On body and off body communication using a compact wideband and high gain wearable textile antenna.

In this paper, a compact low-profile dual-band wearable textile antenna is proposed for on-body and off-body communications. The presented antenna works efficiently in the 5G n79 frequency band (4.4 - 5 GHz) and the ISM band (5.725 - 5.875 GHz). The designed antenna has an ultra-wide impedance bandwidth of 2.01 GHz and peak realized gains of 10.5 dBi and 12 dBi at 4.5 GHz and 5.8 GHz, respectively. The antenna has a small footprint (π × 0.3λ02), which is inspired by circular fractal geometry. The performance of the presented wearable antenna is evaluated at various body parts, including the arm, wrist, and chest. The link margin is evaluated in the on-body and off-body communication scenarios, i.e., communication with the implantable antenna and the outside-body antenna, which is 80 dB and 65 dB at 4.5 GHz and 5.8 GHz, respectively. The 1 gm/10 gm specific absorption rate values at 4.5 GHz and 5.8 GHz are 0.12/0.098 and 0.11/0.082, respectively, which are significantly lower than the standard values, making the proposed antenna suitable for modern wearable applications.

Compact Ultra-Wideband Wilkinson Power Divider in Parallel Stripline with Modified Isolation Branches.

An efficient design method for a compact and ultra-wideband multi-stage Wilkinson power divider in a parallel stripline (PSL) is proposed. To enhance the frequency bandwidth of the proposed power divider while reducing its size, the isolation branch is modified; that is, two capacitors are connected to both sides of a resistor at each isolation branch. For an efficient design process, the PSL power divider is equivalently represented by two microstrip power dividers, and the design equations are derived. Based on the design equations, an in-house algorithm is utilized to optimally determine the design parameters, including the line impedance, resistance, and capacitance of each stage. For example, a three-stage PSL power divider is designed with three λ/4 transmission lines at a base frequency of 5 GHz. To verify the accuracy of the design procedure, 3D EM simulations and measurements are performed, and the results show good agreement. Compared with the conventional three-stage Wilkinson power divider, the proposed PSL power divider achieves a wider frequency bandwidth of 1.16 to 6.51 GHz (139.5%) and a 23% shorter transmission line length of 207°, while exhibiting an insertion loss of 0.7 to 1.4 dB.

Ultra-Wideband Vertical Transition in Coplanar Stripline for Ultra-High-Speed Digital Interfaces.

A design method for an ultra-wideband coplanar-stripline-based vertical transition that can be used for ultra-high-speed digital interfaces is proposed. A conventional via structure, based on a differential line (DL), inherently possesses performance limitations (<10 GHz) due to difficulties in maintaining constant line impedance and smooth electric field transformation, in addition to the effects of signal skews, FR4 fiber weave, and unbalanced EM interferences. DL-based digital interfaces may not meet the demands of ultra-high-speed digital data transmission required for the upcoming 6G communications. The use of a coplanar stripline (CPS), a type of planar balanced line (BL), for the vertical transition, along with the ultra-wideband DL-to-CPS transition, mostly removes the inherent and unfavorable issues of the DL and enables ultra-high-speed digital data transmission. The design process of the transition is simplified using the analytical design formulas, derived using the conformal mapping method, of the transition. The characteristic line impedances of the transition are calculated and found to be in close agreement with the results obtained from EM simulations. Utilizing these results, the CPS-based vertical transition, maintaining the characteristic line impedance of 100 Ω, is designed and fabricated. The measured results confirm its ultra-wideband characteristics, with a maximum of 1.6 dB insertion loss and more than 10 dB return loss in the frequency range of DC to 30 GHz. Therefore, the proposed CPS-based vertical transition offers a significantly wider frequency bandwidth, i.e., more than three times that of conventional DL-based via structures.

Ultra-thin flexible rectenna integrated with power management unit for wireless power harvester/charging of smartwatch/wristband.

