Challenges in Developing a Real-Time Bee-Counting Radar

Detailed within is an attempt to implement a real-time radar signal classification system to monitor and count bee activity at the hive entry. There is interest in keeping records of the productivity of honeybees. Activity at the entrance can be a good measure of overall health and capacity, and a radar-based approach could be cheap, low power, and versatile, beyond other techniques. Fully automated systems would enable simultaneous, large-scale capturing of bee activity patterns from multiple hives, providing vital data for ecological research and business practice improvement. Data from a Doppler radar were gathered from managed beehives on a farm. Recordings were split into 0.4 s windows, and Log Area Ratios (LARs) were computed from the data. Support vector machine models were trained to recognize flight behavior from the LARs, using visual confirmation recorded by a camera. Spectrogram deep learning was also investigated using the same data. Once complete, this process would allow for removing the camera and accurately counting the events by radar-based machine learning alone. Challenging signals from more complex bee flights hindered progress. System accuracy of 70% was achieved, but clutter impacted the overall results requiring intelligent filtering to remove environmental effects from the data.

Real-Time Heart Rate Detection Method Based on 77 GHz FMCW Radar

This paper proposes a real-time heart rate detection method based on 77 GHz FMCW radar. Firstly, the method establishes a new motion model according to respiratory and heartbeat rules, and extracts the motion signals of the chest and the abdomen; then, the random body motion (RBM) signal is eliminated by a combination of polynomial fitting and recursive least squares (RLS) adaptive filtering; lastly, multi-detection-point adaptive harmonics cancellation (AHC) is used to eliminate respiratory harmonics. In addition, the method introduces a spectrum analysis algorithm based on linear predictive coding (LPC). The experimental results show that the method can effectively eliminate the RBM signal and respiratory harmonics, and that the average real-time heart rate detection error rate is 2.925%.

Linear Predictive Approaches Separate Field Potentials in Animal Model of Parkinson's Disease

Parkinson's disease (PD) causes impaired movement and cognition. PD can involve profound changes in cortical and subcortical brain activity as measured by electroencephalography or intracranial recordings of local field potentials (LFP). Such signals can adaptively guide deep-brain stimulation (DBS) as part of PD therapy. However, adaptive DBS requires the identification of triggers of neuronal activity dependent on real time monitoring and analysis. Current methods do not always identify PD-related signals and can entail delays. We test an alternative approach based on linear predictive coding (LPC), which fits autoregressive (AR) models to time-series data. Parameters of these AR models can be calculated by fast algorithms in real time. We compare LFPs from the striatum in an animal model of PD with dopamine depletion in the absence and presence of the dopamine precursor levodopa, which is used to treat motor symptoms of PD. We show that in dopamine-depleted mice a first order AR model characterized by a single LPC parameter obtained by LFP sampling at 1 kHz for just 1 min can distinguish between levodopa-treated and saline-treated mice and outperform current methods. This suggests that LPC may be useful in online analysis of neuronal signals to guide DBS in real time and could contribute to DBS-based treatment of PD.

Performance Improvement of the Goertzel Algorithm in Estimating of Protein Coding Regions Using Modified Anti-notch Filter and Linear Predictive Coding Model

The aim of this paper is to improve the performance of the conventional Goertzel algorithm in determining the protein coding regions in deoxyribonucleic acid (DNA) sequences. First, the symbolic DNA sequences are converted into numerical signals using electron ion interaction potential method. Then by combining the modified anti-notch filter and linear predictive coding model, we proposed an efficient algorithm to achieve the performance improvement in the Goertzel algorithm for estimating genetic regions. Finally, a thresholding method is applied to precisely identify the exon and intron regions. The proposed algorithm is applied to several genes, including genes available in databases BG570 and HMR195 and the results are compared to other methods based on the nucleotide level evaluation criteria. Results demonstrate that our proposed method reduces the number of incorrect nucleotides which are estimated to be in the noncoding region. In addition, the area under the receiver operating characteristic curve has improved by the factor of 1.35 and 1.12 in HMR195 and BG570 datasets respectively, in comparison with the conventional Goertzel algorithm.

Spectrum analysis of Chinese vowels formant in patients with tongue carcinoma underwent hemiglossectomy

OBJECTIVES

Tongue is the most important phonatory organ in stomatognathic system. Radical resection of tongue squamous cell carcinoma can cause tongue defect and result in serious oral dysfunction, especially in phonetic function. This study aims to reveal the influence of tongue cancer, tongue defect and tongue reconstructions to phonetic function of tongue cancer patients.

STUDY DESIGN

Formant spectrum analysis of Chinese vowels was performed by linear predictive coding (LPC) in tongue squamous cell carcinoma patients (before surgery and 3 months, 9 months and 2 years after surgery) and normal people. Patients with tongue squamous cell carcinoma were divided into reconstruction group and non-reconstruction group. In reconstruction group, patients underwent tongue reconstruction with radial forearm free flap (RFFF) and lateral arm free flap (LAFF), respectively.

RESULTS

45 patients and 40 normal people were included. Differences were statistically significant between patients and normal persons, between patients before surgery and after surgery, between non-reconstruction group and construction group 2 years after operation. No statistical significance was found between patients underwent tongue reconstruction with RFFF or LAFF 2 years after operation.

CONCLUSIONS

This study showed that tongue cancer and tongue defect after radical resections affected phonetic function of patients. Tongue reconstruction with free flaps could restore phonetic function to some extent. The efficiency of tongue reconstruction with RFFF and LAFF respectively were similar.

A standard set of American-English voiced stop-consonant stimuli from morphed natural speech

Linear predictive coding (LPC) analysis was used to create morphed natural tokens of English voiced stop consonants ranging from /b/ to /d/ and /d/ to /g/ in four vowel contexts (/i/, /æ/, /a/, /u/). Both vowel-consonant-vowel (VCV) and consonant-vowel (CV) stimuli were created. A total of 320 natural-sounding acoustic speech stimuli were created, comprising 16 stimulus series. A behavioral experiment demonstrated that the stimuli varied perceptually from /b/ to /d/ to /g/, and provided useful reference data for the ambiguity of each token. Acoustic analyses indicated that the stimuli compared favorably to standard characteristics of naturally-produced consonants, and that the LPC morphing procedure successfully modulated multiple acoustic parameters associated with place of articulation. The entire set of stimuli is freely available on the Internet (http://www.psy.cmu.edu/~lholt/php/StephensHoltStimuli.php) for use in research applications.