intensity signals normally covered by dominant signals can be observed. The usefulness of the experiments is exemplified with selective suppression of sugar signals from the NMR spectra of fruit juice and a plant sample. Other possible applications include blood, milk, and wine samples.

Model-based adaptive filtering of harmonic perturbations applied to high-frequency noninvasive valvometry

In this paper, a model-based adaptive filter is used to suppress electrical noise in a high-frequency noninvasive valvometry device, which is part of an autonomous biosensor system using bivalve mollusks valve-activity measurements for ecological monitoring purposes. The proposed model-based adaptive filter uses the dynamic regressor extension and mixing method to allow a decoupled estimation of the parameters. Once the desired regression form of the output model is obtained, a fixed-time estimation approach is used to identify its parameters. By applying these two techniques, a flexible filter structure is obtained with the property of retaining the major relevant components of interest of the original valve-activity signals, even in the case when the unwanted signal frequency components are in the same frequency range as the useful variables.

A Performance based comparative study on the Modified version of Empirical Mode Decomposition with traditional Empirical Mode Decomposition

Abstract Electrocardiogram, popularly known as ECG is a diagnostic test which detects the heart’s electrical system. It is an inevitable diagnostic technique in the medical field which helps to detect any heart related diseases accurately. But there are high chances of addition of any random unwanted signals or noises during the recording process. This hinders the exact diagnosis and hence it is important to denoise these signals. There exist various denoising techniques including Empirical mode decomposition (EMD), Non local means (NLM), various filters, etc. A modified method of EMD is presented here which performs smaller number of iterations and forms only a few Intrinsic mode functions (IMF) when compared with traditional EMD method. The presented work is a performance comparison study between the modified version of EMD which has lesser latency with the traditional EMD.

Machine learning based null allocation design for adaptive beamforming in unmanned aerial vehicle communications

In order to communicate with target unmanned aerial vehicles (UAVs), ground control stations (GCSs) typically adopt adaptive beamforming with high antenna gain and co-channel interference rejection. Multiple interfering signals arriving from different directions arise from other UAVs and other GCSs, and the beamformer installed in the home GCS will usually attempt to null unwanted signals from all these directions of arrival (DoAs) without analyzing the distribution of the angles of arrival. Consequently, the beamformer will fail to allocate nulls in some directions, and the signal-to-interference-plus-noise (SINR) performance of the home GCS is impaired. In this paper, a new approach to null allocation is proposed, based on machine learning using k-means clustering. The design first involves the collection of information about the DoAs and the corresponding received signal strengths of all the interfering signals into a two-dimensional dataset. Secondly, this dataset is broken down into clusters by using k-means clustering, and the cluster centroids are calculated. In each cluster, the interfering signal that has the shortest Euclidean distance to the centroid is identified as the approximated centroid. Only the approximated centroids are selected as input to the beamformer, with the aim that each complete cluster of interference sources can be nulled by allocating one null per cluster. To optimize the number of clusters k used in the null allocation process, the design adopts the particle swarm optimization technique to adaptively update the value of k to maximize the SINR at the home GCS. Simulation results show that our design yields a maximum SINR improvement of about 12 dB when compared to cases where no null allocation is considered. Moreover, our design also outperforms null steering in the UAV scenarios. Advantageously, this enhanced performance is obtained without the need for additional power amplification or hardware modification to the beamformer.

The Kendrick analysis for polymer mass spectrometry.

The mass spectrum of a polymer often displays repetitive patterns with peak series spaced by the repeating unit(s) of the polymeric backbones, sometimes complexified with different adducts, chain terminations, or charge states. Exploring the complex mass spectral data or filtering the unwanted signal is tedious whether performed manually or automatically. In contrast, the now 60-year-old Kendrick (mass defect) analysis, when adapted to polymer ions, produces visual two-dimensional maps with intuitive alignments of the repetitive patterns and favourable deconvolution of features overlaid in the one-dimensional mass spectrum. This special feature article reports on an up-to-date and theoretically sound use of Kendrick plots as a data processing tool. The approach requires no prior knowledge of the sample but offers promising dynamic capabilities for visualizing, filtering, and sometimes assigning congested mass spectra. Examples of applications of the approach to polymers are discussed throughout the text, but the same tools can be readily extended to other applications, including the analysis of polymers present as pollutants/contaminants, and to other analytes incorporating a repetitive moiety, for example, oils or lipids. In each of these instances, data processing can benefit from the application of an updated and interactive Kendrick analysis.

