A Smart Knee Implant Using Triboelectric Energy Harvesters

Although the number of total knee replacement (TKR) surgeries is growing rapidly, functionality and pain-reduction outcomes remain unsatisfactory for many patients. Continual monitoring of knee loads after surgery offers the potential to improve surgical procedures and implant designs. The goal of this study is to characterize a triboelectric energy harvester under body loads and to design compatible frontend electronics to digitize the load data. The harvester prototype would be placed between the tibial component and polyethylene bearing of a TKR implant. The harvester generates power from the compressive load. To examine the harvester output and the feasibility of powering a digitization circuitry, a triboelectric energy harvester prototype is fabricated and tested. An axial tibiofemoral load profile from normal walking (gait) is approximated as a 1 Hz sine wave signal and is applied to the harvester. Because the root mean square of voltages generated via this phenomenon is proportional to the applied load, the device can be simultaneously employed for energy harvesting and load sensing. With an approximated knee cyclic load of 2.3 kN at 1 Hz, the harvester generated output voltage of 18 V RMS, and an average power of 6 µW at the optimal resistance of 58MΩ. The harvested signal is rectified through a negative voltage converter rectifier and regulated through a linear-dropout regulator with a combined efficiency of 71%. The output of the regulator is used to charge a supercapacitor. The energy stored in the supercapacitor is used for low resolution sensing of the load through a peak detector and analog-to-digital converter. According to our analysis, sensing the load several times a day is feasible by relying only on harvested power. The results found from this work demonstrate that triboelectric energy harvesting is a promising technique for self-powering load sensors inside knee implants

SRTGAN: Triplet Loss based Generative Adversarial Network for Real-World Super-Resolution

Many applications such as forensics, surveillance, satellite imaging, medical imaging, etc., demand High-Resolution (HR) images. However, obtaining an HR image is not always possible due to the limitations of optical sensors and their costs. An alternative solution called Single Image Super-Resolution (SISR) is a software-driven approach that aims to take a Low-Resolution (LR) image and obtain the HR image. Most supervised SISR solutions use ground truth HR image as a target and do not include the information provided in the LR image, which could be valuable. In this work, we introduce Triplet Loss-based Generative Adversarial Network hereafter referred as SRTGAN for Image Super-Resolution problem on real-world degradation. We introduce a new triplet-based adversarial loss function that exploits the information provided in the LR image by using it as a negative sample. Allowing the patch-based discriminator with access to both HR and LR images optimizes to better differentiate between HR and LR images; hence, improving the adversary. Further, we propose to fuse the adversarial loss, content loss, perceptual loss, and quality loss to obtain Super-Resolution (SR) image with high perceptual fidelity. We validate the superior performance of the proposed method over the other existing methods on the RealSR dataset in terms of quantitative and qualitative metrics.Comment: Affiliated with the Sardar Vallabhbhai National Institute of Technology (SVNIT), India and Norwegian University of Science and Technology (NTNU), Norway. Presented at the 7th International Conference on Computer Vision and Image Processing (CVIP) 202

Von Hipple–Lindau: Unusual case presentation with peripheral and juxtapapillary retinal hemangioma

Von Hipple-Lindua (VHL) syndrome is an autosomal dominant neoplastic disorder in which multiple benign or malignant tumours and cysts develop in central nervous system and visceral organs. Retinal capillary hemangioma is the most frequent and often the earliest manifestation of VHL syndrome. We report a case with multisystemic involvement diagnosed as a case of multiple endocrine neoplasia (MEN) syndrome but the presence of multiple, unilateral peripheral and juxtapapillary retinal capillary hemangioma was able to clinche the definative diagnosis of VHL and thus helped in appropriate management of the patient

Comparison of 30-2 Standard and Fast programs of Swedish Interactive Threshold Algorithm of Humphrey Field Analyzer for perimetry in patients with intracranial tumors

Purpose: To find out whether 30-2 Swedish Interactive Threshold Algorithm (SITA) Fast is comparable to 30-2 SITA Standard as a tool for perimetry among the patients with intracranial tumors. Methods: This was a prospective cross-sectional study involving 80 patients aged ≥18 years with imaging proven intracranial tumors and visual acuity better than 20/60. The patients underwent multiple visual field examinations using the two algorithms till consistent and repeatable results were obtained. Results: A total of 140 eyes of 80 patients were analyzed. Almost 60% of patients undergoing perimetry with SITA Standard required two or more sessions to obtain consistent results, whereas the same could be obtained in 81.42% with SITA Fast in the first session itself. Of 140 eyes, 70 eyes had recordable field defects and the rest had no defects as detected by either of the two algorithms. Mean deviation (MD) (P = 0.56), pattern standard deviation (PSD) (P = 0.22), visual field index (P = 0.83) and number of depressed points at P < 5%, 2%, 1%, and 0.5% on MD and PSD probability plots showed no statistically significant difference between two algorithms. Bland–Altman test showed that considerable variability existed between two algorithms. Conclusion: Perimetry performed by SITA Standard and SITA Fast algorithm of Humphrey Field Analyzer gives comparable results among the patients of intracranial tumors. Being more time efficient and with a shorter learning curve, SITA Fast my be recommended as a standard test for the purpose of perimetry among these patients

Machine Translation Systems for English Captions to Hindi Language Using Deep Learning

Machine Translation is the process of translating text from one language to another which helps to reduce the conversation gap among people from different cultural backgrounds. The task performed by the Machine Translation System is to automatically translate between pairs of different natural languages, where Neural Machine Translation System stands out from all because it provides fluent translation along with reasonable translation accuracy. The Convolution Neural Network encoder is used to find patterns in the images and encode it into a vector that is passed to the Long Short Term Memory decoder which finds the caption word-by-word to best describe the image. Upon reaching the end-line token, the entire description of the image in English is generated and that is our output for that particular image. Automatically creating the description of an image in English using any natural language sentences and then translating it using Neural Machine Translation to Hindi is a very challenging task. It requires expertise in both image processing as well as natural language processing. In this paper, the aim is to compare the two Machine Translation Systems: Google Translation System and the proposed Neural Machine Translation System to convert the text obtained from an image in English to Hindi language

Malakoplakia of the ureter: An unusual case

Malakoplakia of the ureter is a rare pathological entity. We discuss a 15-year-old girl with malakoplakia of the ureter. She presented with obstructive uropathy associated with left flank pain. Radiological investigations showed left lower ureteric stricture without bladder or kidney involvement. She was treated by excision of terminal ureter and ureteroneocystostomy. Histopathologic examination of the excised specimen showed malakoplakia. Postoperative course was uneventful and on follow-up, she has normal serum creatinine and no recurrence of the disease

Remarkably selective biocompatible turn-on fluorescent probe for detection of Fe3+ in human blood samples and cells

The robust nature of a biocompatible fluorescent probe is demonstrated, by its detection of Fe3+ even after repeated rounds of quenching (reversibility) by acetate in real human blood samples and cells in vitro. Significantly trace levels of Fe3+ ions up to 8.2 nM could be detected, remaining unaffected by the existence of various other metal ions. The obtained results are validated by AAS and ICP-OES methods. A portable test strip is also fabricated for quick on field detection of Fe3+. As iron is a ubiquitous metal in cells and plays a prominent role in biological processes, the use of this probe to image Fe3+ in cells is a substantial development towards biosensing. Cytotoxicity studies also proved the nontoxic nature of this probe