This paper proposes a circularly polarized ultra-thin flexible antenna with a flexible rectifier and power management unit (PMU) for smartwatch/wristband applications. The flexible antenna is compact (0.17λ0 × 0.20λ0 × 0.0004λ0) and has a stepped ground plane. A parasitic element is used at the substrate bottom to reduce the specific absorption rate (SAR) and enhance the gain up to 3.2 dBi, at the resonating frequency of WLAN/Wi-Fi (2.45 GHz). The SAR of the proposed design is also analysed at the resonating frequency, and it satisfies the guidelines of the International Commission on Non-Ionizing Radiation Protection (ICNIRP) and IEEE C95.1-2019 human safety standards. An impedance matching circuit is used between the antenna and the RF energy harvester to improve conversion efficiency. Polarization mismatch is avoided with the help of circular polarization, achieved by tuning stubs of size 0.02λ0 × 0.044λ0. The integration of the antenna and rectenna results in a good conversion efficiency of 78.2% at - 5 dBm of input power with a load resistance of 2 KΩ. The availability of RF signals allows the user to charge the smartwatch/wristband by connecting the PMU circuit with the RF energy harvester.

Compact Dual Circularly-Polarized Quad-Element MIMO/Diversity Antenna for Sub-6 GHz Communication Systems.

In this paper, a compact dual circularly-polarized (CP) planar multiple-input-multiple-output (MIMO) antenna is presented for a sub-6 GHz frequency band. The antenna consists of four identical resonating elements, which are placed in a mirrored-image pattern to obtain polarization diversity. Element 2 is a mirror image of element 1, and elements 3 and 4 are mirror images of elements 1 and 2. Each antenna element comprises an elliptical resonator, a 50-Ω microstrip feed line, and a rectangular stub integrated with the feed to increase the surface current path of the antenna, shifting the resonating frequency to the lower side. Additionally, the rectangular stub is lengthened towards the right side (along the +x-axis direction in the antenna element 1), which balances the magnitude and 90° phase variance among the horizontal (Ex) and vertical (Ey) fields. The proposed MIMO antenna supports both types of circular polarization, where radiators 1 and 3 radiate right-hand CP (RHCP) rays and radiators 2 and 4 radiate left-hand CP (LHCP) rays. Developing a compact-size MIMO antenna is a challenging task, especially when the antenna elements share the same ground plane and are placed less than half a wavelength apart. The mutual coupling in the proposed antenna is reduced by increasing the spacing between the elements without the use of any extra decoupling structure. Optimal spacing is maintained to achieve compact geometry with less inter-element correlation. The radiators are closely placed with an edge-to-edge spacing of 0.08λ0, where λ0 is the free space wavelength at 3.6 GHz. A peak gain of 5 dBi, efficiency of 90%, an envelope correlation coefficient (ECC) of less than 0.1, and isolation of more than 18 dB are obtained between different ports of the prototype antenna. The overall size of the antenna element is 17 mm × 17 mm × 1.6 mm, and the MIMO antenna is 40 mm × 40 mm × 1.6 mm.

Part Affinity Fields and CoordConv for Detecting Landmarks of Lumbar Vertebrae and Sacrum in X-ray Images.

With the prevalence of degenerative diseases due to the increase in the aging population, we have encountered many spine-related disorders. Since the spine is a crucial part of the body, fast and accurate diagnosis is critically important. Generally, clinicians use X-ray images to diagnose the spine, but X-ray images are commonly occluded by the shadows of some bones, making it hard to identify the whole spine. Therefore, recently, various deep-learning-based spinal X-ray image analysis approaches have been proposed to help diagnose the spine. However, these approaches did not consider the characteristics of frequent occlusion in the X-ray image and the properties of the vertebra shape. Therefore, based on the X-ray image properties and vertebra shape, we present a novel landmark detection network specialized in lumbar X-ray images. The proposed network consists of two stages: The first step detects the centers of the lumbar vertebrae and the upper end plate of the first sacral vertebra (S1), and the second step detects the four corner points of each lumbar vertebra and two corner points of S1 from the image obtained in the first step. We used random spine cutout augmentation in the first step to robustify the network against the commonly obscured X-ray images. Furthermore, in the second step, we used CoordConv to make the network recognize the location distribution of landmarks and part affinity fields to understand the morphological features of the vertebrae, resulting in more accurate landmark detection. The proposed network was evaluated using 304 X-ray images, and it achieved 98.02% accuracy in center detection and 8.34% relative distance error in corner detection. This indicates that our network can detect spinal landmarks reliably enough to support radiologists in analyzing the lumbar X-ray images.

Learning-Based Ordering Characters on Ancient Document.