Implementation and Realisation of Micro Digital Sun Sensor Processing in FPGA

AbstractThis paper presents the implementation and realization of complex processing algorithms of newly developed, high accuracy, compact, two axes Micro Digital Sun Sensor (MDSS) using linear CMOS detector and N‐shaped slit mask for Indian Satellite missions. Sun sensor provides the orientation of satellite with respect to Sun. It is one of the prime attitude sensors for spacecraft. Due to low mass, compact size and low power consumption, the MDSS will be used for future micro/nano‐satellite missions as well as for communication Satellite missions. The electrical model of MDSS is built in Laboratory for Electro‐Optics Systems (LEOS). It is around 10 times lesser in weight and 8 times compact than the high accuracy Sun sensors presently available in ISRO. The MDSS has accuracy of 0.02°. In this paper, we have presented details of the simulation and test results of Micro DSS processing.The MDSS consists of an optical head and FPGA based processing unit. The optical head consists of CMOS linear array detector and N shaped slit mask. The N slit mask is mounted at a predefined height above the detector to provide the sensor field of view of ±64° x ±64°. The sunlight falls on the detector through the N‐shaped slit and three Sun spots are formed on the detector. After removing the unwanted signals from the images using auto threshold method, the image identification and centroid computation are carried out using moment method algorithm in real time and using the real time image clustering and centroid computing algorithm. By applying sensor transfer function, the 3 centroid information are converted to Sun aspect angle information which are the sensor outputs used for determining orientation of Satellite.All the processing is done in a single FPGA, like control signals generation for detector, real time centroid calculations, complex image clustering, transfer equation realization, interfacing with output, command module and MIL‐STD‐1553B communication. The complex transfer equation which includes trigonometric functions is implemented inside the FPGA using Co‐Ordinate Rotation Digital Computer (CORDIC) algorithm without any processor. The processing unit consists of ACTEL RTAX or Proasic FPGA with MIL‐STD 1553B and LVDS peripheral interface.

Quantitative lameness assessment in the horse based on upper body movement symmetry: The effect of different filtering techniques on the quantification of motion symmetry

Quantitative gait analysis in horses is rapidly gaining importance, both clinically and in research. The number of available systems is increasing, but the methods of signal analysis differ between systems and research groups. Our objectives are to describe and evaluate the effects of different methods of signal analysis for processing of data from equine kinematic gait analysis. To this end, we use theoretical signals based on previously published work, followed by the evaluation of the performance of each technique using real data from horses with induced lameness. Two infinite impulse response (IIR), high-pass filters (Butterworth and Chebyshev), a signal decomposition method and a moving average filtering technique were evaluated. First, we describe methods to fine-tune each filter to the optimal settings based on residual analysis. Second the performance of each filter is evaluated based on differences in calculated symmetry parameters from horses with induced lameness. We show that optimisation of filtering techniques is crucial when processing signals used for objective lameness quantification. Improper selection of the cut-off frequency for IIR filters can result in false negative results (average values above or below predefined reference values). The IIR Butterworth filter and the signal decomposition method achieved the best reduction of unwanted signal components. Knowledge of the available filtering techniques is a pre-requisite for adequate signal processing of gait data from quantitative analysis systems in horses.

Whole-Brain Myelin Imaging Using 3D Double-Echo Sliding Inversion Recovery Ultrashort Echo Time (DESIRE UTE) MRI.

Background Signal contamination from long T2 water is a major challenge in direct imaging of myelin with MRI. Nulling of the unwanted long T2 signals can be achieved with an inversion recovery (IR) preparation pulse to null long T2 white matter within the brain. The remaining ultrashort T2 signal from myelin can be detected with an ultrashort echo time (UTE) sequence. Purpose To develop patient-specific whole-brain myelin imaging with a three-dimensional double-echo sliding inversion recovery (DESIRE) UTE sequence. Materials and Methods The DESIRE UTE sequence generates a series of IR images with different inversion times during a single scan. The optimal inversion time for nulling long T2 signal is determined by finding minimal signal on the second echo. Myelin images are generated by subtracting the second echo image from the first UTE image. To validate this method, a prospective study was performed in phantoms, cadaveric brain specimens, healthy volunteers, and patients with multiple sclerosis (MS). A total of 20 healthy volunteers (mean age, 40 years ± 13 [standard deviation], 10 women) and 20 patients with MS (mean age, 58 years ± 8; 15 women) who underwent MRI between November 2017 and February 2019 were prospectively included. Analysis of variance was performed to evaluate the signal difference between MS lesions and normal-appearing white matter in patients with MS. Results High signal intensity and corresponding T2* and T1 of the extracted myelin vesicles provided evidence for direct imaging of ultrashort-T2 myelin protons using the UTE sequence. Gadobenate dimeglumine phantoms with a wide range of T1 values were selectively suppressed with DESIRE UTE. In the ex vivo brain study of MS lesions, signal loss was observed in MS lesions and was conformed with histologic analysis. In the human study, there was a significant reduction in normalized signal intensity in MS lesions compared with that in normal-appearing white matter (0.19 ± 0.10 vs 0.76 ± 0.11, respectively; P < .001). Conclusion The double-echo sliding inversion recovery ultrashort echo time sequence can generate whole-brain myelin images specifically with a clinical 3-T scanner. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Port in this issue.

