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An adaptive observer with online estimation of rotor and stator resistances is considered for induction motors, while only one phase current is measured. Generally, an induction motor drive controller needs at least two phase-current sensors. Nevertheless, failure of one current sensor results in degradation of motor drive performance and reliability, and also state and parameter estimation errors. Furthermore, any controller or observer in induction motor drives should be robust to rotor and stator resistance variations. The proposed observer is capable of concurrent estimation of stator currents and rotor fluxes with online tuning of rotor and stator resistances, while rotor speed and only one phase current are available. Stability and convergence of the observer are analytically verified based on the partial stability theory. The observer equations and adaptation laws can be easily implemented, which makes it attractive for industrial development of fault tolerant drives. A complex programmable logic device is implemented for the experimental setup that controls an intelligent power module including insulated gate bipolar transistors . Extensive simulation and experimental tests verify the asymptotic convergence of the proposed observer.NOMENCLATURE v ds (v qs ) Stator voltage along the stationary d-axis (q-axis), in volts. i ds (i qs ) Stator current along the stationary d-axis (q-axis), in amperes. λ dr (λ q r ) Rotor flux along the stationary d-axis (q-axis), in webers. R r (R s ) Rotor (stator) resistance, in ohms (Ω). L r (L s ) Rotor (stator) inductance, in henry. L m Magnetizing inductance, in henry. ω r Rotor electrical speed, in rad/sec. a r (=R r /L r ), inverse of rotor time constant. σ =1−L 2 m /L s L r . β =L m /L r .

Acute myelogenous leukemia (AML) is a subtype of acute leukemia, which is characterized by the accumulation of myeloid blasts in the bone marrow. Careful microscopic examination of stained blood smear or bone marrow aspirate is still the most significant diagnostic methodology for initial AML screening and considered as the first step toward diagnosis. It is time-consuming and due to the elusive nature of the signs and symptoms of AML; wrong diagnosis may occur by pathologists. Therefore, the need for automation of leukemia detection has arisen. In this paper, an automatic technique for identification and detection of AML and its prevalent subtypes, i.e., M2–M5 is presented. At first, microscopic images are acquired from blood smears of patients with AML and normal cases. After applying image preprocessing, color segmentation strategy is applied for segmenting white blood cells from other blood components and then discriminative features, i.e., irregularity, nucleus-cytoplasm ratio, Hausdorff dimension, shape, color, and texture features are extracted from the entire nucleus in the whole images containing multiple nuclei. Images are classified to cancerous and noncancerous images by binary support vector machine (SVM) classifier with 10-fold cross validation technique. Classifier performance is evaluated by three parameters, i.e., sensitivity, specificity, and accuracy. Cancerous images are also classified into their prevalent subtypes by multi-SVM classifier. The results show that the proposed algorithm has achieved an acceptable performance for diagnosis of AML and its common subtypes. Therefore, it can be used as an assistant diagnostic tool for pathologists.

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