Automatic Speech Recognition (ASR) is an active field of research due to its large number of applications and the proliferation of interfaces or computing devices that can support speech processing. However, the bulk of applications are based on well-resourced languages that overshadow under-resourced ones. Yet, ASR represents an undeniable means to promote such languages, especially when designing human-to-human or human-to-machine systems involving illiterate people. An approach to design an ASR system targeting under-resourced languages is to start with a limited vocabulary. ASR using a limited vocabulary is a subset of the speech recognition problem that focuses on the recognition of a small number of words or sentences. This paper aims to provide a comprehensive view of mechanisms behind ASR systems as well as techniques, tools, projects, recent contributions, and possible future directions in ASR using a limited vocabulary. This work consequently provides a way forward when designing an ASR system using limited vocabulary. Although an emphasis is put on limited vocabulary, most of the tools and techniques reported in this survey can be applied to ASR systems in general. AbbreviationsACC: Accuracy; AM: Acoustic Model; ASR: Automatic Speech Recognition; BD-4SK-ASR: Basic Dataset for Sorani Kurdish Automatic Speech Recognition; CER: Character Error Rate; CMU: Carnegie Mellon University; CNN: Convolutional Neural Network; CNTK: CogNitive ToolKit; CUED: Cambridge University Engineering Department; DCT:Discrete Cosine Transformation; DL: Deep Learning; DNN: Deep Neural Network; DRL: Deep Reinforcement Learning; DWT: Discrete Wavelet Transform; FFT: Fast Fourier Transformation; GMM: Gaussian Mixture Model; HMM: Hidden Markov Model; HTK: Hidden Markov Model ToolKit; JASPER: Just Another Speech Recognizer; LDA: Linear Discriminant Analysis; LER: Letter Error Rate; LGB: Light Gradient Boosting Machine; LM:Language Model; LPC: Linear Predictive Coding; LVCSR: Large Vocabulary Continuous Speech Recognition; LVQ: Learning Vector Quantization Algorithm; MFCC: Mel-Frequency Cepstrum Coefficient; ML: Machine Learning; PCM:Pulse-Code Modulation; PPVT: Peabody Picture Vocabulary Test; RASTA: RelAtive SpecTral; RLAT: Rapid Language Adaptation Toolkit; S2ST: Speech-to-Speech Translation; SAPI: Speech Application Programming Interface; SDK: Software Development Kit; SVASR:Small Vocabulary Automatic Speech Recognition; WER: Word Error Rate

Deep learning has been successfully showing promising results in plant disease detection, fruit counting, yield estimation, and gaining an increasing interest in agriculture. Deep learning models are generally based on several millions of parameters that generate exceptionally large weight matrices. The latter requires large memory and computational power for training, testing, and deploying. Unfortunately, these requirements make it difficult to deploy on low-cost devices with limited resources that are present at the fieldwork. In addition, the lack or the bad quality of connectivity in farms does not allow remote computation. An approach that has been used to save memory and speed up the processing is to compress the models. In this work, we tackle the challenges related to the resource limitation by compressing some state-of-the-art models very often used in image classification. For this we apply model pruning and quantization to LeNet5, VGG16, and AlexNet. Original and compressed models were applied to the benchmark of plant seedling classification (V2 Plant Seedlings Dataset) and Flavia database. Results reveal that it is possible to compress the size of these models by a factor of 38 and to reduce the FLOPs of VGG16 by a factor of 99 without considerable loss of accuracy.

By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.

The energy limitation remains one of the biggest constraints in drone path planning, since it prevents drones from performing long surveillance missions. To assist drones in such missions, recharging stations have recently been introduced. They are platforms where the drone can autonomously land to recharge its battery before continuing the mission. However, the cost of those platforms remains a significant obstacle to their adoption. Consequently, it is important to reduce their number while planning the path of the drone. This work introduces the Single Drone Multiple Recharging Stations on Large Farm problem (SD-MRS-LF). A large farm is considered as an area of interest to cover with a set of candidate locations where recharging stations can be installed. The aim is to determine the path of the drone that minimizes the number of locations for recharging stations as well as the completion time of the surveillance mission. This path planning problem falls within the realm of computational geometry and is related to similar problems that are encountered in the field of robotics. The problem is complicated due to environmental constraints on farms such as wind speed and direction, which produce asymmetries in the optimal solution. A back-and-forth-k-opt simulated annealing (BFKSA) approach is proposed to solve the defined problem. The new approach is compared to the basic back-and-forth (BF) and a K-opt variant of the well-known simulated annealing (KSA) approach over a set of 20 random topologies in different environmental conditions. The results from computational experiments show that the BFKSA approach outperforms the others, in terms of providing feasible solutions and minimizing the number of recharges.

Drones are used increasingly for agricultural surveillance. The limited flight range of drones poses a problem for surveillance of large, inaccessible areas. One possible solution is to place autonomous, solar-powered charging stations within the area of interest, where the drone can recharge during its mission. This paper designs and implements a software system for planning low-cost drone coverage of large areas. The software produces a feasible, cost-minimizing charging station placement, as well as a drone path specification. Multiple optimizations are required, which are formulated as integer linear programs. In extensive simulations, the resulting drone paths achieved 70–90 percent of theoretical optimal performance in terms of minimizing mission time for a given number of charging stations, for a variety of field configurations.

Agriculture has proven to be the most effective and efficient economic activity in many developing countries, contributing to economic growth. However, it faces numerous challenges that hinder productivity. Improving productivity necessitates both efficient approaches for selecting suitable crops for cultivation and adherence to the technical itineraries of crops. While the technical itineraries for most crops are well-known, choosing the best crops before commencing agricultural activities remains challenging for farmers, primarily due to the many factors and uncertainties involved. Various research studies have proposed techniques to assist farmers at this early stage. This paper examines the specific methods employed, the different factors at play, the sources and nature of data, and the overall performance achieved in each study. The outcomes of this research reveal trends and provide insights into potential future work in crop selection and rotation. These findings can contribute to developing improved crop selection systems by proposing suitable techniques and identifying crucial parameters.