Montpellier’s Energy-saving Challenges: Case Studies Of Successful Strategies – Believing that it is vital for women to be at the forefront of the ever-evolving world of technology, this year Cignity launched herDIGITALstory in Hyderabad, Telangana. This platform is designed to support and empower women in technology. The event aimed to create a community of practitioners where women passionate about emerging technologies such as AI, ML, Data and Blockchain can come together to learn, grow and network. Overall, herDIGITALstory is a platform created to increase the visibility, voice and leadership of women in the digital world, and to create a more inclusive future.

Rama Devi Lanka, New Technology Leader, ITE&C, Telangana was the chief guest. She is an award-winning emerging technology leader at ITE&C, Telangana, known for her innovative projects in AI, blockchain and drones that have made significant contributions to agriculture, healthcare and governance. Along with Abhilasha, other speakers included Dr. Neha Baranwal, a senior AI researcher with expertise in deep learning, NLP, and multimodal human-robot interaction, and Sirisha Peyeti, senior vice president and head of solutions – digital engineering services at Cigniti, with over two speakers with decades of experience in IT services.

Montpellier’s Energy-saving Challenges: Case Studies Of Successful Strategies

Montpellier's Energy-saving Challenges: Case Studies Of Successful Strategies

One of the key moments of the event was Abhilashi Sinha’s insightful talk on the huge potential of large language models (LLM) for enterprises. Abhilasha Sinha, solutions architect at nologies, spoke about the potential of large language models (LLM) at the event. LLMs are powerful tools that can generate human-like text and answer questions.

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With great power comes great responsibility. Abhilasha shared insights on how to balance the opportunities and challenges of an LLM for Enterprise. These models have enormous potential for transforming enterprises and improving their operations. However, she emphasized the need for responsible use of LLM, as there may be bias in the study data and ethical issues with generating fake news or malicious content.

The world of technology and innovation seems to be developing at an unprecedented pace. We can expect to see some interesting developments in the coming months and years. Looking to the future, it is clear that technological advances will continue to shape and transform all industries. The potential for innovation and progress is enormous, from breakthroughs in AI and quantum computing to the continued growth of various industries.

These developments will undoubtedly bring new challenges and opportunities for individuals and organizations in all sectors. However, by embracing these changes and adapting to new trends, we can harness the power of technology to create a brighter, more sustainable future for all. It’s an exciting time to be a part of an ever-evolving industry and technology, and we can’t wait to see what the future holds.

Concluding her speech, she emphasized the need to remember how technological progress affects our world. With the right approach and mindset, we can certainly harness the power of technology for the greater good and help build a better, more sustainable future.

Montpellier’s Water Utility Back In Public Hands: A Process Of Refocusing Private Management Tools

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Montpellier's Energy-saving Challenges: Case Studies Of Successful Strategies

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Pdf) Fatal Accidental Lipid Overdose With Intravenous Composite Lipid Emulsion In A Premature Newborn: A Case Report

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Jonathan Mikel Jonathan Mikel Scilit Preprints.org Google Scholar 1, * , Laurent Latorre Laurent Latorre Scilit Preprints.org Google Scholar 1 and Simon Chamaile-Jammes Simon Chamaile-Jammes Scilit Preprints.org Google Scholar 2

Received: 3 April 2023 / Revised: 19 April 2023 / Accepted: 24 April 2023 / Published: 27 April 2023

Biologizing refers to the use of animal recording devices to study wildlife behavior. In the case of audio recording, such devices generate large amounts of data over several months and therefore require some level of automation in the processing of the collected raw data. Scientists have widely implemented offline deep learning classification algorithms to extract meaningful information from large data sets, mainly using time-frequency signal representations such as spectrograms. Due to the high deployment costs of animal-borne devices, the autonomy/weight ratio remains a major issue. Mainly power consumption is solved by the built-in storage (without wireless transmission), but the energy costs associated with data storage are far from negligible. In this paper, we evaluate different strategies for reducing the amount of stored data, making the fair assumption that the audio will be categorized by a deep learning classifier at some point in the process. This assumption opens up several scenarios, from simple storage of raw audio combined with subsequent offline classification on the one hand, to a fully embedded AI engine on the other hand with built-in audio compression or feature extraction in between. This paper explores three approaches focused on data dimensionality reduction: (i) traditional embedded audio compression, namely ADPCM and MP3, (ii) full edge deep learning classification, and (iii) embedded preprocessing that only computes and stores spectrograms for further autonomous classification. We characterized each approach in terms of total (sensor + CPU + storage) marginal power consumption (i.e., recording autonomy) and classification accuracy. Our results demonstrate that ADPCM encoding provides 17.6% energy savings compared to the baseline system (i.e., uncompressed raw audio samples). Using such compressed data, the state-of-the-art spectrogram-based classification model still achieves 91.25% accuracy on open speech datasets. Performing on-board data preparation can significantly reduce the amount of stored data, allowing for 19.8% power savings compared to the baseline system, while achieving 89% classification accuracy. These results show that, although significant data reduction can be achieved with the on-board spectrogram computation, this represents a small advantage in device autonomy compared to ADPCM coding, with the added disadvantage of loss of original audio information.

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Biologization is the study of animal behavior using electronic devices. Determining behavior requires large amounts of data, made possible by increasingly energy-efficient embedded recording systems. However, manual processing of the generated data sets is becoming increasingly time-consuming, if not impossible. In this study, we focus on data recording and analysis. Although commercial devices for embedded geolocation or motion analysis are common and readily available, this is not the case with audio recorders carried by animals. Scientists either adapt passive acoustic monitoring (PAM) devices [1] or rely on academic efforts to develop non-standard designs [2, 3]. In this context, over the past few years we have developed our own embedded audio recording solution. The design of our devices is largely driven by the ratio of weight over autonomy and demonstrates advanced power efficiency, capable of collecting around 900 hours of 16-bit 8kHz audio data (~50GB of uncompressed audio samples) from a single small 1800mAh lithium device. Ion battery [4]. In this application, the recorded data is not transmitted over the radio frequency connection, but is stored on the SD memory card for obvious reasons of energy saving. However, our measurements demonstrate that storage activities have a significant impact on overall power consumption when audio data reduction is not applied.

Data reduction is a common problem for power-constrained sensor nodes and wireless sensor networks (WSNs). For example, in weather monitoring applications, filtering algorithms [5, 6] have been proposed to prohibit data transmission when the collected data does not contain valuable information. These algorithms are based on the idea that missing information can be predicted with small errors on the application server side. To the best of our knowledge, these algorithms have not been applied to audio data, which represents a very special case of high-speed uncorrelated time series data, where predictive models seem difficult to build. Furthermore, in the case of WSNs, CPU overhead competes with the high energy cost of data transmission, leaving room for more complex algorithms. In our case, the data is stored on the edge with much lower energy consumption and therefore more CPU constraints.

Another common approach to reducing audio data is to open timing windows only when events of interest are expected. This is done by rigidly scheduling recording periods (eg daylight only) or audio segmentation that dynamically determines the presence of

Montpellier's Energy-saving Challenges: Case Studies Of Successful Strategies

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