A Novel Revcar Platform for Switchable and Gated Tumor Targeting

Hematological malignancies are successfully treated with chimeric antigen receptor (CAR) armed T cells. Despite the clinical success, CAR T cell therapy struggles still with some problems including the selection of tumor escape variants and on-target, off-tumor side reactions as well as massive cytokine release and uncontrollability of CAR T cell activity in the patients. In order to enable controllability of CAR T cells and to avoid unspecific side effects, we established a novel switchable, split and adaptable CAR platform technology, termed RevCAR system. The novel RevCARs lack the single chain variable fragment (scFv) commonly used as extracellular domain in conventional CARs. Instead of the scFv, RevCARs contain only a small peptide epitope as extracellular portion. This design reduces the CAR size, avoids unspecific antigen binding and prevents antigen independent tonic signaling caused by scFv dimerization. As RevCAR T cells do not recognize anything, they are per se inert. Only in the presence of a corresponding bispecific antibody based target module (RevTM) they can be specifically redirected to tumor cells. Therefor RevTMs consist of two scFvs. One recognizes the RevCAR peptide epitope and the other one simultaneously binds to a tumor associated antigen (TAA). By dosing of the RevTM, which has a very short half-life, the reactivity of RevCAR T cells can be switched on and off reversibly. Another advantage is that the RevCAR system can be flexibly adapted to any tumor antigen simply by exchanging the RevTM. Furthermore, the small RevCAR size is favorable for inserting more than one RevCAR in the same T cell thus facilitating the mode of gated targeting which is a highly attractive approach to minimize the risk for on-target, off-tumor toxicities against healthy tissues and to increase tumor specificity of conventional CAR T cells. For 'AND' gate targeting via the RevCAR system, two different RevCARs were constructed and expressed simultaneously in the same T cell. The two RevCARs differed with respect to the extracellular peptide epitope and the intracellular signaling domain. Moreover, the respective transmembrane domain was selected to isolate the respective RevCAR signal. The first RevCAR is designed to transmit the activation signal, the second RevCAR to deliver a costimulatory signal. For efficient RevCAR T cell activation, both RevCARs must be engaged via their respective RevTM which on the one hand binds to one of the two RevCAR epitopes and on the other hand to one of two TAAs expressed on the same target cell. Here, we present two RevCAR/RevTM systems for retargeting of AML cells as well as solid tumor cells including via gated targeting. In summary, we show proof of concept for a novel switchable RevCAR system that can be used for retargeting of AML cells as well as solid tumors. The novel modular RevCAR platform is characterized by small size, lacks unwanted tonic signaling effects, allows the control of RevCAR T cell activity, enables gated targeting strategies, and can be adapted to any tumor antigen and tumor type. Disclosures Koristka: Intellia Therapeutics: Employment. Bachmann:GEMoaB Monoclonals: Equity Ownership, Patents &amp; Royalties.

Retinoid receptor turnover mediated by sumoylation, ubiquitination and the valosin-containing protein is disrupted in glioblastoma

Resistance to therapeutic use of retinoids in glioma has been observed for over 20 years; however, the exact mechanism of resistance remains unknown. To understand retinoic acid resistance in glioma, we studied the turnover mechanism of retinoid receptor proteins in neural stem cells and glioma stem-like cells. Here, we show that in normal neural stem cells, proteasomal degradation of retinoid receptors involves sumoylation, ubiquitination and recognition by the valosin-containing protein (VCP/p97/Cdc48). We find that Sumo1 modification has a dual role to stabilize the retinoid receptor from unwanted degradation and signal additional modification via ubiquitination. Subsequently, the modified receptor binds to the VCP chaperone and both proteins are degraded by the proteasome. Additionally, we reveal that all trans retinoic acid (ATRA) induces VCP expression, creating a positive feedback loop that enhances degradation. In contrast, the pathway is impaired in the glioma stem-like cells resulting in the accumulation of sumoylated and high molecular weight forms of retinoid receptors that lack transcriptional activity and fail to be recognized by the proteasome. Moreover, modified receptor accumulation occurs before ATRA treatment; therefore, the transcritptional defect in glioma is due to a block in the proteasomal degradation pathway that occurs after the sumo modification step.

Cross-Polarization Discrimination and Port-to-Port Isolation Enhancement of Dual-Polarized Antenna Structures Enabling Polarization MIMO

This letter presents port-to-port isolation and cross-polarization discrimination (XPD) enhancement techniques for dual-polarized (DP) antenna topologies. Both port-to-port isolation and XPD of DP-antenna degrade due to unwanted cross-coupling signals from one port to another port. Since high port-to-port isolation and XPD are imperative for realization of DP multiple-input–multiple-output (DP-MIMO), the proposed antiphase excitation scheme enabling undesired cross-coupling cancellation for DP-antenna element and array presents significant promise for the implementation. The fabricated differential DP-antenna designed for 57–71 GHz frequency bands exhibits more than 30 dB of port-to-port isolation over 8 GHz of bandwidth and also demonstrates up to 36 dB of XPD with >25 dB over the whole bandwidth. The modulated transmitter (TX) signal measurements on the proposed differential DP-antenna integrated TX exhibits on average less than 1 dB of error-vector-magnitude degradation in comparison with the single-polarized case, which proves the benefits of the proposed isolation enhancement technique for DP-MIMO.