Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ

Functional outcome of proximal 1/3rd, distal 1/3rd and diaphysial tibial fractures in adults operated with expert tibial nailing

Background: Fractures of the tibial shaft are increasing due to high velocity trauma and industrialisation. Not only they are common but also difficult to treat. Until recently surgeons had to rely on non-operative treatment, V nailing, plates and screws and external fixator but they had their drawbacks like prolonged immobilisation infection, delayed union and non-union. Numerous modifications in nail and screw design have led to development of the expert tibial nail. Multidirectional interlocking screws ensure that alignment can be well maintained and stability preserved in short proximal or distal tibial segments.Methods: 30 patients were admitted and operated during September 2014 to September 2016 in Mamata general hospital Khammam. All patients were evaluated with Klemm Borner’s criteria and complications following surgery.Results: 87% of patients achieved good or excellent results, fair results were obtained in 3 (10%) patient and poor result in one (3%) patient. 2 (6%) patients had malunion, 2 (6%) patients had delayed union, 1 (3%) patient had deep infection led to implant failure.Conclusions: Intramedullary nailing is a safe and effective technique for the treatment of tibial metaphyseal fractures. It avoids the additional soft-tissue dissection associated with traditional open procedures as well as the complications associated with external fixators. Expert tibial nail can give excellent functional and clinical results. Complications such as failure of the bone-implant construct or post-operative malallignment are avoidable if careful pre-operative planning is allied with meticulous surgical technique

A Viewpoint Selection-based Coverage Planning Algorithm for Autonomous Structural Inspection using Multiple UAVs

Autonomous Unmanned Aerial Vehicles (UAVs) are being used extensively in structural inspection tasks to inspect bridges, buildings and other structures. The main objective of a UAV in a structural inspection task is to completely cover the exposed surface of the structure, while collecting information from the surface through its onboard sensors including camera, LIDAR, laser, etc. Using a single UAV for autonomously covering a large 3D structure such as a bridge might not be feasible owing to battery limitations of the UAV. To address this problem, we propose a multi-UAV, viewpoint-based approach, where, first, a set of points or locations around the structure is identified so that the entire structure can be covered while visiting these locations. Subsequently, the locations are allocated among a team of UAVs so that each UAV visits a subset of the locations while ensuring that the UAVs do not collide with each other and the effort (energy) expended by the UAVs for visiting the locations is balanced across the UAVs

Two-Layer Radix Sorter Architecture for a Power-Efficient FPGA Bloom Filter

Bloom filters are a very important tool for many applications including genomics, where they are used as a compact data structure for counting k-mers, represent de Bruijn graphs, and more. However, their performance is often bound by the large filter size requirement in genomics, and their random-access nature. Although accelerators such as FPGAs and GPUs can easily remove the computation overhead of the multiple hash functions, the random access performance of off-chip memory is still a bottleneck, calling for costly high-performance memory. The solution we propose is BunchBloomer, which improves the cost-effectiveness of FPGA Bloom filter accelerators by making better use of cheaper, lower-power DDR memory. As a part of this project, I work on creating the architecture of a two-layer radix sorter to group table updates into bursts directed to the same 8 KiB memory region, which can be efficiently cached in on-chip memory. The sorters can sustain high performance data ingestion by processing four 32-bit tuples at once, while still utilizing a reasonable amount of chip space. The overall BunchBloomer device achieves much better power efficiency compared to a traditional multicore server or even a conventional FPGA Bloom filter accelerator equipped with Hybrid Memory Cubes

Fault detection and isolation in electrical machines using deep Neural networks

With the single-tube and double-tube fault of seven-level converter, this paper presents a new way to learn the faults feature based on the deep neural network of sparse autoencoder. Sparse autoencoder is an unsupervised learning method, it can learn the feature information of the fault data according to training. The feature information is used to train the softmax classifier by softmax regression to realize the aim of classification. Comparing with the traditional neural network of BP neural network, the experimental results show that the method to classify the fault of seven level converter based on deep neural network of sparse autoencoder can achieve higher accuracy.by M.Sai, Parth Tarun Upadhyay and Babji Srinivasa

SMART CLOCK BASED ON REAL-TIME TRAFFIC DATA

Abstract The internet of things (IoT) and Machine learning (ML) are two rapid growing technologies which connects devices to the internet and enables to be controlled and analyze the data from any internet connected device. Using these, this paper demonstrates an idea to upgrade an ordinary clock to a smarter one which can automatically adjust the alarm ring time based on live road traffic present on user route while user is in sleep state or busy, which makes sure the user reaches on time he marked to his destination and also clock has the ability to controls home appliances using smart mode. To implement this, we have used Distance Matrix API from Google maps to get live road traffic data, Telegram app for user interface and Raspberry Pi 3 microcontroller as prototype design

An unusual case of placental chorangioma and intrauterine death: Implications for proper antenatal care and prompt diagnosis

We present an unusual case of placental chorangioma in a 32-year-old female with poor antenatal care. Abdominopelvic ultrasound revealed polyhydramnios with no fetal cardiac activity and suspected placental cyst. An emergency caesarean was performed, and she had still birth of male baby. The cut specimen of the placenta revealed a well-circumscribed marginal mass of 4 cm. Our case emphasizes the importance of regular antenatal screening for early detection of placental abnormalities. While chorangiomas are rare, they should be considered in the differential diagnosis of placental masses. Prompt diagnosis and appropriate management are essential to reduce the maternal and fetal complications associated with chorangiomas. Histological examination of the placenta plays a vital role in differentiating chorangioma from other placental abnormalities with different clinical implications

Image Quality Enhancement for Wheat rust Diseased Leaf Image using Histogram Equalization & CLAHE

In the domain of agriculture, few crops play an important role as wheat is one of them. It is one of the most important one’s across the globe. Nearly providing 15% food production across the world, it is also a winter cereal crop and a most essential food. The real challenge is to enhance the images of wheat crop in the agricultural area. because some of these are captured in real space environments may not be that clear to predict the type of disease of the crop that it is suffering from. So, we enhance the captured images using few existing techniques using the image histograms and the further details are extracted from these enhanced images, which make the disease judgement easy. We try to enhance the pixel intensity of the image using histogram equalization technique and by exploring various other models which deal with CLAHE which stands for Contrast Limited Adaptive Histogram Equalization then we finally conclude with results of the enhanced image by comparing with the originally clicked images which has fine detailed information about the rust in the crop