Does the motor system need intermittent control?

Explanation of motor control is dominated by continuous neurophysiological pathways (e.g. trans-cortical, spinal) and the continuous control paradigm. Using new theoretical development, methodology and evidence, we propose intermittent control, which incorporates a serial ballistic process within the main feedback loop, provides a more general and more accurate paradigm necessary to explain attributes highly advantageous for competitive survival and performance

Optimizing sequencing protocols for leaderboard metagenomics by combining long and short reads.

As metagenomic studies move to increasing numbers of samples, communities like the human gut may benefit more from the assembly of abundant microbes in many samples, rather than the exhaustive assembly of fewer samples. We term this approach leaderboard metagenome sequencing. To explore protocol optimization for leaderboard metagenomics in real samples, we introduce a benchmark of library prep and sequencing using internal references generated by synthetic long-read technology, allowing us to evaluate high-throughput library preparation methods against gold-standard reference genomes derived from the samples themselves. We introduce a low-cost protocol for high-throughput library preparation and sequencing

A system for room acoustic simulation for one's own voice

The real-time simulation of room acoustical environments for one’s own voice, using generic software, has been difficult until very recently due to the computational load involved: requiring real-time convolution of a person’s voice with a potentially large number of long room impulse responses. This thesis is presenting a room acoustical simulation system with a software-based solution to perform real-time convolutions with headtracking; to simulate the effect of room acoustical environments on the sound of one’s own voice, using binaural technology. In order to gather data to implement headtracking in the system, human head- movements are characterized while reading a text aloud. The rooms that are simulated with the system are actual rooms that are characterized by measuring the room impulse response from the mouth to ears of the same head (oral binaural room impulse response, OBRIR). By repeating this process at 2o increments in the yaw angle on the horizontal plane, the rooms are binaurally scanned around a given position to obtain a collection of OBRIRs, which is then used by the software-based convolution system. In the rooms that are simulated with the system, a person equipped with a near- mouth microphone and near-ear loudspeakers can speak or sing, and hear their voice as it would sound in the measured rooms, while physically being in an anechoic room. By continually updating the person’s head orientation using headtracking, the corresponding OBRIR is chosen for convolution with their voice. The system described in this thesis achieves the low latency that is required to simulate nearby reflections, and it can perform convolution with long room impulse responses. The perceptual validity of the system is studied with two experiments, involving human participants reading aloud a set-text. The system presented in this thesis can be used to design experiments that study the various aspects of the auditory perception of the sound of one’s own voice in room environments. The system can also be adapted to incorporate a module that enables listening to the sound of one’s own voice in commercial applications such as architectural acoustic room simulation software, teleconferencing systems, virtual reality and gaming applications, etc

A system for room acoustic simulation for one's own voice

The real-time simulation of room acoustical environments for one’s own voice, using generic software, has been difficult until very recently due to the computational load involved: requiring real-time convolution of a person’s voice with a potentially large number of long room impulse responses. This thesis is presenting a room acoustical simulation system with a software-based solution to perform real-time convolutions with headtracking; to simulate the effect of room acoustical environments on the sound of one’s own voice, using binaural technology. In order to gather data to implement headtracking in the system, human head- movements are characterized while reading a text aloud. The rooms that are simulated with the system are actual rooms that are characterized by measuring the room impulse response from the mouth to ears of the same head (oral binaural room impulse response, OBRIR). By repeating this process at 2o increments in the yaw angle on the horizontal plane, the rooms are binaurally scanned around a given position to obtain a collection of OBRIRs, which is then used by the software-based convolution system. In the rooms that are simulated with the system, a person equipped with a near- mouth microphone and near-ear loudspeakers can speak or sing, and hear their voice as it would sound in the measured rooms, while physically being in an anechoic room. By continually updating the person’s head orientation using headtracking, the corresponding OBRIR is chosen for convolution with their voice. The system described in this thesis achieves the low latency that is required to simulate nearby reflections, and it can perform convolution with long room impulse responses. The perceptual validity of the system is studied with two experiments, involving human participants reading aloud a set-text. The system presented in this thesis can be used to design experiments that study the various aspects of the auditory perception of the sound of one’s own voice in room environments. The system can also be adapted to incorporate a module that enables listening to the sound of one’s own voice in commercial applications such as architectural acoustic room simulation software, teleconferencing systems, virtual reality and gaming applications, etc

Optimizing Banana Type Identification: An Support Vector Machine Classification-Based Approach for Cavendish, Mas, and Tanduk Varieties

This research focuses on addressing the need for improved efficiency in the agricultural sector, particularly in banana processing in Indonesia, where the demand for bananas is consistently high. To improve the efficiency of banana processing, the research proposes the development of a machine learning based solution for automatic banana type selection. This solution uses image data of three banana types (Cavendish, Mas, and Tanduked) captured by a microscopic camera. The images are subjected to feature extraction, and a Support Vector Machine (SVM) algorithm is used to train the model. The results are implemented in a graphical user interface (GUI). The experimental results show promising results, with an accuracy of 86.67%, a precision of 87.78%, and an error rate of 13.33%, achieved with SVM parameters of C = 1000 and a linear kernel. This automated approach provides a practical and sustainable solution to the labor-intensive manual banana variety selection process, thus increasing the efficiency of the banana processing industry