A novel compact-slotted four element multiple input multiple output (MIMO) planar monopole antenna is proposed for 5G mmWave N257/N258 and N262 band applications. The antenna, with dimensions of 12 mm × 11.6 mm × 0.508 mm ($1.036{\lambda }_o \times$$1.001{\lambda }_o\times 0.043{\lambda }_o$ where ${\lambda }_o$ is computed at lowest cutoff frequency), is fabricated on a Rogers RT/duroid 5880 (tm) substrate with a relative permittivity of 2.2 and a dielectric loss tangent of 0.0009. The suggested antenna consists of four U-shaped radiating elements (patches) on top of the dielectric material and a slotted ground on the bottom. The radiating elements are fed by a 50-ohm microstrip line feed. To improve the impedance performance of the MIMO antenna, a rectangular strip of 1.3 mm × 0.2 mm and a couple of rectangular slots are added to each radiating element. The first operating band at 27.1 GHz, ranging from 25.9 GHz to 27.8 GHz, is achieved by using slotted U-shaped radiating elements. The second operating band at 48.7 GHz, ranging from 47.1 GHz to 49.9 GHz, is obtained by etching hexagonal slots on the ground. The antenna design achieves an isolation of >27 dB through the orthogonal positioning of radiating elements and slots on the ground. The designed antenna operates at 27 GHz (N257/N258) and 48.7 GHz (N262) bands, exhibiting stable radiation patterns, a peak gain of >5.95 dBi, radiation efficiency of >90%, an envelope correlation coefficient of <10⁻⁶, a total active reflection coefficient of ≤−10 dB, channel capacity losses of <0.03 bits/s/Hz, and a mean effective gain of ≤−3 dB. The simulated and measured results of the antenna show good agreement, making it well-suited for 5G mmWave communication applications.

Various sectors, including smart homes, healthcare, transportation, agriculture, and manufacturing, derive significant advantages from Internet of Things (IoT) technology. This innovative method of interacting with the world enhances efficiency, convenience, and productivity. Nonetheless, it raises concerns regarding security, privacy, and data governance. This article introduces a cognitive hybrid Deep Learning (DL) approach, facilitated by Software-Defined Networking (SDN), to address security concerns in IoT networks through intrusion detection. The principal objective of this methodology is to consistently and effectively detect cybersecurity risks within the IoT ecosystem. Inspired by the cognitive computing paradigm, the proposed system can analyze, comprehend, and respond to diverse traffic targeting IoT devices. The suggested model is trained and evaluated utilizing the advanced N-BaIoT and CICDDoS2019 datasets. The experimental results exhibit a high degree of accuracy, producing an acceptable false positive rate alongside a tolerable testing duration. Moreover, the architecture takes into account the constrained resources of IoT devices, guaranteeing they are not excessively taxed during the process. The proposed model achieved an accuracy rate of 99.86%, precision of 99.96%, recall of 99.903%, and F1-Score of 99.93%. The proposed model surpassed existing hybrid DL methods.

This paper mainly studies Multi Band Spectral Subtraction (MBSS) for speech enhancement based on the spectrum representation in the frequency domain with three different scales(linear, log, mel) and their effect on performance measures in presence of additive nonstationary noise at different ranges of input SNR. Since speech is non-stationary signal, noise distribution is nonuniform i.e few frequency components are affected severely than others. A common method to restore the original speech in presence of noise is speech enhancement by suppressing the back ground noise. Multi Band Spectral Subtraction is one among the speech enhancement techniques which performs spectral subtraction by dividing noisy speech spectrum into uniformly spaced non over lapping frequency bands and spectral over subtraction is performed in each band separately. The performance of this method is evaluated in terms of objective measures such as Cepstrum distance, Log Likelihood Ratio, Weighted Spectral Slope distance, segmental SNR and Perceptual Evaluation of Speech Quality.

This paper presents a method to reduce the musical noise encountered with the most of the frequency domain speech enhancement algorithms. Musical Noise is a phenomenon which occurs due to random spectral speaks in each speech frame, because of large variance and inaccurate estimate of spectra of noisy speech and noise signals. In order to get low variance spectral estimate, this paper uses a method based on wavelet thresholding the multitaper spectrum combined with noise estimation algorithm, which estimates noise spectrum based on the spectral average of past and present according to a predetermined weighting factor to reduce the musical noise. To evaluate the performance of this method, sine multitapers were used and the spectral coefficients are threshold using Wavelet thresholding to get low variance spectrum .In this paper, both scale dependent, independent thresholdings with soft and hard thresholding using Daubauchies wavelet were used to evaluate the proposed method in terms of objective quality measures under eight different types of real-world noises at three distortions of input SNR. To predict the speech quality in presence of noise, objective quality measures like Segmental SNR ,Weighted Spectral Slope Distance ,Log Likelihood Ratio, Perceptual Evaluation of Speech Quality (PESQ) and composite measures are compared against wavelet de-noising techniques, Spectral Subtraction and Multiband Spectral Subtraction provides consistent performance to all eight different noises in most of the cases considered.