Abstract This study focuses on enhancing Bluetooth low energy (BLE) receiver performance for IoT sensor devices. BLE is widely employed for communication at short ranges, requiring high sensitivity as well as dynamic range (DR) for robust communication and efficient resource utilization. To address these requisites, this work proposes a 2.4 GHz wide DR BLE receiver front-end featuring automatic gain control (AGC). The key innovation lies in integrating a current reuse based low noise-split transconductance amplifier (CLNSTA) with double cascode programming transistors, aimed at enhancing front-end linearity. Moreover, we introduce a novel power detector (PD) with active feedback, effectively extending the operational range of the AGC loop. This combination of the CLNSTA and advanced PD further improves sensitivity and optimal performance across a spectrum of signal strengths, attributed to the CLNSTA’s low noise figure and enhanced 1-dB compression point (P1dB). Simulation results demonstrate the achieved gain is 31.4 dB, P1dB of − 3.01 dBm and third order input intercept point (IIP3) of − 11.62 dBm at 2.4 GHz. With AGC implementation, IIP3 improves by 22.42 dBm and P1dB by 18.29 dBm, achieving a total DR of 88.39 dB. The proposed AGC front end consumes only 2.7 mW when using a 1.8 V supply, offering enhanced sensitivity and performance across varying signal strengths and environmental conditions.

Bluetooth low energy (BLE) is a widely used short-range communication protocol and BLE receivers frequently encounter transient high-power RF (radio frequency) signals from antennas, leading to receiver saturation. To address this issue, this work proposes a 2.4 GHz programmable gain control low-noise amplifier (PGCLNA), which is a key element in an automatic gain control (AGC) system for BLE receiver applications. The proposed PGCLNA achieves discrete levels of gain using the g m -boosting technique by splitting the negative feedback. The proposed circuit is simulated in UMC 180 nm technology and the maximum gain achieved is 41.4 dB, with a step decrease of 15 dB in discrete levels. The gain variations do not degrade the performance of S11, which is always less than -16 dB. The noise figure (NF) is always minimum of 5.5 dB and in high gain mode, the minimum value of NF is 1.82 dB. The third-order intercept point (IIP3) is -7.621 dBm in the best circumstances and always remains above -10.69 dBm. These results are achieved by utilising 5.4 mW power at 1.8 V of supply voltage. The circuit occupies an area of 732 mu m x 771 mu m. The proposed PGCLNA design offers a solution to accommodate a wide range of input signals in BLE receivers, excellent gain control, low noise and robust performance characteristics, making it an essential component in an AGC system for BLE receiver applications.

With the emergence of image capturing devices and increase in usage of internet, massive volume of network data was occupied with digital images. For efficient data transmission, the image may undergo several processing units in from the point it captures to the display/storage device. It may result in loss of the perceptual image quality. Therefore, it is necessary to estimate the image quality to measure the quality of experience. It was found that the convolutional neural networks serve as potential tool for effective feature extraction in many image processing applications. Particularly, with the first layer as Gabor filters, the robustness of the network can be reinforced with learnable Gabor parameters. This paper proposes Gabor Convolutional Neural Network method for No-Reference Image Quality Assessment. Their well-defined spatial structured filters are promising in extracting quality features from the local patches and maps them to perceptual quality scores. Our proposed architecture was tested over synthetic and authentic databases such as LIVE, TID2013, CSIQ, LIVE-MD, MDID2016, LIVE Wild and KoNiQ-10k. The proposed approach was also tested on the Waterloo 3D phase-II database, which contains high-resolution images of both the eyes individually with their respective DMOS scores. The proposed approach out performs over LIVE-MD and LIVE Wild and competes with existing algorithm over other databases.

In Internet of Things (IoT) applications and wireless sensor networks(WSNs), ultra low power receivers are commonly used. The methodologies and circuits used to achieve ultra-low power receivers are compared in this research. The supply voltage scaling and current reuse techniques discussed in this paper are widely used. The current reuse technique is used to design the low noise converter (LNC), self oscillating mixer (SOM), low noise amplifier-mixer-VCO (LMV), LMV-filter architecture, and QLMVF architecture. This paper describes how to overcome design challenges in order to obtain exceptionally low-power building blocks.

Stereoscopic video quality is a perceptual phenomena that is related to the human visual system (HVS). In this paper, we present a spatio-depth saliency and motion strength based full reference stereoscopic video quality metric (FRSVQA). Initially, we obtain a spatial distortion map on every video frame to estimate spatial quality. The spatial distortion map is then refined by the depth salient maps to estimate depth quality. We also estimate the temporal quality by refining the spatial distortion map with the inter-frame difference map at the locations specified by motion edges. The spatial, depth and temporal qualities are systematically combined and averaged over the frames to estimate the overall stereo video quality metric.