Digitalizing and translating a scanned document image entails detecting the characters using a detector and translating the characters in the order they were detected with a translator. However, it is impossible to translate these characters correctly because the detector often detects them in any order. As a result, since it is critical to organize the recognized characters for proper translation, we propose ordering characters from documents with multiple variations using the strength of the learning-based model that learns the necessary operations from the data. In this task, it is difficult to order the characters written on antique handwritten documents that have deviations such as a bent or split line, as opposed to official records that have lines placed uprightly one by one. Because dealing with these many variants using a human-designed algorithm is problematic, we arrange characters printed on papers with diverse variations by taking advantage of a training model that can learn the appropriate function from data. Our method outputs both line id and y-axis and combines them to assign the sequential index. It is difficult to train using simply local regions because sequential character indexes in a large range include long-range dependencies. To solve this problem, we use network architecture to expand the receptive field as wide as possible. The network must learn to give various indexes to characters in similar places for each document because the number and area of characters vary for each document. We offer the ground truth assign method based on the absolute position to assign similar indexes to characters in similar places. Furthermore, even if the network uses absolute ground truth, the network may assign the incorrect line if the center coordinates of characters are biased in one direction. As a result, we employed the Region of Interest (ROI) from the pretrained coordinate layer, which contains position and trend information. We used the modified edit distance to compare the similarity of character indexes from the ground truth and our technique. In addition, we computed the modified fisher criterion to assess the degree of the clustering line. Consequently, our edit distance is just 0.43 times that of the human-designed algorithm, and our fisher criterion is 1.46 times that of the human-designed algorithm, improving the performance of human-designed algorithm.

Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network.

Recent image-style transfer methods use the structure of a VGG feature network to encode and decode the feature map of the image. Since the network is designed for the general image-classification task, it has a number of channels and, accordingly, requires a huge amount of memory and high computational power, which is not mandatory for such a relatively simple task as image-style transfer. In this paper, we propose a new technique to size down the previously used style transfer network for eliminating the redundancy of the VGG feature network in memory consumption and computational cost. Our method automatically finds a number of consistently inactive convolution channels during the network training phase by using two new losses, i.e., channel loss and xor loss. The former maximizes the number of inactive channels and the latter fixes the positions of these inactive channels to be the same for the image. Our method improves the image generation speed to be up to 49% faster and reduces the number of parameters by 20% while maintaining style transferring performance. Additionally, our losses are also effective in pruning the VGG16 classifier network, i.e., parameter reduction by 26% and top-1 accuracy improvement by 0.16% on CIFAR-10.

A Review of Multi-Modal Learning from the Text-Guided Visual Processing Viewpoint.

For decades, co-relating different data domains to attain the maximum potential of machines has driven research, especially in neural networks. Similarly, text and visual data (images and videos) are two distinct data domains with extensive research in the past. Recently, using natural language to process 2D or 3D images and videos with the immense power of neural nets has witnessed a promising future. Despite the diverse range of remarkable work in this field, notably in the past few years, rapid improvements have also solved future challenges for researchers. Moreover, the connection between these two domains is mainly subjected to GAN, thus limiting the horizons of this field. This review analyzes Text-to-Image (T2I) synthesis as a broader picture, Text-guided Visual-output (T2Vo), with the primary goal being to highlight the gaps by proposing a more comprehensive taxonomy. We broadly categorize text-guided visual output into three main divisions and meaningful subdivisions by critically examining an extensive body of literature from top-tier computer vision venues and closely related fields, such as machine learning and human-computer interaction, aiming at state-of-the-art models with a comparative analysis. This study successively follows previous surveys on T2I, adding value by analogously evaluating the diverse range of existing methods, including different generative models, several types of visual output, critical examination of various approaches, and highlighting the shortcomings, suggesting the future direction of research.

Ultra-Wideband Differential Line-to-Balanced Line Transitions for Super-High-Speed Digital Transmission.

A conventional differential line (DL), commonly used on typical digital circuit boards for transmitting high-speed digital data, has fundamental limitations on the maximum signal bandwidth (~10 GHz), mainly due to signal skew, multiple line coupling, and EM interference. Therefore, to support super-high-speed digital data transmission, especially for beyond 5G communications, a practical high-performance transmission structure for digital signals is required. Balanced lines (BLs) can transmit the differential signals with multiple advantages of ultra-wide bandwidth, common-mode rejection, reduced crosstalk, phase recovery, and skew reduction, which enable super-high-speed transmission. In order to utilize the BLs in the DL-based digital circuit, connecting structures between a DL and BLs are required, but the DL-to-BL transition structures dominate the operating bandwidth and signal properties. Therefore, in this paper, properties, and design methods for two ultra-wideband DL-to-BL transitions, i.e., DL-to-CPS (coplanar stripline) and DL-to-PSL (parallel stripline) transitions, are presented. Both implemented DL-to-CPS and DL-to-PSL transitions provide high-quality performance up to 40 GHz or higher, significantly enhancing the frequency bandwidth for the transmission of digital signals while providing compatibility with the DL-based PCBs. The fabricated DL-to-CPS transition performs well from DC to 40 GHz with an insertion loss of less than 0.86 dB and a return loss of more than 10 dB, and the fabricated DL-to-PSL transition also provides good performance from DC to 40 GHz, with an insertion loss of less than 1.34 dB and a return loss of more than 10 dB. Therefore, the proposed DL-to-BL transitions can be applied to achieve super-high-speed digital data transmission with over 40 GHz bandwidth, which is more than four times the bandwidth of the DL, supporting over 200 Gbps of digital data transmission on PCBs for the next generation of advanced communications.

