Online handwriting Arabic recognition system using k-nearest neighbors classifier and DCT features

With advances in machine learning techniques, handwriting recognition systems have gained a great deal of importance. Lately, the increasing popularity of handheld computers, digital notebooks, and smartphones give the field of online handwriting recognition more interest. In this paper, we propose an enhanced method for the recognition of Arabic handwriting words using a directions-based segmentation technique and discrete cosine transform (DCT) coefficients as structural features. The main contribution of this research was combining a total of 18 structural features which were extracted by DCT coefficients and using the k-nearest neighbors (KNN) classifier to classify the segmented characters based on the extracted features. A dataset is used to validate the proposed method consisting of 2500 words in total. The obtained average 99.10% accuracy in recognition of handwritten characters shows that the proposed approach, through its multiple phases, is efficient in separating, distinguishing, and classifying Arabic handwritten characters using the KNN classifier. The availability of an online dataset of Arabic handwriting words is the main issue in this field. However, the dataset used will be available for research via the website.

Ecological Momentary Assessment (EMA) of Positive and Negative Affect and Associations with Blood Glucose (BG) in Teens with Type 1 Diabetes (T1D)

Background: Affect (i.e., emotions) can be associated with diabetes self-care and ambient glucose in teens with type 1 diabetes (T1D). We used momentary sampling to examine associations of daily affectwithblood glucose (BG) monitoring,BG levels,and BG variability in teens with T1D. Method: Over 2 weeks, 32 teens reported positive and negative affect (Positive and Negative Affect Scale) and BG levels on handheld computers 4x/day, coordinated with planned daily BG checks. BG values were classified as: in-range (70-180 mg/dL); low (<70 mg/dL); severe low (<54 mg/dL); high (>180 mg/dL); severe high (>250 mg/dL). Daily BG variability was derived from BG coefficient of variation (BGCV). To determine associations of positive and negative affect with BG checks, BG levels, and BGCV, separate generalized estimating equations were performed, adjusting for demographic and diabetes-related variables, for the overall sample and stratified by HbA1c (≤8%, >8%). Results: Teens (44% male, ages 14-18, 63% pump-treated, HbA1c 8.8 ± 1.4%) reported 51% in-range, 6% low (2% severe low), and 44% high (19% severe high) BG. In teens with HbA1c ≤8%, positive affect was associated with in-range BG (OR = 1.08, 95% CI = 1.04-1.13, P = .0002), reduced odds of very low glucose (OR = 0.35, 95% CI = 0.16-0.74, P = .006), and less daily BGCV (β = −0.9; 95% CI = −1.6, −0.2; P = .01). In teens with HbA1c >8%, negative affect was associated with less likelihood of checking BG (OR = 0.75, 95% CI = 0.64-0.87, P = .0003). Conclusions: Our findings shed light on individual differences in metabolic reactivity based on glycemic levels and the importance of incorporating affect into automated insulin delivery systems.

Emerging trends in the developments of low power design technique

In this paper is introduced a low power design technique for developing more reliable, functional, and more cost-effective handheld cellular telephones, portable computers, and peripherals. The portability requirements of handheld computers and other portable devices have placed tremendous pressure on electronic equipment designers, who need to deal with restrictions in the size of electronic units and power consumption. Even though battery technology is continuously improving, including reduced power consumption of processors and displays, extensive and continuous use of network services aggravates these issues. Now the onus is on the research and industrial communities to extend battery life and reduce weight. Equally, research on new techniques and technologies continues, to carefully manage energy consumption in mobile devices, while still providing continuous and fast connections to services and applications. This paper also discusses the novel trends in the developments and advancements in the area of low power Very Large Scale Integration (VLSI) design, dynamic power dissipation static power loss in Complementary Metal Oxide Semiconductor (CMOS), and advanced low power technique. Though low power as a well-established domain that undergone lots of developments from transistor sizing, process shrinkage, voltage scaling, clock gating, etc., to adiabatic logic are elaborated.