The human visual system pays attention to salient regions while perceiving an image. When viewing a stereoscopic 3-D (S3D) image, we hypothesize that while most of the contribution to saliency is provided by the 2-D image, a small but significant contribution is provided by the depth component. Further, we claim that only a subset of image edges contribute to depth perception while viewing an S3D image. In this paper, we propose a systematic approach for depth saliency estimation, called salient edges with respect to depth perception (SED) which localizes the depth-salient edges in an S3D image. We demonstrate the utility of SED in full reference stereoscopic image quality assessment. We consider gradient magnitude and inter-gradient maps for predicting structural similarity. A coarse quality map is estimated first by comparing the 2-D saliency and gradient maps of reference and test stereo pairs. We average this quality map to estimate luminance quality and refine this quality map using SED maps for evaluating depth quality. Finally, we combine this luminance and depth quality to obtain an overall stereo image quality. We perform a comprehensive evaluation of our metric on seven publicly available S3D IQA databases. The proposed metric shows competitive performance on all seven databases with state-of-the-art performance on three of them.

Conventional image quality metrics such as SSIM, FSIM etc out performs well in standard definition (SD) and enhanced definition (ED) images. But with the rapid advancement in technology and miniaturization of devices, the present camera devices support high resolution images such as high definition (HD), full HD and ultra HD. Existing 2D IQA methods doesn't perform better over high resolution images, especially over full HD and above resolution images. So we present a saliency based approach whose performance is in par with existing methods but shows state-of-the-method over full HD databases. We also consider gradient magnitudes and inter-gradient maps for predicting structural similarity. The choice of usage of saliency and gradient methods results in best performance over full HD database. Our proposed algorithm also implemented in different scales of image where the scale number depends on the size of the image.

In this work, we present a full-reference stereo image quality assessment algorithm that is based on the sparse representations of luminance images and depth maps. The primary challenge lies in dealing with the sparsity of disparity maps in conjunction with the sparsity of luminance images. Although analysing the sparsity of images is sufficient to bring out the quality of luminance images, the effectiveness of sparsity in quantifying depth quality is yet to be fully understood. We present a full reference Sparsity-based Quality Assessment of Stereo Images (SQASI) that is aimed at this understanding.

Stereoscopic image quality typically depends on two factors: i) the quality of the luminance image perception, and ii) the quality of depth perception. The effect of distortion on luminance perception and depth perception is usually different, even though depth is estimated from luminance images. Therefore, we present a full reference stereoscopic image quality assessment (FRSIQA) algorithm that rates stereoscopic images in proportion to the quality of individual luminance image perception and the quality of depth perception. The luminance and depth quality is obtained by applying the robust Multiscale-SSIM (MS-SSIM) index on both luminance and disparity maps respectively. We propose a novel multi-scale approach for combining the luminance and depth scores from the left and right images into a single quality score per stereo image. We also explained that a small amount of distortion does not significantly affect depth perception. Further, heavy distortion in stereo pairs will result in significant loss of depth perception. Our algorithm performs competitively over standard databases and is called the 3D-MS-SSIM index.

We present two contributions in this work: (i) a bivariate generalized Gaussian distribution (BGGD) model for the joint distribution of luminance and disparity subband coefficients of natural stereoscopic scenes and (ii) a no-reference (NR) stereo image quality assessment algorithm based on the BGGD model. We first empirically show that a BGGD accurately models the joint distribution of luminance and disparity subband coefficients. We then show that the model parameters form good discriminatory features for NR quality assessment. Additionally, we rely on the previously established result that luminance and disparity subband coefficients of natural stereo scenes are correlated, and show that correlation also forms a good feature for NR quality assessment. These features are computed for both the left and right luminance-disparity pairs in the stereo image and consolidated into one feature vector per stereo pair. This feature set and the stereo pair׳s difference mean opinion score (DMOS) (labels) are used for supervised learning with a support vector machine (SVM). Support vector regression is used to estimate the perceptual quality of a test stereo image pair. The performance of the algorithm is evaluated over popular databases and shown to be competitive with the state-of-the-art no-reference quality assessment algorithms. Further, the strength of the proposed algorithm is demonstrated by its consistently good performance over both symmetric and asymmetric distortion types. Our algorithm is called Stereo QUality Evaluator (StereoQUE).