Unveiling the effect of gas treatment on the electronic structure of carbon nanotube-supported Pd catalysts for electroreduction of H2O2 and Heck reaction.

This study explored the impact of gas treatments on the structures of multi-walled carbon nanotubes supported Pd (CNT-Pd) catalysts used for electrocatalytic H2O2 reduction and the Heck cross-coupling reaction. The CNT-Pd catalyst was prepared by anchoring Pd nanoparticles on thiolated CNTs. XPS was conducted to examine the surface composition and electronic structure changes of the CNT-Pd catalyst before and after gas treatment. The XPS results revealed that as-prepared CNT-Pd contains at least two different oxidation states on the surface, whereon their proportions depend on the gas used for treatment. Treatment with H2 leads to Pd(0) enrichment near the surface, while O2 treatment causes Pd(Ⅱ) enrichment of CNT-Pd. All catalysts containing both Pd(0) and Pd(Ⅱ) were active toward H2O2 reduction, and the Heck cross-coupling reaction of n-butyl acrylate and 4-iodotoluene; increased proportion of metallic Pd(0) boosted the catalytic reaction. However, the catalyst stability increased as the amount of Pd(II) increased.

Optimal Energetic-Trap Distribution of Nano-Scaled Charge Trap Nitride for Wider Vth Window in 3D NAND Flash Using a Machine-Learning Method.

A machine-learning (ML) technique was used to optimize the energetic-trap distributions of nano-scaled charge trap nitride (CTN) in 3D NAND Flash to widen the threshold voltage (Vth) window, which is crucial for NAND operation. The energetic-trap distribution is a critical material property of the CTN that affects the Vth window between the erase and program Vth. An artificial neural network (ANN) was used to model the relationship between the energetic-trap distributions as an input parameter and the Vth window as an output parameter. A well-trained ANN was used with the gradient-descent method to determine the specific inputs that maximize the outputs. The trap densities (NTD and NTA) and their standard deviations (σTD and σTA) were found to most strongly impact the Vth window. As they increased, the Vth window increased because of the availability of a larger number of trap sites. Finally, when the ML-optimized energetic-trap distributions were simulated, the Vth window increased by 49% compared with the experimental value under the same bias condition. Therefore, the developed ML technique can be applied to optimize cell transistor processes by determining the material properties of the CTN in 3D NAND Flash.

Total Style Transfer with a Single Feed-Forward Network.

The development of recent image style transfer methods allows the quick transformation of an input content image into an arbitrary style. However, these methods have a limitation that the scale-across style pattern of a style image cannot be fully transferred into a content image. In this paper, we propose a new style transfer method, named total style transfer, that resolves this limitation by utilizing intra/inter-scale statistics of multi-scaled feature maps without losing the merits of the existing methods. First, we use a more general feature transform layer that employs intra/inter-scale statistics of multi-scaled feature maps and transforms the multi-scaled style of a content image into that of a style image. Secondly, we generate a multi-scaled stylized image by using only a single decoder network with skip-connections, in which multi-scaled features are merged. Finally, we optimize the style loss for the decoder network in the intra/inter-scale statistics of image style. Our improved total style transfer can generate a stylized image with a scale-across style pattern from a pair of content and style images in one forwarding pass. Our method achieved less memory consumption and faster feed-forwarding speed compared with the recent cascade scheme and the lowest style loss among the recent style transfer methods.

Electrically Small Circularly Polarized UWB Intraocular Antenna System for Retinal Prosthesis.

