Electronic knees provide a wide range of mobility for amputees but the high cost of these knees, due to the wide variety of sensors used and the ensuing complexity of the hardware and algorithms used there in, makes them inaccessible to most amputees in developing countries. The goal of this paper is to develop a low-cost sensor to measure the angular change of the ‘stump’ socket, and that of the thigh movement, with the aim of reducing the overall cost of the electronic knees. The proposed measurement system, named as Stump Angle Measurement (SAM) system, uses a low cost accelerometer, which provides a direct feedback of angular change of the thigh movement, and ultimately that of the hip joint. Preliminary results of SAM module show that the properties of feedback signal alone (amplitude and frequency) can be used to vary the speed of actuator (knee joint) resulting in a wider mobility for the amputee. The proposed system also reduces complexity of the hardware as well as algorithms used in modern electronic knee, thereby reducing the overall cost of knee prosthesis and making it more accessible to amputees in developing countries like India. Keywords: Amputee, Electronic Knee, Prosthesis, Sensor
This paper investigates the impact of interrupted and uninterrupted charging strategies of plug-in electric vehicles (PEVs) on the aggregated load profile while considering the user convenience, i.e., the desired state-of-charge of battery at the departure time. First, this paper introduces a new coordinated charging algorithm with interrupted charging intervals. Then, coordinated charging algorithms with uninterrupted and interrupted charging strategies are compared, with heuristic prioritizing policies, on different base-load characteristics under different PEV penetration rates. The impact is quantified in tenns of the variance of the aggregated load profile. The impact of the priority assignment policies on the aggregated load variance is also explored.
Introduction: Primary functions of heel and forefoot fat pad - shock absorber at heel strike, energy dissipation, load bearing, grip and insulation. •Reliability of weight bearing heel pad thickness measurements by ultrasound has been determined by Rome et al. Importance of soft tissue fillers has been recently popularised by Coleman. Methods and materials: Harvesting done by standard low pressure liposuction using small cannula. Grafting using small needle depositing the small globules of fat in multiple layers of soft tissue. There is an expectation that up to 50% of the fat will be lost and so upto 19mls of fat placed per foot. Patients were kept NWB for 4–6 weeks post op and then allowed to mobilise fully. Case notes were prospectively collated and analysed. Pre and post-op ultrasound scans were performed to document the depth of the heel/forefoot fat pad. Clinical pictures were taken and post-op patient satisfaction scores were done as well. Results: We treated 9 feet in 5 patients. 5 heel fat pad transfers and 4 forefoot. Pain completely relieved in all feet. No complications. Average pre-op VAS - 3/ Post-op – 9. Average pre-op AOFAS score - 70/ post-op - 105. Follow-up 6months - maximum 23 months. Conclusion: Fat transfer is usually used for cosmetic reasons and occasionally to improve scars. Very few reports from South America have been published for patients using high heels giving pain but none for patients with a pathological anomaly. The technique seems to highly effective with no complications so far. It is currently being used on other painful problems in other areas of the sole with equal success. Abdominal fat transfer is an innovative technique aimed at getting rid of the ‘heel pad syndrome’.
In the original publication, the name of one of the coauthors has been published incorrectly. The correct name should be Nigel Kiely. Furthermore, the affiliation of Dr. Rohit Singh and Dr. Stuart Hay is included now. Royal Shrewsbury Hospital, Shrewsbury, Shropshire SY38XQ, UK. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
In this paper, we propose a deep-learning-based channel estimation scheme in an orthogonal frequency division multiplexing (OFDM) system. Our proposed method, named Single Slot Recurrence Along Frequency Network (SisRafNet), is based on a novel study of recurrent models for exploiting sequential behavior of channels across frequencies. Utilizing the fact that wireless channels have a high degree of correlation across frequencies, we employ recurrent neural network techniques within a single OFDM slot, thus overcoming the latency and memory constraints typically associated with recurrence based methods. The proposed SisRafNet delivers superior estimation performance compared to existing deep-learning-based channel estimation techniques and the performance has been validated on a wide range of 3rd Generation Partnership Project (3GPP) compliant channel scenarios at multiple signal-to-noise ratios.
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