OBJECTIVE: This paper presents the design of an electrically small circularly polarized (CP) 3 × 3 mm2 antenna system as an intraocular unit for retinal prosthesis application. METHODS: The system is operating in ISM and ultra-wideband (UWB) bands to target high programmability of retina stimulation and recording, respectively. The electrical dimensions, including the ground plane, are λ0/41 × λ0/41 × λ0/191. Physical limitations of the antenna are discussed based on Hansen and Collin's limitations. The proposed wire patch antenna exhibits wideband characteristics by combining multiple modes of the patch antenna in the presence of an interface PCB circuit. RESULTS: By loading polyimide encapsulated patch with stubs, dominant TM010 mode is combined with the higher order modes TM020-TM070 to exhibit wide -10 dB impedance bandwidth of 2-11 GHz. Annular rings and shorting pins in the ground plane provide CP radiation at 2.45, 5.8, and 8 GHz with 3-dB axial-ratio bandwidth of 0.3, 0.16, and 1.2 GHz, and far-field left hand circularly polarized (LHCP) gain of -18.4, -7.6, and -4.7 dBic, respectively, in broadside direction. A biocompatible antenna system is designed using Ansys HFSS in the presence of a detailed multilayer canonical eye model. Additionally, it is examined in an anatomical HFSS head model. Near and far-field electric field distribution is studied along with peak 1-g average specific absorption rate (SAR) calculations. CONCLUSION: The proposed antenna is fabricated, and the performance, including coupled power from an external antenna, is measured in a custom made eye model including head phantom. A reasonable agreement is obtained between simulated and measured results. SIGNIFICANCE: To generate an artificial vision, image perception capability could be improved with implantable UWB communication systems that feature particularly high data-rate and small size.

Toward Exploiting Second-Order Feature Statistics for Arbitrary Image Style Transfer.

Generating images of artistic style from input images, also known as image style transfer, has been improved in the quality of output style and the speed of image generation since deep neural networks have been applied in the field of computer vision research. However, the previous approaches used feature alignment techniques that were too simple in their transform layer to cover the characteristics of style features of images. In addition, they used an inconsistent combination of transform layers and loss functions in the training phase to embed arbitrary styles in a decoder network. To overcome these shortcomings, the second-order statistics of the encoded features are exploited to build an optimal arbitrary image style transfer technique. First, a new correlation-aware loss and a correlation-aware feature alignment technique are proposed. Using this consistent combination of loss and feature alignment methods strongly matches the second-order statistics of content features to those of the target-style features and, accordingly, the style capacity of the decoder network is increased. Secondly, a new component-wise style controlling method is proposed. This method can generate various styles from one or several style images by using style-specific components from second-order feature statistics. We experimentally prove that the proposed method achieves improvements in both the style capacity of the decoder network and the style variety without losing the ability of real-time processing (less than 200 ms) on Graphics Processing Unit (GPU) devices.

Arbitrary Font Generation by Encoder Learning of Disentangled Features.

Making a new font requires graphical designs for all base characters, and this designing process consumes lots of time and human resources. Especially for languages including a large number of combinations of consonants and vowels, it is a heavy burden to design all such combinations independently. Automatic font generation methods have been proposed to reduce this labor-intensive design problem. Most of the methods are GAN-based approaches, and they are limited to generate the trained fonts. In some previous methods, they used two encoders, one for content, the other for style, but their disentanglement of content and style is not sufficiently effective in generating arbitrary fonts. Arbitrary font generation is a challenging task because learning text and font design separately from given font images is very difficult, where the font images have both text content and font style in each image. In this paper, we propose a new automatic font generation method to solve this disentanglement problem. First, we use two stacked inputs, i.e., images with the same text but different font style as content input and images with the same font style but different text as style input. Second, we propose new consistency losses that force any combination of encoded features of the stacked inputs to have the same values. In our experiments, we proved that our method can extract consistent features of text contents and font styles by separating content and style encoders and this works well for generating unseen font design from a small number of reference font images that are human-designed. Comparing to the previous methods, the font designs generated with our method showed better quality both qualitatively and quantitatively than those with the previous methods for Korean, Chinese, and English characters. e.g., 17.84 lower FID in unseen font compared to other methods.

Atomic-Layer-Deposited SiOx/SnOx Nanolaminate Structure for Moisture and Hydrogen Gas Diffusion Barriers.

High-density SnOx and SiOx thin films were deposited via atomic layer deposition (ALD) at low temperatures (100 °C) using tetrakis(dimethylamino)tin(IV) (TDMASn) and di-isopropylaminosilane (DIPAS) as precursors and hydrogen peroxide (H2O2) and O2 plasma as reactants, respectively. The thin-film encapsulation (TFE) properties of SnOx and SiOx were demonstrated with thickness dependence measurements of the water vapor transmission rate (WVTR) evaluated at 50 °C and 90% relative humidity, and different TFE performance tendencies were observed between thermal and plasma ALD SnOx. The film density, crystallinity, and pinholes formed in the SnOx film appeared to be closely related to the diffusion barrier properties of the film. Based on the above results, a nanolaminate (NL) structure consisting of SiOx and SnOx deposited using plasma-enhanced ALD was measured using WVTR (H2O molecule diffusion) at 2.43 × 10-5 g/m2 day with a 10/10 nm NL structure and time-lag gas permeation measurement (H2 gas diffusion) for applications as passivation layers in various electronic devices.

Uncorrelated feature encoding for faster image style transfer.

Recent image style transfer methods use a pre-trained convolutional neural network as their feature encoder. However, the pre-trained network is not optimal for image style transfer but rather for image classification. Furthermore, they require time-consuming feature alignment to consider the existing correlation among channels of the encoded feature map. In this paper, we propose an end-to-end learning method that optimizes both encoder and decoder networks for style transfer task and relieves the computational complexity of the existing correlation-aware feature alignment. First, we performed end-to-end learning that updates not only decoder but also encoder parameters for the task of image style transfer in the network training phase. Second, in addition to the previous style and content losses, we use uncorrelation loss, i.e., the total correlation coefficient among responses of encoder channels. Our uncorrelation loss allows the encoder network to generate a feature map of channels without correlation. Subsequently, our method results in faster forward processing with only a light-weighted transformer of correlation-unaware feature alignment. Moreover, our method drastically reduced the channel redundancy of the encoded feature during the network training process. This provides us a possibility to perform channel elimination with negligible degradation in generated style quality. Our method is applicable to multiple scaled style transfer by using the cascade network scheme and allows a user to control style strength through the usage of a content-style trade-off parameter.

Dual Circularly Polarized Planar Four-Port MIMO Antenna with Wide Axial-Ratio Bandwidth.

A broadband compact-sized planar four-port multiple-input-multiple-output (MIMO) antenna with polarization diversity is presented. The proposed dual circularly polarized (CP) MIMO antenna consists of four G-shaped monopole elements, two of which are left-hand CP and the other two are right-hand CP. A vertical line strip in the G-shaped radiating element acts in balancing the vertical and horizontal electric field components to obtain 90° phase difference between them for circular polarization. Also, an I-shaped strip is incorporated between the ground planes of the G-shaped antenna elements to obtain equal voltage level in the proposed MIMO configuration. The dual circular polarization mechanism of the proposed MIMO/diversity antenna is analysed from the vector current distributions. The impedance bandwidth (S11 ≤ -10 dB) of the MIMO antenna is 105.9% (4-13 GHz) and the 3 dB axial ratio bandwidth (ARBW) is 67.7% (4.2-8.5 GHz), which is suitable for C-band applications. The overall size of the MIMO antenna is 70 × 68 × 1.6 mm3, and the minimum isolation between the resonating elements is 18 dB. The envelope correlation coefficient is less than 0.25, and the peak gain within the resonating band is 6.4 dBi.

Ultra-Miniature Circularly Polarized CPW-Fed Implantable Antenna Design and its Validation for Biotelemetry Applications.

The paper presents a coplanar waveguide (CPW)-fed ultra-miniaturized patch antenna operating in Industrial, Scientific and Medical (ISM) band (2.4-2.5 GHz) for biotelemetry applications. The proposed antenna structure is circular in shape and its ground plane is loaded with a pair of slots for obtaining circular polarization. In the proposed design, asymmetric square slots generate phase condition for right-hand circularly polarized (RHCP) radiation. And, by merely changing the position of the slots, either RHCP or left-hand circularly polarized (LHCP) radiation can be excited. In the proposed design, a meandered central strip is used for miniaturization. The simulations of the proposed antenna are carried out using Ansys HFSS software with a single-layer and multilayer human tissue models. The antenna shows good performance for different tissue properties owing to its wide axial ratio bandwidth and impedance bandwidth. The antenna is fabricated and measurements are carried out in skin mimicking phantom and pork. Simulated and measured performances of the antenna are in close agreement. The power link budget is also calculated using an exterior circularly polarized (CP) receiving antenna.